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projektAI/venv/Lib/site-packages/pandas/core/generic.py
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from __future__ import annotations
import collections
from datetime import timedelta
import functools
import gc
import json
import operator
import pickle
import re
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
FrozenSet,
Hashable,
List,
Mapping,
Optional,
Sequence,
Set,
Tuple,
Type,
Union,
cast,
)
import warnings
import weakref
import numpy as np
from pandas._config import config
from pandas._libs import lib
from pandas._libs.tslibs import Period, Tick, Timestamp, to_offset
from pandas._typing import (
Axis,
CompressionOptions,
FilePathOrBuffer,
FrameOrSeries,
IndexKeyFunc,
IndexLabel,
JSONSerializable,
Label,
Level,
Renamer,
StorageOptions,
TimedeltaConvertibleTypes,
TimestampConvertibleTypes,
ValueKeyFunc,
final,
)
from pandas.compat._optional import import_optional_dependency
from pandas.compat.numpy import function as nv
from pandas.errors import AbstractMethodError, InvalidIndexError
from pandas.util._decorators import doc, rewrite_axis_style_signature
from pandas.util._validators import (
validate_ascending,
validate_bool_kwarg,
validate_fillna_kwargs,
validate_percentile,
)
from pandas.core.dtypes.common import (
ensure_int64,
ensure_object,
ensure_str,
is_bool,
is_bool_dtype,
is_datetime64_any_dtype,
is_datetime64tz_dtype,
is_dict_like,
is_dtype_equal,
is_extension_array_dtype,
is_float,
is_list_like,
is_number,
is_numeric_dtype,
is_object_dtype,
is_re_compilable,
is_scalar,
is_timedelta64_dtype,
pandas_dtype,
)
from pandas.core.dtypes.generic import ABCDataFrame, ABCSeries
from pandas.core.dtypes.inference import is_hashable
from pandas.core.dtypes.missing import isna, notna
import pandas as pd
from pandas.core import arraylike, indexing, missing, nanops
import pandas.core.algorithms as algos
from pandas.core.base import PandasObject, SelectionMixin
import pandas.core.common as com
from pandas.core.construction import create_series_with_explicit_dtype
from pandas.core.flags import Flags
from pandas.core.indexes import base as ibase
from pandas.core.indexes.api import (
DatetimeIndex,
Index,
MultiIndex,
PeriodIndex,
RangeIndex,
ensure_index,
)
from pandas.core.internals import BlockManager
from pandas.core.missing import find_valid_index
from pandas.core.ops import align_method_FRAME
from pandas.core.shared_docs import _shared_docs
from pandas.core.sorting import get_indexer_indexer
from pandas.core.window import Expanding, ExponentialMovingWindow, Rolling, Window
from pandas.io.formats import format as fmt
from pandas.io.formats.format import (
DataFrameFormatter,
DataFrameRenderer,
format_percentiles,
)
from pandas.io.formats.printing import pprint_thing
if TYPE_CHECKING:
from pandas._libs.tslibs import BaseOffset
from pandas.core.frame import DataFrame
from pandas.core.resample import Resampler
from pandas.core.series import Series
from pandas.core.window.indexers import BaseIndexer
# goal is to be able to define the docs close to function, while still being
# able to share
_shared_docs = {**_shared_docs}
_shared_doc_kwargs = {
"axes": "keywords for axes",
"klass": "Series/DataFrame",
"axes_single_arg": "int or labels for object",
"args_transpose": "axes to permute (int or label for object)",
"optional_by": """
by : str or list of str
Name or list of names to sort by""",
}
bool_t = bool # Need alias because NDFrame has def bool:
class NDFrame(PandasObject, SelectionMixin, indexing.IndexingMixin):
"""
N-dimensional analogue of DataFrame. Store multi-dimensional in a
size-mutable, labeled data structure
Parameters
----------
data : BlockManager
axes : list
copy : bool, default False
"""
_internal_names: List[str] = [
"_mgr",
"_cacher",
"_item_cache",
"_cache",
"_is_copy",
"_subtyp",
"_name",
"_index",
"_default_kind",
"_default_fill_value",
"_metadata",
"__array_struct__",
"__array_interface__",
"_flags",
]
_internal_names_set: Set[str] = set(_internal_names)
_accessors: Set[str] = set()
_hidden_attrs: FrozenSet[str] = frozenset(
["_AXIS_NAMES", "_AXIS_NUMBERS", "get_values", "tshift"]
)
_metadata: List[str] = []
_is_copy = None
_mgr: BlockManager
_attrs: Dict[Optional[Hashable], Any]
_typ: str
# ----------------------------------------------------------------------
# Constructors
def __init__(
self,
data: BlockManager,
copy: bool = False,
attrs: Optional[Mapping[Optional[Hashable], Any]] = None,
):
# copy kwarg is retained for mypy compat, is not used
object.__setattr__(self, "_is_copy", None)
object.__setattr__(self, "_mgr", data)
object.__setattr__(self, "_item_cache", {})
if attrs is None:
attrs = {}
else:
attrs = dict(attrs)
object.__setattr__(self, "_attrs", attrs)
object.__setattr__(self, "_flags", Flags(self, allows_duplicate_labels=True))
@classmethod
def _init_mgr(cls, mgr, axes, dtype=None, copy: bool = False) -> BlockManager:
""" passed a manager and a axes dict """
for a, axe in axes.items():
if axe is not None:
axe = ensure_index(axe)
bm_axis = cls._get_block_manager_axis(a)
mgr = mgr.reindex_axis(axe, axis=bm_axis, copy=False)
# make a copy if explicitly requested
if copy:
mgr = mgr.copy()
if dtype is not None:
# avoid further copies if we can
if len(mgr.blocks) > 1 or mgr.blocks[0].values.dtype != dtype:
mgr = mgr.astype(dtype=dtype)
return mgr
# ----------------------------------------------------------------------
# attrs and flags
@property
def attrs(self) -> Dict[Optional[Hashable], Any]:
"""
Dictionary of global attributes of this dataset.
.. warning::
attrs is experimental and may change without warning.
See Also
--------
DataFrame.flags : Global flags applying to this object.
"""
if self._attrs is None:
self._attrs = {}
return self._attrs
@attrs.setter
def attrs(self, value: Mapping[Optional[Hashable], Any]) -> None:
self._attrs = dict(value)
@final
@property
def flags(self) -> Flags:
"""
Get the properties associated with this pandas object.
The available flags are
* :attr:`Flags.allows_duplicate_labels`
See Also
--------
Flags : Flags that apply to pandas objects.
DataFrame.attrs : Global metadata applying to this dataset.
Notes
-----
"Flags" differ from "metadata". Flags reflect properties of the
pandas object (the Series or DataFrame). Metadata refer to properties
of the dataset, and should be stored in :attr:`DataFrame.attrs`.
Examples
--------
>>> df = pd.DataFrame({"A": [1, 2]})
>>> df.flags
<Flags(allows_duplicate_labels=True)>
Flags can be get or set using ``.``
>>> df.flags.allows_duplicate_labels
True
>>> df.flags.allows_duplicate_labels = False
Or by slicing with a key
>>> df.flags["allows_duplicate_labels"]
False
>>> df.flags["allows_duplicate_labels"] = True
"""
return self._flags
@final
def set_flags(
self: FrameOrSeries,
*,
copy: bool = False,
allows_duplicate_labels: Optional[bool] = None,
) -> FrameOrSeries:
"""
Return a new object with updated flags.
Parameters
----------
allows_duplicate_labels : bool, optional
Whether the returned object allows duplicate labels.
Returns
-------
Series or DataFrame
The same type as the caller.
See Also
--------
DataFrame.attrs : Global metadata applying to this dataset.
DataFrame.flags : Global flags applying to this object.
Notes
-----
This method returns a new object that's a view on the same data
as the input. Mutating the input or the output values will be reflected
in the other.
This method is intended to be used in method chains.
"Flags" differ from "metadata". Flags reflect properties of the
pandas object (the Series or DataFrame). Metadata refer to properties
of the dataset, and should be stored in :attr:`DataFrame.attrs`.
Examples
--------
>>> df = pd.DataFrame({"A": [1, 2]})
>>> df.flags.allows_duplicate_labels
True
>>> df2 = df.set_flags(allows_duplicate_labels=False)
>>> df2.flags.allows_duplicate_labels
False
"""
df = self.copy(deep=copy)
if allows_duplicate_labels is not None:
df.flags["allows_duplicate_labels"] = allows_duplicate_labels
return df
@final
@classmethod
def _validate_dtype(cls, dtype):
""" validate the passed dtype """
if dtype is not None:
dtype = pandas_dtype(dtype)
# a compound dtype
if dtype.kind == "V":
raise NotImplementedError(
"compound dtypes are not implemented "
f"in the {cls.__name__} constructor"
)
return dtype
# ----------------------------------------------------------------------
# Construction
@property
def _constructor(self: FrameOrSeries) -> Type[FrameOrSeries]:
"""
Used when a manipulation result has the same dimensions as the
original.
"""
raise AbstractMethodError(self)
@property
def _constructor_sliced(self):
"""
Used when a manipulation result has one lower dimension(s) as the
original, such as DataFrame single columns slicing.
"""
raise AbstractMethodError(self)
@property
def _constructor_expanddim(self):
"""
Used when a manipulation result has one higher dimension as the
original, such as Series.to_frame()
"""
raise NotImplementedError
# ----------------------------------------------------------------------
# Internals
@final
@property
def _data(self):
# GH#33054 retained because some downstream packages uses this,
# e.g. fastparquet
return self._mgr
# ----------------------------------------------------------------------
# Axis
_stat_axis_number = 0
_stat_axis_name = "index"
_ix = None
_AXIS_ORDERS: List[str]
_AXIS_TO_AXIS_NUMBER: Dict[Axis, int] = {0: 0, "index": 0, "rows": 0}
_AXIS_REVERSED: bool
_info_axis_number: int
_info_axis_name: str
_AXIS_LEN: int
@property
def _AXIS_NUMBERS(self) -> Dict[str, int]:
""".. deprecated:: 1.1.0"""
warnings.warn("_AXIS_NUMBERS has been deprecated.", FutureWarning, stacklevel=3)
return {"index": 0}
@property
def _AXIS_NAMES(self) -> Dict[int, str]:
""".. deprecated:: 1.1.0"""
warnings.warn("_AXIS_NAMES has been deprecated.", FutureWarning, stacklevel=3)
return {0: "index"}
@final
def _construct_axes_dict(self, axes=None, **kwargs):
"""Return an axes dictionary for myself."""
d = {a: self._get_axis(a) for a in (axes or self._AXIS_ORDERS)}
d.update(kwargs)
return d
@final
@classmethod
def _construct_axes_from_arguments(
cls, args, kwargs, require_all: bool = False, sentinel=None
):
"""
Construct and returns axes if supplied in args/kwargs.
If require_all, raise if all axis arguments are not supplied
return a tuple of (axes, kwargs).
sentinel specifies the default parameter when an axis is not
supplied; useful to distinguish when a user explicitly passes None
in scenarios where None has special meaning.
"""
# construct the args
args = list(args)
for a in cls._AXIS_ORDERS:
# look for a argument by position
if a not in kwargs:
try:
kwargs[a] = args.pop(0)
except IndexError as err:
if require_all:
raise TypeError(
"not enough/duplicate arguments specified!"
) from err
axes = {a: kwargs.pop(a, sentinel) for a in cls._AXIS_ORDERS}
return axes, kwargs
@final
@classmethod
def _get_axis_number(cls, axis: Axis) -> int:
try:
return cls._AXIS_TO_AXIS_NUMBER[axis]
except KeyError:
raise ValueError(f"No axis named {axis} for object type {cls.__name__}")
@final
@classmethod
def _get_axis_name(cls, axis: Axis) -> str:
axis_number = cls._get_axis_number(axis)
return cls._AXIS_ORDERS[axis_number]
@final
def _get_axis(self, axis: Axis) -> Index:
axis_number = self._get_axis_number(axis)
assert axis_number in {0, 1}
return self.index if axis_number == 0 else self.columns
@final
@classmethod
def _get_block_manager_axis(cls, axis: Axis) -> int:
"""Map the axis to the block_manager axis."""
axis = cls._get_axis_number(axis)
if cls._AXIS_REVERSED:
m = cls._AXIS_LEN - 1
return m - axis
return axis
@final
def _get_axis_resolvers(self, axis: str) -> Dict[str, Union[Series, MultiIndex]]:
# index or columns
axis_index = getattr(self, axis)
d = {}
prefix = axis[0]
for i, name in enumerate(axis_index.names):
if name is not None:
key = level = name
else:
# prefix with 'i' or 'c' depending on the input axis
# e.g., you must do ilevel_0 for the 0th level of an unnamed
# multiiindex
key = f"{prefix}level_{i}"
level = i
level_values = axis_index.get_level_values(level)
s = level_values.to_series()
s.index = axis_index
d[key] = s
# put the index/columns itself in the dict
if isinstance(axis_index, MultiIndex):
dindex = axis_index
else:
dindex = axis_index.to_series()
d[axis] = dindex
return d
@final
def _get_index_resolvers(self) -> Dict[Label, Union[Series, MultiIndex]]:
from pandas.core.computation.parsing import clean_column_name
d: Dict[str, Union[Series, MultiIndex]] = {}
for axis_name in self._AXIS_ORDERS:
d.update(self._get_axis_resolvers(axis_name))
return {clean_column_name(k): v for k, v in d.items() if not isinstance(k, int)}
@final
def _get_cleaned_column_resolvers(self) -> Dict[Label, Series]:
"""
Return the special character free column resolvers of a dataframe.
Column names with special characters are 'cleaned up' so that they can
be referred to by backtick quoting.
Used in :meth:`DataFrame.eval`.
"""
from pandas.core.computation.parsing import clean_column_name
if isinstance(self, ABCSeries):
return {clean_column_name(self.name): self}
return {
clean_column_name(k): v for k, v in self.items() if not isinstance(k, int)
}
@property
def _info_axis(self) -> Index:
return getattr(self, self._info_axis_name)
@property
def _stat_axis(self) -> Index:
return getattr(self, self._stat_axis_name)
@property
def shape(self) -> Tuple[int, ...]:
"""
Return a tuple of axis dimensions
"""
return tuple(len(self._get_axis(a)) for a in self._AXIS_ORDERS)
@property
def axes(self) -> List[Index]:
"""
Return index label(s) of the internal NDFrame
"""
# we do it this way because if we have reversed axes, then
# the block manager shows then reversed
return [self._get_axis(a) for a in self._AXIS_ORDERS]
@property
def ndim(self) -> int:
"""
Return an int representing the number of axes / array dimensions.
Return 1 if Series. Otherwise return 2 if DataFrame.
See Also
--------
ndarray.ndim : Number of array dimensions.
Examples
--------
>>> s = pd.Series({'a': 1, 'b': 2, 'c': 3})
>>> s.ndim
1
>>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> df.ndim
2
"""
return self._mgr.ndim
@property
def size(self) -> int:
"""
Return an int representing the number of elements in this object.
Return the number of rows if Series. Otherwise return the number of
rows times number of columns if DataFrame.
See Also
--------
ndarray.size : Number of elements in the array.
Examples
--------
>>> s = pd.Series({'a': 1, 'b': 2, 'c': 3})
>>> s.size
3
>>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> df.size
4
"""
return np.prod(self.shape)
@final
@property
def _selected_obj(self: FrameOrSeries) -> FrameOrSeries:
""" internal compat with SelectionMixin """
return self
@final
@property
def _obj_with_exclusions(self: FrameOrSeries) -> FrameOrSeries:
""" internal compat with SelectionMixin """
return self
def set_axis(self, labels, axis: Axis = 0, inplace: bool = False):
"""
Assign desired index to given axis.
Indexes for%(extended_summary_sub)s row labels can be changed by assigning
a list-like or Index.
Parameters
----------
labels : list-like, Index
The values for the new index.
axis : %(axes_single_arg)s, default 0
The axis to update. The value 0 identifies the rows%(axis_description_sub)s.
inplace : bool, default False
Whether to return a new %(klass)s instance.
Returns
-------
renamed : %(klass)s or None
An object of type %(klass)s or None if ``inplace=True``.
See Also
--------
%(klass)s.rename_axis : Alter the name of the index%(see_also_sub)s.
"""
self._check_inplace_and_allows_duplicate_labels(inplace)
return self._set_axis_nocheck(labels, axis, inplace)
@final
def _set_axis_nocheck(self, labels, axis: Axis, inplace: bool):
# NDFrame.rename with inplace=False calls set_axis(inplace=True) on a copy.
if inplace:
setattr(self, self._get_axis_name(axis), labels)
else:
obj = self.copy()
obj.set_axis(labels, axis=axis, inplace=True)
return obj
def _set_axis(self, axis: int, labels: Index) -> None:
labels = ensure_index(labels)
self._mgr.set_axis(axis, labels)
self._clear_item_cache()
@final
def swapaxes(self: FrameOrSeries, axis1, axis2, copy=True) -> FrameOrSeries:
"""
Interchange axes and swap values axes appropriately.
Returns
-------
y : same as input
"""
i = self._get_axis_number(axis1)
j = self._get_axis_number(axis2)
if i == j:
if copy:
return self.copy()
return self
mapping = {i: j, j: i}
new_axes = (self._get_axis(mapping.get(k, k)) for k in range(self._AXIS_LEN))
new_values = self.values.swapaxes(i, j)
if copy:
new_values = new_values.copy()
# ignore needed because of NDFrame constructor is different than
# DataFrame/Series constructors.
return self._constructor(
new_values, *new_axes # type: ignore[arg-type]
).__finalize__(self, method="swapaxes")
@final
def droplevel(self: FrameOrSeries, level, axis=0) -> FrameOrSeries:
"""
Return DataFrame with requested index / column level(s) removed.
.. versionadded:: 0.24.0
Parameters
----------
level : int, str, or list-like
If a string is given, must be the name of a level
If list-like, elements must be names or positional indexes
of levels.
axis : {0 or 'index', 1 or 'columns'}, default 0
Axis along which the level(s) is removed:
* 0 or 'index': remove level(s) in column.
* 1 or 'columns': remove level(s) in row.
Returns
-------
DataFrame
DataFrame with requested index / column level(s) removed.
Examples
--------
>>> df = pd.DataFrame([
... [1, 2, 3, 4],
... [5, 6, 7, 8],
... [9, 10, 11, 12]
... ]).set_index([0, 1]).rename_axis(['a', 'b'])
>>> df.columns = pd.MultiIndex.from_tuples([
... ('c', 'e'), ('d', 'f')
... ], names=['level_1', 'level_2'])
>>> df
level_1 c d
level_2 e f
a b
1 2 3 4
5 6 7 8
9 10 11 12
>>> df.droplevel('a')
level_1 c d
level_2 e f
b
2 3 4
6 7 8
10 11 12
>>> df.droplevel('level_2', axis=1)
level_1 c d
a b
1 2 3 4
5 6 7 8
9 10 11 12
"""
labels = self._get_axis(axis)
new_labels = labels.droplevel(level)
result = self.set_axis(new_labels, axis=axis, inplace=False)
return result
def pop(self, item: Label) -> Union[Series, Any]:
result = self[item]
del self[item]
if self.ndim == 2:
result._reset_cacher()
return result
@final
def squeeze(self, axis=None):
"""
Squeeze 1 dimensional axis objects into scalars.
Series or DataFrames with a single element are squeezed to a scalar.
DataFrames with a single column or a single row are squeezed to a
Series. Otherwise the object is unchanged.
This method is most useful when you don't know if your
object is a Series or DataFrame, but you do know it has just a single
column. In that case you can safely call `squeeze` to ensure you have a
Series.
Parameters
----------
axis : {0 or 'index', 1 or 'columns', None}, default None
A specific axis to squeeze. By default, all length-1 axes are
squeezed.
Returns
-------
DataFrame, Series, or scalar
The projection after squeezing `axis` or all the axes.
See Also
--------
Series.iloc : Integer-location based indexing for selecting scalars.
DataFrame.iloc : Integer-location based indexing for selecting Series.
Series.to_frame : Inverse of DataFrame.squeeze for a
single-column DataFrame.
Examples
--------
>>> primes = pd.Series([2, 3, 5, 7])
Slicing might produce a Series with a single value:
>>> even_primes = primes[primes % 2 == 0]
>>> even_primes
0 2
dtype: int64
>>> even_primes.squeeze()
2
Squeezing objects with more than one value in every axis does nothing:
>>> odd_primes = primes[primes % 2 == 1]
>>> odd_primes
1 3
2 5
3 7
dtype: int64
>>> odd_primes.squeeze()
1 3
2 5
3 7
dtype: int64
Squeezing is even more effective when used with DataFrames.
>>> df = pd.DataFrame([[1, 2], [3, 4]], columns=['a', 'b'])
>>> df
a b
0 1 2
1 3 4
Slicing a single column will produce a DataFrame with the columns
having only one value:
>>> df_a = df[['a']]
>>> df_a
a
0 1
1 3
So the columns can be squeezed down, resulting in a Series:
>>> df_a.squeeze('columns')
0 1
1 3
Name: a, dtype: int64
Slicing a single row from a single column will produce a single
scalar DataFrame:
>>> df_0a = df.loc[df.index < 1, ['a']]
>>> df_0a
a
0 1
Squeezing the rows produces a single scalar Series:
>>> df_0a.squeeze('rows')
a 1
Name: 0, dtype: int64
Squeezing all axes will project directly into a scalar:
>>> df_0a.squeeze()
1
"""
axis = range(self._AXIS_LEN) if axis is None else (self._get_axis_number(axis),)
return self.iloc[
tuple(
0 if i in axis and len(a) == 1 else slice(None)
for i, a in enumerate(self.axes)
)
]
# ----------------------------------------------------------------------
# Rename
def rename(
self: FrameOrSeries,
mapper: Optional[Renamer] = None,
*,
index: Optional[Renamer] = None,
columns: Optional[Renamer] = None,
axis: Optional[Axis] = None,
copy: bool = True,
inplace: bool = False,
level: Optional[Level] = None,
errors: str = "ignore",
) -> Optional[FrameOrSeries]:
"""
Alter axes input function or functions. Function / dict values must be
unique (1-to-1). Labels not contained in a dict / Series will be left
as-is. Extra labels listed don't throw an error. Alternatively, change
``Series.name`` with a scalar value (Series only).
Parameters
----------
%(axes)s : scalar, list-like, dict-like or function, optional
Scalar or list-like will alter the ``Series.name`` attribute,
and raise on DataFrame.
dict-like or functions are transformations to apply to
that axis' values
copy : bool, default True
Also copy underlying data.
inplace : bool, default False
Whether to return a new {klass}. If True then value of copy is
ignored.
level : int or level name, default None
In case of a MultiIndex, only rename labels in the specified
level.
errors : {'ignore', 'raise'}, default 'ignore'
If 'raise', raise a `KeyError` when a dict-like `mapper`, `index`,
or `columns` contains labels that are not present in the Index
being transformed.
If 'ignore', existing keys will be renamed and extra keys will be
ignored.
Returns
-------
renamed : {klass} (new object)
Raises
------
KeyError
If any of the labels is not found in the selected axis and
"errors='raise'".
See Also
--------
NDFrame.rename_axis
Examples
--------
>>> s = pd.Series([1, 2, 3])
>>> s
0 1
1 2
2 3
dtype: int64
>>> s.rename("my_name") # scalar, changes Series.name
0 1
1 2
2 3
Name: my_name, dtype: int64
>>> s.rename(lambda x: x ** 2) # function, changes labels
0 1
1 2
4 3
dtype: int64
>>> s.rename({1: 3, 2: 5}) # mapping, changes labels
0 1
3 2
5 3
dtype: int64
Since ``DataFrame`` doesn't have a ``.name`` attribute,
only mapping-type arguments are allowed.
>>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
>>> df.rename(2)
Traceback (most recent call last):
...
TypeError: 'int' object is not callable
``DataFrame.rename`` supports two calling conventions
* ``(index=index_mapper, columns=columns_mapper, ...)``
* ``(mapper, axis={'index', 'columns'}, ...)``
We *highly* recommend using keyword arguments to clarify your
intent.
>>> df.rename(index=str, columns={"A": "a", "B": "c"})
a c
0 1 4
1 2 5
2 3 6
>>> df.rename(index=str, columns={"A": "a", "C": "c"})
a B
0 1 4
1 2 5
2 3 6
Using axis-style parameters
>>> df.rename(str.lower, axis='columns')
a b
0 1 4
1 2 5
2 3 6
>>> df.rename({1: 2, 2: 4}, axis='index')
A B
0 1 4
2 2 5
4 3 6
See the :ref:`user guide <basics.rename>` for more.
"""
if mapper is None and index is None and columns is None:
raise TypeError("must pass an index to rename")
if index is not None or columns is not None:
if axis is not None:
raise TypeError(
"Cannot specify both 'axis' and any of 'index' or 'columns'"
)
elif mapper is not None:
raise TypeError(
"Cannot specify both 'mapper' and any of 'index' or 'columns'"
)
else:
# use the mapper argument
if axis and self._get_axis_number(axis) == 1:
columns = mapper
else:
index = mapper
self._check_inplace_and_allows_duplicate_labels(inplace)
result = self if inplace else self.copy(deep=copy)
for axis_no, replacements in enumerate((index, columns)):
if replacements is None:
continue
ax = self._get_axis(axis_no)
f = com.get_rename_function(replacements)
if level is not None:
level = ax._get_level_number(level)
# GH 13473
if not callable(replacements):
indexer = ax.get_indexer_for(replacements)
if errors == "raise" and len(indexer[indexer == -1]):
missing_labels = [
label
for index, label in enumerate(replacements)
if indexer[index] == -1
]
raise KeyError(f"{missing_labels} not found in axis")
new_index = ax._transform_index(f, level)
result._set_axis_nocheck(new_index, axis=axis_no, inplace=True)
result._clear_item_cache()
if inplace:
self._update_inplace(result)
return None
else:
return result.__finalize__(self, method="rename")
@rewrite_axis_style_signature("mapper", [("copy", True), ("inplace", False)])
def rename_axis(self, mapper=lib.no_default, **kwargs):
"""
Set the name of the axis for the index or columns.
Parameters
----------
mapper : scalar, list-like, optional
Value to set the axis name attribute.
index, columns : scalar, list-like, dict-like or function, optional
A scalar, list-like, dict-like or functions transformations to
apply to that axis' values.
Note that the ``columns`` parameter is not allowed if the
object is a Series. This parameter only apply for DataFrame
type objects.
Use either ``mapper`` and ``axis`` to
specify the axis to target with ``mapper``, or ``index``
and/or ``columns``.
.. versionchanged:: 0.24.0
axis : {0 or 'index', 1 or 'columns'}, default 0
The axis to rename.
copy : bool, default True
Also copy underlying data.
inplace : bool, default False
Modifies the object directly, instead of creating a new Series
or DataFrame.
Returns
-------
Series, DataFrame, or None
The same type as the caller or None if ``inplace=True``.
See Also
--------
Series.rename : Alter Series index labels or name.
DataFrame.rename : Alter DataFrame index labels or name.
Index.rename : Set new names on index.
Notes
-----
``DataFrame.rename_axis`` supports two calling conventions
* ``(index=index_mapper, columns=columns_mapper, ...)``
* ``(mapper, axis={'index', 'columns'}, ...)``
The first calling convention will only modify the names of
the index and/or the names of the Index object that is the columns.
In this case, the parameter ``copy`` is ignored.
The second calling convention will modify the names of the
corresponding index if mapper is a list or a scalar.
However, if mapper is dict-like or a function, it will use the
deprecated behavior of modifying the axis *labels*.
We *highly* recommend using keyword arguments to clarify your
intent.
Examples
--------
**Series**
>>> s = pd.Series(["dog", "cat", "monkey"])
>>> s
0 dog
1 cat
2 monkey
dtype: object
>>> s.rename_axis("animal")
animal
0 dog
1 cat
2 monkey
dtype: object
**DataFrame**
>>> df = pd.DataFrame({"num_legs": [4, 4, 2],
... "num_arms": [0, 0, 2]},
... ["dog", "cat", "monkey"])
>>> df
num_legs num_arms
dog 4 0
cat 4 0
monkey 2 2
>>> df = df.rename_axis("animal")
>>> df
num_legs num_arms
animal
dog 4 0
cat 4 0
monkey 2 2
>>> df = df.rename_axis("limbs", axis="columns")
>>> df
limbs num_legs num_arms
animal
dog 4 0
cat 4 0
monkey 2 2
**MultiIndex**
>>> df.index = pd.MultiIndex.from_product([['mammal'],
... ['dog', 'cat', 'monkey']],
... names=['type', 'name'])
>>> df
limbs num_legs num_arms
type name
mammal dog 4 0
cat 4 0
monkey 2 2
>>> df.rename_axis(index={'type': 'class'})
limbs num_legs num_arms
class name
mammal dog 4 0
cat 4 0
monkey 2 2
>>> df.rename_axis(columns=str.upper)
LIMBS num_legs num_arms
type name
mammal dog 4 0
cat 4 0
monkey 2 2
"""
axes, kwargs = self._construct_axes_from_arguments(
(), kwargs, sentinel=lib.no_default
)
copy = kwargs.pop("copy", True)
inplace = kwargs.pop("inplace", False)
axis = kwargs.pop("axis", 0)
if axis is not None:
axis = self._get_axis_number(axis)
if kwargs:
raise TypeError(
"rename_axis() got an unexpected keyword "
f'argument "{list(kwargs.keys())[0]}"'
)
inplace = validate_bool_kwarg(inplace, "inplace")
if mapper is not lib.no_default:
# Use v0.23 behavior if a scalar or list
non_mapper = is_scalar(mapper) or (
is_list_like(mapper) and not is_dict_like(mapper)
)
if non_mapper:
return self._set_axis_name(mapper, axis=axis, inplace=inplace)
else:
raise ValueError("Use `.rename` to alter labels with a mapper.")
else:
# Use new behavior. Means that index and/or columns
# is specified
result = self if inplace else self.copy(deep=copy)
for axis in range(self._AXIS_LEN):
v = axes.get(self._get_axis_name(axis))
if v is lib.no_default:
continue
non_mapper = is_scalar(v) or (is_list_like(v) and not is_dict_like(v))
if non_mapper:
newnames = v
else:
f = com.get_rename_function(v)
curnames = self._get_axis(axis).names
newnames = [f(name) for name in curnames]
result._set_axis_name(newnames, axis=axis, inplace=True)
if not inplace:
return result
@final
def _set_axis_name(self, name, axis=0, inplace=False):
"""
Set the name(s) of the axis.
Parameters
----------
name : str or list of str
Name(s) to set.
axis : {0 or 'index', 1 or 'columns'}, default 0
The axis to set the label. The value 0 or 'index' specifies index,
and the value 1 or 'columns' specifies columns.
inplace : bool, default False
If `True`, do operation inplace and return None.
Returns
-------
Series, DataFrame, or None
The same type as the caller or `None` if `inplace` is `True`.
See Also
--------
DataFrame.rename : Alter the axis labels of :class:`DataFrame`.
Series.rename : Alter the index labels or set the index name
of :class:`Series`.
Index.rename : Set the name of :class:`Index` or :class:`MultiIndex`.
Examples
--------
>>> df = pd.DataFrame({"num_legs": [4, 4, 2]},
... ["dog", "cat", "monkey"])
>>> df
num_legs
dog 4
cat 4
monkey 2
>>> df._set_axis_name("animal")
num_legs
animal
dog 4
cat 4
monkey 2
>>> df.index = pd.MultiIndex.from_product(
... [["mammal"], ['dog', 'cat', 'monkey']])
>>> df._set_axis_name(["type", "name"])
num_legs
type name
mammal dog 4
cat 4
monkey 2
"""
axis = self._get_axis_number(axis)
idx = self._get_axis(axis).set_names(name)
inplace = validate_bool_kwarg(inplace, "inplace")
renamed = self if inplace else self.copy()
renamed.set_axis(idx, axis=axis, inplace=True)
if not inplace:
return renamed
# ----------------------------------------------------------------------
# Comparison Methods
@final
def _indexed_same(self, other) -> bool:
return all(
self._get_axis(a).equals(other._get_axis(a)) for a in self._AXIS_ORDERS
)
@final
def equals(self, other: object) -> bool:
"""
Test whether two objects contain the same elements.
This function allows two Series or DataFrames to be compared against
each other to see if they have the same shape and elements. NaNs in
the same location are considered equal.
The row/column index do not need to have the same type, as long
as the values are considered equal. Corresponding columns must be of
the same dtype.
Parameters
----------
other : Series or DataFrame
The other Series or DataFrame to be compared with the first.
Returns
-------
bool
True if all elements are the same in both objects, False
otherwise.
See Also
--------
Series.eq : Compare two Series objects of the same length
and return a Series where each element is True if the element
in each Series is equal, False otherwise.
DataFrame.eq : Compare two DataFrame objects of the same shape and
return a DataFrame where each element is True if the respective
element in each DataFrame is equal, False otherwise.
testing.assert_series_equal : Raises an AssertionError if left and
right are not equal. Provides an easy interface to ignore
inequality in dtypes, indexes and precision among others.
testing.assert_frame_equal : Like assert_series_equal, but targets
DataFrames.
numpy.array_equal : Return True if two arrays have the same shape
and elements, False otherwise.
Examples
--------
>>> df = pd.DataFrame({1: [10], 2: [20]})
>>> df
1 2
0 10 20
DataFrames df and exactly_equal have the same types and values for
their elements and column labels, which will return True.
>>> exactly_equal = pd.DataFrame({1: [10], 2: [20]})
>>> exactly_equal
1 2
0 10 20
>>> df.equals(exactly_equal)
True
DataFrames df and different_column_type have the same element
types and values, but have different types for the column labels,
which will still return True.
>>> different_column_type = pd.DataFrame({1.0: [10], 2.0: [20]})
>>> different_column_type
1.0 2.0
0 10 20
>>> df.equals(different_column_type)
True
DataFrames df and different_data_type have different types for the
same values for their elements, and will return False even though
their column labels are the same values and types.
>>> different_data_type = pd.DataFrame({1: [10.0], 2: [20.0]})
>>> different_data_type
1 2
0 10.0 20.0
>>> df.equals(different_data_type)
False
"""
if not (isinstance(other, type(self)) or isinstance(self, type(other))):
return False
other = cast(NDFrame, other)
return self._mgr.equals(other._mgr)
# -------------------------------------------------------------------------
# Unary Methods
@final
def __neg__(self):
values = self._values
if is_bool_dtype(values):
arr = operator.inv(values)
elif (
is_numeric_dtype(values)
or is_timedelta64_dtype(values)
or is_object_dtype(values)
):
arr = operator.neg(values)
else:
raise TypeError(f"Unary negative expects numeric dtype, not {values.dtype}")
return self.__array_wrap__(arr)
@final
def __pos__(self):
values = self._values
if is_bool_dtype(values):
arr = values
elif (
is_numeric_dtype(values)
or is_timedelta64_dtype(values)
or is_object_dtype(values)
):
arr = operator.pos(values)
else:
raise TypeError(
"Unary plus expects bool, numeric, timedelta, "
f"or object dtype, not {values.dtype}"
)
return self.__array_wrap__(arr)
@final
def __invert__(self):
if not self.size:
# inv fails with 0 len
return self
new_data = self._mgr.apply(operator.invert)
result = self._constructor(new_data).__finalize__(self, method="__invert__")
return result
@final
def __nonzero__(self):
raise ValueError(
f"The truth value of a {type(self).__name__} is ambiguous. "
"Use a.empty, a.bool(), a.item(), a.any() or a.all()."
)
__bool__ = __nonzero__
@final
def bool(self):
"""
Return the bool of a single element Series or DataFrame.
This must be a boolean scalar value, either True or False. It will raise a
ValueError if the Series or DataFrame does not have exactly 1 element, or that
element is not boolean (integer values 0 and 1 will also raise an exception).
Returns
-------
bool
The value in the Series or DataFrame.
See Also
--------
Series.astype : Change the data type of a Series, including to boolean.
DataFrame.astype : Change the data type of a DataFrame, including to boolean.
numpy.bool_ : NumPy boolean data type, used by pandas for boolean values.
Examples
--------
The method will only work for single element objects with a boolean value:
>>> pd.Series([True]).bool()
True
>>> pd.Series([False]).bool()
False
>>> pd.DataFrame({'col': [True]}).bool()
True
>>> pd.DataFrame({'col': [False]}).bool()
False
"""
v = self.squeeze()
if isinstance(v, (bool, np.bool_)):
return bool(v)
elif is_scalar(v):
raise ValueError(
"bool cannot act on a non-boolean single element "
f"{type(self).__name__}"
)
self.__nonzero__()
@final
def __abs__(self: FrameOrSeries) -> FrameOrSeries:
return self.abs()
@final
def __round__(self: FrameOrSeries, decimals: int = 0) -> FrameOrSeries:
return self.round(decimals)
# -------------------------------------------------------------------------
# Label or Level Combination Helpers
#
# A collection of helper methods for DataFrame/Series operations that
# accept a combination of column/index labels and levels. All such
# operations should utilize/extend these methods when possible so that we
# have consistent precedence and validation logic throughout the library.
@final
def _is_level_reference(self, key, axis=0):
"""
Test whether a key is a level reference for a given axis.
To be considered a level reference, `key` must be a string that:
- (axis=0): Matches the name of an index level and does NOT match
a column label.
- (axis=1): Matches the name of a column level and does NOT match
an index label.
Parameters
----------
key : str
Potential level name for the given axis
axis : int, default 0
Axis that levels are associated with (0 for index, 1 for columns)
Returns
-------
is_level : bool
"""
axis = self._get_axis_number(axis)
return (
key is not None
and is_hashable(key)
and key in self.axes[axis].names
and not self._is_label_reference(key, axis=axis)
)
@final
def _is_label_reference(self, key, axis=0) -> bool_t:
"""
Test whether a key is a label reference for a given axis.
To be considered a label reference, `key` must be a string that:
- (axis=0): Matches a column label
- (axis=1): Matches an index label
Parameters
----------
key: str
Potential label name
axis: int, default 0
Axis perpendicular to the axis that labels are associated with
(0 means search for column labels, 1 means search for index labels)
Returns
-------
is_label: bool
"""
axis = self._get_axis_number(axis)
other_axes = (ax for ax in range(self._AXIS_LEN) if ax != axis)
return (
key is not None
and is_hashable(key)
and any(key in self.axes[ax] for ax in other_axes)
)
@final
def _is_label_or_level_reference(self, key: str, axis: int = 0) -> bool_t:
"""
Test whether a key is a label or level reference for a given axis.
To be considered either a label or a level reference, `key` must be a
string that:
- (axis=0): Matches a column label or an index level
- (axis=1): Matches an index label or a column level
Parameters
----------
key: str
Potential label or level name
axis: int, default 0
Axis that levels are associated with (0 for index, 1 for columns)
Returns
-------
is_label_or_level: bool
"""
return self._is_level_reference(key, axis=axis) or self._is_label_reference(
key, axis=axis
)
@final
def _check_label_or_level_ambiguity(self, key, axis: int = 0) -> None:
"""
Check whether `key` is ambiguous.
By ambiguous, we mean that it matches both a level of the input
`axis` and a label of the other axis.
Parameters
----------
key: str or object
Label or level name.
axis: int, default 0
Axis that levels are associated with (0 for index, 1 for columns).
Raises
------
ValueError: `key` is ambiguous
"""
axis = self._get_axis_number(axis)
other_axes = (ax for ax in range(self._AXIS_LEN) if ax != axis)
if (
key is not None
and is_hashable(key)
and key in self.axes[axis].names
and any(key in self.axes[ax] for ax in other_axes)
):
# Build an informative and grammatical warning
level_article, level_type = (
("an", "index") if axis == 0 else ("a", "column")
)
label_article, label_type = (
("a", "column") if axis == 0 else ("an", "index")
)
msg = (
f"'{key}' is both {level_article} {level_type} level and "
f"{label_article} {label_type} label, which is ambiguous."
)
raise ValueError(msg)
@final
def _get_label_or_level_values(self, key: str, axis: int = 0) -> np.ndarray:
"""
Return a 1-D array of values associated with `key`, a label or level
from the given `axis`.
Retrieval logic:
- (axis=0): Return column values if `key` matches a column label.
Otherwise return index level values if `key` matches an index
level.
- (axis=1): Return row values if `key` matches an index label.
Otherwise return column level values if 'key' matches a column
level
Parameters
----------
key: str
Label or level name.
axis: int, default 0
Axis that levels are associated with (0 for index, 1 for columns)
Returns
-------
values: np.ndarray
Raises
------
KeyError
if `key` matches neither a label nor a level
ValueError
if `key` matches multiple labels
FutureWarning
if `key` is ambiguous. This will become an ambiguity error in a
future version
"""
axis = self._get_axis_number(axis)
other_axes = [ax for ax in range(self._AXIS_LEN) if ax != axis]
if self._is_label_reference(key, axis=axis):
self._check_label_or_level_ambiguity(key, axis=axis)
values = self.xs(key, axis=other_axes[0])._values
elif self._is_level_reference(key, axis=axis):
values = self.axes[axis].get_level_values(key)._values
else:
raise KeyError(key)
# Check for duplicates
if values.ndim > 1:
if other_axes and isinstance(self._get_axis(other_axes[0]), MultiIndex):
multi_message = (
"\n"
"For a multi-index, the label must be a "
"tuple with elements corresponding to each level."
)
else:
multi_message = ""
label_axis_name = "column" if axis == 0 else "index"
raise ValueError(
f"The {label_axis_name} label '{key}' is not unique.{multi_message}"
)
return values
@final
def _drop_labels_or_levels(self, keys, axis: int = 0):
"""
Drop labels and/or levels for the given `axis`.
For each key in `keys`:
- (axis=0): If key matches a column label then drop the column.
Otherwise if key matches an index level then drop the level.
- (axis=1): If key matches an index label then drop the row.
Otherwise if key matches a column level then drop the level.
Parameters
----------
keys: str or list of str
labels or levels to drop
axis: int, default 0
Axis that levels are associated with (0 for index, 1 for columns)
Returns
-------
dropped: DataFrame
Raises
------
ValueError
if any `keys` match neither a label nor a level
"""
axis = self._get_axis_number(axis)
# Validate keys
keys = com.maybe_make_list(keys)
invalid_keys = [
k for k in keys if not self._is_label_or_level_reference(k, axis=axis)
]
if invalid_keys:
raise ValueError(
"The following keys are not valid labels or "
f"levels for axis {axis}: {invalid_keys}"
)
# Compute levels and labels to drop
levels_to_drop = [k for k in keys if self._is_level_reference(k, axis=axis)]
labels_to_drop = [k for k in keys if not self._is_level_reference(k, axis=axis)]
# Perform copy upfront and then use inplace operations below.
# This ensures that we always perform exactly one copy.
# ``copy`` and/or ``inplace`` options could be added in the future.
dropped = self.copy()
if axis == 0:
# Handle dropping index levels
if levels_to_drop:
dropped.reset_index(levels_to_drop, drop=True, inplace=True)
# Handle dropping columns labels
if labels_to_drop:
dropped.drop(labels_to_drop, axis=1, inplace=True)
else:
# Handle dropping column levels
if levels_to_drop:
if isinstance(dropped.columns, MultiIndex):
# Drop the specified levels from the MultiIndex
dropped.columns = dropped.columns.droplevel(levels_to_drop)
else:
# Drop the last level of Index by replacing with
# a RangeIndex
dropped.columns = RangeIndex(dropped.columns.size)
# Handle dropping index labels
if labels_to_drop:
dropped.drop(labels_to_drop, axis=0, inplace=True)
return dropped
# ----------------------------------------------------------------------
# Iteration
def __hash__(self) -> int:
raise TypeError(
f"{repr(type(self).__name__)} objects are mutable, "
f"thus they cannot be hashed"
)
def __iter__(self):
"""
Iterate over info axis.
Returns
-------
iterator
Info axis as iterator.
"""
return iter(self._info_axis)
# can we get a better explanation of this?
def keys(self):
"""
Get the 'info axis' (see Indexing for more).
This is index for Series, columns for DataFrame.
Returns
-------
Index
Info axis.
"""
return self._info_axis
def items(self):
"""
Iterate over (label, values) on info axis
This is index for Series and columns for DataFrame.
Returns
-------
Generator
"""
for h in self._info_axis:
yield h, self[h]
@doc(items)
def iteritems(self):
return self.items()
def __len__(self) -> int:
"""Returns length of info axis"""
return len(self._info_axis)
@final
def __contains__(self, key) -> bool_t:
"""True if the key is in the info axis"""
return key in self._info_axis
@property
def empty(self) -> bool_t:
"""
Indicator whether DataFrame is empty.
True if DataFrame is entirely empty (no items), meaning any of the
axes are of length 0.
Returns
-------
bool
If DataFrame is empty, return True, if not return False.
See Also
--------
Series.dropna : Return series without null values.
DataFrame.dropna : Return DataFrame with labels on given axis omitted
where (all or any) data are missing.
Notes
-----
If DataFrame contains only NaNs, it is still not considered empty. See
the example below.
Examples
--------
An example of an actual empty DataFrame. Notice the index is empty:
>>> df_empty = pd.DataFrame({'A' : []})
>>> df_empty
Empty DataFrame
Columns: [A]
Index: []
>>> df_empty.empty
True
If we only have NaNs in our DataFrame, it is not considered empty! We
will need to drop the NaNs to make the DataFrame empty:
>>> df = pd.DataFrame({'A' : [np.nan]})
>>> df
A
0 NaN
>>> df.empty
False
>>> df.dropna().empty
True
"""
return any(len(self._get_axis(a)) == 0 for a in self._AXIS_ORDERS)
# ----------------------------------------------------------------------
# Array Interface
# This is also set in IndexOpsMixin
# GH#23114 Ensure ndarray.__op__(DataFrame) returns NotImplemented
__array_priority__ = 1000
def __array__(self, dtype=None) -> np.ndarray:
return np.asarray(self._values, dtype=dtype)
def __array_wrap__(
self,
result: np.ndarray,
context: Optional[Tuple[Callable, Tuple[Any, ...], int]] = None,
):
"""
Gets called after a ufunc and other functions.
Parameters
----------
result: np.ndarray
The result of the ufunc or other function called on the NumPy array
returned by __array__
context: tuple of (func, tuple, int)
This parameter is returned by ufuncs as a 3-element tuple: (name of the
ufunc, arguments of the ufunc, domain of the ufunc), but is not set by
other numpy functions.q
Notes
-----
Series implements __array_ufunc_ so this not called for ufunc on Series.
"""
result = lib.item_from_zerodim(result)
if is_scalar(result):
# e.g. we get here with np.ptp(series)
# ptp also requires the item_from_zerodim
return result
d = self._construct_axes_dict(self._AXIS_ORDERS, copy=False)
return self._constructor(result, **d).__finalize__(
self, method="__array_wrap__"
)
def __array_ufunc__(
self, ufunc: Callable, method: str, *inputs: Any, **kwargs: Any
):
return arraylike.array_ufunc(self, ufunc, method, *inputs, **kwargs)
# ideally we would define this to avoid the getattr checks, but
# is slower
# @property
# def __array_interface__(self):
# """ provide numpy array interface method """
# values = self.values
# return dict(typestr=values.dtype.str,shape=values.shape,data=values)
# ----------------------------------------------------------------------
# Picklability
@final
def __getstate__(self) -> Dict[str, Any]:
meta = {k: getattr(self, k, None) for k in self._metadata}
return {
"_mgr": self._mgr,
"_typ": self._typ,
"_metadata": self._metadata,
"attrs": self.attrs,
"_flags": {k: self.flags[k] for k in self.flags._keys},
**meta,
}
@final
def __setstate__(self, state):
if isinstance(state, BlockManager):
self._mgr = state
elif isinstance(state, dict):
if "_data" in state and "_mgr" not in state:
# compat for older pickles
state["_mgr"] = state.pop("_data")
typ = state.get("_typ")
if typ is not None:
attrs = state.get("_attrs", {})
object.__setattr__(self, "_attrs", attrs)
flags = state.get("_flags", {"allows_duplicate_labels": True})
object.__setattr__(self, "_flags", Flags(self, **flags))
# set in the order of internal names
# to avoid definitional recursion
# e.g. say fill_value needing _mgr to be
# defined
meta = set(self._internal_names + self._metadata)
for k in list(meta):
if k in state and k != "_flags":
v = state[k]
object.__setattr__(self, k, v)
for k, v in state.items():
if k not in meta:
object.__setattr__(self, k, v)
else:
raise NotImplementedError("Pre-0.12 pickles are no longer supported")
elif len(state) == 2:
raise NotImplementedError("Pre-0.12 pickles are no longer supported")
self._item_cache = {}
# ----------------------------------------------------------------------
# Rendering Methods
def __repr__(self) -> str:
# string representation based upon iterating over self
# (since, by definition, `PandasContainers` are iterable)
prepr = f"[{','.join(map(pprint_thing, self))}]"
return f"{type(self).__name__}({prepr})"
@final
def _repr_latex_(self):
"""
Returns a LaTeX representation for a particular object.
Mainly for use with nbconvert (jupyter notebook conversion to pdf).
"""
if config.get_option("display.latex.repr"):
return self.to_latex()
else:
return None
@final
def _repr_data_resource_(self):
"""
Not a real Jupyter special repr method, but we use the same
naming convention.
"""
if config.get_option("display.html.table_schema"):
data = self.head(config.get_option("display.max_rows"))
as_json = data.to_json(orient="table")
as_json = cast(str, as_json)
payload = json.loads(as_json, object_pairs_hook=collections.OrderedDict)
return payload
# ----------------------------------------------------------------------
# I/O Methods
@final
@doc(klass="object", storage_options=_shared_docs["storage_options"])
def to_excel(
self,
excel_writer,
sheet_name: str = "Sheet1",
na_rep: str = "",
float_format: Optional[str] = None,
columns=None,
header=True,
index=True,
index_label=None,
startrow=0,
startcol=0,
engine=None,
merge_cells=True,
encoding=None,
inf_rep="inf",
verbose=True,
freeze_panes=None,
storage_options: StorageOptions = None,
) -> None:
"""
Write {klass} to an Excel sheet.
To write a single {klass} to an Excel .xlsx file it is only necessary to
specify a target file name. To write to multiple sheets it is necessary to
create an `ExcelWriter` object with a target file name, and specify a sheet
in the file to write to.
Multiple sheets may be written to by specifying unique `sheet_name`.
With all data written to the file it is necessary to save the changes.
Note that creating an `ExcelWriter` object with a file name that already
exists will result in the contents of the existing file being erased.
Parameters
----------
excel_writer : path-like, file-like, or ExcelWriter object
File path or existing ExcelWriter.
sheet_name : str, default 'Sheet1'
Name of sheet which will contain DataFrame.
na_rep : str, default ''
Missing data representation.
float_format : str, optional
Format string for floating point numbers. For example
``float_format="%.2f"`` will format 0.1234 to 0.12.
columns : sequence or list of str, optional
Columns to write.
header : bool or list of str, default True
Write out the column names. If a list of string is given it is
assumed to be aliases for the column names.
index : bool, default True
Write row names (index).
index_label : str or sequence, optional
Column label for index column(s) if desired. If not specified, and
`header` and `index` are True, then the index names are used. A
sequence should be given if the DataFrame uses MultiIndex.
startrow : int, default 0
Upper left cell row to dump data frame.
startcol : int, default 0
Upper left cell column to dump data frame.
engine : str, optional
Write engine to use, 'openpyxl' or 'xlsxwriter'. You can also set this
via the options ``io.excel.xlsx.writer``, ``io.excel.xls.writer``, and
``io.excel.xlsm.writer``.
.. deprecated:: 1.2.0
As the `xlwt <https://pypi.org/project/xlwt/>`__ package is no longer
maintained, the ``xlwt`` engine will be removed in a future version
of pandas.
merge_cells : bool, default True
Write MultiIndex and Hierarchical Rows as merged cells.
encoding : str, optional
Encoding of the resulting excel file. Only necessary for xlwt,
other writers support unicode natively.
inf_rep : str, default 'inf'
Representation for infinity (there is no native representation for
infinity in Excel).
verbose : bool, default True
Display more information in the error logs.
freeze_panes : tuple of int (length 2), optional
Specifies the one-based bottommost row and rightmost column that
is to be frozen.
{storage_options}
.. versionadded:: 1.2.0
See Also
--------
to_csv : Write DataFrame to a comma-separated values (csv) file.
ExcelWriter : Class for writing DataFrame objects into excel sheets.
read_excel : Read an Excel file into a pandas DataFrame.
read_csv : Read a comma-separated values (csv) file into DataFrame.
Notes
-----
For compatibility with :meth:`~DataFrame.to_csv`,
to_excel serializes lists and dicts to strings before writing.
Once a workbook has been saved it is not possible write further data
without rewriting the whole workbook.
Examples
--------
Create, write to and save a workbook:
>>> df1 = pd.DataFrame([['a', 'b'], ['c', 'd']],
... index=['row 1', 'row 2'],
... columns=['col 1', 'col 2'])
>>> df1.to_excel("output.xlsx") # doctest: +SKIP
To specify the sheet name:
>>> df1.to_excel("output.xlsx",
... sheet_name='Sheet_name_1') # doctest: +SKIP
If you wish to write to more than one sheet in the workbook, it is
necessary to specify an ExcelWriter object:
>>> df2 = df1.copy()
>>> with pd.ExcelWriter('output.xlsx') as writer: # doctest: +SKIP
... df1.to_excel(writer, sheet_name='Sheet_name_1')
... df2.to_excel(writer, sheet_name='Sheet_name_2')
ExcelWriter can also be used to append to an existing Excel file:
>>> with pd.ExcelWriter('output.xlsx',
... mode='a') as writer: # doctest: +SKIP
... df.to_excel(writer, sheet_name='Sheet_name_3')
To set the library that is used to write the Excel file,
you can pass the `engine` keyword (the default engine is
automatically chosen depending on the file extension):
>>> df1.to_excel('output1.xlsx', engine='xlsxwriter') # doctest: +SKIP
"""
df = self if isinstance(self, ABCDataFrame) else self.to_frame()
from pandas.io.formats.excel import ExcelFormatter
formatter = ExcelFormatter(
df,
na_rep=na_rep,
cols=columns,
header=header,
float_format=float_format,
index=index,
index_label=index_label,
merge_cells=merge_cells,
inf_rep=inf_rep,
)
formatter.write(
excel_writer,
sheet_name=sheet_name,
startrow=startrow,
startcol=startcol,
freeze_panes=freeze_panes,
engine=engine,
storage_options=storage_options,
)
@final
@doc(storage_options=_shared_docs["storage_options"])
def to_json(
self,
path_or_buf: Optional[FilePathOrBuffer] = None,
orient: Optional[str] = None,
date_format: Optional[str] = None,
double_precision: int = 10,
force_ascii: bool_t = True,
date_unit: str = "ms",
default_handler: Optional[Callable[[Any], JSONSerializable]] = None,
lines: bool_t = False,
compression: CompressionOptions = "infer",
index: bool_t = True,
indent: Optional[int] = None,
storage_options: StorageOptions = None,
) -> Optional[str]:
"""
Convert the object to a JSON string.
Note NaN's and None will be converted to null and datetime objects
will be converted to UNIX timestamps.
Parameters
----------
path_or_buf : str or file handle, optional
File path or object. If not specified, the result is returned as
a string.
orient : str
Indication of expected JSON string format.
* Series:
- default is 'index'
- allowed values are: {{'split', 'records', 'index', 'table'}}.
* DataFrame:
- default is 'columns'
- allowed values are: {{'split', 'records', 'index', 'columns',
'values', 'table'}}.
* The format of the JSON string:
- 'split' : dict like {{'index' -> [index], 'columns' -> [columns],
'data' -> [values]}}
- 'records' : list like [{{column -> value}}, ... , {{column -> value}}]
- 'index' : dict like {{index -> {{column -> value}}}}
- 'columns' : dict like {{column -> {{index -> value}}}}
- 'values' : just the values array
- 'table' : dict like {{'schema': {{schema}}, 'data': {{data}}}}
Describing the data, where data component is like ``orient='records'``.
date_format : {{None, 'epoch', 'iso'}}
Type of date conversion. 'epoch' = epoch milliseconds,
'iso' = ISO8601. The default depends on the `orient`. For
``orient='table'``, the default is 'iso'. For all other orients,
the default is 'epoch'.
double_precision : int, default 10
The number of decimal places to use when encoding
floating point values.
force_ascii : bool, default True
Force encoded string to be ASCII.
date_unit : str, default 'ms' (milliseconds)
The time unit to encode to, governs timestamp and ISO8601
precision. One of 's', 'ms', 'us', 'ns' for second, millisecond,
microsecond, and nanosecond respectively.
default_handler : callable, default None
Handler to call if object cannot otherwise be converted to a
suitable format for JSON. Should receive a single argument which is
the object to convert and return a serialisable object.
lines : bool, default False
If 'orient' is 'records' write out line delimited json format. Will
throw ValueError if incorrect 'orient' since others are not list
like.
compression : {{'infer', 'gzip', 'bz2', 'zip', 'xz', None}}
A string representing the compression to use in the output file,
only used when the first argument is a filename. By default, the
compression is inferred from the filename.
.. versionchanged:: 0.24.0
'infer' option added and set to default
index : bool, default True
Whether to include the index values in the JSON string. Not
including the index (``index=False``) is only supported when
orient is 'split' or 'table'.
indent : int, optional
Length of whitespace used to indent each record.
.. versionadded:: 1.0.0
{storage_options}
.. versionadded:: 1.2.0
Returns
-------
None or str
If path_or_buf is None, returns the resulting json format as a
string. Otherwise returns None.
See Also
--------
read_json : Convert a JSON string to pandas object.
Notes
-----
The behavior of ``indent=0`` varies from the stdlib, which does not
indent the output but does insert newlines. Currently, ``indent=0``
and the default ``indent=None`` are equivalent in pandas, though this
may change in a future release.
``orient='table'`` contains a 'pandas_version' field under 'schema'.
This stores the version of `pandas` used in the latest revision of the
schema.
Examples
--------
>>> import json
>>> df = pd.DataFrame(
... [["a", "b"], ["c", "d"]],
... index=["row 1", "row 2"],
... columns=["col 1", "col 2"],
... )
>>> result = df.to_json(orient="split")
>>> parsed = json.loads(result)
>>> json.dumps(parsed, indent=4) # doctest: +SKIP
{{
"columns": [
"col 1",
"col 2"
],
"index": [
"row 1",
"row 2"
],
"data": [
[
"a",
"b"
],
[
"c",
"d"
]
]
}}
Encoding/decoding a Dataframe using ``'records'`` formatted JSON.
Note that index labels are not preserved with this encoding.
>>> result = df.to_json(orient="records")
>>> parsed = json.loads(result)
>>> json.dumps(parsed, indent=4) # doctest: +SKIP
[
{{
"col 1": "a",
"col 2": "b"
}},
{{
"col 1": "c",
"col 2": "d"
}}
]
Encoding/decoding a Dataframe using ``'index'`` formatted JSON:
>>> result = df.to_json(orient="index")
>>> parsed = json.loads(result)
>>> json.dumps(parsed, indent=4) # doctest: +SKIP
{{
"row 1": {{
"col 1": "a",
"col 2": "b"
}},
"row 2": {{
"col 1": "c",
"col 2": "d"
}}
}}
Encoding/decoding a Dataframe using ``'columns'`` formatted JSON:
>>> result = df.to_json(orient="columns")
>>> parsed = json.loads(result)
>>> json.dumps(parsed, indent=4) # doctest: +SKIP
{{
"col 1": {{
"row 1": "a",
"row 2": "c"
}},
"col 2": {{
"row 1": "b",
"row 2": "d"
}}
}}
Encoding/decoding a Dataframe using ``'values'`` formatted JSON:
>>> result = df.to_json(orient="values")
>>> parsed = json.loads(result)
>>> json.dumps(parsed, indent=4) # doctest: +SKIP
[
[
"a",
"b"
],
[
"c",
"d"
]
]
Encoding with Table Schema:
>>> result = df.to_json(orient="table")
>>> parsed = json.loads(result)
>>> json.dumps(parsed, indent=4) # doctest: +SKIP
{{
"schema": {{
"fields": [
{{
"name": "index",
"type": "string"
}},
{{
"name": "col 1",
"type": "string"
}},
{{
"name": "col 2",
"type": "string"
}}
],
"primaryKey": [
"index"
],
"pandas_version": "0.20.0"
}},
"data": [
{{
"index": "row 1",
"col 1": "a",
"col 2": "b"
}},
{{
"index": "row 2",
"col 1": "c",
"col 2": "d"
}}
]
}}
"""
from pandas.io import json
if date_format is None and orient == "table":
date_format = "iso"
elif date_format is None:
date_format = "epoch"
config.is_nonnegative_int(indent)
indent = indent or 0
return json.to_json(
path_or_buf=path_or_buf,
obj=self,
orient=orient,
date_format=date_format,
double_precision=double_precision,
force_ascii=force_ascii,
date_unit=date_unit,
default_handler=default_handler,
lines=lines,
compression=compression,
index=index,
indent=indent,
storage_options=storage_options,
)
@final
def to_hdf(
self,
path_or_buf,
key: str,
mode: str = "a",
complevel: Optional[int] = None,
complib: Optional[str] = None,
append: bool_t = False,
format: Optional[str] = None,
index: bool_t = True,
min_itemsize: Optional[Union[int, Dict[str, int]]] = None,
nan_rep=None,
dropna: Optional[bool_t] = None,
data_columns: Optional[Union[bool_t, List[str]]] = None,
errors: str = "strict",
encoding: str = "UTF-8",
) -> None:
"""
Write the contained data to an HDF5 file using HDFStore.
Hierarchical Data Format (HDF) is self-describing, allowing an
application to interpret the structure and contents of a file with
no outside information. One HDF file can hold a mix of related objects
which can be accessed as a group or as individual objects.
In order to add another DataFrame or Series to an existing HDF file
please use append mode and a different a key.
For more information see the :ref:`user guide <io.hdf5>`.
Parameters
----------
path_or_buf : str or pandas.HDFStore
File path or HDFStore object.
key : str
Identifier for the group in the store.
mode : {'a', 'w', 'r+'}, default 'a'
Mode to open file:
- 'w': write, a new file is created (an existing file with
the same name would be deleted).
- 'a': append, an existing file is opened for reading and
writing, and if the file does not exist it is created.
- 'r+': similar to 'a', but the file must already exist.
complevel : {0-9}, optional
Specifies a compression level for data.
A value of 0 disables compression.
complib : {'zlib', 'lzo', 'bzip2', 'blosc'}, default 'zlib'
Specifies the compression library to be used.
As of v0.20.2 these additional compressors for Blosc are supported
(default if no compressor specified: 'blosc:blosclz'):
{'blosc:blosclz', 'blosc:lz4', 'blosc:lz4hc', 'blosc:snappy',
'blosc:zlib', 'blosc:zstd'}.
Specifying a compression library which is not available issues
a ValueError.
append : bool, default False
For Table formats, append the input data to the existing.
format : {'fixed', 'table', None}, default 'fixed'
Possible values:
- 'fixed': Fixed format. Fast writing/reading. Not-appendable,
nor searchable.
- 'table': Table format. Write as a PyTables Table structure
which may perform worse but allow more flexible operations
like searching / selecting subsets of the data.
- If None, pd.get_option('io.hdf.default_format') is checked,
followed by fallback to "fixed"
errors : str, default 'strict'
Specifies how encoding and decoding errors are to be handled.
See the errors argument for :func:`open` for a full list
of options.
encoding : str, default "UTF-8"
min_itemsize : dict or int, optional
Map column names to minimum string sizes for columns.
nan_rep : Any, optional
How to represent null values as str.
Not allowed with append=True.
data_columns : list of columns or True, optional
List of columns to create as indexed data columns for on-disk
queries, or True to use all columns. By default only the axes
of the object are indexed. See :ref:`io.hdf5-query-data-columns`.
Applicable only to format='table'.
See Also
--------
DataFrame.read_hdf : Read from HDF file.
DataFrame.to_parquet : Write a DataFrame to the binary parquet format.
DataFrame.to_sql : Write to a sql table.
DataFrame.to_feather : Write out feather-format for DataFrames.
DataFrame.to_csv : Write out to a csv file.
Examples
--------
>>> df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]},
... index=['a', 'b', 'c'])
>>> df.to_hdf('data.h5', key='df', mode='w')
We can add another object to the same file:
>>> s = pd.Series([1, 2, 3, 4])
>>> s.to_hdf('data.h5', key='s')
Reading from HDF file:
>>> pd.read_hdf('data.h5', 'df')
A B
a 1 4
b 2 5
c 3 6
>>> pd.read_hdf('data.h5', 's')
0 1
1 2
2 3
3 4
dtype: int64
Deleting file with data:
>>> import os
>>> os.remove('data.h5')
"""
from pandas.io import pytables
pytables.to_hdf(
path_or_buf,
key,
self,
mode=mode,
complevel=complevel,
complib=complib,
append=append,
format=format,
index=index,
min_itemsize=min_itemsize,
nan_rep=nan_rep,
dropna=dropna,
data_columns=data_columns,
errors=errors,
encoding=encoding,
)
@final
def to_sql(
self,
name: str,
con,
schema=None,
if_exists: str = "fail",
index: bool_t = True,
index_label=None,
chunksize=None,
dtype=None,
method=None,
) -> None:
"""
Write records stored in a DataFrame to a SQL database.
Databases supported by SQLAlchemy [1]_ are supported. Tables can be
newly created, appended to, or overwritten.
Parameters
----------
name : str
Name of SQL table.
con : sqlalchemy.engine.(Engine or Connection) or sqlite3.Connection
Using SQLAlchemy makes it possible to use any DB supported by that
library. Legacy support is provided for sqlite3.Connection objects. The user
is responsible for engine disposal and connection closure for the SQLAlchemy
connectable See `here \
<https://docs.sqlalchemy.org/en/13/core/connections.html>`_.
schema : str, optional
Specify the schema (if database flavor supports this). If None, use
default schema.
if_exists : {'fail', 'replace', 'append'}, default 'fail'
How to behave if the table already exists.
* fail: Raise a ValueError.
* replace: Drop the table before inserting new values.
* append: Insert new values to the existing table.
index : bool, default True
Write DataFrame index as a column. Uses `index_label` as the column
name in the table.
index_label : str or sequence, default None
Column label for index column(s). If None is given (default) and
`index` is True, then the index names are used.
A sequence should be given if the DataFrame uses MultiIndex.
chunksize : int, optional
Specify the number of rows in each batch to be written at a time.
By default, all rows will be written at once.
dtype : dict or scalar, optional
Specifying the datatype for columns. If a dictionary is used, the
keys should be the column names and the values should be the
SQLAlchemy types or strings for the sqlite3 legacy mode. If a
scalar is provided, it will be applied to all columns.
method : {None, 'multi', callable}, optional
Controls the SQL insertion clause used:
* None : Uses standard SQL ``INSERT`` clause (one per row).
* 'multi': Pass multiple values in a single ``INSERT`` clause.
* callable with signature ``(pd_table, conn, keys, data_iter)``.
Details and a sample callable implementation can be found in the
section :ref:`insert method <io.sql.method>`.
.. versionadded:: 0.24.0
Raises
------
ValueError
When the table already exists and `if_exists` is 'fail' (the
default).
See Also
--------
read_sql : Read a DataFrame from a table.
Notes
-----
Timezone aware datetime columns will be written as
``Timestamp with timezone`` type with SQLAlchemy if supported by the
database. Otherwise, the datetimes will be stored as timezone unaware
timestamps local to the original timezone.
.. versionadded:: 0.24.0
References
----------
.. [1] https://docs.sqlalchemy.org
.. [2] https://www.python.org/dev/peps/pep-0249/
Examples
--------
Create an in-memory SQLite database.
>>> from sqlalchemy import create_engine
>>> engine = create_engine('sqlite://', echo=False)
Create a table from scratch with 3 rows.
>>> df = pd.DataFrame({'name' : ['User 1', 'User 2', 'User 3']})
>>> df
name
0 User 1
1 User 2
2 User 3
>>> df.to_sql('users', con=engine)
>>> engine.execute("SELECT * FROM users").fetchall()
[(0, 'User 1'), (1, 'User 2'), (2, 'User 3')]
An `sqlalchemy.engine.Connection` can also be passed to `con`:
>>> with engine.begin() as connection:
... df1 = pd.DataFrame({'name' : ['User 4', 'User 5']})
... df1.to_sql('users', con=connection, if_exists='append')
This is allowed to support operations that require that the same
DBAPI connection is used for the entire operation.
>>> df2 = pd.DataFrame({'name' : ['User 6', 'User 7']})
>>> df2.to_sql('users', con=engine, if_exists='append')
>>> engine.execute("SELECT * FROM users").fetchall()
[(0, 'User 1'), (1, 'User 2'), (2, 'User 3'),
(0, 'User 4'), (1, 'User 5'), (0, 'User 6'),
(1, 'User 7')]
Overwrite the table with just ``df2``.
>>> df2.to_sql('users', con=engine, if_exists='replace',
... index_label='id')
>>> engine.execute("SELECT * FROM users").fetchall()
[(0, 'User 6'), (1, 'User 7')]
Specify the dtype (especially useful for integers with missing values).
Notice that while pandas is forced to store the data as floating point,
the database supports nullable integers. When fetching the data with
Python, we get back integer scalars.
>>> df = pd.DataFrame({"A": [1, None, 2]})
>>> df
A
0 1.0
1 NaN
2 2.0
>>> from sqlalchemy.types import Integer
>>> df.to_sql('integers', con=engine, index=False,
... dtype={"A": Integer()})
>>> engine.execute("SELECT * FROM integers").fetchall()
[(1,), (None,), (2,)]
"""
from pandas.io import sql
sql.to_sql(
self,
name,
con,
schema=schema,
if_exists=if_exists,
index=index,
index_label=index_label,
chunksize=chunksize,
dtype=dtype,
method=method,
)
@final
@doc(storage_options=_shared_docs["storage_options"])
def to_pickle(
self,
path,
compression: CompressionOptions = "infer",
protocol: int = pickle.HIGHEST_PROTOCOL,
storage_options: StorageOptions = None,
) -> None:
"""
Pickle (serialize) object to file.
Parameters
----------
path : str
File path where the pickled object will be stored.
compression : {{'infer', 'gzip', 'bz2', 'zip', 'xz', None}}, \
default 'infer'
A string representing the compression to use in the output file. By
default, infers from the file extension in specified path.
Compression mode may be any of the following possible
values: {{‘infer’, ‘gzip’, ‘bz2’, ‘zip’, ‘xz’, None}}. If compression
mode is ‘infer’ and path_or_buf is path-like, then detect
compression mode from the following extensions:
‘.gz’, ‘.bz2’, ‘.zip’ or ‘.xz’. (otherwise no compression).
If dict given and mode is ‘zip’ or inferred as ‘zip’, other entries
passed as additional compression options.
protocol : int
Int which indicates which protocol should be used by the pickler,
default HIGHEST_PROTOCOL (see [1]_ paragraph 12.1.2). The possible
values are 0, 1, 2, 3, 4, 5. A negative value for the protocol
parameter is equivalent to setting its value to HIGHEST_PROTOCOL.
.. [1] https://docs.python.org/3/library/pickle.html.
{storage_options}
.. versionadded:: 1.2.0
See Also
--------
read_pickle : Load pickled pandas object (or any object) from file.
DataFrame.to_hdf : Write DataFrame to an HDF5 file.
DataFrame.to_sql : Write DataFrame to a SQL database.
DataFrame.to_parquet : Write a DataFrame to the binary parquet format.
Examples
--------
>>> original_df = pd.DataFrame({{"foo": range(5), "bar": range(5, 10)}})
>>> original_df
foo bar
0 0 5
1 1 6
2 2 7
3 3 8
4 4 9
>>> original_df.to_pickle("./dummy.pkl")
>>> unpickled_df = pd.read_pickle("./dummy.pkl")
>>> unpickled_df
foo bar
0 0 5
1 1 6
2 2 7
3 3 8
4 4 9
>>> import os
>>> os.remove("./dummy.pkl")
"""
from pandas.io.pickle import to_pickle
to_pickle(
self,
path,
compression=compression,
protocol=protocol,
storage_options=storage_options,
)
@final
def to_clipboard(
self, excel: bool_t = True, sep: Optional[str] = None, **kwargs
) -> None:
r"""
Copy object to the system clipboard.
Write a text representation of object to the system clipboard.
This can be pasted into Excel, for example.
Parameters
----------
excel : bool, default True
Produce output in a csv format for easy pasting into excel.
- True, use the provided separator for csv pasting.
- False, write a string representation of the object to the clipboard.
sep : str, default ``'\t'``
Field delimiter.
**kwargs
These parameters will be passed to DataFrame.to_csv.
See Also
--------
DataFrame.to_csv : Write a DataFrame to a comma-separated values
(csv) file.
read_clipboard : Read text from clipboard and pass to read_table.
Notes
-----
Requirements for your platform.
- Linux : `xclip`, or `xsel` (with `PyQt4` modules)
- Windows : none
- OS X : none
Examples
--------
Copy the contents of a DataFrame to the clipboard.
>>> df = pd.DataFrame([[1, 2, 3], [4, 5, 6]], columns=['A', 'B', 'C'])
>>> df.to_clipboard(sep=',') # doctest: +SKIP
... # Wrote the following to the system clipboard:
... # ,A,B,C
... # 0,1,2,3
... # 1,4,5,6
We can omit the index by passing the keyword `index` and setting
it to false.
>>> df.to_clipboard(sep=',', index=False) # doctest: +SKIP
... # Wrote the following to the system clipboard:
... # A,B,C
... # 1,2,3
... # 4,5,6
"""
from pandas.io import clipboards
clipboards.to_clipboard(self, excel=excel, sep=sep, **kwargs)
@final
def to_xarray(self):
"""
Return an xarray object from the pandas object.
Returns
-------
xarray.DataArray or xarray.Dataset
Data in the pandas structure converted to Dataset if the object is
a DataFrame, or a DataArray if the object is a Series.
See Also
--------
DataFrame.to_hdf : Write DataFrame to an HDF5 file.
DataFrame.to_parquet : Write a DataFrame to the binary parquet format.
Notes
-----
See the `xarray docs <https://xarray.pydata.org/en/stable/>`__
Examples
--------
>>> df = pd.DataFrame([('falcon', 'bird', 389.0, 2),
... ('parrot', 'bird', 24.0, 2),
... ('lion', 'mammal', 80.5, 4),
... ('monkey', 'mammal', np.nan, 4)],
... columns=['name', 'class', 'max_speed',
... 'num_legs'])
>>> df
name class max_speed num_legs
0 falcon bird 389.0 2
1 parrot bird 24.0 2
2 lion mammal 80.5 4
3 monkey mammal NaN 4
>>> df.to_xarray()
<xarray.Dataset>
Dimensions: (index: 4)
Coordinates:
* index (index) int64 0 1 2 3
Data variables:
name (index) object 'falcon' 'parrot' 'lion' 'monkey'
class (index) object 'bird' 'bird' 'mammal' 'mammal'
max_speed (index) float64 389.0 24.0 80.5 nan
num_legs (index) int64 2 2 4 4
>>> df['max_speed'].to_xarray()
<xarray.DataArray 'max_speed' (index: 4)>
array([389. , 24. , 80.5, nan])
Coordinates:
* index (index) int64 0 1 2 3
>>> dates = pd.to_datetime(['2018-01-01', '2018-01-01',
... '2018-01-02', '2018-01-02'])
>>> df_multiindex = pd.DataFrame({'date': dates,
... 'animal': ['falcon', 'parrot',
... 'falcon', 'parrot'],
... 'speed': [350, 18, 361, 15]})
>>> df_multiindex = df_multiindex.set_index(['date', 'animal'])
>>> df_multiindex
speed
date animal
2018-01-01 falcon 350
parrot 18
2018-01-02 falcon 361
parrot 15
>>> df_multiindex.to_xarray()
<xarray.Dataset>
Dimensions: (animal: 2, date: 2)
Coordinates:
* date (date) datetime64[ns] 2018-01-01 2018-01-02
* animal (animal) object 'falcon' 'parrot'
Data variables:
speed (date, animal) int64 350 18 361 15
"""
xarray = import_optional_dependency("xarray")
if self.ndim == 1:
return xarray.DataArray.from_series(self)
else:
return xarray.Dataset.from_dataframe(self)
@final
@doc(returns=fmt.return_docstring)
def to_latex(
self,
buf=None,
columns=None,
col_space=None,
header=True,
index=True,
na_rep="NaN",
formatters=None,
float_format=None,
sparsify=None,
index_names=True,
bold_rows=False,
column_format=None,
longtable=None,
escape=None,
encoding=None,
decimal=".",
multicolumn=None,
multicolumn_format=None,
multirow=None,
caption=None,
label=None,
position=None,
):
r"""
Render object to a LaTeX tabular, longtable, or nested table/tabular.
Requires ``\usepackage{{booktabs}}``. The output can be copy/pasted
into a main LaTeX document or read from an external file
with ``\input{{table.tex}}``.
.. versionchanged:: 1.0.0
Added caption and label arguments.
.. versionchanged:: 1.2.0
Added position argument, changed meaning of caption argument.
Parameters
----------
buf : str, Path or StringIO-like, optional, default None
Buffer to write to. If None, the output is returned as a string.
columns : list of label, optional
The subset of columns to write. Writes all columns by default.
col_space : int, optional
The minimum width of each column.
header : bool or list of str, default True
Write out the column names. If a list of strings is given,
it is assumed to be aliases for the column names.
index : bool, default True
Write row names (index).
na_rep : str, default 'NaN'
Missing data representation.
formatters : list of functions or dict of {{str: function}}, optional
Formatter functions to apply to columns' elements by position or
name. The result of each function must be a unicode string.
List must be of length equal to the number of columns.
float_format : one-parameter function or str, optional, default None
Formatter for floating point numbers. For example
``float_format="%.2f"`` and ``float_format="{{:0.2f}}".format`` will
both result in 0.1234 being formatted as 0.12.
sparsify : bool, optional
Set to False for a DataFrame with a hierarchical index to print
every multiindex key at each row. By default, the value will be
read from the config module.
index_names : bool, default True
Prints the names of the indexes.
bold_rows : bool, default False
Make the row labels bold in the output.
column_format : str, optional
The columns format as specified in `LaTeX table format
<https://en.wikibooks.org/wiki/LaTeX/Tables>`__ e.g. 'rcl' for 3
columns. By default, 'l' will be used for all columns except
columns of numbers, which default to 'r'.
longtable : bool, optional
By default, the value will be read from the pandas config
module. Use a longtable environment instead of tabular. Requires
adding a \usepackage{{longtable}} to your LaTeX preamble.
escape : bool, optional
By default, the value will be read from the pandas config
module. When set to False prevents from escaping latex special
characters in column names.
encoding : str, optional
A string representing the encoding to use in the output file,
defaults to 'utf-8'.
decimal : str, default '.'
Character recognized as decimal separator, e.g. ',' in Europe.
multicolumn : bool, default True
Use \multicolumn to enhance MultiIndex columns.
The default will be read from the config module.
multicolumn_format : str, default 'l'
The alignment for multicolumns, similar to `column_format`
The default will be read from the config module.
multirow : bool, default False
Use \multirow to enhance MultiIndex rows. Requires adding a
\usepackage{{multirow}} to your LaTeX preamble. Will print
centered labels (instead of top-aligned) across the contained
rows, separating groups via clines. The default will be read
from the pandas config module.
caption : str or tuple, optional
Tuple (full_caption, short_caption),
which results in ``\caption[short_caption]{{full_caption}}``;
if a single string is passed, no short caption will be set.
.. versionadded:: 1.0.0
.. versionchanged:: 1.2.0
Optionally allow caption to be a tuple ``(full_caption, short_caption)``.
label : str, optional
The LaTeX label to be placed inside ``\label{{}}`` in the output.
This is used with ``\ref{{}}`` in the main ``.tex`` file.
.. versionadded:: 1.0.0
position : str, optional
The LaTeX positional argument for tables, to be placed after
``\begin{{}}`` in the output.
.. versionadded:: 1.2.0
{returns}
See Also
--------
DataFrame.to_string : Render a DataFrame to a console-friendly
tabular output.
DataFrame.to_html : Render a DataFrame as an HTML table.
Examples
--------
>>> df = pd.DataFrame(dict(name=['Raphael', 'Donatello'],
... mask=['red', 'purple'],
... weapon=['sai', 'bo staff']))
>>> print(df.to_latex(index=False)) # doctest: +NORMALIZE_WHITESPACE
\begin{{tabular}}{{lll}}
\toprule
name & mask & weapon \\
\midrule
Raphael & red & sai \\
Donatello & purple & bo staff \\
\bottomrule
\end{{tabular}}
"""
# Get defaults from the pandas config
if self.ndim == 1:
self = self.to_frame()
if longtable is None:
longtable = config.get_option("display.latex.longtable")
if escape is None:
escape = config.get_option("display.latex.escape")
if multicolumn is None:
multicolumn = config.get_option("display.latex.multicolumn")
if multicolumn_format is None:
multicolumn_format = config.get_option("display.latex.multicolumn_format")
if multirow is None:
multirow = config.get_option("display.latex.multirow")
self = cast("DataFrame", self)
formatter = DataFrameFormatter(
self,
columns=columns,
col_space=col_space,
na_rep=na_rep,
header=header,
index=index,
formatters=formatters,
float_format=float_format,
bold_rows=bold_rows,
sparsify=sparsify,
index_names=index_names,
escape=escape,
decimal=decimal,
)
return DataFrameRenderer(formatter).to_latex(
buf=buf,
column_format=column_format,
longtable=longtable,
encoding=encoding,
multicolumn=multicolumn,
multicolumn_format=multicolumn_format,
multirow=multirow,
caption=caption,
label=label,
position=position,
)
@final
@doc(storage_options=_shared_docs["storage_options"])
def to_csv(
self,
path_or_buf: Optional[FilePathOrBuffer] = None,
sep: str = ",",
na_rep: str = "",
float_format: Optional[str] = None,
columns: Optional[Sequence[Label]] = None,
header: Union[bool_t, List[str]] = True,
index: bool_t = True,
index_label: Optional[IndexLabel] = None,
mode: str = "w",
encoding: Optional[str] = None,
compression: CompressionOptions = "infer",
quoting: Optional[int] = None,
quotechar: str = '"',
line_terminator: Optional[str] = None,
chunksize: Optional[int] = None,
date_format: Optional[str] = None,
doublequote: bool_t = True,
escapechar: Optional[str] = None,
decimal: str = ".",
errors: str = "strict",
storage_options: StorageOptions = None,
) -> Optional[str]:
r"""
Write object to a comma-separated values (csv) file.
.. versionchanged:: 0.24.0
The order of arguments for Series was changed.
Parameters
----------
path_or_buf : str or file handle, default None
File path or object, if None is provided the result is returned as
a string. If a non-binary file object is passed, it should be opened
with `newline=''`, disabling universal newlines. If a binary
file object is passed, `mode` might need to contain a `'b'`.
.. versionchanged:: 0.24.0
Was previously named "path" for Series.
.. versionchanged:: 1.2.0
Support for binary file objects was introduced.
sep : str, default ','
String of length 1. Field delimiter for the output file.
na_rep : str, default ''
Missing data representation.
float_format : str, default None
Format string for floating point numbers.
columns : sequence, optional
Columns to write.
header : bool or list of str, default True
Write out the column names. If a list of strings is given it is
assumed to be aliases for the column names.
.. versionchanged:: 0.24.0
Previously defaulted to False for Series.
index : bool, default True
Write row names (index).
index_label : str or sequence, or False, default None
Column label for index column(s) if desired. If None is given, and
`header` and `index` are True, then the index names are used. A
sequence should be given if the object uses MultiIndex. If
False do not print fields for index names. Use index_label=False
for easier importing in R.
mode : str
Python write mode, default 'w'.
encoding : str, optional
A string representing the encoding to use in the output file,
defaults to 'utf-8'. `encoding` is not supported if `path_or_buf`
is a non-binary file object.
compression : str or dict, default 'infer'
If str, represents compression mode. If dict, value at 'method' is
the compression mode. Compression mode may be any of the following
possible values: {{'infer', 'gzip', 'bz2', 'zip', 'xz', None}}. If
compression mode is 'infer' and `path_or_buf` is path-like, then
detect compression mode from the following extensions: '.gz',
'.bz2', '.zip' or '.xz'. (otherwise no compression). If dict given
and mode is one of {{'zip', 'gzip', 'bz2'}}, or inferred as
one of the above, other entries passed as
additional compression options.
.. versionchanged:: 1.0.0
May now be a dict with key 'method' as compression mode
and other entries as additional compression options if
compression mode is 'zip'.
.. versionchanged:: 1.1.0
Passing compression options as keys in dict is
supported for compression modes 'gzip' and 'bz2'
as well as 'zip'.
.. versionchanged:: 1.2.0
Compression is supported for binary file objects.
.. versionchanged:: 1.2.0
Previous versions forwarded dict entries for 'gzip' to
`gzip.open` instead of `gzip.GzipFile` which prevented
setting `mtime`.
quoting : optional constant from csv module
Defaults to csv.QUOTE_MINIMAL. If you have set a `float_format`
then floats are converted to strings and thus csv.QUOTE_NONNUMERIC
will treat them as non-numeric.
quotechar : str, default '\"'
String of length 1. Character used to quote fields.
line_terminator : str, optional
The newline character or character sequence to use in the output
file. Defaults to `os.linesep`, which depends on the OS in which
this method is called ('\n' for linux, '\r\n' for Windows, i.e.).
.. versionchanged:: 0.24.0
chunksize : int or None
Rows to write at a time.
date_format : str, default None
Format string for datetime objects.
doublequote : bool, default True
Control quoting of `quotechar` inside a field.
escapechar : str, default None
String of length 1. Character used to escape `sep` and `quotechar`
when appropriate.
decimal : str, default '.'
Character recognized as decimal separator. E.g. use ',' for
European data.
errors : str, default 'strict'
Specifies how encoding and decoding errors are to be handled.
See the errors argument for :func:`open` for a full list
of options.
.. versionadded:: 1.1.0
{storage_options}
.. versionadded:: 1.2.0
Returns
-------
None or str
If path_or_buf is None, returns the resulting csv format as a
string. Otherwise returns None.
See Also
--------
read_csv : Load a CSV file into a DataFrame.
to_excel : Write DataFrame to an Excel file.
Examples
--------
>>> df = pd.DataFrame({{'name': ['Raphael', 'Donatello'],
... 'mask': ['red', 'purple'],
... 'weapon': ['sai', 'bo staff']}})
>>> df.to_csv(index=False)
'name,mask,weapon\nRaphael,red,sai\nDonatello,purple,bo staff\n'
Create 'out.zip' containing 'out.csv'
>>> compression_opts = dict(method='zip',
... archive_name='out.csv') # doctest: +SKIP
>>> df.to_csv('out.zip', index=False,
... compression=compression_opts) # doctest: +SKIP
"""
df = self if isinstance(self, ABCDataFrame) else self.to_frame()
formatter = DataFrameFormatter(
frame=df,
header=header,
index=index,
na_rep=na_rep,
float_format=float_format,
decimal=decimal,
)
return DataFrameRenderer(formatter).to_csv(
path_or_buf,
line_terminator=line_terminator,
sep=sep,
encoding=encoding,
errors=errors,
compression=compression,
quoting=quoting,
columns=columns,
index_label=index_label,
mode=mode,
chunksize=chunksize,
quotechar=quotechar,
date_format=date_format,
doublequote=doublequote,
escapechar=escapechar,
storage_options=storage_options,
)
# ----------------------------------------------------------------------
# Lookup Caching
@final
def _set_as_cached(self, item, cacher) -> None:
"""
Set the _cacher attribute on the calling object with a weakref to
cacher.
"""
self._cacher = (item, weakref.ref(cacher))
@final
def _reset_cacher(self) -> None:
"""
Reset the cacher.
"""
if hasattr(self, "_cacher"):
del self._cacher
@final
def _maybe_cache_changed(self, item, value) -> None:
"""
The object has called back to us saying maybe it has changed.
"""
loc = self._info_axis.get_loc(item)
self._mgr.iset(loc, value)
@final
@property
def _is_cached(self) -> bool_t:
"""Return boolean indicating if self is cached or not."""
return getattr(self, "_cacher", None) is not None
@final
def _get_cacher(self):
"""return my cacher or None"""
cacher = getattr(self, "_cacher", None)
if cacher is not None:
cacher = cacher[1]()
return cacher
@final
def _maybe_update_cacher(
self, clear: bool_t = False, verify_is_copy: bool_t = True
) -> None:
"""
See if we need to update our parent cacher if clear, then clear our
cache.
Parameters
----------
clear : bool, default False
Clear the item cache.
verify_is_copy : bool, default True
Provide is_copy checks.
"""
cacher = getattr(self, "_cacher", None)
if cacher is not None:
ref = cacher[1]()
# we are trying to reference a dead referent, hence
# a copy
if ref is None:
del self._cacher
else:
if len(self) == len(ref):
# otherwise, either self or ref has swapped in new arrays
ref._maybe_cache_changed(cacher[0], self)
else:
# GH#33675 we have swapped in a new array, so parent
# reference to self is now invalid
ref._item_cache.pop(cacher[0], None)
if verify_is_copy:
self._check_setitem_copy(stacklevel=5, t="referent")
if clear:
self._clear_item_cache()
@final
def _clear_item_cache(self) -> None:
self._item_cache.clear()
# ----------------------------------------------------------------------
# Indexing Methods
def take(
self: FrameOrSeries, indices, axis=0, is_copy: Optional[bool_t] = None, **kwargs
) -> FrameOrSeries:
"""
Return the elements in the given *positional* indices along an axis.
This means that we are not indexing according to actual values in
the index attribute of the object. We are indexing according to the
actual position of the element in the object.
Parameters
----------
indices : array-like
An array of ints indicating which positions to take.
axis : {0 or 'index', 1 or 'columns', None}, default 0
The axis on which to select elements. ``0`` means that we are
selecting rows, ``1`` means that we are selecting columns.
is_copy : bool
Before pandas 1.0, ``is_copy=False`` can be specified to ensure
that the return value is an actual copy. Starting with pandas 1.0,
``take`` always returns a copy, and the keyword is therefore
deprecated.
.. deprecated:: 1.0.0
**kwargs
For compatibility with :meth:`numpy.take`. Has no effect on the
output.
Returns
-------
taken : same type as caller
An array-like containing the elements taken from the object.
See Also
--------
DataFrame.loc : Select a subset of a DataFrame by labels.
DataFrame.iloc : Select a subset of a DataFrame by positions.
numpy.take : Take elements from an array along an axis.
Examples
--------
>>> df = pd.DataFrame([('falcon', 'bird', 389.0),
... ('parrot', 'bird', 24.0),
... ('lion', 'mammal', 80.5),
... ('monkey', 'mammal', np.nan)],
... columns=['name', 'class', 'max_speed'],
... index=[0, 2, 3, 1])
>>> df
name class max_speed
0 falcon bird 389.0
2 parrot bird 24.0
3 lion mammal 80.5
1 monkey mammal NaN
Take elements at positions 0 and 3 along the axis 0 (default).
Note how the actual indices selected (0 and 1) do not correspond to
our selected indices 0 and 3. That's because we are selecting the 0th
and 3rd rows, not rows whose indices equal 0 and 3.
>>> df.take([0, 3])
name class max_speed
0 falcon bird 389.0
1 monkey mammal NaN
Take elements at indices 1 and 2 along the axis 1 (column selection).
>>> df.take([1, 2], axis=1)
class max_speed
0 bird 389.0
2 bird 24.0
3 mammal 80.5
1 mammal NaN
We may take elements using negative integers for positive indices,
starting from the end of the object, just like with Python lists.
>>> df.take([-1, -2])
name class max_speed
1 monkey mammal NaN
3 lion mammal 80.5
"""
if is_copy is not None:
warnings.warn(
"is_copy is deprecated and will be removed in a future version. "
"'take' always returns a copy, so there is no need to specify this.",
FutureWarning,
stacklevel=2,
)
nv.validate_take((), kwargs)
self._consolidate_inplace()
new_data = self._mgr.take(
indices, axis=self._get_block_manager_axis(axis), verify=True
)
return self._constructor(new_data).__finalize__(self, method="take")
@final
def _take_with_is_copy(self: FrameOrSeries, indices, axis=0) -> FrameOrSeries:
"""
Internal version of the `take` method that sets the `_is_copy`
attribute to keep track of the parent dataframe (using in indexing
for the SettingWithCopyWarning).
See the docstring of `take` for full explanation of the parameters.
"""
result = self.take(indices=indices, axis=axis)
# Maybe set copy if we didn't actually change the index.
if not result._get_axis(axis).equals(self._get_axis(axis)):
result._set_is_copy(self)
return result
@final
def xs(self, key, axis=0, level=None, drop_level: bool_t = True):
"""
Return cross-section from the Series/DataFrame.
This method takes a `key` argument to select data at a particular
level of a MultiIndex.
Parameters
----------
key : label or tuple of label
Label contained in the index, or partially in a MultiIndex.
axis : {0 or 'index', 1 or 'columns'}, default 0
Axis to retrieve cross-section on.
level : object, defaults to first n levels (n=1 or len(key))
In case of a key partially contained in a MultiIndex, indicate
which levels are used. Levels can be referred by label or position.
drop_level : bool, default True
If False, returns object with same levels as self.
Returns
-------
Series or DataFrame
Cross-section from the original Series or DataFrame
corresponding to the selected index levels.
See Also
--------
DataFrame.loc : Access a group of rows and columns
by label(s) or a boolean array.
DataFrame.iloc : Purely integer-location based indexing
for selection by position.
Notes
-----
`xs` can not be used to set values.
MultiIndex Slicers is a generic way to get/set values on
any level or levels.
It is a superset of `xs` functionality, see
:ref:`MultiIndex Slicers <advanced.mi_slicers>`.
Examples
--------
>>> d = {'num_legs': [4, 4, 2, 2],
... 'num_wings': [0, 0, 2, 2],
... 'class': ['mammal', 'mammal', 'mammal', 'bird'],
... 'animal': ['cat', 'dog', 'bat', 'penguin'],
... 'locomotion': ['walks', 'walks', 'flies', 'walks']}
>>> df = pd.DataFrame(data=d)
>>> df = df.set_index(['class', 'animal', 'locomotion'])
>>> df
num_legs num_wings
class animal locomotion
mammal cat walks 4 0
dog walks 4 0
bat flies 2 2
bird penguin walks 2 2
Get values at specified index
>>> df.xs('mammal')
num_legs num_wings
animal locomotion
cat walks 4 0
dog walks 4 0
bat flies 2 2
Get values at several indexes
>>> df.xs(('mammal', 'dog'))
num_legs num_wings
locomotion
walks 4 0
Get values at specified index and level
>>> df.xs('cat', level=1)
num_legs num_wings
class locomotion
mammal walks 4 0
Get values at several indexes and levels
>>> df.xs(('bird', 'walks'),
... level=[0, 'locomotion'])
num_legs num_wings
animal
penguin 2 2
Get values at specified column and axis
>>> df.xs('num_wings', axis=1)
class animal locomotion
mammal cat walks 0
dog walks 0
bat flies 2
bird penguin walks 2
Name: num_wings, dtype: int64
"""
axis = self._get_axis_number(axis)
labels = self._get_axis(axis)
if level is not None:
if not isinstance(labels, MultiIndex):
raise TypeError("Index must be a MultiIndex")
loc, new_ax = labels.get_loc_level(key, level=level, drop_level=drop_level)
# create the tuple of the indexer
_indexer = [slice(None)] * self.ndim
_indexer[axis] = loc
indexer = tuple(_indexer)
result = self.iloc[indexer]
setattr(result, result._get_axis_name(axis), new_ax)
return result
if axis == 1:
if drop_level:
return self[key]
index = self.columns
else:
index = self.index
self._consolidate_inplace()
if isinstance(index, MultiIndex):
try:
loc, new_index = index._get_loc_level(
key, level=0, drop_level=drop_level
)
except TypeError as e:
raise TypeError(f"Expected label or tuple of labels, got {key}") from e
else:
loc = index.get_loc(key)
if isinstance(loc, np.ndarray):
if loc.dtype == np.bool_:
(inds,) = loc.nonzero()
return self._take_with_is_copy(inds, axis=axis)
else:
return self._take_with_is_copy(loc, axis=axis)
if not is_scalar(loc):
new_index = index[loc]
if is_scalar(loc) and axis == 0:
# In this case loc should be an integer
if self.ndim == 1:
# if we encounter an array-like and we only have 1 dim
# that means that their are list/ndarrays inside the Series!
# so just return them (GH 6394)
return self._values[loc]
new_values = self._mgr.fast_xs(loc)
result = self._constructor_sliced(
new_values,
index=self.columns,
name=self.index[loc],
dtype=new_values.dtype,
)
elif is_scalar(loc):
result = self.iloc[:, slice(loc, loc + 1)]
elif axis == 1:
result = self.iloc[:, loc]
else:
result = self.iloc[loc]
result.index = new_index
# this could be a view
# but only in a single-dtyped view sliceable case
result._set_is_copy(self, copy=not result._is_view)
return result
def __getitem__(self, item):
raise AbstractMethodError(self)
@final
def _get_item_cache(self, item):
"""Return the cached item, item represents a label indexer."""
cache = self._item_cache
res = cache.get(item)
if res is None:
# All places that call _get_item_cache have unique columns,
# pending resolution of GH#33047
loc = self.columns.get_loc(item)
values = self._mgr.iget(loc)
res = self._box_col_values(values, loc).__finalize__(self)
cache[item] = res
res._set_as_cached(item, self)
# for a chain
res._is_copy = self._is_copy
return res
def _slice(self: FrameOrSeries, slobj: slice, axis=0) -> FrameOrSeries:
"""
Construct a slice of this container.
Slicing with this method is *always* positional.
"""
assert isinstance(slobj, slice), type(slobj)
axis = self._get_block_manager_axis(axis)
result = self._constructor(self._mgr.get_slice(slobj, axis=axis))
result = result.__finalize__(self)
# this could be a view
# but only in a single-dtyped view sliceable case
is_copy = axis != 0 or result._is_view
result._set_is_copy(self, copy=is_copy)
return result
def _iset_item(self, loc: int, value) -> None:
self._mgr.iset(loc, value)
self._clear_item_cache()
def _set_item(self, key, value) -> None:
try:
loc = self._info_axis.get_loc(key)
except KeyError:
# This item wasn't present, just insert at end
self._mgr.insert(len(self._info_axis), key, value)
return
NDFrame._iset_item(self, loc, value)
@final
def _set_is_copy(self, ref, copy: bool_t = True) -> None:
if not copy:
self._is_copy = None
else:
assert ref is not None
self._is_copy = weakref.ref(ref)
@final
def _check_is_chained_assignment_possible(self) -> bool_t:
"""
Check if we are a view, have a cacher, and are of mixed type.
If so, then force a setitem_copy check.
Should be called just near setting a value
Will return a boolean if it we are a view and are cached, but a
single-dtype meaning that the cacher should be updated following
setting.
"""
if self._is_view and self._is_cached:
ref = self._get_cacher()
if ref is not None and ref._is_mixed_type:
self._check_setitem_copy(stacklevel=4, t="referent", force=True)
return True
elif self._is_copy:
self._check_setitem_copy(stacklevel=4, t="referent")
return False
@final
def _check_setitem_copy(self, stacklevel=4, t="setting", force=False):
"""
Parameters
----------
stacklevel : int, default 4
the level to show of the stack when the error is output
t : str, the type of setting error
force : bool, default False
If True, then force showing an error.
validate if we are doing a setitem on a chained copy.
If you call this function, be sure to set the stacklevel such that the
user will see the error *at the level of setting*
It is technically possible to figure out that we are setting on
a copy even WITH a multi-dtyped pandas object. In other words, some
blocks may be views while other are not. Currently _is_view will ALWAYS
return False for multi-blocks to avoid having to handle this case.
df = DataFrame(np.arange(0,9), columns=['count'])
df['group'] = 'b'
# This technically need not raise SettingWithCopy if both are view
# (which is not # generally guaranteed but is usually True. However,
# this is in general not a good practice and we recommend using .loc.
df.iloc[0:5]['group'] = 'a'
"""
# return early if the check is not needed
if not (force or self._is_copy):
return
value = config.get_option("mode.chained_assignment")
if value is None:
return
# see if the copy is not actually referred; if so, then dissolve
# the copy weakref
if self._is_copy is not None and not isinstance(self._is_copy, str):
r = self._is_copy()
if not gc.get_referents(r) or (r is not None and r.shape == self.shape):
self._is_copy = None
return
# a custom message
if isinstance(self._is_copy, str):
t = self._is_copy
elif t == "referent":
t = (
"\n"
"A value is trying to be set on a copy of a slice from a "
"DataFrame\n\n"
"See the caveats in the documentation: "
"https://pandas.pydata.org/pandas-docs/stable/user_guide/"
"indexing.html#returning-a-view-versus-a-copy"
)
else:
t = (
"\n"
"A value is trying to be set on a copy of a slice from a "
"DataFrame.\n"
"Try using .loc[row_indexer,col_indexer] = value "
"instead\n\nSee the caveats in the documentation: "
"https://pandas.pydata.org/pandas-docs/stable/user_guide/"
"indexing.html#returning-a-view-versus-a-copy"
)
if value == "raise":
raise com.SettingWithCopyError(t)
elif value == "warn":
warnings.warn(t, com.SettingWithCopyWarning, stacklevel=stacklevel)
def __delitem__(self, key) -> None:
"""
Delete item
"""
deleted = False
maybe_shortcut = False
if self.ndim == 2 and isinstance(self.columns, MultiIndex):
try:
maybe_shortcut = key not in self.columns._engine
except TypeError:
pass
if maybe_shortcut:
# Allow shorthand to delete all columns whose first len(key)
# elements match key:
if not isinstance(key, tuple):
key = (key,)
for col in self.columns:
if isinstance(col, tuple) and col[: len(key)] == key:
del self[col]
deleted = True
if not deleted:
# If the above loop ran and didn't delete anything because
# there was no match, this call should raise the appropriate
# exception:
loc = self.axes[-1].get_loc(key)
self._mgr.idelete(loc)
# delete from the caches
try:
del self._item_cache[key]
except KeyError:
pass
# ----------------------------------------------------------------------
# Unsorted
@final
def _check_inplace_and_allows_duplicate_labels(self, inplace):
if inplace and not self.flags.allows_duplicate_labels:
raise ValueError(
"Cannot specify 'inplace=True' when "
"'self.flags.allows_duplicate_labels' is False."
)
@final
def get(self, key, default=None):
"""
Get item from object for given key (ex: DataFrame column).
Returns default value if not found.
Parameters
----------
key : object
Returns
-------
value : same type as items contained in object
"""
try:
return self[key]
except (KeyError, ValueError, IndexError):
return default
@final
@property
def _is_view(self) -> bool_t:
"""Return boolean indicating if self is view of another array """
return self._mgr.is_view
@final
def reindex_like(
self: FrameOrSeries,
other,
method: Optional[str] = None,
copy: bool_t = True,
limit=None,
tolerance=None,
) -> FrameOrSeries:
"""
Return an object with matching indices as other object.
Conform the object to the same index on all axes. Optional
filling logic, placing NaN in locations having no value
in the previous index. A new object is produced unless the
new index is equivalent to the current one and copy=False.
Parameters
----------
other : Object of the same data type
Its row and column indices are used to define the new indices
of this object.
method : {None, 'backfill'/'bfill', 'pad'/'ffill', 'nearest'}
Method to use for filling holes in reindexed DataFrame.
Please note: this is only applicable to DataFrames/Series with a
monotonically increasing/decreasing index.
* None (default): don't fill gaps
* pad / ffill: propagate last valid observation forward to next
valid
* backfill / bfill: use next valid observation to fill gap
* nearest: use nearest valid observations to fill gap.
copy : bool, default True
Return a new object, even if the passed indexes are the same.
limit : int, default None
Maximum number of consecutive labels to fill for inexact matches.
tolerance : optional
Maximum distance between original and new labels for inexact
matches. The values of the index at the matching locations must
satisfy the equation ``abs(index[indexer] - target) <= tolerance``.
Tolerance may be a scalar value, which applies the same tolerance
to all values, or list-like, which applies variable tolerance per
element. List-like includes list, tuple, array, Series, and must be
the same size as the index and its dtype must exactly match the
index's type.
Returns
-------
Series or DataFrame
Same type as caller, but with changed indices on each axis.
See Also
--------
DataFrame.set_index : Set row labels.
DataFrame.reset_index : Remove row labels or move them to new columns.
DataFrame.reindex : Change to new indices or expand indices.
Notes
-----
Same as calling
``.reindex(index=other.index, columns=other.columns,...)``.
Examples
--------
>>> df1 = pd.DataFrame([[24.3, 75.7, 'high'],
... [31, 87.8, 'high'],
... [22, 71.6, 'medium'],
... [35, 95, 'medium']],
... columns=['temp_celsius', 'temp_fahrenheit',
... 'windspeed'],
... index=pd.date_range(start='2014-02-12',
... end='2014-02-15', freq='D'))
>>> df1
temp_celsius temp_fahrenheit windspeed
2014-02-12 24.3 75.7 high
2014-02-13 31.0 87.8 high
2014-02-14 22.0 71.6 medium
2014-02-15 35.0 95.0 medium
>>> df2 = pd.DataFrame([[28, 'low'],
... [30, 'low'],
... [35.1, 'medium']],
... columns=['temp_celsius', 'windspeed'],
... index=pd.DatetimeIndex(['2014-02-12', '2014-02-13',
... '2014-02-15']))
>>> df2
temp_celsius windspeed
2014-02-12 28.0 low
2014-02-13 30.0 low
2014-02-15 35.1 medium
>>> df2.reindex_like(df1)
temp_celsius temp_fahrenheit windspeed
2014-02-12 28.0 NaN low
2014-02-13 30.0 NaN low
2014-02-14 NaN NaN NaN
2014-02-15 35.1 NaN medium
"""
d = other._construct_axes_dict(
axes=self._AXIS_ORDERS,
method=method,
copy=copy,
limit=limit,
tolerance=tolerance,
)
return self.reindex(**d)
def drop(
self,
labels=None,
axis=0,
index=None,
columns=None,
level=None,
inplace: bool_t = False,
errors: str = "raise",
):
inplace = validate_bool_kwarg(inplace, "inplace")
if labels is not None:
if index is not None or columns is not None:
raise ValueError("Cannot specify both 'labels' and 'index'/'columns'")
axis_name = self._get_axis_name(axis)
axes = {axis_name: labels}
elif index is not None or columns is not None:
axes, _ = self._construct_axes_from_arguments((index, columns), {})
else:
raise ValueError(
"Need to specify at least one of 'labels', 'index' or 'columns'"
)
obj = self
for axis, labels in axes.items():
if labels is not None:
obj = obj._drop_axis(labels, axis, level=level, errors=errors)
if inplace:
self._update_inplace(obj)
else:
return obj
@final
def _drop_axis(
self: FrameOrSeries, labels, axis, level=None, errors: str = "raise"
) -> FrameOrSeries:
"""
Drop labels from specified axis. Used in the ``drop`` method
internally.
Parameters
----------
labels : single label or list-like
axis : int or axis name
level : int or level name, default None
For MultiIndex
errors : {'ignore', 'raise'}, default 'raise'
If 'ignore', suppress error and existing labels are dropped.
"""
axis = self._get_axis_number(axis)
axis_name = self._get_axis_name(axis)
axis = self._get_axis(axis)
if axis.is_unique:
if level is not None:
if not isinstance(axis, MultiIndex):
raise AssertionError("axis must be a MultiIndex")
new_axis = axis.drop(labels, level=level, errors=errors)
else:
new_axis = axis.drop(labels, errors=errors)
result = self.reindex(**{axis_name: new_axis})
# Case for non-unique axis
else:
labels = ensure_object(com.index_labels_to_array(labels))
if level is not None:
if not isinstance(axis, MultiIndex):
raise AssertionError("axis must be a MultiIndex")
indexer = ~axis.get_level_values(level).isin(labels)
# GH 18561 MultiIndex.drop should raise if label is absent
if errors == "raise" and indexer.all():
raise KeyError(f"{labels} not found in axis")
else:
indexer = ~axis.isin(labels)
# Check if label doesn't exist along axis
labels_missing = (axis.get_indexer_for(labels) == -1).any()
if errors == "raise" and labels_missing:
raise KeyError(f"{labels} not found in axis")
slicer = [slice(None)] * self.ndim
slicer[self._get_axis_number(axis_name)] = indexer
result = self.loc[tuple(slicer)]
return result
@final
def _update_inplace(self, result, verify_is_copy: bool_t = True) -> None:
"""
Replace self internals with result.
Parameters
----------
result : same type as self
verify_is_copy : bool, default True
Provide is_copy checks.
"""
# NOTE: This does *not* call __finalize__ and that's an explicit
# decision that we may revisit in the future.
self._reset_cache()
self._clear_item_cache()
self._mgr = result._mgr
self._maybe_update_cacher(verify_is_copy=verify_is_copy)
@final
def add_prefix(self: FrameOrSeries, prefix: str) -> FrameOrSeries:
"""
Prefix labels with string `prefix`.
For Series, the row labels are prefixed.
For DataFrame, the column labels are prefixed.
Parameters
----------
prefix : str
The string to add before each label.
Returns
-------
Series or DataFrame
New Series or DataFrame with updated labels.
See Also
--------
Series.add_suffix: Suffix row labels with string `suffix`.
DataFrame.add_suffix: Suffix column labels with string `suffix`.
Examples
--------
>>> s = pd.Series([1, 2, 3, 4])
>>> s
0 1
1 2
2 3
3 4
dtype: int64
>>> s.add_prefix('item_')
item_0 1
item_1 2
item_2 3
item_3 4
dtype: int64
>>> df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [3, 4, 5, 6]})
>>> df
A B
0 1 3
1 2 4
2 3 5
3 4 6
>>> df.add_prefix('col_')
col_A col_B
0 1 3
1 2 4
2 3 5
3 4 6
"""
f = functools.partial("{prefix}{}".format, prefix=prefix)
mapper = {self._info_axis_name: f}
# error: Incompatible return value type (got "Optional[FrameOrSeries]",
# expected "FrameOrSeries")
# error: Argument 1 to "rename" of "NDFrame" has incompatible type
# "**Dict[str, partial[str]]"; expected "Union[str, int, None]"
return self.rename(**mapper) # type: ignore[return-value, arg-type]
@final
def add_suffix(self: FrameOrSeries, suffix: str) -> FrameOrSeries:
"""
Suffix labels with string `suffix`.
For Series, the row labels are suffixed.
For DataFrame, the column labels are suffixed.
Parameters
----------
suffix : str
The string to add after each label.
Returns
-------
Series or DataFrame
New Series or DataFrame with updated labels.
See Also
--------
Series.add_prefix: Prefix row labels with string `prefix`.
DataFrame.add_prefix: Prefix column labels with string `prefix`.
Examples
--------
>>> s = pd.Series([1, 2, 3, 4])
>>> s
0 1
1 2
2 3
3 4
dtype: int64
>>> s.add_suffix('_item')
0_item 1
1_item 2
2_item 3
3_item 4
dtype: int64
>>> df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [3, 4, 5, 6]})
>>> df
A B
0 1 3
1 2 4
2 3 5
3 4 6
>>> df.add_suffix('_col')
A_col B_col
0 1 3
1 2 4
2 3 5
3 4 6
"""
f = functools.partial("{}{suffix}".format, suffix=suffix)
mapper = {self._info_axis_name: f}
# error: Incompatible return value type (got "Optional[FrameOrSeries]",
# expected "FrameOrSeries")
# error: Argument 1 to "rename" of "NDFrame" has incompatible type
# "**Dict[str, partial[str]]"; expected "Union[str, int, None]"
return self.rename(**mapper) # type: ignore[return-value, arg-type]
def sort_values(
self,
axis=0,
ascending=True,
inplace: bool_t = False,
kind: str = "quicksort",
na_position: str = "last",
ignore_index: bool_t = False,
key: ValueKeyFunc = None,
):
"""
Sort by the values along either axis.
Parameters
----------%(optional_by)s
axis : %(axes_single_arg)s, default 0
Axis to be sorted.
ascending : bool or list of bool, default True
Sort ascending vs. descending. Specify list for multiple sort
orders. If this is a list of bools, must match the length of
the by.
inplace : bool, default False
If True, perform operation in-place.
kind : {'quicksort', 'mergesort', 'heapsort'}, default 'quicksort'
Choice of sorting algorithm. See also ndarray.np.sort for more
information. `mergesort` is the only stable algorithm. For
DataFrames, this option is only applied when sorting on a single
column or label.
na_position : {'first', 'last'}, default 'last'
Puts NaNs at the beginning if `first`; `last` puts NaNs at the
end.
ignore_index : bool, default False
If True, the resulting axis will be labeled 0, 1, …, n - 1.
.. versionadded:: 1.0.0
key : callable, optional
Apply the key function to the values
before sorting. This is similar to the `key` argument in the
builtin :meth:`sorted` function, with the notable difference that
this `key` function should be *vectorized*. It should expect a
``Series`` and return a Series with the same shape as the input.
It will be applied to each column in `by` independently.
.. versionadded:: 1.1.0
Returns
-------
DataFrame or None
DataFrame with sorted values or None if ``inplace=True``.
See Also
--------
DataFrame.sort_index : Sort a DataFrame by the index.
Series.sort_values : Similar method for a Series.
Examples
--------
>>> df = pd.DataFrame({
... 'col1': ['A', 'A', 'B', np.nan, 'D', 'C'],
... 'col2': [2, 1, 9, 8, 7, 4],
... 'col3': [0, 1, 9, 4, 2, 3],
... 'col4': ['a', 'B', 'c', 'D', 'e', 'F']
... })
>>> df
col1 col2 col3 col4
0 A 2 0 a
1 A 1 1 B
2 B 9 9 c
3 NaN 8 4 D
4 D 7 2 e
5 C 4 3 F
Sort by col1
>>> df.sort_values(by=['col1'])
col1 col2 col3 col4
0 A 2 0 a
1 A 1 1 B
2 B 9 9 c
5 C 4 3 F
4 D 7 2 e
3 NaN 8 4 D
Sort by multiple columns
>>> df.sort_values(by=['col1', 'col2'])
col1 col2 col3 col4
1 A 1 1 B
0 A 2 0 a
2 B 9 9 c
5 C 4 3 F
4 D 7 2 e
3 NaN 8 4 D
Sort Descending
>>> df.sort_values(by='col1', ascending=False)
col1 col2 col3 col4
4 D 7 2 e
5 C 4 3 F
2 B 9 9 c
0 A 2 0 a
1 A 1 1 B
3 NaN 8 4 D
Putting NAs first
>>> df.sort_values(by='col1', ascending=False, na_position='first')
col1 col2 col3 col4
3 NaN 8 4 D
4 D 7 2 e
5 C 4 3 F
2 B 9 9 c
0 A 2 0 a
1 A 1 1 B
Sorting with a key function
>>> df.sort_values(by='col4', key=lambda col: col.str.lower())
col1 col2 col3 col4
0 A 2 0 a
1 A 1 1 B
2 B 9 9 c
3 NaN 8 4 D
4 D 7 2 e
5 C 4 3 F
Natural sort with the key argument,
using the `natsort <https://github.com/SethMMorton/natsort>` package.
>>> df = pd.DataFrame({
... "time": ['0hr', '128hr', '72hr', '48hr', '96hr'],
... "value": [10, 20, 30, 40, 50]
... })
>>> df
time value
0 0hr 10
1 128hr 20
2 72hr 30
3 48hr 40
4 96hr 50
>>> from natsort import index_natsorted
>>> df.sort_values(
... by="time",
... key=lambda x: np.argsort(index_natsorted(df["time"]))
... )
time value
0 0hr 10
3 48hr 40
2 72hr 30
4 96hr 50
1 128hr 20
"""
raise AbstractMethodError(self)
def sort_index(
self,
axis=0,
level=None,
ascending: Union[Union[bool_t, int], Sequence[Union[bool_t, int]]] = True,
inplace: bool_t = False,
kind: str = "quicksort",
na_position: str = "last",
sort_remaining: bool_t = True,
ignore_index: bool_t = False,
key: IndexKeyFunc = None,
):
inplace = validate_bool_kwarg(inplace, "inplace")
axis = self._get_axis_number(axis)
ascending = validate_ascending(ascending)
target = self._get_axis(axis)
indexer = get_indexer_indexer(
target, level, ascending, kind, na_position, sort_remaining, key
)
if indexer is None:
if inplace:
return
else:
return self.copy()
baxis = self._get_block_manager_axis(axis)
new_data = self._mgr.take(indexer, axis=baxis, verify=False)
# reconstruct axis if needed
new_data.axes[baxis] = new_data.axes[baxis]._sort_levels_monotonic()
if ignore_index:
axis = 1 if isinstance(self, ABCDataFrame) else 0
new_data.axes[axis] = ibase.default_index(len(indexer))
result = self._constructor(new_data)
if inplace:
return self._update_inplace(result)
else:
return result.__finalize__(self, method="sort_index")
@doc(
klass=_shared_doc_kwargs["klass"],
axes=_shared_doc_kwargs["axes"],
optional_labels="",
optional_axis="",
)
def reindex(self: FrameOrSeries, *args, **kwargs) -> FrameOrSeries:
"""
Conform {klass} to new index with optional filling logic.
Places NA/NaN in locations having no value in the previous index. A new object
is produced unless the new index is equivalent to the current one and
``copy=False``.
Parameters
----------
{optional_labels}
{axes} : array-like, optional
New labels / index to conform to, should be specified using
keywords. Preferably an Index object to avoid duplicating data.
{optional_axis}
method : {{None, 'backfill'/'bfill', 'pad'/'ffill', 'nearest'}}
Method to use for filling holes in reindexed DataFrame.
Please note: this is only applicable to DataFrames/Series with a
monotonically increasing/decreasing index.
* None (default): don't fill gaps
* pad / ffill: Propagate last valid observation forward to next
valid.
* backfill / bfill: Use next valid observation to fill gap.
* nearest: Use nearest valid observations to fill gap.
copy : bool, default True
Return a new object, even if the passed indexes are the same.
level : int or name
Broadcast across a level, matching Index values on the
passed MultiIndex level.
fill_value : scalar, default np.NaN
Value to use for missing values. Defaults to NaN, but can be any
"compatible" value.
limit : int, default None
Maximum number of consecutive elements to forward or backward fill.
tolerance : optional
Maximum distance between original and new labels for inexact
matches. The values of the index at the matching locations most
satisfy the equation ``abs(index[indexer] - target) <= tolerance``.
Tolerance may be a scalar value, which applies the same tolerance
to all values, or list-like, which applies variable tolerance per
element. List-like includes list, tuple, array, Series, and must be
the same size as the index and its dtype must exactly match the
index's type.
Returns
-------
{klass} with changed index.
See Also
--------
DataFrame.set_index : Set row labels.
DataFrame.reset_index : Remove row labels or move them to new columns.
DataFrame.reindex_like : Change to same indices as other DataFrame.
Examples
--------
``DataFrame.reindex`` supports two calling conventions
* ``(index=index_labels, columns=column_labels, ...)``
* ``(labels, axis={{'index', 'columns'}}, ...)``
We *highly* recommend using keyword arguments to clarify your
intent.
Create a dataframe with some fictional data.
>>> index = ['Firefox', 'Chrome', 'Safari', 'IE10', 'Konqueror']
>>> df = pd.DataFrame({{'http_status': [200, 200, 404, 404, 301],
... 'response_time': [0.04, 0.02, 0.07, 0.08, 1.0]}},
... index=index)
>>> df
http_status response_time
Firefox 200 0.04
Chrome 200 0.02
Safari 404 0.07
IE10 404 0.08
Konqueror 301 1.00
Create a new index and reindex the dataframe. By default
values in the new index that do not have corresponding
records in the dataframe are assigned ``NaN``.
>>> new_index = ['Safari', 'Iceweasel', 'Comodo Dragon', 'IE10',
... 'Chrome']
>>> df.reindex(new_index)
http_status response_time
Safari 404.0 0.07
Iceweasel NaN NaN
Comodo Dragon NaN NaN
IE10 404.0 0.08
Chrome 200.0 0.02
We can fill in the missing values by passing a value to
the keyword ``fill_value``. Because the index is not monotonically
increasing or decreasing, we cannot use arguments to the keyword
``method`` to fill the ``NaN`` values.
>>> df.reindex(new_index, fill_value=0)
http_status response_time
Safari 404 0.07
Iceweasel 0 0.00
Comodo Dragon 0 0.00
IE10 404 0.08
Chrome 200 0.02
>>> df.reindex(new_index, fill_value='missing')
http_status response_time
Safari 404 0.07
Iceweasel missing missing
Comodo Dragon missing missing
IE10 404 0.08
Chrome 200 0.02
We can also reindex the columns.
>>> df.reindex(columns=['http_status', 'user_agent'])
http_status user_agent
Firefox 200 NaN
Chrome 200 NaN
Safari 404 NaN
IE10 404 NaN
Konqueror 301 NaN
Or we can use "axis-style" keyword arguments
>>> df.reindex(['http_status', 'user_agent'], axis="columns")
http_status user_agent
Firefox 200 NaN
Chrome 200 NaN
Safari 404 NaN
IE10 404 NaN
Konqueror 301 NaN
To further illustrate the filling functionality in
``reindex``, we will create a dataframe with a
monotonically increasing index (for example, a sequence
of dates).
>>> date_index = pd.date_range('1/1/2010', periods=6, freq='D')
>>> df2 = pd.DataFrame({{"prices": [100, 101, np.nan, 100, 89, 88]}},
... index=date_index)
>>> df2
prices
2010-01-01 100.0
2010-01-02 101.0
2010-01-03 NaN
2010-01-04 100.0
2010-01-05 89.0
2010-01-06 88.0
Suppose we decide to expand the dataframe to cover a wider
date range.
>>> date_index2 = pd.date_range('12/29/2009', periods=10, freq='D')
>>> df2.reindex(date_index2)
prices
2009-12-29 NaN
2009-12-30 NaN
2009-12-31 NaN
2010-01-01 100.0
2010-01-02 101.0
2010-01-03 NaN
2010-01-04 100.0
2010-01-05 89.0
2010-01-06 88.0
2010-01-07 NaN
The index entries that did not have a value in the original data frame
(for example, '2009-12-29') are by default filled with ``NaN``.
If desired, we can fill in the missing values using one of several
options.
For example, to back-propagate the last valid value to fill the ``NaN``
values, pass ``bfill`` as an argument to the ``method`` keyword.
>>> df2.reindex(date_index2, method='bfill')
prices
2009-12-29 100.0
2009-12-30 100.0
2009-12-31 100.0
2010-01-01 100.0
2010-01-02 101.0
2010-01-03 NaN
2010-01-04 100.0
2010-01-05 89.0
2010-01-06 88.0
2010-01-07 NaN
Please note that the ``NaN`` value present in the original dataframe
(at index value 2010-01-03) will not be filled by any of the
value propagation schemes. This is because filling while reindexing
does not look at dataframe values, but only compares the original and
desired indexes. If you do want to fill in the ``NaN`` values present
in the original dataframe, use the ``fillna()`` method.
See the :ref:`user guide <basics.reindexing>` for more.
"""
# TODO: Decide if we care about having different examples for different
# kinds
# construct the args
axes, kwargs = self._construct_axes_from_arguments(args, kwargs)
method = missing.clean_reindex_fill_method(kwargs.pop("method", None))
level = kwargs.pop("level", None)
copy = kwargs.pop("copy", True)
limit = kwargs.pop("limit", None)
tolerance = kwargs.pop("tolerance", None)
fill_value = kwargs.pop("fill_value", None)
# Series.reindex doesn't use / need the axis kwarg
# We pop and ignore it here, to make writing Series/Frame generic code
# easier
kwargs.pop("axis", None)
if kwargs:
raise TypeError(
"reindex() got an unexpected keyword "
f'argument "{list(kwargs.keys())[0]}"'
)
self._consolidate_inplace()
# if all axes that are requested to reindex are equal, then only copy
# if indicated must have index names equal here as well as values
if all(
self._get_axis(axis).identical(ax)
for axis, ax in axes.items()
if ax is not None
):
if copy:
return self.copy()
return self
# check if we are a multi reindex
if self._needs_reindex_multi(axes, method, level):
return self._reindex_multi(axes, copy, fill_value)
# perform the reindex on the axes
return self._reindex_axes(
axes, level, limit, tolerance, method, fill_value, copy
).__finalize__(self, method="reindex")
@final
def _reindex_axes(
self: FrameOrSeries, axes, level, limit, tolerance, method, fill_value, copy
) -> FrameOrSeries:
"""Perform the reindex for all the axes."""
obj = self
for a in self._AXIS_ORDERS:
labels = axes[a]
if labels is None:
continue
ax = self._get_axis(a)
new_index, indexer = ax.reindex(
labels, level=level, limit=limit, tolerance=tolerance, method=method
)
axis = self._get_axis_number(a)
obj = obj._reindex_with_indexers(
{axis: [new_index, indexer]},
fill_value=fill_value,
copy=copy,
allow_dups=False,
)
return obj
@final
def _needs_reindex_multi(self, axes, method, level) -> bool_t:
"""Check if we do need a multi reindex."""
return (
(com.count_not_none(*axes.values()) == self._AXIS_LEN)
and method is None
and level is None
and not self._is_mixed_type
)
def _reindex_multi(self, axes, copy, fill_value):
raise AbstractMethodError(self)
@final
def _reindex_with_indexers(
self: FrameOrSeries,
reindexers,
fill_value=None,
copy: bool_t = False,
allow_dups: bool_t = False,
) -> FrameOrSeries:
"""allow_dups indicates an internal call here """
# reindex doing multiple operations on different axes if indicated
new_data = self._mgr
for axis in sorted(reindexers.keys()):
index, indexer = reindexers[axis]
baxis = self._get_block_manager_axis(axis)
if index is None:
continue
index = ensure_index(index)
if indexer is not None:
indexer = ensure_int64(indexer)
# TODO: speed up on homogeneous DataFrame objects
new_data = new_data.reindex_indexer(
index,
indexer,
axis=baxis,
fill_value=fill_value,
allow_dups=allow_dups,
copy=copy,
)
# If we've made a copy once, no need to make another one
copy = False
if copy and new_data is self._mgr:
new_data = new_data.copy()
return self._constructor(new_data).__finalize__(self)
def filter(
self: FrameOrSeries,
items=None,
like: Optional[str] = None,
regex: Optional[str] = None,
axis=None,
) -> FrameOrSeries:
"""
Subset the dataframe rows or columns according to the specified index labels.
Note that this routine does not filter a dataframe on its
contents. The filter is applied to the labels of the index.
Parameters
----------
items : list-like
Keep labels from axis which are in items.
like : str
Keep labels from axis for which "like in label == True".
regex : str (regular expression)
Keep labels from axis for which re.search(regex, label) == True.
axis : {0 or ‘index’, 1 or ‘columns’, None}, default None
The axis to filter on, expressed either as an index (int)
or axis name (str). By default this is the info axis,
'index' for Series, 'columns' for DataFrame.
Returns
-------
same type as input object
See Also
--------
DataFrame.loc : Access a group of rows and columns
by label(s) or a boolean array.
Notes
-----
The ``items``, ``like``, and ``regex`` parameters are
enforced to be mutually exclusive.
``axis`` defaults to the info axis that is used when indexing
with ``[]``.
Examples
--------
>>> df = pd.DataFrame(np.array(([1, 2, 3], [4, 5, 6])),
... index=['mouse', 'rabbit'],
... columns=['one', 'two', 'three'])
>>> df
one two three
mouse 1 2 3
rabbit 4 5 6
>>> # select columns by name
>>> df.filter(items=['one', 'three'])
one three
mouse 1 3
rabbit 4 6
>>> # select columns by regular expression
>>> df.filter(regex='e$', axis=1)
one three
mouse 1 3
rabbit 4 6
>>> # select rows containing 'bbi'
>>> df.filter(like='bbi', axis=0)
one two three
rabbit 4 5 6
"""
nkw = com.count_not_none(items, like, regex)
if nkw > 1:
raise TypeError(
"Keyword arguments `items`, `like`, or `regex` "
"are mutually exclusive"
)
if axis is None:
axis = self._info_axis_name
labels = self._get_axis(axis)
if items is not None:
name = self._get_axis_name(axis)
return self.reindex(**{name: [r for r in items if r in labels]})
elif like:
def f(x) -> bool:
assert like is not None # needed for mypy
return like in ensure_str(x)
values = labels.map(f)
return self.loc(axis=axis)[values]
elif regex:
def f(x) -> bool:
return matcher.search(ensure_str(x)) is not None
matcher = re.compile(regex)
values = labels.map(f)
return self.loc(axis=axis)[values]
else:
raise TypeError("Must pass either `items`, `like`, or `regex`")
@final
def head(self: FrameOrSeries, n: int = 5) -> FrameOrSeries:
"""
Return the first `n` rows.
This function returns the first `n` rows for the object based
on position. It is useful for quickly testing if your object
has the right type of data in it.
For negative values of `n`, this function returns all rows except
the last `n` rows, equivalent to ``df[:-n]``.
Parameters
----------
n : int, default 5
Number of rows to select.
Returns
-------
same type as caller
The first `n` rows of the caller object.
See Also
--------
DataFrame.tail: Returns the last `n` rows.
Examples
--------
>>> df = pd.DataFrame({'animal': ['alligator', 'bee', 'falcon', 'lion',
... 'monkey', 'parrot', 'shark', 'whale', 'zebra']})
>>> df
animal
0 alligator
1 bee
2 falcon
3 lion
4 monkey
5 parrot
6 shark
7 whale
8 zebra
Viewing the first 5 lines
>>> df.head()
animal
0 alligator
1 bee
2 falcon
3 lion
4 monkey
Viewing the first `n` lines (three in this case)
>>> df.head(3)
animal
0 alligator
1 bee
2 falcon
For negative values of `n`
>>> df.head(-3)
animal
0 alligator
1 bee
2 falcon
3 lion
4 monkey
5 parrot
"""
return self.iloc[:n]
@final
def tail(self: FrameOrSeries, n: int = 5) -> FrameOrSeries:
"""
Return the last `n` rows.
This function returns last `n` rows from the object based on
position. It is useful for quickly verifying data, for example,
after sorting or appending rows.
For negative values of `n`, this function returns all rows except
the first `n` rows, equivalent to ``df[n:]``.
Parameters
----------
n : int, default 5
Number of rows to select.
Returns
-------
type of caller
The last `n` rows of the caller object.
See Also
--------
DataFrame.head : The first `n` rows of the caller object.
Examples
--------
>>> df = pd.DataFrame({'animal': ['alligator', 'bee', 'falcon', 'lion',
... 'monkey', 'parrot', 'shark', 'whale', 'zebra']})
>>> df
animal
0 alligator
1 bee
2 falcon
3 lion
4 monkey
5 parrot
6 shark
7 whale
8 zebra
Viewing the last 5 lines
>>> df.tail()
animal
4 monkey
5 parrot
6 shark
7 whale
8 zebra
Viewing the last `n` lines (three in this case)
>>> df.tail(3)
animal
6 shark
7 whale
8 zebra
For negative values of `n`
>>> df.tail(-3)
animal
3 lion
4 monkey
5 parrot
6 shark
7 whale
8 zebra
"""
if n == 0:
return self.iloc[0:0]
return self.iloc[-n:]
@final
def sample(
self: FrameOrSeries,
n=None,
frac=None,
replace=False,
weights=None,
random_state=None,
axis=None,
) -> FrameOrSeries:
"""
Return a random sample of items from an axis of object.
You can use `random_state` for reproducibility.
Parameters
----------
n : int, optional
Number of items from axis to return. Cannot be used with `frac`.
Default = 1 if `frac` = None.
frac : float, optional
Fraction of axis items to return. Cannot be used with `n`.
replace : bool, default False
Allow or disallow sampling of the same row more than once.
weights : str or ndarray-like, optional
Default 'None' results in equal probability weighting.
If passed a Series, will align with target object on index. Index
values in weights not found in sampled object will be ignored and
index values in sampled object not in weights will be assigned
weights of zero.
If called on a DataFrame, will accept the name of a column
when axis = 0.
Unless weights are a Series, weights must be same length as axis
being sampled.
If weights do not sum to 1, they will be normalized to sum to 1.
Missing values in the weights column will be treated as zero.
Infinite values not allowed.
random_state : int, array-like, BitGenerator, np.random.RandomState, optional
If int, array-like, or BitGenerator (NumPy>=1.17), seed for
random number generator
If np.random.RandomState, use as numpy RandomState object.
.. versionchanged:: 1.1.0
array-like and BitGenerator (for NumPy>=1.17) object now passed to
np.random.RandomState() as seed
axis : {0 or ‘index’, 1 or ‘columns’, None}, default None
Axis to sample. Accepts axis number or name. Default is stat axis
for given data type (0 for Series and DataFrames).
Returns
-------
Series or DataFrame
A new object of same type as caller containing `n` items randomly
sampled from the caller object.
See Also
--------
DataFrameGroupBy.sample: Generates random samples from each group of a
DataFrame object.
SeriesGroupBy.sample: Generates random samples from each group of a
Series object.
numpy.random.choice: Generates a random sample from a given 1-D numpy
array.
Notes
-----
If `frac` > 1, `replacement` should be set to `True`.
Examples
--------
>>> df = pd.DataFrame({'num_legs': [2, 4, 8, 0],
... 'num_wings': [2, 0, 0, 0],
... 'num_specimen_seen': [10, 2, 1, 8]},
... index=['falcon', 'dog', 'spider', 'fish'])
>>> df
num_legs num_wings num_specimen_seen
falcon 2 2 10
dog 4 0 2
spider 8 0 1
fish 0 0 8
Extract 3 random elements from the ``Series`` ``df['num_legs']``:
Note that we use `random_state` to ensure the reproducibility of
the examples.
>>> df['num_legs'].sample(n=3, random_state=1)
fish 0
spider 8
falcon 2
Name: num_legs, dtype: int64
A random 50% sample of the ``DataFrame`` with replacement:
>>> df.sample(frac=0.5, replace=True, random_state=1)
num_legs num_wings num_specimen_seen
dog 4 0 2
fish 0 0 8
An upsample sample of the ``DataFrame`` with replacement:
Note that `replace` parameter has to be `True` for `frac` parameter > 1.
>>> df.sample(frac=2, replace=True, random_state=1)
num_legs num_wings num_specimen_seen
dog 4 0 2
fish 0 0 8
falcon 2 2 10
falcon 2 2 10
fish 0 0 8
dog 4 0 2
fish 0 0 8
dog 4 0 2
Using a DataFrame column as weights. Rows with larger value in the
`num_specimen_seen` column are more likely to be sampled.
>>> df.sample(n=2, weights='num_specimen_seen', random_state=1)
num_legs num_wings num_specimen_seen
falcon 2 2 10
fish 0 0 8
"""
if axis is None:
axis = self._stat_axis_number
axis = self._get_axis_number(axis)
axis_length = self.shape[axis]
# Process random_state argument
rs = com.random_state(random_state)
# Check weights for compliance
if weights is not None:
# If a series, align with frame
if isinstance(weights, ABCSeries):
weights = weights.reindex(self.axes[axis])
# Strings acceptable if a dataframe and axis = 0
if isinstance(weights, str):
if isinstance(self, ABCDataFrame):
if axis == 0:
try:
weights = self[weights]
except KeyError as err:
raise KeyError(
"String passed to weights not a valid column"
) from err
else:
raise ValueError(
"Strings can only be passed to "
"weights when sampling from rows on "
"a DataFrame"
)
else:
raise ValueError(
"Strings cannot be passed as weights "
"when sampling from a Series."
)
weights = pd.Series(weights, dtype="float64")
if len(weights) != axis_length:
raise ValueError(
"Weights and axis to be sampled must be of same length"
)
if (weights == np.inf).any() or (weights == -np.inf).any():
raise ValueError("weight vector may not include `inf` values")
if (weights < 0).any():
raise ValueError("weight vector many not include negative values")
# If has nan, set to zero.
weights = weights.fillna(0)
# Renormalize if don't sum to 1
if weights.sum() != 1:
if weights.sum() != 0:
weights = weights / weights.sum()
else:
raise ValueError("Invalid weights: weights sum to zero")
weights = weights._values
# If no frac or n, default to n=1.
if n is None and frac is None:
n = 1
elif frac is not None and frac > 1 and not replace:
raise ValueError(
"Replace has to be set to `True` when "
"upsampling the population `frac` > 1."
)
elif n is not None and frac is None and n % 1 != 0:
raise ValueError("Only integers accepted as `n` values")
elif n is None and frac is not None:
n = int(round(frac * axis_length))
elif n is not None and frac is not None:
raise ValueError("Please enter a value for `frac` OR `n`, not both")
# Check for negative sizes
if n < 0:
raise ValueError(
"A negative number of rows requested. Please provide positive value."
)
locs = rs.choice(axis_length, size=n, replace=replace, p=weights)
return self.take(locs, axis=axis)
@final
@doc(klass=_shared_doc_kwargs["klass"])
def pipe(self, func, *args, **kwargs):
r"""
Apply func(self, \*args, \*\*kwargs).
Parameters
----------
func : function
Function to apply to the {klass}.
``args``, and ``kwargs`` are passed into ``func``.
Alternatively a ``(callable, data_keyword)`` tuple where
``data_keyword`` is a string indicating the keyword of
``callable`` that expects the {klass}.
args : iterable, optional
Positional arguments passed into ``func``.
kwargs : mapping, optional
A dictionary of keyword arguments passed into ``func``.
Returns
-------
object : the return type of ``func``.
See Also
--------
DataFrame.apply : Apply a function along input axis of DataFrame.
DataFrame.applymap : Apply a function elementwise on a whole DataFrame.
Series.map : Apply a mapping correspondence on a
:class:`~pandas.Series`.
Notes
-----
Use ``.pipe`` when chaining together functions that expect
Series, DataFrames or GroupBy objects. Instead of writing
>>> func(g(h(df), arg1=a), arg2=b, arg3=c) # doctest: +SKIP
You can write
>>> (df.pipe(h)
... .pipe(g, arg1=a)
... .pipe(func, arg2=b, arg3=c)
... ) # doctest: +SKIP
If you have a function that takes the data as (say) the second
argument, pass a tuple indicating which keyword expects the
data. For example, suppose ``f`` takes its data as ``arg2``:
>>> (df.pipe(h)
... .pipe(g, arg1=a)
... .pipe((func, 'arg2'), arg1=a, arg3=c)
... ) # doctest: +SKIP
"""
return com.pipe(self, func, *args, **kwargs)
# ----------------------------------------------------------------------
# Attribute access
@final
def __finalize__(
self: FrameOrSeries, other, method: Optional[str] = None, **kwargs
) -> FrameOrSeries:
"""
Propagate metadata from other to self.
Parameters
----------
other : the object from which to get the attributes that we are going
to propagate
method : str, optional
A passed method name providing context on where ``__finalize__``
was called.
.. warning::
The value passed as `method` are not currently considered
stable across pandas releases.
"""
if isinstance(other, NDFrame):
for name in other.attrs:
self.attrs[name] = other.attrs[name]
self.flags.allows_duplicate_labels = other.flags.allows_duplicate_labels
# For subclasses using _metadata.
for name in set(self._metadata) & set(other._metadata):
assert isinstance(name, str)
object.__setattr__(self, name, getattr(other, name, None))
if method == "concat":
allows_duplicate_labels = all(
x.flags.allows_duplicate_labels for x in other.objs
)
self.flags.allows_duplicate_labels = allows_duplicate_labels
return self
def __getattr__(self, name: str):
"""
After regular attribute access, try looking up the name
This allows simpler access to columns for interactive use.
"""
# Note: obj.x will always call obj.__getattribute__('x') prior to
# calling obj.__getattr__('x').
if (
name in self._internal_names_set
or name in self._metadata
or name in self._accessors
):
return object.__getattribute__(self, name)
else:
if self._info_axis._can_hold_identifiers_and_holds_name(name):
return self[name]
return object.__getattribute__(self, name)
def __setattr__(self, name: str, value) -> None:
"""
After regular attribute access, try setting the name
This allows simpler access to columns for interactive use.
"""
# first try regular attribute access via __getattribute__, so that
# e.g. ``obj.x`` and ``obj.x = 4`` will always reference/modify
# the same attribute.
try:
object.__getattribute__(self, name)
return object.__setattr__(self, name, value)
except AttributeError:
pass
# if this fails, go on to more involved attribute setting
# (note that this matches __getattr__, above).
if name in self._internal_names_set:
object.__setattr__(self, name, value)
elif name in self._metadata:
object.__setattr__(self, name, value)
else:
try:
existing = getattr(self, name)
if isinstance(existing, Index):
object.__setattr__(self, name, value)
elif name in self._info_axis:
self[name] = value
else:
object.__setattr__(self, name, value)
except (AttributeError, TypeError):
if isinstance(self, ABCDataFrame) and (is_list_like(value)):
warnings.warn(
"Pandas doesn't allow columns to be "
"created via a new attribute name - see "
"https://pandas.pydata.org/pandas-docs/"
"stable/indexing.html#attribute-access",
stacklevel=2,
)
object.__setattr__(self, name, value)
@final
def _dir_additions(self) -> Set[str]:
"""
add the string-like attributes from the info_axis.
If info_axis is a MultiIndex, its first level values are used.
"""
additions = super()._dir_additions()
if self._info_axis._can_hold_strings:
additions.update(self._info_axis._dir_additions_for_owner)
return additions
# ----------------------------------------------------------------------
# Consolidation of internals
@final
def _protect_consolidate(self, f):
"""
Consolidate _mgr -- if the blocks have changed, then clear the
cache
"""
blocks_before = len(self._mgr.blocks)
result = f()
if len(self._mgr.blocks) != blocks_before:
self._clear_item_cache()
return result
@final
def _consolidate_inplace(self) -> None:
"""Consolidate data in place and return None"""
def f():
self._mgr = self._mgr.consolidate()
self._protect_consolidate(f)
@final
def _consolidate(self):
"""
Compute NDFrame with "consolidated" internals (data of each dtype
grouped together in a single ndarray).
Returns
-------
consolidated : same type as caller
"""
f = lambda: self._mgr.consolidate()
cons_data = self._protect_consolidate(f)
return self._constructor(cons_data).__finalize__(self)
@final
@property
def _is_mixed_type(self) -> bool_t:
if self._mgr.is_single_block:
return False
if self._mgr.any_extension_types:
# Even if they have the same dtype, we cant consolidate them,
# so we pretend this is "mixed'"
return True
return self.dtypes.nunique() > 1
@final
def _check_inplace_setting(self, value) -> bool_t:
""" check whether we allow in-place setting with this type of value """
if self._is_mixed_type:
if not self._mgr.is_numeric_mixed_type:
# allow an actual np.nan thru
if is_float(value) and np.isnan(value):
return True
raise TypeError(
"Cannot do inplace boolean setting on "
"mixed-types with a non np.nan value"
)
return True
@final
def _get_numeric_data(self):
return self._constructor(self._mgr.get_numeric_data()).__finalize__(self)
@final
def _get_bool_data(self):
return self._constructor(self._mgr.get_bool_data()).__finalize__(self)
# ----------------------------------------------------------------------
# Internal Interface Methods
@property
def values(self) -> np.ndarray:
"""
Return a Numpy representation of the DataFrame.
.. warning::
We recommend using :meth:`DataFrame.to_numpy` instead.
Only the values in the DataFrame will be returned, the axes labels
will be removed.
Returns
-------
numpy.ndarray
The values of the DataFrame.
See Also
--------
DataFrame.to_numpy : Recommended alternative to this method.
DataFrame.index : Retrieve the index labels.
DataFrame.columns : Retrieving the column names.
Notes
-----
The dtype will be a lower-common-denominator dtype (implicit
upcasting); that is to say if the dtypes (even of numeric types)
are mixed, the one that accommodates all will be chosen. Use this
with care if you are not dealing with the blocks.
e.g. If the dtypes are float16 and float32, dtype will be upcast to
float32. If dtypes are int32 and uint8, dtype will be upcast to
int32. By :func:`numpy.find_common_type` convention, mixing int64
and uint64 will result in a float64 dtype.
Examples
--------
A DataFrame where all columns are the same type (e.g., int64) results
in an array of the same type.
>>> df = pd.DataFrame({'age': [ 3, 29],
... 'height': [94, 170],
... 'weight': [31, 115]})
>>> df
age height weight
0 3 94 31
1 29 170 115
>>> df.dtypes
age int64
height int64
weight int64
dtype: object
>>> df.values
array([[ 3, 94, 31],
[ 29, 170, 115]])
A DataFrame with mixed type columns(e.g., str/object, int64, float32)
results in an ndarray of the broadest type that accommodates these
mixed types (e.g., object).
>>> df2 = pd.DataFrame([('parrot', 24.0, 'second'),
... ('lion', 80.5, 1),
... ('monkey', np.nan, None)],
... columns=('name', 'max_speed', 'rank'))
>>> df2.dtypes
name object
max_speed float64
rank object
dtype: object
>>> df2.values
array([['parrot', 24.0, 'second'],
['lion', 80.5, 1],
['monkey', nan, None]], dtype=object)
"""
self._consolidate_inplace()
return self._mgr.as_array(transpose=self._AXIS_REVERSED)
@property
def _values(self) -> np.ndarray:
"""internal implementation"""
return self.values
@property
def dtypes(self):
"""
Return the dtypes in the DataFrame.
This returns a Series with the data type of each column.
The result's index is the original DataFrame's columns. Columns
with mixed types are stored with the ``object`` dtype. See
:ref:`the User Guide <basics.dtypes>` for more.
Returns
-------
pandas.Series
The data type of each column.
Examples
--------
>>> df = pd.DataFrame({'float': [1.0],
... 'int': [1],
... 'datetime': [pd.Timestamp('20180310')],
... 'string': ['foo']})
>>> df.dtypes
float float64
int int64
datetime datetime64[ns]
string object
dtype: object
"""
data = self._mgr.get_dtypes()
return self._constructor_sliced(data, index=self._info_axis, dtype=np.object_)
@final
def _to_dict_of_blocks(self, copy: bool_t = True):
"""
Return a dict of dtype -> Constructor Types that
each is a homogeneous dtype.
Internal ONLY
"""
return {
k: self._constructor(v).__finalize__(self)
for k, v, in self._mgr.to_dict(copy=copy).items()
}
def astype(
self: FrameOrSeries, dtype, copy: bool_t = True, errors: str = "raise"
) -> FrameOrSeries:
"""
Cast a pandas object to a specified dtype ``dtype``.
Parameters
----------
dtype : data type, or dict of column name -> data type
Use a numpy.dtype or Python type to cast entire pandas object to
the same type. Alternatively, use {col: dtype, ...}, where col is a
column label and dtype is a numpy.dtype or Python type to cast one
or more of the DataFrame's columns to column-specific types.
copy : bool, default True
Return a copy when ``copy=True`` (be very careful setting
``copy=False`` as changes to values then may propagate to other
pandas objects).
errors : {'raise', 'ignore'}, default 'raise'
Control raising of exceptions on invalid data for provided dtype.
- ``raise`` : allow exceptions to be raised
- ``ignore`` : suppress exceptions. On error return original object.
Returns
-------
casted : same type as caller
See Also
--------
to_datetime : Convert argument to datetime.
to_timedelta : Convert argument to timedelta.
to_numeric : Convert argument to a numeric type.
numpy.ndarray.astype : Cast a numpy array to a specified type.
Examples
--------
Create a DataFrame:
>>> d = {'col1': [1, 2], 'col2': [3, 4]}
>>> df = pd.DataFrame(data=d)
>>> df.dtypes
col1 int64
col2 int64
dtype: object
Cast all columns to int32:
>>> df.astype('int32').dtypes
col1 int32
col2 int32
dtype: object
Cast col1 to int32 using a dictionary:
>>> df.astype({'col1': 'int32'}).dtypes
col1 int32
col2 int64
dtype: object
Create a series:
>>> ser = pd.Series([1, 2], dtype='int32')
>>> ser
0 1
1 2
dtype: int32
>>> ser.astype('int64')
0 1
1 2
dtype: int64
Convert to categorical type:
>>> ser.astype('category')
0 1
1 2
dtype: category
Categories (2, int64): [1, 2]
Convert to ordered categorical type with custom ordering:
>>> cat_dtype = pd.api.types.CategoricalDtype(
... categories=[2, 1], ordered=True)
>>> ser.astype(cat_dtype)
0 1
1 2
dtype: category
Categories (2, int64): [2 < 1]
Note that using ``copy=False`` and changing data on a new
pandas object may propagate changes:
>>> s1 = pd.Series([1, 2])
>>> s2 = s1.astype('int64', copy=False)
>>> s2[0] = 10
>>> s1 # note that s1[0] has changed too
0 10
1 2
dtype: int64
Create a series of dates:
>>> ser_date = pd.Series(pd.date_range('20200101', periods=3))
>>> ser_date
0 2020-01-01
1 2020-01-02
2 2020-01-03
dtype: datetime64[ns]
Datetimes are localized to UTC first before
converting to the specified timezone:
>>> ser_date.astype('datetime64[ns, US/Eastern]')
0 2019-12-31 19:00:00-05:00
1 2020-01-01 19:00:00-05:00
2 2020-01-02 19:00:00-05:00
dtype: datetime64[ns, US/Eastern]
"""
if is_dict_like(dtype):
if self.ndim == 1: # i.e. Series
if len(dtype) > 1 or self.name not in dtype:
raise KeyError(
"Only the Series name can be used for "
"the key in Series dtype mappings."
)
new_type = dtype[self.name]
return self.astype(new_type, copy, errors)
for col_name in dtype.keys():
if col_name not in self:
raise KeyError(
"Only a column name can be used for the "
"key in a dtype mappings argument."
)
results = []
for col_name, col in self.items():
if col_name in dtype:
results.append(
col.astype(dtype=dtype[col_name], copy=copy, errors=errors)
)
else:
results.append(col.copy() if copy else col)
elif is_extension_array_dtype(dtype) and self.ndim > 1:
# GH 18099/22869: columnwise conversion to extension dtype
# GH 24704: use iloc to handle duplicate column names
results = [
self.iloc[:, i].astype(dtype, copy=copy)
for i in range(len(self.columns))
]
else:
# else, only a single dtype is given
new_data = self._mgr.astype(dtype=dtype, copy=copy, errors=errors)
return self._constructor(new_data).__finalize__(self, method="astype")
# GH 33113: handle empty frame or series
if not results:
return self.copy()
# GH 19920: retain column metadata after concat
result = pd.concat(results, axis=1, copy=False)
result.columns = self.columns
return result
@final
def copy(self: FrameOrSeries, deep: bool_t = True) -> FrameOrSeries:
"""
Make a copy of this object's indices and data.
When ``deep=True`` (default), a new object will be created with a
copy of the calling object's data and indices. Modifications to
the data or indices of the copy will not be reflected in the
original object (see notes below).
When ``deep=False``, a new object will be created without copying
the calling object's data or index (only references to the data
and index are copied). Any changes to the data of the original
will be reflected in the shallow copy (and vice versa).
Parameters
----------
deep : bool, default True
Make a deep copy, including a copy of the data and the indices.
With ``deep=False`` neither the indices nor the data are copied.
Returns
-------
copy : Series or DataFrame
Object type matches caller.
Notes
-----
When ``deep=True``, data is copied but actual Python objects
will not be copied recursively, only the reference to the object.
This is in contrast to `copy.deepcopy` in the Standard Library,
which recursively copies object data (see examples below).
While ``Index`` objects are copied when ``deep=True``, the underlying
numpy array is not copied for performance reasons. Since ``Index`` is
immutable, the underlying data can be safely shared and a copy
is not needed.
Examples
--------
>>> s = pd.Series([1, 2], index=["a", "b"])
>>> s
a 1
b 2
dtype: int64
>>> s_copy = s.copy()
>>> s_copy
a 1
b 2
dtype: int64
**Shallow copy versus default (deep) copy:**
>>> s = pd.Series([1, 2], index=["a", "b"])
>>> deep = s.copy()
>>> shallow = s.copy(deep=False)
Shallow copy shares data and index with original.
>>> s is shallow
False
>>> s.values is shallow.values and s.index is shallow.index
True
Deep copy has own copy of data and index.
>>> s is deep
False
>>> s.values is deep.values or s.index is deep.index
False
Updates to the data shared by shallow copy and original is reflected
in both; deep copy remains unchanged.
>>> s[0] = 3
>>> shallow[1] = 4
>>> s
a 3
b 4
dtype: int64
>>> shallow
a 3
b 4
dtype: int64
>>> deep
a 1
b 2
dtype: int64
Note that when copying an object containing Python objects, a deep copy
will copy the data, but will not do so recursively. Updating a nested
data object will be reflected in the deep copy.
>>> s = pd.Series([[1, 2], [3, 4]])
>>> deep = s.copy()
>>> s[0][0] = 10
>>> s
0 [10, 2]
1 [3, 4]
dtype: object
>>> deep
0 [10, 2]
1 [3, 4]
dtype: object
"""
data = self._mgr.copy(deep=deep)
self._clear_item_cache()
return self._constructor(data).__finalize__(self, method="copy")
@final
def __copy__(self: FrameOrSeries, deep: bool_t = True) -> FrameOrSeries:
return self.copy(deep=deep)
@final
def __deepcopy__(self: FrameOrSeries, memo=None) -> FrameOrSeries:
"""
Parameters
----------
memo, default None
Standard signature. Unused
"""
return self.copy(deep=True)
@final
def _convert(
self: FrameOrSeries,
datetime: bool_t = False,
numeric: bool_t = False,
timedelta: bool_t = False,
) -> FrameOrSeries:
"""
Attempt to infer better dtype for object columns
Parameters
----------
datetime : bool, default False
If True, convert to date where possible.
numeric : bool, default False
If True, attempt to convert to numbers (including strings), with
unconvertible values becoming NaN.
timedelta : bool, default False
If True, convert to timedelta where possible.
Returns
-------
converted : same as input object
"""
validate_bool_kwarg(datetime, "datetime")
validate_bool_kwarg(numeric, "numeric")
validate_bool_kwarg(timedelta, "timedelta")
return self._constructor(
self._mgr.convert(
datetime=datetime,
numeric=numeric,
timedelta=timedelta,
copy=True,
)
).__finalize__(self)
@final
def infer_objects(self: FrameOrSeries) -> FrameOrSeries:
"""
Attempt to infer better dtypes for object columns.
Attempts soft conversion of object-dtyped
columns, leaving non-object and unconvertible
columns unchanged. The inference rules are the
same as during normal Series/DataFrame construction.
Returns
-------
converted : same type as input object
See Also
--------
to_datetime : Convert argument to datetime.
to_timedelta : Convert argument to timedelta.
to_numeric : Convert argument to numeric type.
convert_dtypes : Convert argument to best possible dtype.
Examples
--------
>>> df = pd.DataFrame({"A": ["a", 1, 2, 3]})
>>> df = df.iloc[1:]
>>> df
A
1 1
2 2
3 3
>>> df.dtypes
A object
dtype: object
>>> df.infer_objects().dtypes
A int64
dtype: object
"""
# numeric=False necessary to only soft convert;
# python objects will still be converted to
# native numpy numeric types
return self._constructor(
self._mgr.convert(datetime=True, numeric=False, timedelta=True, copy=True)
).__finalize__(self, method="infer_objects")
@final
def convert_dtypes(
self: FrameOrSeries,
infer_objects: bool_t = True,
convert_string: bool_t = True,
convert_integer: bool_t = True,
convert_boolean: bool_t = True,
convert_floating: bool_t = True,
) -> FrameOrSeries:
"""
Convert columns to best possible dtypes using dtypes supporting ``pd.NA``.
.. versionadded:: 1.0.0
Parameters
----------
infer_objects : bool, default True
Whether object dtypes should be converted to the best possible types.
convert_string : bool, default True
Whether object dtypes should be converted to ``StringDtype()``.
convert_integer : bool, default True
Whether, if possible, conversion can be done to integer extension types.
convert_boolean : bool, defaults True
Whether object dtypes should be converted to ``BooleanDtypes()``.
convert_floating : bool, defaults True
Whether, if possible, conversion can be done to floating extension types.
If `convert_integer` is also True, preference will be give to integer
dtypes if the floats can be faithfully casted to integers.
.. versionadded:: 1.2.0
Returns
-------
Series or DataFrame
Copy of input object with new dtype.
See Also
--------
infer_objects : Infer dtypes of objects.
to_datetime : Convert argument to datetime.
to_timedelta : Convert argument to timedelta.
to_numeric : Convert argument to a numeric type.
Notes
-----
By default, ``convert_dtypes`` will attempt to convert a Series (or each
Series in a DataFrame) to dtypes that support ``pd.NA``. By using the options
``convert_string``, ``convert_integer``, ``convert_boolean`` and
``convert_boolean``, it is possible to turn off individual conversions
to ``StringDtype``, the integer extension types, ``BooleanDtype``
or floating extension types, respectively.
For object-dtyped columns, if ``infer_objects`` is ``True``, use the inference
rules as during normal Series/DataFrame construction. Then, if possible,
convert to ``StringDtype``, ``BooleanDtype`` or an appropriate integer
or floating extension type, otherwise leave as ``object``.
If the dtype is integer, convert to an appropriate integer extension type.
If the dtype is numeric, and consists of all integers, convert to an
appropriate integer extension type. Otherwise, convert to an
appropriate floating extension type.
.. versionchanged:: 1.2
Starting with pandas 1.2, this method also converts float columns
to the nullable floating extension type.
In the future, as new dtypes are added that support ``pd.NA``, the results
of this method will change to support those new dtypes.
Examples
--------
>>> df = pd.DataFrame(
... {
... "a": pd.Series([1, 2, 3], dtype=np.dtype("int32")),
... "b": pd.Series(["x", "y", "z"], dtype=np.dtype("O")),
... "c": pd.Series([True, False, np.nan], dtype=np.dtype("O")),
... "d": pd.Series(["h", "i", np.nan], dtype=np.dtype("O")),
... "e": pd.Series([10, np.nan, 20], dtype=np.dtype("float")),
... "f": pd.Series([np.nan, 100.5, 200], dtype=np.dtype("float")),
... }
... )
Start with a DataFrame with default dtypes.
>>> df
a b c d e f
0 1 x True h 10.0 NaN
1 2 y False i NaN 100.5
2 3 z NaN NaN 20.0 200.0
>>> df.dtypes
a int32
b object
c object
d object
e float64
f float64
dtype: object
Convert the DataFrame to use best possible dtypes.
>>> dfn = df.convert_dtypes()
>>> dfn
a b c d e f
0 1 x True h 10 <NA>
1 2 y False i <NA> 100.5
2 3 z <NA> <NA> 20 200.0
>>> dfn.dtypes
a Int32
b string
c boolean
d string
e Int64
f Float64
dtype: object
Start with a Series of strings and missing data represented by ``np.nan``.
>>> s = pd.Series(["a", "b", np.nan])
>>> s
0 a
1 b
2 NaN
dtype: object
Obtain a Series with dtype ``StringDtype``.
>>> s.convert_dtypes()
0 a
1 b
2 <NA>
dtype: string
"""
if self.ndim == 1:
return self._convert_dtypes(
infer_objects,
convert_string,
convert_integer,
convert_boolean,
convert_floating,
)
else:
results = [
col._convert_dtypes(
infer_objects,
convert_string,
convert_integer,
convert_boolean,
convert_floating,
)
for col_name, col in self.items()
]
result = pd.concat(results, axis=1, copy=False)
return result
# ----------------------------------------------------------------------
# Filling NA's
@doc(**_shared_doc_kwargs)
def fillna(
self: FrameOrSeries,
value=None,
method=None,
axis=None,
inplace: bool_t = False,
limit=None,
downcast=None,
) -> Optional[FrameOrSeries]:
"""
Fill NA/NaN values using the specified method.
Parameters
----------
value : scalar, dict, Series, or DataFrame
Value to use to fill holes (e.g. 0), alternately a
dict/Series/DataFrame of values specifying which value to use for
each index (for a Series) or column (for a DataFrame). Values not
in the dict/Series/DataFrame will not be filled. This value cannot
be a list.
method : {{'backfill', 'bfill', 'pad', 'ffill', None}}, default None
Method to use for filling holes in reindexed Series
pad / ffill: propagate last valid observation forward to next valid
backfill / bfill: use next valid observation to fill gap.
axis : {axes_single_arg}
Axis along which to fill missing values.
inplace : bool, default False
If True, fill in-place. Note: this will modify any
other views on this object (e.g., a no-copy slice for a column in a
DataFrame).
limit : int, default None
If method is specified, this is the maximum number of consecutive
NaN values to forward/backward fill. In other words, if there is
a gap with more than this number of consecutive NaNs, it will only
be partially filled. If method is not specified, this is the
maximum number of entries along the entire axis where NaNs will be
filled. Must be greater than 0 if not None.
downcast : dict, default is None
A dict of item->dtype of what to downcast if possible,
or the string 'infer' which will try to downcast to an appropriate
equal type (e.g. float64 to int64 if possible).
Returns
-------
{klass} or None
Object with missing values filled or None if ``inplace=True``.
See Also
--------
interpolate : Fill NaN values using interpolation.
reindex : Conform object to new index.
asfreq : Convert TimeSeries to specified frequency.
Examples
--------
>>> df = pd.DataFrame([[np.nan, 2, np.nan, 0],
... [3, 4, np.nan, 1],
... [np.nan, np.nan, np.nan, 5],
... [np.nan, 3, np.nan, 4]],
... columns=list('ABCD'))
>>> df
A B C D
0 NaN 2.0 NaN 0
1 3.0 4.0 NaN 1
2 NaN NaN NaN 5
3 NaN 3.0 NaN 4
Replace all NaN elements with 0s.
>>> df.fillna(0)
A B C D
0 0.0 2.0 0.0 0
1 3.0 4.0 0.0 1
2 0.0 0.0 0.0 5
3 0.0 3.0 0.0 4
We can also propagate non-null values forward or backward.
>>> df.fillna(method='ffill')
A B C D
0 NaN 2.0 NaN 0
1 3.0 4.0 NaN 1
2 3.0 4.0 NaN 5
3 3.0 3.0 NaN 4
Replace all NaN elements in column 'A', 'B', 'C', and 'D', with 0, 1,
2, and 3 respectively.
>>> values = {{'A': 0, 'B': 1, 'C': 2, 'D': 3}}
>>> df.fillna(value=values)
A B C D
0 0.0 2.0 2.0 0
1 3.0 4.0 2.0 1
2 0.0 1.0 2.0 5
3 0.0 3.0 2.0 4
Only replace the first NaN element.
>>> df.fillna(value=values, limit=1)
A B C D
0 0.0 2.0 2.0 0
1 3.0 4.0 NaN 1
2 NaN 1.0 NaN 5
3 NaN 3.0 NaN 4
"""
inplace = validate_bool_kwarg(inplace, "inplace")
value, method = validate_fillna_kwargs(value, method)
self._consolidate_inplace()
# set the default here, so functions examining the signaure
# can detect if something was set (e.g. in groupby) (GH9221)
if axis is None:
axis = 0
axis = self._get_axis_number(axis)
if value is None:
if not self._mgr.is_single_block and axis == 1:
if inplace:
raise NotImplementedError()
result = self.T.fillna(method=method, limit=limit).T
# need to downcast here because of all of the transposes
result._mgr = result._mgr.downcast()
return result
new_data = self._mgr.interpolate(
method=method,
axis=axis,
limit=limit,
inplace=inplace,
coerce=True,
downcast=downcast,
)
else:
if self.ndim == 1:
if isinstance(value, (dict, ABCSeries)):
value = create_series_with_explicit_dtype(
value, dtype_if_empty=object
)
value = value.reindex(self.index, copy=False)
value = value._values
elif not is_list_like(value):
pass
else:
raise TypeError(
'"value" parameter must be a scalar, dict '
"or Series, but you passed a "
f'"{type(value).__name__}"'
)
new_data = self._mgr.fillna(
value=value, limit=limit, inplace=inplace, downcast=downcast
)
elif isinstance(value, (dict, ABCSeries)):
if axis == 1:
raise NotImplementedError(
"Currently only can fill "
"with dict/Series column "
"by column"
)
result = self if inplace else self.copy()
for k, v in value.items():
if k not in result:
continue
obj = result[k]
obj.fillna(v, limit=limit, inplace=True, downcast=downcast)
return result if not inplace else None
elif not is_list_like(value):
new_data = self._mgr.fillna(
value=value, limit=limit, inplace=inplace, downcast=downcast
)
elif isinstance(value, ABCDataFrame) and self.ndim == 2:
new_data = self.where(self.notna(), value)._data
else:
raise ValueError(f"invalid fill value with a {type(value)}")
result = self._constructor(new_data)
if inplace:
return self._update_inplace(result)
else:
return result.__finalize__(self, method="fillna")
@final
@doc(klass=_shared_doc_kwargs["klass"])
def ffill(
self: FrameOrSeries,
axis=None,
inplace: bool_t = False,
limit=None,
downcast=None,
) -> Optional[FrameOrSeries]:
"""
Synonym for :meth:`DataFrame.fillna` with ``method='ffill'``.
Returns
-------
{klass} or None
Object with missing values filled or None if ``inplace=True``.
"""
return self.fillna(
method="ffill", axis=axis, inplace=inplace, limit=limit, downcast=downcast
)
pad = ffill
@final
@doc(klass=_shared_doc_kwargs["klass"])
def bfill(
self: FrameOrSeries,
axis=None,
inplace: bool_t = False,
limit=None,
downcast=None,
) -> Optional[FrameOrSeries]:
"""
Synonym for :meth:`DataFrame.fillna` with ``method='bfill'``.
Returns
-------
{klass} or None
Object with missing values filled or None if ``inplace=True``.
"""
return self.fillna(
method="bfill", axis=axis, inplace=inplace, limit=limit, downcast=downcast
)
backfill = bfill
@doc(klass=_shared_doc_kwargs["klass"])
def replace(
self,
to_replace=None,
value=None,
inplace: bool_t = False,
limit: Optional[int] = None,
regex=False,
method="pad",
):
"""
Replace values given in `to_replace` with `value`.
Values of the {klass} are replaced with other values dynamically.
This differs from updating with ``.loc`` or ``.iloc``, which require
you to specify a location to update with some value.
Parameters
----------
to_replace : str, regex, list, dict, Series, int, float, or None
How to find the values that will be replaced.
* numeric, str or regex:
- numeric: numeric values equal to `to_replace` will be
replaced with `value`
- str: string exactly matching `to_replace` will be replaced
with `value`
- regex: regexs matching `to_replace` will be replaced with
`value`
* list of str, regex, or numeric:
- First, if `to_replace` and `value` are both lists, they
**must** be the same length.
- Second, if ``regex=True`` then all of the strings in **both**
lists will be interpreted as regexs otherwise they will match
directly. This doesn't matter much for `value` since there
are only a few possible substitution regexes you can use.
- str, regex and numeric rules apply as above.
* dict:
- Dicts can be used to specify different replacement values
for different existing values. For example,
``{{'a': 'b', 'y': 'z'}}`` replaces the value 'a' with 'b' and
'y' with 'z'. To use a dict in this way the `value`
parameter should be `None`.
- For a DataFrame a dict can specify that different values
should be replaced in different columns. For example,
``{{'a': 1, 'b': 'z'}}`` looks for the value 1 in column 'a'
and the value 'z' in column 'b' and replaces these values
with whatever is specified in `value`. The `value` parameter
should not be ``None`` in this case. You can treat this as a
special case of passing two lists except that you are
specifying the column to search in.
- For a DataFrame nested dictionaries, e.g.,
``{{'a': {{'b': np.nan}}}}``, are read as follows: look in column
'a' for the value 'b' and replace it with NaN. The `value`
parameter should be ``None`` to use a nested dict in this
way. You can nest regular expressions as well. Note that
column names (the top-level dictionary keys in a nested
dictionary) **cannot** be regular expressions.
* None:
- This means that the `regex` argument must be a string,
compiled regular expression, or list, dict, ndarray or
Series of such elements. If `value` is also ``None`` then
this **must** be a nested dictionary or Series.
See the examples section for examples of each of these.
value : scalar, dict, list, str, regex, default None
Value to replace any values matching `to_replace` with.
For a DataFrame a dict of values can be used to specify which
value to use for each column (columns not in the dict will not be
filled). Regular expressions, strings and lists or dicts of such
objects are also allowed.
inplace : bool, default False
If True, in place. Note: this will modify any
other views on this object (e.g. a column from a DataFrame).
Returns the caller if this is True.
limit : int or None, default None
Maximum size gap to forward or backward fill.
regex : bool or same types as `to_replace`, default False
Whether to interpret `to_replace` and/or `value` as regular
expressions. If this is ``True`` then `to_replace` *must* be a
string. Alternatively, this could be a regular expression or a
list, dict, or array of regular expressions in which case
`to_replace` must be ``None``.
method : {{'pad', 'ffill', 'bfill', `None`}}
The method to use when for replacement, when `to_replace` is a
scalar, list or tuple and `value` is ``None``.
Returns
-------
{klass} or None
Object after replacement or None if ``inplace=True``.
Raises
------
AssertionError
* If `regex` is not a ``bool`` and `to_replace` is not
``None``.
TypeError
* If `to_replace` is not a scalar, array-like, ``dict``, or ``None``
* If `to_replace` is a ``dict`` and `value` is not a ``list``,
``dict``, ``ndarray``, or ``Series``
* If `to_replace` is ``None`` and `regex` is not compilable
into a regular expression or is a list, dict, ndarray, or
Series.
* When replacing multiple ``bool`` or ``datetime64`` objects and
the arguments to `to_replace` does not match the type of the
value being replaced
ValueError
* If a ``list`` or an ``ndarray`` is passed to `to_replace` and
`value` but they are not the same length.
See Also
--------
{klass}.fillna : Fill NA values.
{klass}.where : Replace values based on boolean condition.
Series.str.replace : Simple string replacement.
Notes
-----
* Regex substitution is performed under the hood with ``re.sub``. The
rules for substitution for ``re.sub`` are the same.
* Regular expressions will only substitute on strings, meaning you
cannot provide, for example, a regular expression matching floating
point numbers and expect the columns in your frame that have a
numeric dtype to be matched. However, if those floating point
numbers *are* strings, then you can do this.
* This method has *a lot* of options. You are encouraged to experiment
and play with this method to gain intuition about how it works.
* When dict is used as the `to_replace` value, it is like
key(s) in the dict are the to_replace part and
value(s) in the dict are the value parameter.
Examples
--------
**Scalar `to_replace` and `value`**
>>> s = pd.Series([0, 1, 2, 3, 4])
>>> s.replace(0, 5)
0 5
1 1
2 2
3 3
4 4
dtype: int64
>>> df = pd.DataFrame({{'A': [0, 1, 2, 3, 4],
... 'B': [5, 6, 7, 8, 9],
... 'C': ['a', 'b', 'c', 'd', 'e']}})
>>> df.replace(0, 5)
A B C
0 5 5 a
1 1 6 b
2 2 7 c
3 3 8 d
4 4 9 e
**List-like `to_replace`**
>>> df.replace([0, 1, 2, 3], 4)
A B C
0 4 5 a
1 4 6 b
2 4 7 c
3 4 8 d
4 4 9 e
>>> df.replace([0, 1, 2, 3], [4, 3, 2, 1])
A B C
0 4 5 a
1 3 6 b
2 2 7 c
3 1 8 d
4 4 9 e
>>> s.replace([1, 2], method='bfill')
0 0
1 3
2 3
3 3
4 4
dtype: int64
**dict-like `to_replace`**
>>> df.replace({{0: 10, 1: 100}})
A B C
0 10 5 a
1 100 6 b
2 2 7 c
3 3 8 d
4 4 9 e
>>> df.replace({{'A': 0, 'B': 5}}, 100)
A B C
0 100 100 a
1 1 6 b
2 2 7 c
3 3 8 d
4 4 9 e
>>> df.replace({{'A': {{0: 100, 4: 400}}}})
A B C
0 100 5 a
1 1 6 b
2 2 7 c
3 3 8 d
4 400 9 e
**Regular expression `to_replace`**
>>> df = pd.DataFrame({{'A': ['bat', 'foo', 'bait'],
... 'B': ['abc', 'bar', 'xyz']}})
>>> df.replace(to_replace=r'^ba.$', value='new', regex=True)
A B
0 new abc
1 foo new
2 bait xyz
>>> df.replace({{'A': r'^ba.$'}}, {{'A': 'new'}}, regex=True)
A B
0 new abc
1 foo bar
2 bait xyz
>>> df.replace(regex=r'^ba.$', value='new')
A B
0 new abc
1 foo new
2 bait xyz
>>> df.replace(regex={{r'^ba.$': 'new', 'foo': 'xyz'}})
A B
0 new abc
1 xyz new
2 bait xyz
>>> df.replace(regex=[r'^ba.$', 'foo'], value='new')
A B
0 new abc
1 new new
2 bait xyz
Compare the behavior of ``s.replace({{'a': None}})`` and
``s.replace('a', None)`` to understand the peculiarities
of the `to_replace` parameter:
>>> s = pd.Series([10, 'a', 'a', 'b', 'a'])
When one uses a dict as the `to_replace` value, it is like the
value(s) in the dict are equal to the `value` parameter.
``s.replace({{'a': None}})`` is equivalent to
``s.replace(to_replace={{'a': None}}, value=None, method=None)``:
>>> s.replace({{'a': None}})
0 10
1 None
2 None
3 b
4 None
dtype: object
When ``value=None`` and `to_replace` is a scalar, list or
tuple, `replace` uses the method parameter (default 'pad') to do the
replacement. So this is why the 'a' values are being replaced by 10
in rows 1 and 2 and 'b' in row 4 in this case.
The command ``s.replace('a', None)`` is actually equivalent to
``s.replace(to_replace='a', value=None, method='pad')``:
>>> s.replace('a', None)
0 10
1 10
2 10
3 b
4 b
dtype: object
"""
if not (
is_scalar(to_replace)
or is_re_compilable(to_replace)
or is_list_like(to_replace)
):
raise TypeError(
"Expecting 'to_replace' to be either a scalar, array-like, "
"dict or None, got invalid type "
f"{repr(type(to_replace).__name__)}"
)
inplace = validate_bool_kwarg(inplace, "inplace")
if not is_bool(regex) and to_replace is not None:
raise ValueError("'to_replace' must be 'None' if 'regex' is not a bool")
self._consolidate_inplace()
if value is None:
# passing a single value that is scalar like
# when value is None (GH5319), for compat
if not is_dict_like(to_replace) and not is_dict_like(regex):
to_replace = [to_replace]
if isinstance(to_replace, (tuple, list)):
if isinstance(self, ABCDataFrame):
from pandas import Series
return self.apply(
Series._replace_single,
args=(to_replace, method, inplace, limit),
)
self = cast("Series", self)
return self._replace_single(to_replace, method, inplace, limit)
if not is_dict_like(to_replace):
if not is_dict_like(regex):
raise TypeError(
'If "to_replace" and "value" are both None '
'and "to_replace" is not a list, then '
"regex must be a mapping"
)
to_replace = regex
regex = True
items = list(to_replace.items())
if items:
keys, values = zip(*items)
else:
keys, values = ([], [])
are_mappings = [is_dict_like(v) for v in values]
if any(are_mappings):
if not all(are_mappings):
raise TypeError(
"If a nested mapping is passed, all values "
"of the top level mapping must be mappings"
)
# passed a nested dict/Series
to_rep_dict = {}
value_dict = {}
for k, v in items:
keys, values = list(zip(*v.items())) or ([], [])
to_rep_dict[k] = list(keys)
value_dict[k] = list(values)
to_replace, value = to_rep_dict, value_dict
else:
to_replace, value = keys, values
return self.replace(
to_replace, value, inplace=inplace, limit=limit, regex=regex
)
else:
# need a non-zero len on all axes
if not self.size:
if inplace:
return
return self.copy()
if is_dict_like(to_replace):
if is_dict_like(value): # {'A' : NA} -> {'A' : 0}
# Note: Checking below for `in foo.keys()` instead of
# `in foo` is needed for when we have a Series and not dict
mapping = {
col: (to_replace[col], value[col])
for col in to_replace.keys()
if col in value.keys() and col in self
}
return self._replace_columnwise(mapping, inplace, regex)
# {'A': NA} -> 0
elif not is_list_like(value):
# Operate column-wise
if self.ndim == 1:
raise ValueError(
"Series.replace cannot use dict-like to_replace "
"and non-None value"
)
mapping = {
col: (to_rep, value) for col, to_rep in to_replace.items()
}
return self._replace_columnwise(mapping, inplace, regex)
else:
raise TypeError("value argument must be scalar, dict, or Series")
elif is_list_like(to_replace):
if not is_list_like(value):
# e.g. to_replace = [NA, ''] and value is 0,
# so we replace NA with 0 and then replace '' with 0
value = [value] * len(to_replace)
# e.g. we have to_replace = [NA, ''] and value = [0, 'missing']
if len(to_replace) != len(value):
raise ValueError(
f"Replacement lists must match in length. "
f"Expecting {len(to_replace)} got {len(value)} "
)
new_data = self._mgr.replace_list(
src_list=to_replace,
dest_list=value,
inplace=inplace,
regex=regex,
)
elif to_replace is None:
if not (
is_re_compilable(regex)
or is_list_like(regex)
or is_dict_like(regex)
):
raise TypeError(
f"'regex' must be a string or a compiled regular expression "
f"or a list or dict of strings or regular expressions, "
f"you passed a {repr(type(regex).__name__)}"
)
return self.replace(
regex, value, inplace=inplace, limit=limit, regex=True
)
else:
# dest iterable dict-like
if is_dict_like(value): # NA -> {'A' : 0, 'B' : -1}
# Operate column-wise
if self.ndim == 1:
raise ValueError(
"Series.replace cannot use dict-value and "
"non-None to_replace"
)
mapping = {col: (to_replace, val) for col, val in value.items()}
return self._replace_columnwise(mapping, inplace, regex)
elif not is_list_like(value): # NA -> 0
new_data = self._mgr.replace(
to_replace=to_replace, value=value, inplace=inplace, regex=regex
)
else:
raise TypeError(
f'Invalid "to_replace" type: {repr(type(to_replace).__name__)}'
)
result = self._constructor(new_data)
if inplace:
return self._update_inplace(result)
else:
return result.__finalize__(self, method="replace")
@final
def interpolate(
self: FrameOrSeries,
method: str = "linear",
axis: Axis = 0,
limit: Optional[int] = None,
inplace: bool_t = False,
limit_direction: Optional[str] = None,
limit_area: Optional[str] = None,
downcast: Optional[str] = None,
**kwargs,
) -> Optional[FrameOrSeries]:
"""
Fill NaN values using an interpolation method.
Please note that only ``method='linear'`` is supported for
DataFrame/Series with a MultiIndex.
Parameters
----------
method : str, default 'linear'
Interpolation technique to use. One of:
* 'linear': Ignore the index and treat the values as equally
spaced. This is the only method supported on MultiIndexes.
* 'time': Works on daily and higher resolution data to interpolate
given length of interval.
* 'index', 'values': use the actual numerical values of the index.
* 'pad': Fill in NaNs using existing values.
* 'nearest', 'zero', 'slinear', 'quadratic', 'cubic', 'spline',
'barycentric', 'polynomial': Passed to
`scipy.interpolate.interp1d`. These methods use the numerical
values of the index. Both 'polynomial' and 'spline' require that
you also specify an `order` (int), e.g.
``df.interpolate(method='polynomial', order=5)``.
* 'krogh', 'piecewise_polynomial', 'spline', 'pchip', 'akima',
'cubicspline': Wrappers around the SciPy interpolation methods of
similar names. See `Notes`.
* 'from_derivatives': Refers to
`scipy.interpolate.BPoly.from_derivatives` which
replaces 'piecewise_polynomial' interpolation method in
scipy 0.18.
axis : {{0 or 'index', 1 or 'columns', None}}, default None
Axis to interpolate along.
limit : int, optional
Maximum number of consecutive NaNs to fill. Must be greater than
0.
inplace : bool, default False
Update the data in place if possible.
limit_direction : {{'forward', 'backward', 'both'}}, Optional
Consecutive NaNs will be filled in this direction.
If limit is specified:
* If 'method' is 'pad' or 'ffill', 'limit_direction' must be 'forward'.
* If 'method' is 'backfill' or 'bfill', 'limit_direction' must be
'backwards'.
If 'limit' is not specified:
* If 'method' is 'backfill' or 'bfill', the default is 'backward'
* else the default is 'forward'
.. versionchanged:: 1.1.0
raises ValueError if `limit_direction` is 'forward' or 'both' and
method is 'backfill' or 'bfill'.
raises ValueError if `limit_direction` is 'backward' or 'both' and
method is 'pad' or 'ffill'.
limit_area : {{`None`, 'inside', 'outside'}}, default None
If limit is specified, consecutive NaNs will be filled with this
restriction.
* ``None``: No fill restriction.
* 'inside': Only fill NaNs surrounded by valid values
(interpolate).
* 'outside': Only fill NaNs outside valid values (extrapolate).
downcast : optional, 'infer' or None, defaults to None
Downcast dtypes if possible.
``**kwargs`` : optional
Keyword arguments to pass on to the interpolating function.
Returns
-------
Series or DataFrame or None
Returns the same object type as the caller, interpolated at
some or all ``NaN`` values or None if ``inplace=True``.
See Also
--------
fillna : Fill missing values using different methods.
scipy.interpolate.Akima1DInterpolator : Piecewise cubic polynomials
(Akima interpolator).
scipy.interpolate.BPoly.from_derivatives : Piecewise polynomial in the
Bernstein basis.
scipy.interpolate.interp1d : Interpolate a 1-D function.
scipy.interpolate.KroghInterpolator : Interpolate polynomial (Krogh
interpolator).
scipy.interpolate.PchipInterpolator : PCHIP 1-d monotonic cubic
interpolation.
scipy.interpolate.CubicSpline : Cubic spline data interpolator.
Notes
-----
The 'krogh', 'piecewise_polynomial', 'spline', 'pchip' and 'akima'
methods are wrappers around the respective SciPy implementations of
similar names. These use the actual numerical values of the index.
For more information on their behavior, see the
`SciPy documentation
<https://docs.scipy.org/doc/scipy/reference/interpolate.html#univariate-interpolation>`__
and `SciPy tutorial
<https://docs.scipy.org/doc/scipy/reference/tutorial/interpolate.html>`__.
Examples
--------
Filling in ``NaN`` in a :class:`~pandas.Series` via linear
interpolation.
>>> s = pd.Series([0, 1, np.nan, 3])
>>> s
0 0.0
1 1.0
2 NaN
3 3.0
dtype: float64
>>> s.interpolate()
0 0.0
1 1.0
2 2.0
3 3.0
dtype: float64
Filling in ``NaN`` in a Series by padding, but filling at most two
consecutive ``NaN`` at a time.
>>> s = pd.Series([np.nan, "single_one", np.nan,
... "fill_two_more", np.nan, np.nan, np.nan,
... 4.71, np.nan])
>>> s
0 NaN
1 single_one
2 NaN
3 fill_two_more
4 NaN
5 NaN
6 NaN
7 4.71
8 NaN
dtype: object
>>> s.interpolate(method='pad', limit=2)
0 NaN
1 single_one
2 single_one
3 fill_two_more
4 fill_two_more
5 fill_two_more
6 NaN
7 4.71
8 4.71
dtype: object
Filling in ``NaN`` in a Series via polynomial interpolation or splines:
Both 'polynomial' and 'spline' methods require that you also specify
an ``order`` (int).
>>> s = pd.Series([0, 2, np.nan, 8])
>>> s.interpolate(method='polynomial', order=2)
0 0.000000
1 2.000000
2 4.666667
3 8.000000
dtype: float64
Fill the DataFrame forward (that is, going down) along each column
using linear interpolation.
Note how the last entry in column 'a' is interpolated differently,
because there is no entry after it to use for interpolation.
Note how the first entry in column 'b' remains ``NaN``, because there
is no entry before it to use for interpolation.
>>> df = pd.DataFrame([(0.0, np.nan, -1.0, 1.0),
... (np.nan, 2.0, np.nan, np.nan),
... (2.0, 3.0, np.nan, 9.0),
... (np.nan, 4.0, -4.0, 16.0)],
... columns=list('abcd'))
>>> df
a b c d
0 0.0 NaN -1.0 1.0
1 NaN 2.0 NaN NaN
2 2.0 3.0 NaN 9.0
3 NaN 4.0 -4.0 16.0
>>> df.interpolate(method='linear', limit_direction='forward', axis=0)
a b c d
0 0.0 NaN -1.0 1.0
1 1.0 2.0 -2.0 5.0
2 2.0 3.0 -3.0 9.0
3 2.0 4.0 -4.0 16.0
Using polynomial interpolation.
>>> df['d'].interpolate(method='polynomial', order=2)
0 1.0
1 4.0
2 9.0
3 16.0
Name: d, dtype: float64
"""
inplace = validate_bool_kwarg(inplace, "inplace")
axis = self._get_axis_number(axis)
fillna_methods = ["ffill", "bfill", "pad", "backfill"]
should_transpose = axis == 1 and method not in fillna_methods
obj = self.T if should_transpose else self
if obj.empty:
return self.copy()
if method not in fillna_methods:
axis = self._info_axis_number
if isinstance(obj.index, MultiIndex) and method != "linear":
raise ValueError(
"Only `method=linear` interpolation is supported on MultiIndexes."
)
# Set `limit_direction` depending on `method`
if limit_direction is None:
limit_direction = (
"backward" if method in ("backfill", "bfill") else "forward"
)
else:
if method in ("pad", "ffill") and limit_direction != "forward":
raise ValueError(
f"`limit_direction` must be 'forward' for method `{method}`"
)
if method in ("backfill", "bfill") and limit_direction != "backward":
raise ValueError(
f"`limit_direction` must be 'backward' for method `{method}`"
)
if obj.ndim == 2 and np.all(obj.dtypes == np.dtype(object)):
raise TypeError(
"Cannot interpolate with all object-dtype columns "
"in the DataFrame. Try setting at least one "
"column to a numeric dtype."
)
# create/use the index
if method == "linear":
# prior default
index = np.arange(len(obj.index))
index = Index(index)
else:
index = obj.index
methods = {"index", "values", "nearest", "time"}
is_numeric_or_datetime = (
is_numeric_dtype(index.dtype)
or is_datetime64_any_dtype(index.dtype)
or is_timedelta64_dtype(index.dtype)
)
if method not in methods and not is_numeric_or_datetime:
raise ValueError(
"Index column must be numeric or datetime type when "
f"using {method} method other than linear. "
"Try setting a numeric or datetime index column before "
"interpolating."
)
if isna(index).any():
raise NotImplementedError(
"Interpolation with NaNs in the index "
"has not been implemented. Try filling "
"those NaNs before interpolating."
)
new_data = obj._mgr.interpolate(
method=method,
axis=axis,
index=index,
limit=limit,
limit_direction=limit_direction,
limit_area=limit_area,
inplace=inplace,
downcast=downcast,
**kwargs,
)
result = self._constructor(new_data)
if should_transpose:
result = result.T
if inplace:
return self._update_inplace(result)
else:
return result.__finalize__(self, method="interpolate")
# ----------------------------------------------------------------------
# Timeseries methods Methods
@final
def asof(self, where, subset=None):
"""
Return the last row(s) without any NaNs before `where`.
The last row (for each element in `where`, if list) without any
NaN is taken.
In case of a :class:`~pandas.DataFrame`, the last row without NaN
considering only the subset of columns (if not `None`)
If there is no good value, NaN is returned for a Series or
a Series of NaN values for a DataFrame
Parameters
----------
where : date or array-like of dates
Date(s) before which the last row(s) are returned.
subset : str or array-like of str, default `None`
For DataFrame, if not `None`, only use these columns to
check for NaNs.
Returns
-------
scalar, Series, or DataFrame
The return can be:
* scalar : when `self` is a Series and `where` is a scalar
* Series: when `self` is a Series and `where` is an array-like,
or when `self` is a DataFrame and `where` is a scalar
* DataFrame : when `self` is a DataFrame and `where` is an
array-like
Return scalar, Series, or DataFrame.
See Also
--------
merge_asof : Perform an asof merge. Similar to left join.
Notes
-----
Dates are assumed to be sorted. Raises if this is not the case.
Examples
--------
A Series and a scalar `where`.
>>> s = pd.Series([1, 2, np.nan, 4], index=[10, 20, 30, 40])
>>> s
10 1.0
20 2.0
30 NaN
40 4.0
dtype: float64
>>> s.asof(20)
2.0
For a sequence `where`, a Series is returned. The first value is
NaN, because the first element of `where` is before the first
index value.
>>> s.asof([5, 20])
5 NaN
20 2.0
dtype: float64
Missing values are not considered. The following is ``2.0``, not
NaN, even though NaN is at the index location for ``30``.
>>> s.asof(30)
2.0
Take all columns into consideration
>>> df = pd.DataFrame({'a': [10, 20, 30, 40, 50],
... 'b': [None, None, None, None, 500]},
... index=pd.DatetimeIndex(['2018-02-27 09:01:00',
... '2018-02-27 09:02:00',
... '2018-02-27 09:03:00',
... '2018-02-27 09:04:00',
... '2018-02-27 09:05:00']))
>>> df.asof(pd.DatetimeIndex(['2018-02-27 09:03:30',
... '2018-02-27 09:04:30']))
a b
2018-02-27 09:03:30 NaN NaN
2018-02-27 09:04:30 NaN NaN
Take a single column into consideration
>>> df.asof(pd.DatetimeIndex(['2018-02-27 09:03:30',
... '2018-02-27 09:04:30']),
... subset=['a'])
a b
2018-02-27 09:03:30 30.0 NaN
2018-02-27 09:04:30 40.0 NaN
"""
if isinstance(where, str):
where = Timestamp(where)
if not self.index.is_monotonic:
raise ValueError("asof requires a sorted index")
is_series = isinstance(self, ABCSeries)
if is_series:
if subset is not None:
raise ValueError("subset is not valid for Series")
else:
if subset is None:
subset = self.columns
if not is_list_like(subset):
subset = [subset]
is_list = is_list_like(where)
if not is_list:
start = self.index[0]
if isinstance(self.index, PeriodIndex):
where = Period(where, freq=self.index.freq)
if where < start:
if not is_series:
return self._constructor_sliced(
index=self.columns, name=where, dtype=np.float64
)
return np.nan
# It's always much faster to use a *while* loop here for
# Series than pre-computing all the NAs. However a
# *while* loop is extremely expensive for DataFrame
# so we later pre-compute all the NAs and use the same
# code path whether *where* is a scalar or list.
# See PR: https://github.com/pandas-dev/pandas/pull/14476
if is_series:
loc = self.index.searchsorted(where, side="right")
if loc > 0:
loc -= 1
values = self._values
while loc > 0 and isna(values[loc]):
loc -= 1
return values[loc]
if not isinstance(where, Index):
where = Index(where) if is_list else Index([where])
nulls = self.isna() if is_series else self[subset].isna().any(1)
if nulls.all():
if is_series:
self = cast("Series", self)
return self._constructor(np.nan, index=where, name=self.name)
elif is_list:
self = cast("DataFrame", self)
return self._constructor(np.nan, index=where, columns=self.columns)
else:
self = cast("DataFrame", self)
return self._constructor_sliced(
np.nan, index=self.columns, name=where[0]
)
locs = self.index.asof_locs(where, ~(nulls._values))
# mask the missing
missing = locs == -1
data = self.take(locs)
data.index = where
data.loc[missing] = np.nan
return data if is_list else data.iloc[-1]
# ----------------------------------------------------------------------
# Action Methods
@doc(klass=_shared_doc_kwargs["klass"])
def isna(self: FrameOrSeries) -> FrameOrSeries:
"""
Detect missing values.
Return a boolean same-sized object indicating if the values are NA.
NA values, such as None or :attr:`numpy.NaN`, gets mapped to True
values.
Everything else gets mapped to False values. Characters such as empty
strings ``''`` or :attr:`numpy.inf` are not considered NA values
(unless you set ``pandas.options.mode.use_inf_as_na = True``).
Returns
-------
{klass}
Mask of bool values for each element in {klass} that
indicates whether an element is an NA value.
See Also
--------
{klass}.isnull : Alias of isna.
{klass}.notna : Boolean inverse of isna.
{klass}.dropna : Omit axes labels with missing values.
isna : Top-level isna.
Examples
--------
Show which entries in a DataFrame are NA.
>>> df = pd.DataFrame(dict(age=[5, 6, np.NaN],
... born=[pd.NaT, pd.Timestamp('1939-05-27'),
... pd.Timestamp('1940-04-25')],
... name=['Alfred', 'Batman', ''],
... toy=[None, 'Batmobile', 'Joker']))
>>> df
age born name toy
0 5.0 NaT Alfred None
1 6.0 1939-05-27 Batman Batmobile
2 NaN 1940-04-25 Joker
>>> df.isna()
age born name toy
0 False True False True
1 False False False False
2 True False False False
Show which entries in a Series are NA.
>>> ser = pd.Series([5, 6, np.NaN])
>>> ser
0 5.0
1 6.0
2 NaN
dtype: float64
>>> ser.isna()
0 False
1 False
2 True
dtype: bool
"""
return isna(self).__finalize__(self, method="isna")
@doc(isna, klass=_shared_doc_kwargs["klass"])
def isnull(self: FrameOrSeries) -> FrameOrSeries:
return isna(self).__finalize__(self, method="isnull")
@doc(klass=_shared_doc_kwargs["klass"])
def notna(self: FrameOrSeries) -> FrameOrSeries:
"""
Detect existing (non-missing) values.
Return a boolean same-sized object indicating if the values are not NA.
Non-missing values get mapped to True. Characters such as empty
strings ``''`` or :attr:`numpy.inf` are not considered NA values
(unless you set ``pandas.options.mode.use_inf_as_na = True``).
NA values, such as None or :attr:`numpy.NaN`, get mapped to False
values.
Returns
-------
{klass}
Mask of bool values for each element in {klass} that
indicates whether an element is not an NA value.
See Also
--------
{klass}.notnull : Alias of notna.
{klass}.isna : Boolean inverse of notna.
{klass}.dropna : Omit axes labels with missing values.
notna : Top-level notna.
Examples
--------
Show which entries in a DataFrame are not NA.
>>> df = pd.DataFrame(dict(age=[5, 6, np.NaN],
... born=[pd.NaT, pd.Timestamp('1939-05-27'),
... pd.Timestamp('1940-04-25')],
... name=['Alfred', 'Batman', ''],
... toy=[None, 'Batmobile', 'Joker']))
>>> df
age born name toy
0 5.0 NaT Alfred None
1 6.0 1939-05-27 Batman Batmobile
2 NaN 1940-04-25 Joker
>>> df.notna()
age born name toy
0 True False True False
1 True True True True
2 False True True True
Show which entries in a Series are not NA.
>>> ser = pd.Series([5, 6, np.NaN])
>>> ser
0 5.0
1 6.0
2 NaN
dtype: float64
>>> ser.notna()
0 True
1 True
2 False
dtype: bool
"""
return notna(self).__finalize__(self, method="notna")
@doc(notna, klass=_shared_doc_kwargs["klass"])
def notnull(self: FrameOrSeries) -> FrameOrSeries:
return notna(self).__finalize__(self, method="notnull")
@final
def _clip_with_scalar(self, lower, upper, inplace: bool_t = False):
if (lower is not None and np.any(isna(lower))) or (
upper is not None and np.any(isna(upper))
):
raise ValueError("Cannot use an NA value as a clip threshold")
result = self
mask = isna(self._values)
with np.errstate(all="ignore"):
if upper is not None:
subset = self.to_numpy() <= upper
result = result.where(subset, upper, axis=None, inplace=False)
if lower is not None:
subset = self.to_numpy() >= lower
result = result.where(subset, lower, axis=None, inplace=False)
if np.any(mask):
result[mask] = np.nan
if inplace:
return self._update_inplace(result)
else:
return result
@final
def _clip_with_one_bound(self, threshold, method, axis, inplace):
if axis is not None:
axis = self._get_axis_number(axis)
# method is self.le for upper bound and self.ge for lower bound
if is_scalar(threshold) and is_number(threshold):
if method.__name__ == "le":
return self._clip_with_scalar(None, threshold, inplace=inplace)
return self._clip_with_scalar(threshold, None, inplace=inplace)
subset = method(threshold, axis=axis) | isna(self)
# GH #15390
# In order for where method to work, the threshold must
# be transformed to NDFrame from other array like structure.
if (not isinstance(threshold, ABCSeries)) and is_list_like(threshold):
if isinstance(self, ABCSeries):
threshold = self._constructor(threshold, index=self.index)
else:
threshold = align_method_FRAME(self, threshold, axis, flex=None)[1]
return self.where(subset, threshold, axis=axis, inplace=inplace)
@final
def clip(
self: FrameOrSeries,
lower=None,
upper=None,
axis=None,
inplace: bool_t = False,
*args,
**kwargs,
) -> FrameOrSeries:
"""
Trim values at input threshold(s).
Assigns values outside boundary to boundary values. Thresholds
can be singular values or array like, and in the latter case
the clipping is performed element-wise in the specified axis.
Parameters
----------
lower : float or array_like, default None
Minimum threshold value. All values below this
threshold will be set to it.
upper : float or array_like, default None
Maximum threshold value. All values above this
threshold will be set to it.
axis : int or str axis name, optional
Align object with lower and upper along the given axis.
inplace : bool, default False
Whether to perform the operation in place on the data.
*args, **kwargs
Additional keywords have no effect but might be accepted
for compatibility with numpy.
Returns
-------
Series or DataFrame or None
Same type as calling object with the values outside the
clip boundaries replaced or None if ``inplace=True``.
See Also
--------
Series.clip : Trim values at input threshold in series.
DataFrame.clip : Trim values at input threshold in dataframe.
numpy.clip : Clip (limit) the values in an array.
Examples
--------
>>> data = {'col_0': [9, -3, 0, -1, 5], 'col_1': [-2, -7, 6, 8, -5]}
>>> df = pd.DataFrame(data)
>>> df
col_0 col_1
0 9 -2
1 -3 -7
2 0 6
3 -1 8
4 5 -5
Clips per column using lower and upper thresholds:
>>> df.clip(-4, 6)
col_0 col_1
0 6 -2
1 -3 -4
2 0 6
3 -1 6
4 5 -4
Clips using specific lower and upper thresholds per column element:
>>> t = pd.Series([2, -4, -1, 6, 3])
>>> t
0 2
1 -4
2 -1
3 6
4 3
dtype: int64
>>> df.clip(t, t + 4, axis=0)
col_0 col_1
0 6 2
1 -3 -4
2 0 3
3 6 8
4 5 3
"""
inplace = validate_bool_kwarg(inplace, "inplace")
axis = nv.validate_clip_with_axis(axis, args, kwargs)
if axis is not None:
axis = self._get_axis_number(axis)
# GH 17276
# numpy doesn't like NaN as a clip value
# so ignore
# GH 19992
# numpy doesn't drop a list-like bound containing NaN
if not is_list_like(lower) and np.any(isna(lower)):
lower = None
if not is_list_like(upper) and np.any(isna(upper)):
upper = None
# GH 2747 (arguments were reversed)
if lower is not None and upper is not None:
if is_scalar(lower) and is_scalar(upper):
lower, upper = min(lower, upper), max(lower, upper)
# fast-path for scalars
if (lower is None or (is_scalar(lower) and is_number(lower))) and (
upper is None or (is_scalar(upper) and is_number(upper))
):
return self._clip_with_scalar(lower, upper, inplace=inplace)
result = self
if lower is not None:
result = result._clip_with_one_bound(
lower, method=self.ge, axis=axis, inplace=inplace
)
if upper is not None:
if inplace:
result = self
result = result._clip_with_one_bound(
upper, method=self.le, axis=axis, inplace=inplace
)
return result
@final
def asfreq(
self: FrameOrSeries,
freq,
method=None,
how: Optional[str] = None,
normalize: bool_t = False,
fill_value=None,
) -> FrameOrSeries:
"""
Convert TimeSeries to specified frequency.
Optionally provide filling method to pad/backfill missing values.
Returns the original data conformed to a new index with the specified
frequency. ``resample`` is more appropriate if an operation, such as
summarization, is necessary to represent the data at the new frequency.
Parameters
----------
freq : DateOffset or str
Frequency DateOffset or string.
method : {'backfill'/'bfill', 'pad'/'ffill'}, default None
Method to use for filling holes in reindexed Series (note this
does not fill NaNs that already were present):
* 'pad' / 'ffill': propagate last valid observation forward to next
valid
* 'backfill' / 'bfill': use NEXT valid observation to fill.
how : {'start', 'end'}, default end
For PeriodIndex only (see PeriodIndex.asfreq).
normalize : bool, default False
Whether to reset output index to midnight.
fill_value : scalar, optional
Value to use for missing values, applied during upsampling (note
this does not fill NaNs that already were present).
Returns
-------
Same type as caller
Object converted to the specified frequency.
See Also
--------
reindex : Conform DataFrame to new index with optional filling logic.
Notes
-----
To learn more about the frequency strings, please see `this link
<https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases>`__.
Examples
--------
Start by creating a series with 4 one minute timestamps.
>>> index = pd.date_range('1/1/2000', periods=4, freq='T')
>>> series = pd.Series([0.0, None, 2.0, 3.0], index=index)
>>> df = pd.DataFrame({'s':series})
>>> df
s
2000-01-01 00:00:00 0.0
2000-01-01 00:01:00 NaN
2000-01-01 00:02:00 2.0
2000-01-01 00:03:00 3.0
Upsample the series into 30 second bins.
>>> df.asfreq(freq='30S')
s
2000-01-01 00:00:00 0.0
2000-01-01 00:00:30 NaN
2000-01-01 00:01:00 NaN
2000-01-01 00:01:30 NaN
2000-01-01 00:02:00 2.0
2000-01-01 00:02:30 NaN
2000-01-01 00:03:00 3.0
Upsample again, providing a ``fill value``.
>>> df.asfreq(freq='30S', fill_value=9.0)
s
2000-01-01 00:00:00 0.0
2000-01-01 00:00:30 9.0
2000-01-01 00:01:00 NaN
2000-01-01 00:01:30 9.0
2000-01-01 00:02:00 2.0
2000-01-01 00:02:30 9.0
2000-01-01 00:03:00 3.0
Upsample again, providing a ``method``.
>>> df.asfreq(freq='30S', method='bfill')
s
2000-01-01 00:00:00 0.0
2000-01-01 00:00:30 NaN
2000-01-01 00:01:00 NaN
2000-01-01 00:01:30 2.0
2000-01-01 00:02:00 2.0
2000-01-01 00:02:30 3.0
2000-01-01 00:03:00 3.0
"""
from pandas.core.resample import asfreq
return asfreq(
self,
freq,
method=method,
how=how,
normalize=normalize,
fill_value=fill_value,
)
@final
def at_time(
self: FrameOrSeries, time, asof: bool_t = False, axis=None
) -> FrameOrSeries:
"""
Select values at particular time of day (e.g., 9:30AM).
Parameters
----------
time : datetime.time or str
axis : {0 or 'index', 1 or 'columns'}, default 0
.. versionadded:: 0.24.0
Returns
-------
Series or DataFrame
Raises
------
TypeError
If the index is not a :class:`DatetimeIndex`
See Also
--------
between_time : Select values between particular times of the day.
first : Select initial periods of time series based on a date offset.
last : Select final periods of time series based on a date offset.
DatetimeIndex.indexer_at_time : Get just the index locations for
values at particular time of the day.
Examples
--------
>>> i = pd.date_range('2018-04-09', periods=4, freq='12H')
>>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
>>> ts
A
2018-04-09 00:00:00 1
2018-04-09 12:00:00 2
2018-04-10 00:00:00 3
2018-04-10 12:00:00 4
>>> ts.at_time('12:00')
A
2018-04-09 12:00:00 2
2018-04-10 12:00:00 4
"""
if axis is None:
axis = self._stat_axis_number
axis = self._get_axis_number(axis)
index = self._get_axis(axis)
if not isinstance(index, DatetimeIndex):
raise TypeError("Index must be DatetimeIndex")
indexer = index.indexer_at_time(time, asof=asof)
return self._take_with_is_copy(indexer, axis=axis)
@final
def between_time(
self: FrameOrSeries,
start_time,
end_time,
include_start: bool_t = True,
include_end: bool_t = True,
axis=None,
) -> FrameOrSeries:
"""
Select values between particular times of the day (e.g., 9:00-9:30 AM).
By setting ``start_time`` to be later than ``end_time``,
you can get the times that are *not* between the two times.
Parameters
----------
start_time : datetime.time or str
Initial time as a time filter limit.
end_time : datetime.time or str
End time as a time filter limit.
include_start : bool, default True
Whether the start time needs to be included in the result.
include_end : bool, default True
Whether the end time needs to be included in the result.
axis : {0 or 'index', 1 or 'columns'}, default 0
Determine range time on index or columns value.
.. versionadded:: 0.24.0
Returns
-------
Series or DataFrame
Data from the original object filtered to the specified dates range.
Raises
------
TypeError
If the index is not a :class:`DatetimeIndex`
See Also
--------
at_time : Select values at a particular time of the day.
first : Select initial periods of time series based on a date offset.
last : Select final periods of time series based on a date offset.
DatetimeIndex.indexer_between_time : Get just the index locations for
values between particular times of the day.
Examples
--------
>>> i = pd.date_range('2018-04-09', periods=4, freq='1D20min')
>>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
>>> ts
A
2018-04-09 00:00:00 1
2018-04-10 00:20:00 2
2018-04-11 00:40:00 3
2018-04-12 01:00:00 4
>>> ts.between_time('0:15', '0:45')
A
2018-04-10 00:20:00 2
2018-04-11 00:40:00 3
You get the times that are *not* between two times by setting
``start_time`` later than ``end_time``:
>>> ts.between_time('0:45', '0:15')
A
2018-04-09 00:00:00 1
2018-04-12 01:00:00 4
"""
if axis is None:
axis = self._stat_axis_number
axis = self._get_axis_number(axis)
index = self._get_axis(axis)
if not isinstance(index, DatetimeIndex):
raise TypeError("Index must be DatetimeIndex")
indexer = index.indexer_between_time(
start_time, end_time, include_start=include_start, include_end=include_end
)
return self._take_with_is_copy(indexer, axis=axis)
@final
def resample(
self,
rule,
axis=0,
closed: Optional[str] = None,
label: Optional[str] = None,
convention: str = "start",
kind: Optional[str] = None,
loffset=None,
base: Optional[int] = None,
on=None,
level=None,
origin: Union[str, TimestampConvertibleTypes] = "start_day",
offset: Optional[TimedeltaConvertibleTypes] = None,
) -> Resampler:
"""
Resample time-series data.
Convenience method for frequency conversion and resampling of time
series. Object must have a datetime-like index (`DatetimeIndex`,
`PeriodIndex`, or `TimedeltaIndex`), or pass datetime-like values
to the `on` or `level` keyword.
Parameters
----------
rule : DateOffset, Timedelta or str
The offset string or object representing target conversion.
axis : {0 or 'index', 1 or 'columns'}, default 0
Which axis to use for up- or down-sampling. For `Series` this
will default to 0, i.e. along the rows. Must be
`DatetimeIndex`, `TimedeltaIndex` or `PeriodIndex`.
closed : {'right', 'left'}, default None
Which side of bin interval is closed. The default is 'left'
for all frequency offsets except for 'M', 'A', 'Q', 'BM',
'BA', 'BQ', and 'W' which all have a default of 'right'.
label : {'right', 'left'}, default None
Which bin edge label to label bucket with. The default is 'left'
for all frequency offsets except for 'M', 'A', 'Q', 'BM',
'BA', 'BQ', and 'W' which all have a default of 'right'.
convention : {'start', 'end', 's', 'e'}, default 'start'
For `PeriodIndex` only, controls whether to use the start or
end of `rule`.
kind : {'timestamp', 'period'}, optional, default None
Pass 'timestamp' to convert the resulting index to a
`DateTimeIndex` or 'period' to convert it to a `PeriodIndex`.
By default the input representation is retained.
loffset : timedelta, default None
Adjust the resampled time labels.
.. deprecated:: 1.1.0
You should add the loffset to the `df.index` after the resample.
See below.
base : int, default 0
For frequencies that evenly subdivide 1 day, the "origin" of the
aggregated intervals. For example, for '5min' frequency, base could
range from 0 through 4. Defaults to 0.
.. deprecated:: 1.1.0
The new arguments that you should use are 'offset' or 'origin'.
on : str, optional
For a DataFrame, column to use instead of index for resampling.
Column must be datetime-like.
level : str or int, optional
For a MultiIndex, level (name or number) to use for
resampling. `level` must be datetime-like.
origin : {'epoch', 'start', 'start_day'}, Timestamp or str, default 'start_day'
The timestamp on which to adjust the grouping. The timezone of origin
must match the timezone of the index.
If a timestamp is not used, these values are also supported:
- 'epoch': `origin` is 1970-01-01
- 'start': `origin` is the first value of the timeseries
- 'start_day': `origin` is the first day at midnight of the timeseries
.. versionadded:: 1.1.0
offset : Timedelta or str, default is None
An offset timedelta added to the origin.
.. versionadded:: 1.1.0
Returns
-------
Resampler object
See Also
--------
groupby : Group by mapping, function, label, or list of labels.
Series.resample : Resample a Series.
DataFrame.resample: Resample a DataFrame.
Notes
-----
See the `user guide
<https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#resampling>`_
for more.
To learn more about the offset strings, please see `this link
<https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects>`__.
Examples
--------
Start by creating a series with 9 one minute timestamps.
>>> index = pd.date_range('1/1/2000', periods=9, freq='T')
>>> series = pd.Series(range(9), index=index)
>>> series
2000-01-01 00:00:00 0
2000-01-01 00:01:00 1
2000-01-01 00:02:00 2
2000-01-01 00:03:00 3
2000-01-01 00:04:00 4
2000-01-01 00:05:00 5
2000-01-01 00:06:00 6
2000-01-01 00:07:00 7
2000-01-01 00:08:00 8
Freq: T, dtype: int64
Downsample the series into 3 minute bins and sum the values
of the timestamps falling into a bin.
>>> series.resample('3T').sum()
2000-01-01 00:00:00 3
2000-01-01 00:03:00 12
2000-01-01 00:06:00 21
Freq: 3T, dtype: int64
Downsample the series into 3 minute bins as above, but label each
bin using the right edge instead of the left. Please note that the
value in the bucket used as the label is not included in the bucket,
which it labels. For example, in the original series the
bucket ``2000-01-01 00:03:00`` contains the value 3, but the summed
value in the resampled bucket with the label ``2000-01-01 00:03:00``
does not include 3 (if it did, the summed value would be 6, not 3).
To include this value close the right side of the bin interval as
illustrated in the example below this one.
>>> series.resample('3T', label='right').sum()
2000-01-01 00:03:00 3
2000-01-01 00:06:00 12
2000-01-01 00:09:00 21
Freq: 3T, dtype: int64
Downsample the series into 3 minute bins as above, but close the right
side of the bin interval.
>>> series.resample('3T', label='right', closed='right').sum()
2000-01-01 00:00:00 0
2000-01-01 00:03:00 6
2000-01-01 00:06:00 15
2000-01-01 00:09:00 15
Freq: 3T, dtype: int64
Upsample the series into 30 second bins.
>>> series.resample('30S').asfreq()[0:5] # Select first 5 rows
2000-01-01 00:00:00 0.0
2000-01-01 00:00:30 NaN
2000-01-01 00:01:00 1.0
2000-01-01 00:01:30 NaN
2000-01-01 00:02:00 2.0
Freq: 30S, dtype: float64
Upsample the series into 30 second bins and fill the ``NaN``
values using the ``pad`` method.
>>> series.resample('30S').pad()[0:5]
2000-01-01 00:00:00 0
2000-01-01 00:00:30 0
2000-01-01 00:01:00 1
2000-01-01 00:01:30 1
2000-01-01 00:02:00 2
Freq: 30S, dtype: int64
Upsample the series into 30 second bins and fill the
``NaN`` values using the ``bfill`` method.
>>> series.resample('30S').bfill()[0:5]
2000-01-01 00:00:00 0
2000-01-01 00:00:30 1
2000-01-01 00:01:00 1
2000-01-01 00:01:30 2
2000-01-01 00:02:00 2
Freq: 30S, dtype: int64
Pass a custom function via ``apply``
>>> def custom_resampler(array_like):
... return np.sum(array_like) + 5
...
>>> series.resample('3T').apply(custom_resampler)
2000-01-01 00:00:00 8
2000-01-01 00:03:00 17
2000-01-01 00:06:00 26
Freq: 3T, dtype: int64
For a Series with a PeriodIndex, the keyword `convention` can be
used to control whether to use the start or end of `rule`.
Resample a year by quarter using 'start' `convention`. Values are
assigned to the first quarter of the period.
>>> s = pd.Series([1, 2], index=pd.period_range('2012-01-01',
... freq='A',
... periods=2))
>>> s
2012 1
2013 2
Freq: A-DEC, dtype: int64
>>> s.resample('Q', convention='start').asfreq()
2012Q1 1.0
2012Q2 NaN
2012Q3 NaN
2012Q4 NaN
2013Q1 2.0
2013Q2 NaN
2013Q3 NaN
2013Q4 NaN
Freq: Q-DEC, dtype: float64
Resample quarters by month using 'end' `convention`. Values are
assigned to the last month of the period.
>>> q = pd.Series([1, 2, 3, 4], index=pd.period_range('2018-01-01',
... freq='Q',
... periods=4))
>>> q
2018Q1 1
2018Q2 2
2018Q3 3
2018Q4 4
Freq: Q-DEC, dtype: int64
>>> q.resample('M', convention='end').asfreq()
2018-03 1.0
2018-04 NaN
2018-05 NaN
2018-06 2.0
2018-07 NaN
2018-08 NaN
2018-09 3.0
2018-10 NaN
2018-11 NaN
2018-12 4.0
Freq: M, dtype: float64
For DataFrame objects, the keyword `on` can be used to specify the
column instead of the index for resampling.
>>> d = {'price': [10, 11, 9, 13, 14, 18, 17, 19],
... 'volume': [50, 60, 40, 100, 50, 100, 40, 50]}
>>> df = pd.DataFrame(d)
>>> df['week_starting'] = pd.date_range('01/01/2018',
... periods=8,
... freq='W')
>>> df
price volume week_starting
0 10 50 2018-01-07
1 11 60 2018-01-14
2 9 40 2018-01-21
3 13 100 2018-01-28
4 14 50 2018-02-04
5 18 100 2018-02-11
6 17 40 2018-02-18
7 19 50 2018-02-25
>>> df.resample('M', on='week_starting').mean()
price volume
week_starting
2018-01-31 10.75 62.5
2018-02-28 17.00 60.0
For a DataFrame with MultiIndex, the keyword `level` can be used to
specify on which level the resampling needs to take place.
>>> days = pd.date_range('1/1/2000', periods=4, freq='D')
>>> d2 = {'price': [10, 11, 9, 13, 14, 18, 17, 19],
... 'volume': [50, 60, 40, 100, 50, 100, 40, 50]}
>>> df2 = pd.DataFrame(d2,
... index=pd.MultiIndex.from_product([days,
... ['morning',
... 'afternoon']]
... ))
>>> df2
price volume
2000-01-01 morning 10 50
afternoon 11 60
2000-01-02 morning 9 40
afternoon 13 100
2000-01-03 morning 14 50
afternoon 18 100
2000-01-04 morning 17 40
afternoon 19 50
>>> df2.resample('D', level=0).sum()
price volume
2000-01-01 21 110
2000-01-02 22 140
2000-01-03 32 150
2000-01-04 36 90
If you want to adjust the start of the bins based on a fixed timestamp:
>>> start, end = '2000-10-01 23:30:00', '2000-10-02 00:30:00'
>>> rng = pd.date_range(start, end, freq='7min')
>>> ts = pd.Series(np.arange(len(rng)) * 3, index=rng)
>>> ts
2000-10-01 23:30:00 0
2000-10-01 23:37:00 3
2000-10-01 23:44:00 6
2000-10-01 23:51:00 9
2000-10-01 23:58:00 12
2000-10-02 00:05:00 15
2000-10-02 00:12:00 18
2000-10-02 00:19:00 21
2000-10-02 00:26:00 24
Freq: 7T, dtype: int64
>>> ts.resample('17min').sum()
2000-10-01 23:14:00 0
2000-10-01 23:31:00 9
2000-10-01 23:48:00 21
2000-10-02 00:05:00 54
2000-10-02 00:22:00 24
Freq: 17T, dtype: int64
>>> ts.resample('17min', origin='epoch').sum()
2000-10-01 23:18:00 0
2000-10-01 23:35:00 18
2000-10-01 23:52:00 27
2000-10-02 00:09:00 39
2000-10-02 00:26:00 24
Freq: 17T, dtype: int64
>>> ts.resample('17min', origin='2000-01-01').sum()
2000-10-01 23:24:00 3
2000-10-01 23:41:00 15
2000-10-01 23:58:00 45
2000-10-02 00:15:00 45
Freq: 17T, dtype: int64
If you want to adjust the start of the bins with an `offset` Timedelta, the two
following lines are equivalent:
>>> ts.resample('17min', origin='start').sum()
2000-10-01 23:30:00 9
2000-10-01 23:47:00 21
2000-10-02 00:04:00 54
2000-10-02 00:21:00 24
Freq: 17T, dtype: int64
>>> ts.resample('17min', offset='23h30min').sum()
2000-10-01 23:30:00 9
2000-10-01 23:47:00 21
2000-10-02 00:04:00 54
2000-10-02 00:21:00 24
Freq: 17T, dtype: int64
To replace the use of the deprecated `base` argument, you can now use `offset`,
in this example it is equivalent to have `base=2`:
>>> ts.resample('17min', offset='2min').sum()
2000-10-01 23:16:00 0
2000-10-01 23:33:00 9
2000-10-01 23:50:00 36
2000-10-02 00:07:00 39
2000-10-02 00:24:00 24
Freq: 17T, dtype: int64
To replace the use of the deprecated `loffset` argument:
>>> from pandas.tseries.frequencies import to_offset
>>> loffset = '19min'
>>> ts_out = ts.resample('17min').sum()
>>> ts_out.index = ts_out.index + to_offset(loffset)
>>> ts_out
2000-10-01 23:33:00 0
2000-10-01 23:50:00 9
2000-10-02 00:07:00 21
2000-10-02 00:24:00 54
2000-10-02 00:41:00 24
Freq: 17T, dtype: int64
"""
from pandas.core.resample import get_resampler
axis = self._get_axis_number(axis)
return get_resampler(
self,
freq=rule,
label=label,
closed=closed,
axis=axis,
kind=kind,
loffset=loffset,
convention=convention,
base=base,
key=on,
level=level,
origin=origin,
offset=offset,
)
@final
def first(self: FrameOrSeries, offset) -> FrameOrSeries:
"""
Select initial periods of time series data based on a date offset.
When having a DataFrame with dates as index, this function can
select the first few rows based on a date offset.
Parameters
----------
offset : str, DateOffset or dateutil.relativedelta
The offset length of the data that will be selected. For instance,
'1M' will display all the rows having their index within the first month.
Returns
-------
Series or DataFrame
A subset of the caller.
Raises
------
TypeError
If the index is not a :class:`DatetimeIndex`
See Also
--------
last : Select final periods of time series based on a date offset.
at_time : Select values at a particular time of the day.
between_time : Select values between particular times of the day.
Examples
--------
>>> i = pd.date_range('2018-04-09', periods=4, freq='2D')
>>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
>>> ts
A
2018-04-09 1
2018-04-11 2
2018-04-13 3
2018-04-15 4
Get the rows for the first 3 days:
>>> ts.first('3D')
A
2018-04-09 1
2018-04-11 2
Notice the data for 3 first calendar days were returned, not the first
3 days observed in the dataset, and therefore data for 2018-04-13 was
not returned.
"""
if not isinstance(self.index, DatetimeIndex):
raise TypeError("'first' only supports a DatetimeIndex index")
if len(self.index) == 0:
return self
offset = to_offset(offset)
end_date = end = self.index[0] + offset
# Tick-like, e.g. 3 weeks
if isinstance(offset, Tick):
if end_date in self.index:
end = self.index.searchsorted(end_date, side="left")
return self.iloc[:end]
return self.loc[:end]
@final
def last(self: FrameOrSeries, offset) -> FrameOrSeries:
"""
Select final periods of time series data based on a date offset.
When having a DataFrame with dates as index, this function can
select the last few rows based on a date offset.
Parameters
----------
offset : str, DateOffset, dateutil.relativedelta
The offset length of the data that will be selected. For instance,
'3D' will display all the rows having their index within the last 3 days.
Returns
-------
Series or DataFrame
A subset of the caller.
Raises
------
TypeError
If the index is not a :class:`DatetimeIndex`
See Also
--------
first : Select initial periods of time series based on a date offset.
at_time : Select values at a particular time of the day.
between_time : Select values between particular times of the day.
Examples
--------
>>> i = pd.date_range('2018-04-09', periods=4, freq='2D')
>>> ts = pd.DataFrame({'A': [1, 2, 3, 4]}, index=i)
>>> ts
A
2018-04-09 1
2018-04-11 2
2018-04-13 3
2018-04-15 4
Get the rows for the last 3 days:
>>> ts.last('3D')
A
2018-04-13 3
2018-04-15 4
Notice the data for 3 last calendar days were returned, not the last
3 observed days in the dataset, and therefore data for 2018-04-11 was
not returned.
"""
if not isinstance(self.index, DatetimeIndex):
raise TypeError("'last' only supports a DatetimeIndex index")
if len(self.index) == 0:
return self
offset = to_offset(offset)
start_date = self.index[-1] - offset
start = self.index.searchsorted(start_date, side="right")
return self.iloc[start:]
@final
def rank(
self: FrameOrSeries,
axis=0,
method: str = "average",
numeric_only: Optional[bool_t] = None,
na_option: str = "keep",
ascending: bool_t = True,
pct: bool_t = False,
) -> FrameOrSeries:
"""
Compute numerical data ranks (1 through n) along axis.
By default, equal values are assigned a rank that is the average of the
ranks of those values.
Parameters
----------
axis : {0 or 'index', 1 or 'columns'}, default 0
Index to direct ranking.
method : {'average', 'min', 'max', 'first', 'dense'}, default 'average'
How to rank the group of records that have the same value (i.e. ties):
* average: average rank of the group
* min: lowest rank in the group
* max: highest rank in the group
* first: ranks assigned in order they appear in the array
* dense: like 'min', but rank always increases by 1 between groups.
numeric_only : bool, optional
For DataFrame objects, rank only numeric columns if set to True.
na_option : {'keep', 'top', 'bottom'}, default 'keep'
How to rank NaN values:
* keep: assign NaN rank to NaN values
* top: assign smallest rank to NaN values if ascending
* bottom: assign highest rank to NaN values if ascending.
ascending : bool, default True
Whether or not the elements should be ranked in ascending order.
pct : bool, default False
Whether or not to display the returned rankings in percentile
form.
Returns
-------
same type as caller
Return a Series or DataFrame with data ranks as values.
See Also
--------
core.groupby.GroupBy.rank : Rank of values within each group.
Examples
--------
>>> df = pd.DataFrame(data={'Animal': ['cat', 'penguin', 'dog',
... 'spider', 'snake'],
... 'Number_legs': [4, 2, 4, 8, np.nan]})
>>> df
Animal Number_legs
0 cat 4.0
1 penguin 2.0
2 dog 4.0
3 spider 8.0
4 snake NaN
The following example shows how the method behaves with the above
parameters:
* default_rank: this is the default behaviour obtained without using
any parameter.
* max_rank: setting ``method = 'max'`` the records that have the
same values are ranked using the highest rank (e.g.: since 'cat'
and 'dog' are both in the 2nd and 3rd position, rank 3 is assigned.)
* NA_bottom: choosing ``na_option = 'bottom'``, if there are records
with NaN values they are placed at the bottom of the ranking.
* pct_rank: when setting ``pct = True``, the ranking is expressed as
percentile rank.
>>> df['default_rank'] = df['Number_legs'].rank()
>>> df['max_rank'] = df['Number_legs'].rank(method='max')
>>> df['NA_bottom'] = df['Number_legs'].rank(na_option='bottom')
>>> df['pct_rank'] = df['Number_legs'].rank(pct=True)
>>> df
Animal Number_legs default_rank max_rank NA_bottom pct_rank
0 cat 4.0 2.5 3.0 2.5 0.625
1 penguin 2.0 1.0 1.0 1.0 0.250
2 dog 4.0 2.5 3.0 2.5 0.625
3 spider 8.0 4.0 4.0 4.0 1.000
4 snake NaN NaN NaN 5.0 NaN
"""
axis = self._get_axis_number(axis)
if na_option not in {"keep", "top", "bottom"}:
msg = "na_option must be one of 'keep', 'top', or 'bottom'"
raise ValueError(msg)
def ranker(data):
ranks = algos.rank(
data.values,
axis=axis,
method=method,
ascending=ascending,
na_option=na_option,
pct=pct,
)
ranks = self._constructor(ranks, **data._construct_axes_dict())
return ranks.__finalize__(self, method="rank")
# if numeric_only is None, and we can't get anything, we try with
# numeric_only=True
if numeric_only is None:
try:
return ranker(self)
except TypeError:
numeric_only = True
if numeric_only:
data = self._get_numeric_data()
else:
data = self
return ranker(data)
@doc(_shared_docs["compare"], klass=_shared_doc_kwargs["klass"])
def compare(
self,
other,
align_axis: Axis = 1,
keep_shape: bool_t = False,
keep_equal: bool_t = False,
):
from pandas.core.reshape.concat import concat
if type(self) is not type(other):
cls_self, cls_other = type(self).__name__, type(other).__name__
raise TypeError(
f"can only compare '{cls_self}' (not '{cls_other}') with '{cls_self}'"
)
mask = ~((self == other) | (self.isna() & other.isna()))
keys = ["self", "other"]
if not keep_equal:
self = self.where(mask)
other = other.where(mask)
if not keep_shape:
if isinstance(self, ABCDataFrame):
cmask = mask.any()
rmask = mask.any(axis=1)
self = self.loc[rmask, cmask]
other = other.loc[rmask, cmask]
else:
self = self[mask]
other = other[mask]
if align_axis in (1, "columns"): # This is needed for Series
axis = 1
else:
axis = self._get_axis_number(align_axis)
diff = concat([self, other], axis=axis, keys=keys)
if axis >= self.ndim:
# No need to reorganize data if stacking on new axis
# This currently applies for stacking two Series on columns
return diff
ax = diff._get_axis(axis)
ax_names = np.array(ax.names)
# set index names to positions to avoid confusion
ax.names = np.arange(len(ax_names))
# bring self-other to inner level
order = list(range(1, ax.nlevels)) + [0]
if isinstance(diff, ABCDataFrame):
diff = diff.reorder_levels(order, axis=axis)
else:
diff = diff.reorder_levels(order)
# restore the index names in order
diff._get_axis(axis=axis).names = ax_names[order]
# reorder axis to keep things organized
indices = (
np.arange(diff.shape[axis]).reshape([2, diff.shape[axis] // 2]).T.flatten()
)
diff = diff.take(indices, axis=axis)
return diff
@doc(**_shared_doc_kwargs)
def align(
self,
other,
join="outer",
axis=None,
level=None,
copy=True,
fill_value=None,
method=None,
limit=None,
fill_axis=0,
broadcast_axis=None,
):
"""
Align two objects on their axes with the specified join method.
Join method is specified for each axis Index.
Parameters
----------
other : DataFrame or Series
join : {{'outer', 'inner', 'left', 'right'}}, default 'outer'
axis : allowed axis of the other object, default None
Align on index (0), columns (1), or both (None).
level : int or level name, default None
Broadcast across a level, matching Index values on the
passed MultiIndex level.
copy : bool, default True
Always returns new objects. If copy=False and no reindexing is
required then original objects are returned.
fill_value : scalar, default np.NaN
Value to use for missing values. Defaults to NaN, but can be any
"compatible" value.
method : {{'backfill', 'bfill', 'pad', 'ffill', None}}, default None
Method to use for filling holes in reindexed Series:
- pad / ffill: propagate last valid observation forward to next valid.
- backfill / bfill: use NEXT valid observation to fill gap.
limit : int, default None
If method is specified, this is the maximum number of consecutive
NaN values to forward/backward fill. In other words, if there is
a gap with more than this number of consecutive NaNs, it will only
be partially filled. If method is not specified, this is the
maximum number of entries along the entire axis where NaNs will be
filled. Must be greater than 0 if not None.
fill_axis : {axes_single_arg}, default 0
Filling axis, method and limit.
broadcast_axis : {axes_single_arg}, default None
Broadcast values along this axis, if aligning two objects of
different dimensions.
Returns
-------
(left, right) : ({klass}, type of other)
Aligned objects.
"""
method = missing.clean_fill_method(method)
if broadcast_axis == 1 and self.ndim != other.ndim:
if isinstance(self, ABCSeries):
# this means other is a DataFrame, and we need to broadcast
# self
cons = self._constructor_expanddim
df = cons(
{c: self for c in other.columns}, **other._construct_axes_dict()
)
return df._align_frame(
other,
join=join,
axis=axis,
level=level,
copy=copy,
fill_value=fill_value,
method=method,
limit=limit,
fill_axis=fill_axis,
)
elif isinstance(other, ABCSeries):
# this means self is a DataFrame, and we need to broadcast
# other
cons = other._constructor_expanddim
df = cons(
{c: other for c in self.columns}, **self._construct_axes_dict()
)
return self._align_frame(
df,
join=join,
axis=axis,
level=level,
copy=copy,
fill_value=fill_value,
method=method,
limit=limit,
fill_axis=fill_axis,
)
if axis is not None:
axis = self._get_axis_number(axis)
if isinstance(other, ABCDataFrame):
return self._align_frame(
other,
join=join,
axis=axis,
level=level,
copy=copy,
fill_value=fill_value,
method=method,
limit=limit,
fill_axis=fill_axis,
)
elif isinstance(other, ABCSeries):
return self._align_series(
other,
join=join,
axis=axis,
level=level,
copy=copy,
fill_value=fill_value,
method=method,
limit=limit,
fill_axis=fill_axis,
)
else: # pragma: no cover
raise TypeError(f"unsupported type: {type(other)}")
@final
def _align_frame(
self,
other,
join="outer",
axis=None,
level=None,
copy: bool_t = True,
fill_value=None,
method=None,
limit=None,
fill_axis=0,
):
# defaults
join_index, join_columns = None, None
ilidx, iridx = None, None
clidx, cridx = None, None
is_series = isinstance(self, ABCSeries)
if axis is None or axis == 0:
if not self.index.equals(other.index):
join_index, ilidx, iridx = self.index.join(
other.index, how=join, level=level, return_indexers=True
)
if axis is None or axis == 1:
if not is_series and not self.columns.equals(other.columns):
join_columns, clidx, cridx = self.columns.join(
other.columns, how=join, level=level, return_indexers=True
)
if is_series:
reindexers = {0: [join_index, ilidx]}
else:
reindexers = {0: [join_index, ilidx], 1: [join_columns, clidx]}
left = self._reindex_with_indexers(
reindexers, copy=copy, fill_value=fill_value, allow_dups=True
)
# other must be always DataFrame
right = other._reindex_with_indexers(
{0: [join_index, iridx], 1: [join_columns, cridx]},
copy=copy,
fill_value=fill_value,
allow_dups=True,
)
if method is not None:
_left = left.fillna(method=method, axis=fill_axis, limit=limit)
assert _left is not None # needed for mypy
left = _left
right = right.fillna(method=method, axis=fill_axis, limit=limit)
# if DatetimeIndex have different tz, convert to UTC
if is_datetime64tz_dtype(left.index.dtype):
if left.index.tz != right.index.tz:
if join_index is not None:
# GH#33671 ensure we don't change the index on
# our original Series (NB: by default deep=False)
left = left.copy()
right = right.copy()
left.index = join_index
right.index = join_index
return (
left.__finalize__(self),
right.__finalize__(other),
)
@final
def _align_series(
self,
other,
join="outer",
axis=None,
level=None,
copy: bool_t = True,
fill_value=None,
method=None,
limit=None,
fill_axis=0,
):
is_series = isinstance(self, ABCSeries)
# series/series compat, other must always be a Series
if is_series:
if axis:
raise ValueError("cannot align series to a series other than axis 0")
# equal
if self.index.equals(other.index):
join_index, lidx, ridx = None, None, None
else:
join_index, lidx, ridx = self.index.join(
other.index, how=join, level=level, return_indexers=True
)
left = self._reindex_indexer(join_index, lidx, copy)
right = other._reindex_indexer(join_index, ridx, copy)
else:
# one has > 1 ndim
fdata = self._mgr
if axis == 0:
join_index = self.index
lidx, ridx = None, None
if not self.index.equals(other.index):
join_index, lidx, ridx = self.index.join(
other.index, how=join, level=level, return_indexers=True
)
if lidx is not None:
fdata = fdata.reindex_indexer(join_index, lidx, axis=1)
elif axis == 1:
join_index = self.columns
lidx, ridx = None, None
if not self.columns.equals(other.index):
join_index, lidx, ridx = self.columns.join(
other.index, how=join, level=level, return_indexers=True
)
if lidx is not None:
fdata = fdata.reindex_indexer(join_index, lidx, axis=0)
else:
raise ValueError("Must specify axis=0 or 1")
if copy and fdata is self._mgr:
fdata = fdata.copy()
left = self._constructor(fdata)
if ridx is None:
right = other
else:
right = other.reindex(join_index, level=level)
# fill
fill_na = notna(fill_value) or (method is not None)
if fill_na:
left = left.fillna(fill_value, method=method, limit=limit, axis=fill_axis)
right = right.fillna(fill_value, method=method, limit=limit)
# if DatetimeIndex have different tz, convert to UTC
if is_series or (not is_series and axis == 0):
if is_datetime64tz_dtype(left.index.dtype):
if left.index.tz != right.index.tz:
if join_index is not None:
# GH#33671 ensure we don't change the index on
# our original Series (NB: by default deep=False)
left = left.copy()
right = right.copy()
left.index = join_index
right.index = join_index
return (
left.__finalize__(self),
right.__finalize__(other),
)
@final
def _where(
self,
cond,
other=np.nan,
inplace=False,
axis=None,
level=None,
errors="raise",
try_cast=False,
):
"""
Equivalent to public method `where`, except that `other` is not
applied as a function even if callable. Used in __setitem__.
"""
inplace = validate_bool_kwarg(inplace, "inplace")
# align the cond to same shape as myself
cond = com.apply_if_callable(cond, self)
if isinstance(cond, NDFrame):
cond, _ = cond.align(self, join="right", broadcast_axis=1)
else:
if not hasattr(cond, "shape"):
cond = np.asanyarray(cond)
if cond.shape != self.shape:
raise ValueError("Array conditional must be same shape as self")
cond = self._constructor(cond, **self._construct_axes_dict())
# make sure we are boolean
fill_value = bool(inplace)
cond = cond.fillna(fill_value)
msg = "Boolean array expected for the condition, not {dtype}"
if not cond.empty:
if not isinstance(cond, ABCDataFrame):
# This is a single-dimensional object.
if not is_bool_dtype(cond):
raise ValueError(msg.format(dtype=cond.dtype))
else:
for dt in cond.dtypes:
if not is_bool_dtype(dt):
raise ValueError(msg.format(dtype=dt))
else:
# GH#21947 we have an empty DataFrame/Series, could be object-dtype
cond = cond.astype(bool)
cond = -cond if inplace else cond
# try to align with other
if isinstance(other, NDFrame):
# align with me
if other.ndim <= self.ndim:
_, other = self.align(
other, join="left", axis=axis, level=level, fill_value=np.nan
)
# if we are NOT aligned, raise as we cannot where index
if axis is None and not all(
other._get_axis(i).equals(ax) for i, ax in enumerate(self.axes)
):
raise InvalidIndexError
# slice me out of the other
else:
raise NotImplementedError(
"cannot align with a higher dimensional NDFrame"
)
if isinstance(other, np.ndarray):
if other.shape != self.shape:
if self.ndim == 1:
icond = cond._values
# GH 2745 / GH 4192
# treat like a scalar
if len(other) == 1:
other = other[0]
# GH 3235
# match True cond to other
elif len(cond[icond]) == len(other):
# try to not change dtype at first
new_other = np.asarray(self)
new_other = new_other.copy()
new_other[icond] = other
other = new_other
else:
raise ValueError(
"Length of replacements must equal series length"
)
else:
raise ValueError(
"other must be the same shape as self when an ndarray"
)
# we are the same shape, so create an actual object for alignment
else:
other = self._constructor(other, **self._construct_axes_dict())
if axis is None:
axis = 0
if self.ndim == getattr(other, "ndim", 0):
align = True
else:
align = self._get_axis_number(axis) == 1
if align and isinstance(other, NDFrame):
other = other.reindex(self._info_axis, axis=self._info_axis_number)
if isinstance(cond, NDFrame):
cond = cond.reindex(self._info_axis, axis=self._info_axis_number)
block_axis = self._get_block_manager_axis(axis)
if inplace:
# we may have different type blocks come out of putmask, so
# reconstruct the block manager
self._check_inplace_setting(other)
new_data = self._mgr.putmask(
mask=cond, new=other, align=align, axis=block_axis
)
result = self._constructor(new_data)
return self._update_inplace(result)
else:
new_data = self._mgr.where(
other=other,
cond=cond,
align=align,
errors=errors,
try_cast=try_cast,
axis=block_axis,
)
result = self._constructor(new_data)
return result.__finalize__(self)
@final
@doc(
klass=_shared_doc_kwargs["klass"],
cond="True",
cond_rev="False",
name="where",
name_other="mask",
)
def where(
self,
cond,
other=np.nan,
inplace=False,
axis=None,
level=None,
errors="raise",
try_cast=False,
):
"""
Replace values where the condition is {cond_rev}.
Parameters
----------
cond : bool {klass}, array-like, or callable
Where `cond` is {cond}, keep the original value. Where
{cond_rev}, replace with corresponding value from `other`.
If `cond` is callable, it is computed on the {klass} and
should return boolean {klass} or array. The callable must
not change input {klass} (though pandas doesn't check it).
other : scalar, {klass}, or callable
Entries where `cond` is {cond_rev} are replaced with
corresponding value from `other`.
If other is callable, it is computed on the {klass} and
should return scalar or {klass}. The callable must not
change input {klass} (though pandas doesn't check it).
inplace : bool, default False
Whether to perform the operation in place on the data.
axis : int, default None
Alignment axis if needed.
level : int, default None
Alignment level if needed.
errors : str, {{'raise', 'ignore'}}, default 'raise'
Note that currently this parameter won't affect
the results and will always coerce to a suitable dtype.
- 'raise' : allow exceptions to be raised.
- 'ignore' : suppress exceptions. On error return original object.
try_cast : bool, default False
Try to cast the result back to the input type (if possible).
Returns
-------
Same type as caller or None if ``inplace=True``.
See Also
--------
:func:`DataFrame.{name_other}` : Return an object of same shape as
self.
Notes
-----
The {name} method is an application of the if-then idiom. For each
element in the calling DataFrame, if ``cond`` is ``{cond}`` the
element is used; otherwise the corresponding element from the DataFrame
``other`` is used.
The signature for :func:`DataFrame.where` differs from
:func:`numpy.where`. Roughly ``df1.where(m, df2)`` is equivalent to
``np.where(m, df1, df2)``.
For further details and examples see the ``{name}`` documentation in
:ref:`indexing <indexing.where_mask>`.
Examples
--------
>>> s = pd.Series(range(5))
>>> s.where(s > 0)
0 NaN
1 1.0
2 2.0
3 3.0
4 4.0
dtype: float64
>>> s.mask(s > 0)
0 0.0
1 NaN
2 NaN
3 NaN
4 NaN
dtype: float64
>>> s.where(s > 1, 10)
0 10
1 10
2 2
3 3
4 4
dtype: int64
>>> s.mask(s > 1, 10)
0 0
1 1
2 10
3 10
4 10
dtype: int64
>>> df = pd.DataFrame(np.arange(10).reshape(-1, 2), columns=['A', 'B'])
>>> df
A B
0 0 1
1 2 3
2 4 5
3 6 7
4 8 9
>>> m = df % 3 == 0
>>> df.where(m, -df)
A B
0 0 -1
1 -2 3
2 -4 -5
3 6 -7
4 -8 9
>>> df.where(m, -df) == np.where(m, df, -df)
A B
0 True True
1 True True
2 True True
3 True True
4 True True
>>> df.where(m, -df) == df.mask(~m, -df)
A B
0 True True
1 True True
2 True True
3 True True
4 True True
"""
other = com.apply_if_callable(other, self)
return self._where(
cond, other, inplace, axis, level, errors=errors, try_cast=try_cast
)
@final
@doc(
where,
klass=_shared_doc_kwargs["klass"],
cond="False",
cond_rev="True",
name="mask",
name_other="where",
)
def mask(
self,
cond,
other=np.nan,
inplace=False,
axis=None,
level=None,
errors="raise",
try_cast=False,
):
inplace = validate_bool_kwarg(inplace, "inplace")
cond = com.apply_if_callable(cond, self)
# see gh-21891
if not hasattr(cond, "__invert__"):
cond = np.array(cond)
return self.where(
~cond,
other=other,
inplace=inplace,
axis=axis,
level=level,
try_cast=try_cast,
errors=errors,
)
@doc(klass=_shared_doc_kwargs["klass"])
def shift(
self: FrameOrSeries, periods=1, freq=None, axis=0, fill_value=None
) -> FrameOrSeries:
"""
Shift index by desired number of periods with an optional time `freq`.
When `freq` is not passed, shift the index without realigning the data.
If `freq` is passed (in this case, the index must be date or datetime,
or it will raise a `NotImplementedError`), the index will be
increased using the periods and the `freq`. `freq` can be inferred
when specified as "infer" as long as either freq or inferred_freq
attribute is set in the index.
Parameters
----------
periods : int
Number of periods to shift. Can be positive or negative.
freq : DateOffset, tseries.offsets, timedelta, or str, optional
Offset to use from the tseries module or time rule (e.g. 'EOM').
If `freq` is specified then the index values are shifted but the
data is not realigned. That is, use `freq` if you would like to
extend the index when shifting and preserve the original data.
If `freq` is specified as "infer" then it will be inferred from
the freq or inferred_freq attributes of the index. If neither of
those attributes exist, a ValueError is thrown.
axis : {{0 or 'index', 1 or 'columns', None}}, default None
Shift direction.
fill_value : object, optional
The scalar value to use for newly introduced missing values.
the default depends on the dtype of `self`.
For numeric data, ``np.nan`` is used.
For datetime, timedelta, or period data, etc. :attr:`NaT` is used.
For extension dtypes, ``self.dtype.na_value`` is used.
.. versionchanged:: 1.1.0
Returns
-------
{klass}
Copy of input object, shifted.
See Also
--------
Index.shift : Shift values of Index.
DatetimeIndex.shift : Shift values of DatetimeIndex.
PeriodIndex.shift : Shift values of PeriodIndex.
tshift : Shift the time index, using the index's frequency if
available.
Examples
--------
>>> df = pd.DataFrame({{"Col1": [10, 20, 15, 30, 45],
... "Col2": [13, 23, 18, 33, 48],
... "Col3": [17, 27, 22, 37, 52]}},
... index=pd.date_range("2020-01-01", "2020-01-05"))
>>> df
Col1 Col2 Col3
2020-01-01 10 13 17
2020-01-02 20 23 27
2020-01-03 15 18 22
2020-01-04 30 33 37
2020-01-05 45 48 52
>>> df.shift(periods=3)
Col1 Col2 Col3
2020-01-01 NaN NaN NaN
2020-01-02 NaN NaN NaN
2020-01-03 NaN NaN NaN
2020-01-04 10.0 13.0 17.0
2020-01-05 20.0 23.0 27.0
>>> df.shift(periods=1, axis="columns")
Col1 Col2 Col3
2020-01-01 NaN 10 13
2020-01-02 NaN 20 23
2020-01-03 NaN 15 18
2020-01-04 NaN 30 33
2020-01-05 NaN 45 48
>>> df.shift(periods=3, fill_value=0)
Col1 Col2 Col3
2020-01-01 0 0 0
2020-01-02 0 0 0
2020-01-03 0 0 0
2020-01-04 10 13 17
2020-01-05 20 23 27
>>> df.shift(periods=3, freq="D")
Col1 Col2 Col3
2020-01-04 10 13 17
2020-01-05 20 23 27
2020-01-06 15 18 22
2020-01-07 30 33 37
2020-01-08 45 48 52
>>> df.shift(periods=3, freq="infer")
Col1 Col2 Col3
2020-01-04 10 13 17
2020-01-05 20 23 27
2020-01-06 15 18 22
2020-01-07 30 33 37
2020-01-08 45 48 52
"""
if periods == 0:
return self.copy()
if freq is None:
# when freq is None, data is shifted, index is not
block_axis = self._get_block_manager_axis(axis)
new_data = self._mgr.shift(
periods=periods, axis=block_axis, fill_value=fill_value
)
return self._constructor(new_data).__finalize__(self, method="shift")
# when freq is given, index is shifted, data is not
index = self._get_axis(axis)
if freq == "infer":
freq = getattr(index, "freq", None)
if freq is None:
freq = getattr(index, "inferred_freq", None)
if freq is None:
msg = "Freq was not set in the index hence cannot be inferred"
raise ValueError(msg)
elif isinstance(freq, str):
freq = to_offset(freq)
if isinstance(index, PeriodIndex):
orig_freq = to_offset(index.freq)
if freq != orig_freq:
assert orig_freq is not None # for mypy
raise ValueError(
f"Given freq {freq.rule_code} does not match "
f"PeriodIndex freq {orig_freq.rule_code}"
)
new_ax = index.shift(periods)
else:
new_ax = index.shift(periods, freq)
result = self.set_axis(new_ax, axis)
return result.__finalize__(self, method="shift")
@final
def slice_shift(self: FrameOrSeries, periods: int = 1, axis=0) -> FrameOrSeries:
"""
Equivalent to `shift` without copying data.
The shifted data will not include the dropped periods and the
shifted axis will be smaller than the original.
.. deprecated:: 1.2.0
slice_shift is deprecated,
use DataFrame/Series.shift instead.
Parameters
----------
periods : int
Number of periods to move, can be positive or negative.
Returns
-------
shifted : same type as caller
Notes
-----
While the `slice_shift` is faster than `shift`, you may pay for it
later during alignment.
"""
msg = (
"The 'slice_shift' method is deprecated "
"and will be removed in a future version. "
"You can use DataFrame/Series.shift instead"
)
warnings.warn(msg, FutureWarning, stacklevel=2)
if periods == 0:
return self
if periods > 0:
vslicer = slice(None, -periods)
islicer = slice(periods, None)
else:
vslicer = slice(-periods, None)
islicer = slice(None, periods)
new_obj = self._slice(vslicer, axis=axis)
shifted_axis = self._get_axis(axis)[islicer]
new_obj.set_axis(shifted_axis, axis=axis, inplace=True)
return new_obj.__finalize__(self, method="slice_shift")
@final
def tshift(
self: FrameOrSeries, periods: int = 1, freq=None, axis: Axis = 0
) -> FrameOrSeries:
"""
Shift the time index, using the index's frequency if available.
.. deprecated:: 1.1.0
Use `shift` instead.
Parameters
----------
periods : int
Number of periods to move, can be positive or negative.
freq : DateOffset, timedelta, or str, default None
Increment to use from the tseries module
or time rule expressed as a string (e.g. 'EOM').
axis : {0 or ‘index’, 1 or ‘columns’, None}, default 0
Corresponds to the axis that contains the Index.
Returns
-------
shifted : Series/DataFrame
Notes
-----
If freq is not specified then tries to use the freq or inferred_freq
attributes of the index. If neither of those attributes exist, a
ValueError is thrown
"""
warnings.warn(
(
"tshift is deprecated and will be removed in a future version. "
"Please use shift instead."
),
FutureWarning,
stacklevel=2,
)
if freq is None:
freq = "infer"
return self.shift(periods, freq, axis)
def truncate(
self: FrameOrSeries, before=None, after=None, axis=None, copy: bool_t = True
) -> FrameOrSeries:
"""
Truncate a Series or DataFrame before and after some index value.
This is a useful shorthand for boolean indexing based on index
values above or below certain thresholds.
Parameters
----------
before : date, str, int
Truncate all rows before this index value.
after : date, str, int
Truncate all rows after this index value.
axis : {0 or 'index', 1 or 'columns'}, optional
Axis to truncate. Truncates the index (rows) by default.
copy : bool, default is True,
Return a copy of the truncated section.
Returns
-------
type of caller
The truncated Series or DataFrame.
See Also
--------
DataFrame.loc : Select a subset of a DataFrame by label.
DataFrame.iloc : Select a subset of a DataFrame by position.
Notes
-----
If the index being truncated contains only datetime values,
`before` and `after` may be specified as strings instead of
Timestamps.
Examples
--------
>>> df = pd.DataFrame({'A': ['a', 'b', 'c', 'd', 'e'],
... 'B': ['f', 'g', 'h', 'i', 'j'],
... 'C': ['k', 'l', 'm', 'n', 'o']},
... index=[1, 2, 3, 4, 5])
>>> df
A B C
1 a f k
2 b g l
3 c h m
4 d i n
5 e j o
>>> df.truncate(before=2, after=4)
A B C
2 b g l
3 c h m
4 d i n
The columns of a DataFrame can be truncated.
>>> df.truncate(before="A", after="B", axis="columns")
A B
1 a f
2 b g
3 c h
4 d i
5 e j
For Series, only rows can be truncated.
>>> df['A'].truncate(before=2, after=4)
2 b
3 c
4 d
Name: A, dtype: object
The index values in ``truncate`` can be datetimes or string
dates.
>>> dates = pd.date_range('2016-01-01', '2016-02-01', freq='s')
>>> df = pd.DataFrame(index=dates, data={'A': 1})
>>> df.tail()
A
2016-01-31 23:59:56 1
2016-01-31 23:59:57 1
2016-01-31 23:59:58 1
2016-01-31 23:59:59 1
2016-02-01 00:00:00 1
>>> df.truncate(before=pd.Timestamp('2016-01-05'),
... after=pd.Timestamp('2016-01-10')).tail()
A
2016-01-09 23:59:56 1
2016-01-09 23:59:57 1
2016-01-09 23:59:58 1
2016-01-09 23:59:59 1
2016-01-10 00:00:00 1
Because the index is a DatetimeIndex containing only dates, we can
specify `before` and `after` as strings. They will be coerced to
Timestamps before truncation.
>>> df.truncate('2016-01-05', '2016-01-10').tail()
A
2016-01-09 23:59:56 1
2016-01-09 23:59:57 1
2016-01-09 23:59:58 1
2016-01-09 23:59:59 1
2016-01-10 00:00:00 1
Note that ``truncate`` assumes a 0 value for any unspecified time
component (midnight). This differs from partial string slicing, which
returns any partially matching dates.
>>> df.loc['2016-01-05':'2016-01-10', :].tail()
A
2016-01-10 23:59:55 1
2016-01-10 23:59:56 1
2016-01-10 23:59:57 1
2016-01-10 23:59:58 1
2016-01-10 23:59:59 1
"""
if axis is None:
axis = self._stat_axis_number
axis = self._get_axis_number(axis)
ax = self._get_axis(axis)
# GH 17935
# Check that index is sorted
if not ax.is_monotonic_increasing and not ax.is_monotonic_decreasing:
raise ValueError("truncate requires a sorted index")
# if we have a date index, convert to dates, otherwise
# treat like a slice
if ax._is_all_dates:
from pandas.core.tools.datetimes import to_datetime
before = to_datetime(before)
after = to_datetime(after)
if before is not None and after is not None:
if before > after:
raise ValueError(f"Truncate: {after} must be after {before}")
if len(ax) > 1 and ax.is_monotonic_decreasing:
before, after = after, before
slicer = [slice(None, None)] * self._AXIS_LEN
slicer[axis] = slice(before, after)
result = self.loc[tuple(slicer)]
if isinstance(ax, MultiIndex):
setattr(result, self._get_axis_name(axis), ax.truncate(before, after))
if copy:
result = result.copy()
return result
@final
def tz_convert(
self: FrameOrSeries, tz, axis=0, level=None, copy: bool_t = True
) -> FrameOrSeries:
"""
Convert tz-aware axis to target time zone.
Parameters
----------
tz : str or tzinfo object
axis : the axis to convert
level : int, str, default None
If axis is a MultiIndex, convert a specific level. Otherwise
must be None.
copy : bool, default True
Also make a copy of the underlying data.
Returns
-------
{klass}
Object with time zone converted axis.
Raises
------
TypeError
If the axis is tz-naive.
"""
axis = self._get_axis_number(axis)
ax = self._get_axis(axis)
def _tz_convert(ax, tz):
if not hasattr(ax, "tz_convert"):
if len(ax) > 0:
ax_name = self._get_axis_name(axis)
raise TypeError(
f"{ax_name} is not a valid DatetimeIndex or PeriodIndex"
)
else:
ax = DatetimeIndex([], tz=tz)
else:
ax = ax.tz_convert(tz)
return ax
# if a level is given it must be a MultiIndex level or
# equivalent to the axis name
if isinstance(ax, MultiIndex):
level = ax._get_level_number(level)
new_level = _tz_convert(ax.levels[level], tz)
ax = ax.set_levels(new_level, level=level)
else:
if level not in (None, 0, ax.name):
raise ValueError(f"The level {level} is not valid")
ax = _tz_convert(ax, tz)
result = self.copy(deep=copy)
result = result.set_axis(ax, axis=axis, inplace=False)
return result.__finalize__(self, method="tz_convert")
@final
def tz_localize(
self: FrameOrSeries,
tz,
axis=0,
level=None,
copy: bool_t = True,
ambiguous="raise",
nonexistent: str = "raise",
) -> FrameOrSeries:
"""
Localize tz-naive index of a Series or DataFrame to target time zone.
This operation localizes the Index. To localize the values in a
timezone-naive Series, use :meth:`Series.dt.tz_localize`.
Parameters
----------
tz : str or tzinfo
axis : the axis to localize
level : int, str, default None
If axis ia a MultiIndex, localize a specific level. Otherwise
must be None.
copy : bool, default True
Also make a copy of the underlying data.
ambiguous : 'infer', bool-ndarray, 'NaT', default 'raise'
When clocks moved backward due to DST, ambiguous times may arise.
For example in Central European Time (UTC+01), when going from
03:00 DST to 02:00 non-DST, 02:30:00 local time occurs both at
00:30:00 UTC and at 01:30:00 UTC. In such a situation, the
`ambiguous` parameter dictates how ambiguous times should be
handled.
- 'infer' will attempt to infer fall dst-transition hours based on
order
- bool-ndarray where True signifies a DST time, False designates
a non-DST time (note that this flag is only applicable for
ambiguous times)
- 'NaT' will return NaT where there are ambiguous times
- 'raise' will raise an AmbiguousTimeError if there are ambiguous
times.
nonexistent : str, default 'raise'
A nonexistent time does not exist in a particular timezone
where clocks moved forward due to DST. Valid values are:
- 'shift_forward' will shift the nonexistent time forward to the
closest existing time
- 'shift_backward' will shift the nonexistent time backward to the
closest existing time
- 'NaT' will return NaT where there are nonexistent times
- timedelta objects will shift nonexistent times by the timedelta
- 'raise' will raise an NonExistentTimeError if there are
nonexistent times.
.. versionadded:: 0.24.0
Returns
-------
Series or DataFrame
Same type as the input.
Raises
------
TypeError
If the TimeSeries is tz-aware and tz is not None.
Examples
--------
Localize local times:
>>> s = pd.Series([1],
... index=pd.DatetimeIndex(['2018-09-15 01:30:00']))
>>> s.tz_localize('CET')
2018-09-15 01:30:00+02:00 1
dtype: int64
Be careful with DST changes. When there is sequential data, pandas
can infer the DST time:
>>> s = pd.Series(range(7),
... index=pd.DatetimeIndex(['2018-10-28 01:30:00',
... '2018-10-28 02:00:00',
... '2018-10-28 02:30:00',
... '2018-10-28 02:00:00',
... '2018-10-28 02:30:00',
... '2018-10-28 03:00:00',
... '2018-10-28 03:30:00']))
>>> s.tz_localize('CET', ambiguous='infer')
2018-10-28 01:30:00+02:00 0
2018-10-28 02:00:00+02:00 1
2018-10-28 02:30:00+02:00 2
2018-10-28 02:00:00+01:00 3
2018-10-28 02:30:00+01:00 4
2018-10-28 03:00:00+01:00 5
2018-10-28 03:30:00+01:00 6
dtype: int64
In some cases, inferring the DST is impossible. In such cases, you can
pass an ndarray to the ambiguous parameter to set the DST explicitly
>>> s = pd.Series(range(3),
... index=pd.DatetimeIndex(['2018-10-28 01:20:00',
... '2018-10-28 02:36:00',
... '2018-10-28 03:46:00']))
>>> s.tz_localize('CET', ambiguous=np.array([True, True, False]))
2018-10-28 01:20:00+02:00 0
2018-10-28 02:36:00+02:00 1
2018-10-28 03:46:00+01:00 2
dtype: int64
If the DST transition causes nonexistent times, you can shift these
dates forward or backward with a timedelta object or `'shift_forward'`
or `'shift_backward'`.
>>> s = pd.Series(range(2),
... index=pd.DatetimeIndex(['2015-03-29 02:30:00',
... '2015-03-29 03:30:00']))
>>> s.tz_localize('Europe/Warsaw', nonexistent='shift_forward')
2015-03-29 03:00:00+02:00 0
2015-03-29 03:30:00+02:00 1
dtype: int64
>>> s.tz_localize('Europe/Warsaw', nonexistent='shift_backward')
2015-03-29 01:59:59.999999999+01:00 0
2015-03-29 03:30:00+02:00 1
dtype: int64
>>> s.tz_localize('Europe/Warsaw', nonexistent=pd.Timedelta('1H'))
2015-03-29 03:30:00+02:00 0
2015-03-29 03:30:00+02:00 1
dtype: int64
"""
nonexistent_options = ("raise", "NaT", "shift_forward", "shift_backward")
if nonexistent not in nonexistent_options and not isinstance(
nonexistent, timedelta
):
raise ValueError(
"The nonexistent argument must be one of 'raise', "
"'NaT', 'shift_forward', 'shift_backward' or "
"a timedelta object"
)
axis = self._get_axis_number(axis)
ax = self._get_axis(axis)
def _tz_localize(ax, tz, ambiguous, nonexistent):
if not hasattr(ax, "tz_localize"):
if len(ax) > 0:
ax_name = self._get_axis_name(axis)
raise TypeError(
f"{ax_name} is not a valid DatetimeIndex or PeriodIndex"
)
else:
ax = DatetimeIndex([], tz=tz)
else:
ax = ax.tz_localize(tz, ambiguous=ambiguous, nonexistent=nonexistent)
return ax
# if a level is given it must be a MultiIndex level or
# equivalent to the axis name
if isinstance(ax, MultiIndex):
level = ax._get_level_number(level)
new_level = _tz_localize(ax.levels[level], tz, ambiguous, nonexistent)
ax = ax.set_levels(new_level, level=level)
else:
if level not in (None, 0, ax.name):
raise ValueError(f"The level {level} is not valid")
ax = _tz_localize(ax, tz, ambiguous, nonexistent)
result = self.copy(deep=copy)
result = result.set_axis(ax, axis=axis, inplace=False)
return result.__finalize__(self, method="tz_localize")
# ----------------------------------------------------------------------
# Numeric Methods
@final
def abs(self: FrameOrSeries) -> FrameOrSeries:
"""
Return a Series/DataFrame with absolute numeric value of each element.
This function only applies to elements that are all numeric.
Returns
-------
abs
Series/DataFrame containing the absolute value of each element.
See Also
--------
numpy.absolute : Calculate the absolute value element-wise.
Notes
-----
For ``complex`` inputs, ``1.2 + 1j``, the absolute value is
:math:`\\sqrt{ a^2 + b^2 }`.
Examples
--------
Absolute numeric values in a Series.
>>> s = pd.Series([-1.10, 2, -3.33, 4])
>>> s.abs()
0 1.10
1 2.00
2 3.33
3 4.00
dtype: float64
Absolute numeric values in a Series with complex numbers.
>>> s = pd.Series([1.2 + 1j])
>>> s.abs()
0 1.56205
dtype: float64
Absolute numeric values in a Series with a Timedelta element.
>>> s = pd.Series([pd.Timedelta('1 days')])
>>> s.abs()
0 1 days
dtype: timedelta64[ns]
Select rows with data closest to certain value using argsort (from
`StackOverflow <https://stackoverflow.com/a/17758115>`__).
>>> df = pd.DataFrame({
... 'a': [4, 5, 6, 7],
... 'b': [10, 20, 30, 40],
... 'c': [100, 50, -30, -50]
... })
>>> df
a b c
0 4 10 100
1 5 20 50
2 6 30 -30
3 7 40 -50
>>> df.loc[(df.c - 43).abs().argsort()]
a b c
1 5 20 50
0 4 10 100
2 6 30 -30
3 7 40 -50
"""
return np.abs(self)
@final
def describe(
self: FrameOrSeries,
percentiles=None,
include=None,
exclude=None,
datetime_is_numeric=False,
) -> FrameOrSeries:
"""
Generate descriptive statistics.
Descriptive statistics include those that summarize the central
tendency, dispersion and shape of a
dataset's distribution, excluding ``NaN`` values.
Analyzes both numeric and object series, as well
as ``DataFrame`` column sets of mixed data types. The output
will vary depending on what is provided. Refer to the notes
below for more detail.
Parameters
----------
percentiles : list-like of numbers, optional
The percentiles to include in the output. All should
fall between 0 and 1. The default is
``[.25, .5, .75]``, which returns the 25th, 50th, and
75th percentiles.
include : 'all', list-like of dtypes or None (default), optional
A white list of data types to include in the result. Ignored
for ``Series``. Here are the options:
- 'all' : All columns of the input will be included in the output.
- A list-like of dtypes : Limits the results to the
provided data types.
To limit the result to numeric types submit
``numpy.number``. To limit it instead to object columns submit
the ``numpy.object`` data type. Strings
can also be used in the style of
``select_dtypes`` (e.g. ``df.describe(include=['O'])``). To
select pandas categorical columns, use ``'category'``
- None (default) : The result will include all numeric columns.
exclude : list-like of dtypes or None (default), optional,
A black list of data types to omit from the result. Ignored
for ``Series``. Here are the options:
- A list-like of dtypes : Excludes the provided data types
from the result. To exclude numeric types submit
``numpy.number``. To exclude object columns submit the data
type ``numpy.object``. Strings can also be used in the style of
``select_dtypes`` (e.g. ``df.describe(include=['O'])``). To
exclude pandas categorical columns, use ``'category'``
- None (default) : The result will exclude nothing.
datetime_is_numeric : bool, default False
Whether to treat datetime dtypes as numeric. This affects statistics
calculated for the column. For DataFrame input, this also
controls whether datetime columns are included by default.
.. versionadded:: 1.1.0
Returns
-------
Series or DataFrame
Summary statistics of the Series or Dataframe provided.
See Also
--------
DataFrame.count: Count number of non-NA/null observations.
DataFrame.max: Maximum of the values in the object.
DataFrame.min: Minimum of the values in the object.
DataFrame.mean: Mean of the values.
DataFrame.std: Standard deviation of the observations.
DataFrame.select_dtypes: Subset of a DataFrame including/excluding
columns based on their dtype.
Notes
-----
For numeric data, the result's index will include ``count``,
``mean``, ``std``, ``min``, ``max`` as well as lower, ``50`` and
upper percentiles. By default the lower percentile is ``25`` and the
upper percentile is ``75``. The ``50`` percentile is the
same as the median.
For object data (e.g. strings or timestamps), the result's index
will include ``count``, ``unique``, ``top``, and ``freq``. The ``top``
is the most common value. The ``freq`` is the most common value's
frequency. Timestamps also include the ``first`` and ``last`` items.
If multiple object values have the highest count, then the
``count`` and ``top`` results will be arbitrarily chosen from
among those with the highest count.
For mixed data types provided via a ``DataFrame``, the default is to
return only an analysis of numeric columns. If the dataframe consists
only of object and categorical data without any numeric columns, the
default is to return an analysis of both the object and categorical
columns. If ``include='all'`` is provided as an option, the result
will include a union of attributes of each type.
The `include` and `exclude` parameters can be used to limit
which columns in a ``DataFrame`` are analyzed for the output.
The parameters are ignored when analyzing a ``Series``.
Examples
--------
Describing a numeric ``Series``.
>>> s = pd.Series([1, 2, 3])
>>> s.describe()
count 3.0
mean 2.0
std 1.0
min 1.0
25% 1.5
50% 2.0
75% 2.5
max 3.0
dtype: float64
Describing a categorical ``Series``.
>>> s = pd.Series(['a', 'a', 'b', 'c'])
>>> s.describe()
count 4
unique 3
top a
freq 2
dtype: object
Describing a timestamp ``Series``.
>>> s = pd.Series([
... np.datetime64("2000-01-01"),
... np.datetime64("2010-01-01"),
... np.datetime64("2010-01-01")
... ])
>>> s.describe(datetime_is_numeric=True)
count 3
mean 2006-09-01 08:00:00
min 2000-01-01 00:00:00
25% 2004-12-31 12:00:00
50% 2010-01-01 00:00:00
75% 2010-01-01 00:00:00
max 2010-01-01 00:00:00
dtype: object
Describing a ``DataFrame``. By default only numeric fields
are returned.
>>> df = pd.DataFrame({'categorical': pd.Categorical(['d','e','f']),
... 'numeric': [1, 2, 3],
... 'object': ['a', 'b', 'c']
... })
>>> df.describe()
numeric
count 3.0
mean 2.0
std 1.0
min 1.0
25% 1.5
50% 2.0
75% 2.5
max 3.0
Describing all columns of a ``DataFrame`` regardless of data type.
>>> df.describe(include='all') # doctest: +SKIP
categorical numeric object
count 3 3.0 3
unique 3 NaN 3
top f NaN a
freq 1 NaN 1
mean NaN 2.0 NaN
std NaN 1.0 NaN
min NaN 1.0 NaN
25% NaN 1.5 NaN
50% NaN 2.0 NaN
75% NaN 2.5 NaN
max NaN 3.0 NaN
Describing a column from a ``DataFrame`` by accessing it as
an attribute.
>>> df.numeric.describe()
count 3.0
mean 2.0
std 1.0
min 1.0
25% 1.5
50% 2.0
75% 2.5
max 3.0
Name: numeric, dtype: float64
Including only numeric columns in a ``DataFrame`` description.
>>> df.describe(include=[np.number])
numeric
count 3.0
mean 2.0
std 1.0
min 1.0
25% 1.5
50% 2.0
75% 2.5
max 3.0
Including only string columns in a ``DataFrame`` description.
>>> df.describe(include=[object]) # doctest: +SKIP
object
count 3
unique 3
top a
freq 1
Including only categorical columns from a ``DataFrame`` description.
>>> df.describe(include=['category'])
categorical
count 3
unique 3
top d
freq 1
Excluding numeric columns from a ``DataFrame`` description.
>>> df.describe(exclude=[np.number]) # doctest: +SKIP
categorical object
count 3 3
unique 3 3
top f a
freq 1 1
Excluding object columns from a ``DataFrame`` description.
>>> df.describe(exclude=[object]) # doctest: +SKIP
categorical numeric
count 3 3.0
unique 3 NaN
top f NaN
freq 1 NaN
mean NaN 2.0
std NaN 1.0
min NaN 1.0
25% NaN 1.5
50% NaN 2.0
75% NaN 2.5
max NaN 3.0
"""
if self.ndim == 2 and self.columns.size == 0:
raise ValueError("Cannot describe a DataFrame without columns")
if percentiles is not None:
# explicit conversion of `percentiles` to list
percentiles = list(percentiles)
# get them all to be in [0, 1]
validate_percentile(percentiles)
# median should always be included
if 0.5 not in percentiles:
percentiles.append(0.5)
percentiles = np.asarray(percentiles)
else:
percentiles = np.array([0.25, 0.5, 0.75])
# sort and check for duplicates
unique_pcts = np.unique(percentiles)
if len(unique_pcts) < len(percentiles):
raise ValueError("percentiles cannot contain duplicates")
percentiles = unique_pcts
formatted_percentiles = format_percentiles(percentiles)
def describe_numeric_1d(series) -> "Series":
stat_index = (
["count", "mean", "std", "min"] + formatted_percentiles + ["max"]
)
d = (
[series.count(), series.mean(), series.std(), series.min()]
+ series.quantile(percentiles).tolist()
+ [series.max()]
)
return pd.Series(d, index=stat_index, name=series.name)
def describe_categorical_1d(data) -> "Series":
names = ["count", "unique"]
objcounts = data.value_counts()
count_unique = len(objcounts[objcounts != 0])
result = [data.count(), count_unique]
dtype = None
if result[1] > 0:
top, freq = objcounts.index[0], objcounts.iloc[0]
if is_datetime64_any_dtype(data.dtype):
if self.ndim == 1:
stacklevel = 4
else:
stacklevel = 5
warnings.warn(
"Treating datetime data as categorical rather than numeric in "
"`.describe` is deprecated and will be removed in a future "
"version of pandas. Specify `datetime_is_numeric=True` to "
"silence this warning and adopt the future behavior now.",
FutureWarning,
stacklevel=stacklevel,
)
tz = data.dt.tz
asint = data.dropna().values.view("i8")
top = Timestamp(top)
if top.tzinfo is not None and tz is not None:
# Don't tz_localize(None) if key is already tz-aware
top = top.tz_convert(tz)
else:
top = top.tz_localize(tz)
names += ["top", "freq", "first", "last"]
result += [
top,
freq,
Timestamp(asint.min(), tz=tz),
Timestamp(asint.max(), tz=tz),
]
else:
names += ["top", "freq"]
result += [top, freq]
# If the DataFrame is empty, set 'top' and 'freq' to None
# to maintain output shape consistency
else:
names += ["top", "freq"]
result += [np.nan, np.nan]
dtype = "object"
return pd.Series(result, index=names, name=data.name, dtype=dtype)
def describe_timestamp_1d(data) -> "Series":
# GH-30164
stat_index = ["count", "mean", "min"] + formatted_percentiles + ["max"]
d = (
[data.count(), data.mean(), data.min()]
+ data.quantile(percentiles).tolist()
+ [data.max()]
)
return pd.Series(d, index=stat_index, name=data.name)
def describe_1d(data) -> "Series":
if is_bool_dtype(data.dtype):
return describe_categorical_1d(data)
elif is_numeric_dtype(data):
return describe_numeric_1d(data)
elif is_datetime64_any_dtype(data.dtype) and datetime_is_numeric:
return describe_timestamp_1d(data)
elif is_timedelta64_dtype(data.dtype):
return describe_numeric_1d(data)
else:
return describe_categorical_1d(data)
if self.ndim == 1:
# Incompatible return value type
# (got "Series", expected "FrameOrSeries") [return-value]
return describe_1d(self) # type:ignore[return-value]
elif (include is None) and (exclude is None):
# when some numerics are found, keep only numerics
default_include = [np.number]
if datetime_is_numeric:
default_include.append("datetime")
data = self.select_dtypes(include=default_include)
if len(data.columns) == 0:
data = self
elif include == "all":
if exclude is not None:
msg = "exclude must be None when include is 'all'"
raise ValueError(msg)
data = self
else:
data = self.select_dtypes(include=include, exclude=exclude)
ldesc = [describe_1d(s) for _, s in data.items()]
# set a convenient order for rows
names: List[Label] = []
ldesc_indexes = sorted((x.index for x in ldesc), key=len)
for idxnames in ldesc_indexes:
for name in idxnames:
if name not in names:
names.append(name)
d = pd.concat([x.reindex(names, copy=False) for x in ldesc], axis=1, sort=False)
d.columns = data.columns.copy()
return d
@final
def pct_change(
self: FrameOrSeries,
periods=1,
fill_method="pad",
limit=None,
freq=None,
**kwargs,
) -> FrameOrSeries:
"""
Percentage change between the current and a prior element.
Computes the percentage change from the immediately previous row by
default. This is useful in comparing the percentage of change in a time
series of elements.
Parameters
----------
periods : int, default 1
Periods to shift for forming percent change.
fill_method : str, default 'pad'
How to handle NAs before computing percent changes.
limit : int, default None
The number of consecutive NAs to fill before stopping.
freq : DateOffset, timedelta, or str, optional
Increment to use from time series API (e.g. 'M' or BDay()).
**kwargs
Additional keyword arguments are passed into
`DataFrame.shift` or `Series.shift`.
Returns
-------
chg : Series or DataFrame
The same type as the calling object.
See Also
--------
Series.diff : Compute the difference of two elements in a Series.
DataFrame.diff : Compute the difference of two elements in a DataFrame.
Series.shift : Shift the index by some number of periods.
DataFrame.shift : Shift the index by some number of periods.
Examples
--------
**Series**
>>> s = pd.Series([90, 91, 85])
>>> s
0 90
1 91
2 85
dtype: int64
>>> s.pct_change()
0 NaN
1 0.011111
2 -0.065934
dtype: float64
>>> s.pct_change(periods=2)
0 NaN
1 NaN
2 -0.055556
dtype: float64
See the percentage change in a Series where filling NAs with last
valid observation forward to next valid.
>>> s = pd.Series([90, 91, None, 85])
>>> s
0 90.0
1 91.0
2 NaN
3 85.0
dtype: float64
>>> s.pct_change(fill_method='ffill')
0 NaN
1 0.011111
2 0.000000
3 -0.065934
dtype: float64
**DataFrame**
Percentage change in French franc, Deutsche Mark, and Italian lira from
1980-01-01 to 1980-03-01.
>>> df = pd.DataFrame({
... 'FR': [4.0405, 4.0963, 4.3149],
... 'GR': [1.7246, 1.7482, 1.8519],
... 'IT': [804.74, 810.01, 860.13]},
... index=['1980-01-01', '1980-02-01', '1980-03-01'])
>>> df
FR GR IT
1980-01-01 4.0405 1.7246 804.74
1980-02-01 4.0963 1.7482 810.01
1980-03-01 4.3149 1.8519 860.13
>>> df.pct_change()
FR GR IT
1980-01-01 NaN NaN NaN
1980-02-01 0.013810 0.013684 0.006549
1980-03-01 0.053365 0.059318 0.061876
Percentage of change in GOOG and APPL stock volume. Shows computing
the percentage change between columns.
>>> df = pd.DataFrame({
... '2016': [1769950, 30586265],
... '2015': [1500923, 40912316],
... '2014': [1371819, 41403351]},
... index=['GOOG', 'APPL'])
>>> df
2016 2015 2014
GOOG 1769950 1500923 1371819
APPL 30586265 40912316 41403351
>>> df.pct_change(axis='columns')
2016 2015 2014
GOOG NaN -0.151997 -0.086016
APPL NaN 0.337604 0.012002
"""
axis = self._get_axis_number(kwargs.pop("axis", self._stat_axis_name))
if fill_method is None:
data = self
else:
_data = self.fillna(method=fill_method, axis=axis, limit=limit)
assert _data is not None # needed for mypy
data = _data
rs = data.div(data.shift(periods=periods, freq=freq, axis=axis, **kwargs)) - 1
if freq is not None:
# Shift method is implemented differently when freq is not None
# We want to restore the original index
rs = rs.loc[~rs.index.duplicated()]
rs = rs.reindex_like(data)
return rs
@final
def _agg_by_level(self, name, axis=0, level=0, skipna=True, **kwargs):
if axis is None:
raise ValueError("Must specify 'axis' when aggregating by level.")
grouped = self.groupby(level=level, axis=axis, sort=False)
if hasattr(grouped, name) and skipna:
return getattr(grouped, name)(**kwargs)
axis = self._get_axis_number(axis)
method = getattr(type(self), name)
applyf = lambda x: method(x, axis=axis, skipna=skipna, **kwargs)
return grouped.aggregate(applyf)
@final
def _logical_func(
self, name: str, func, axis=0, bool_only=None, skipna=True, level=None, **kwargs
):
nv.validate_logical_func((), kwargs, fname=name)
if level is not None:
if bool_only is not None:
raise NotImplementedError(
"Option bool_only is not implemented with option level."
)
return self._agg_by_level(name, axis=axis, level=level, skipna=skipna)
if self.ndim > 1 and axis is None:
# Reduce along one dimension then the other, to simplify DataFrame._reduce
res = self._logical_func(
name, func, axis=0, bool_only=bool_only, skipna=skipna, **kwargs
)
return res._logical_func(name, func, skipna=skipna, **kwargs)
return self._reduce(
func,
name=name,
axis=axis,
skipna=skipna,
numeric_only=bool_only,
filter_type="bool",
)
def any(self, axis=0, bool_only=None, skipna=True, level=None, **kwargs):
return self._logical_func(
"any", nanops.nanany, axis, bool_only, skipna, level, **kwargs
)
def all(self, axis=0, bool_only=None, skipna=True, level=None, **kwargs):
return self._logical_func(
"all", nanops.nanall, axis, bool_only, skipna, level, **kwargs
)
@final
def _accum_func(self, name: str, func, axis=None, skipna=True, *args, **kwargs):
skipna = nv.validate_cum_func_with_skipna(skipna, args, kwargs, name)
if axis is None:
axis = self._stat_axis_number
else:
axis = self._get_axis_number(axis)
if axis == 1:
return self.T._accum_func(
name, func, axis=0, skipna=skipna, *args, **kwargs
).T
def block_accum_func(blk_values):
values = blk_values.T if hasattr(blk_values, "T") else blk_values
result = nanops.na_accum_func(values, func, skipna=skipna)
result = result.T if hasattr(result, "T") else result
return result
result = self._mgr.apply(block_accum_func)
return self._constructor(result).__finalize__(self, method=name)
def cummax(self, axis=None, skipna=True, *args, **kwargs):
return self._accum_func(
"cummax", np.maximum.accumulate, axis, skipna, *args, **kwargs
)
def cummin(self, axis=None, skipna=True, *args, **kwargs):
return self._accum_func(
"cummin", np.minimum.accumulate, axis, skipna, *args, **kwargs
)
def cumsum(self, axis=None, skipna=True, *args, **kwargs):
return self._accum_func("cumsum", np.cumsum, axis, skipna, *args, **kwargs)
def cumprod(self, axis=None, skipna=True, *args, **kwargs):
return self._accum_func("cumprod", np.cumprod, axis, skipna, *args, **kwargs)
@final
def _stat_function_ddof(
self,
name: str,
func,
axis=None,
skipna=None,
level=None,
ddof=1,
numeric_only=None,
**kwargs,
):
nv.validate_stat_ddof_func((), kwargs, fname=name)
if skipna is None:
skipna = True
if axis is None:
axis = self._stat_axis_number
if level is not None:
return self._agg_by_level(
name, axis=axis, level=level, skipna=skipna, ddof=ddof
)
return self._reduce(
func, name, axis=axis, numeric_only=numeric_only, skipna=skipna, ddof=ddof
)
def sem(
self, axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs
):
return self._stat_function_ddof(
"sem", nanops.nansem, axis, skipna, level, ddof, numeric_only, **kwargs
)
def var(
self, axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs
):
return self._stat_function_ddof(
"var", nanops.nanvar, axis, skipna, level, ddof, numeric_only, **kwargs
)
def std(
self, axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs
):
return self._stat_function_ddof(
"std", nanops.nanstd, axis, skipna, level, ddof, numeric_only, **kwargs
)
@final
def _stat_function(
self,
name: str,
func,
axis=None,
skipna=None,
level=None,
numeric_only=None,
**kwargs,
):
if name == "median":
nv.validate_median((), kwargs)
else:
nv.validate_stat_func((), kwargs, fname=name)
if skipna is None:
skipna = True
if axis is None:
axis = self._stat_axis_number
if level is not None:
return self._agg_by_level(name, axis=axis, level=level, skipna=skipna)
return self._reduce(
func, name=name, axis=axis, skipna=skipna, numeric_only=numeric_only
)
def min(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs):
return self._stat_function(
"min", nanops.nanmin, axis, skipna, level, numeric_only, **kwargs
)
def max(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs):
return self._stat_function(
"max", nanops.nanmax, axis, skipna, level, numeric_only, **kwargs
)
def mean(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs):
return self._stat_function(
"mean", nanops.nanmean, axis, skipna, level, numeric_only, **kwargs
)
def median(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs):
return self._stat_function(
"median", nanops.nanmedian, axis, skipna, level, numeric_only, **kwargs
)
def skew(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs):
return self._stat_function(
"skew", nanops.nanskew, axis, skipna, level, numeric_only, **kwargs
)
def kurt(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs):
return self._stat_function(
"kurt", nanops.nankurt, axis, skipna, level, numeric_only, **kwargs
)
kurtosis = kurt
@final
def _min_count_stat_function(
self,
name: str,
func,
axis=None,
skipna=None,
level=None,
numeric_only=None,
min_count=0,
**kwargs,
):
if name == "sum":
nv.validate_sum((), kwargs)
elif name == "prod":
nv.validate_prod((), kwargs)
else:
nv.validate_stat_func((), kwargs, fname=name)
if skipna is None:
skipna = True
if axis is None:
axis = self._stat_axis_number
if level is not None:
return self._agg_by_level(
name, axis=axis, level=level, skipna=skipna, min_count=min_count
)
return self._reduce(
func,
name=name,
axis=axis,
skipna=skipna,
numeric_only=numeric_only,
min_count=min_count,
)
def sum(
self,
axis=None,
skipna=None,
level=None,
numeric_only=None,
min_count=0,
**kwargs,
):
return self._min_count_stat_function(
"sum", nanops.nansum, axis, skipna, level, numeric_only, min_count, **kwargs
)
def prod(
self,
axis=None,
skipna=None,
level=None,
numeric_only=None,
min_count=0,
**kwargs,
):
return self._min_count_stat_function(
"prod",
nanops.nanprod,
axis,
skipna,
level,
numeric_only,
min_count,
**kwargs,
)
product = prod
def mad(self, axis=None, skipna=None, level=None):
"""
{desc}
Parameters
----------
axis : {axis_descr}
Axis for the function to be applied on.
skipna : bool, default None
Exclude NA/null values when computing the result.
level : int or level name, default None
If the axis is a MultiIndex (hierarchical), count along a
particular level, collapsing into a {name1}.
Returns
-------
{name1} or {name2} (if level specified)\
{see_also}\
{examples}
"""
if skipna is None:
skipna = True
if axis is None:
axis = self._stat_axis_number
if level is not None:
return self._agg_by_level("mad", axis=axis, level=level, skipna=skipna)
data = self._get_numeric_data()
if axis == 0:
demeaned = data - data.mean(axis=0)
else:
demeaned = data.sub(data.mean(axis=1), axis=0)
return np.abs(demeaned).mean(axis=axis, skipna=skipna)
@classmethod
def _add_numeric_operations(cls):
"""
Add the operations to the cls; evaluate the doc strings again
"""
axis_descr, name1, name2 = _doc_parms(cls)
@doc(
_bool_doc,
desc=_any_desc,
name1=name1,
name2=name2,
axis_descr=axis_descr,
see_also=_any_see_also,
examples=_any_examples,
empty_value=False,
)
def any(self, axis=0, bool_only=None, skipna=True, level=None, **kwargs):
return NDFrame.any(self, axis, bool_only, skipna, level, **kwargs)
# pandas\core\generic.py:10725: error: Cannot assign to a method
# [assignment]
cls.any = any # type: ignore[assignment]
@doc(
_bool_doc,
desc=_all_desc,
name1=name1,
name2=name2,
axis_descr=axis_descr,
see_also=_all_see_also,
examples=_all_examples,
empty_value=True,
)
def all(self, axis=0, bool_only=None, skipna=True, level=None, **kwargs):
return NDFrame.all(self, axis, bool_only, skipna, level, **kwargs)
# pandas\core\generic.py:10719: error: Cannot assign to a method
# [assignment]
# pandas\core\generic.py:10719: error: Incompatible types in assignment
# (expression has type "Callable[[Iterable[object]], bool]", variable
# has type "Callable[[NDFrame, Any, Any, Any, Any, KwArg(Any)], Any]")
# [assignment]
cls.all = all # type: ignore[assignment]
# error: Argument 1 to "doc" has incompatible type "Optional[str]"; expected
# "Union[str, Callable[..., Any]]"
@doc(
NDFrame.mad.__doc__, # type: ignore[arg-type]
desc="Return the mean absolute deviation of the values "
"over the requested axis.",
name1=name1,
name2=name2,
axis_descr=axis_descr,
see_also="",
examples="",
)
def mad(self, axis=None, skipna=None, level=None):
return NDFrame.mad(self, axis, skipna, level)
# pandas\core\generic.py:10736: error: Cannot assign to a method
# [assignment]
cls.mad = mad # type: ignore[assignment]
@doc(
_num_ddof_doc,
desc="Return unbiased standard error of the mean over requested "
"axis.\n\nNormalized by N-1 by default. This can be changed "
"using the ddof argument",
name1=name1,
name2=name2,
axis_descr=axis_descr,
)
def sem(
self,
axis=None,
skipna=None,
level=None,
ddof=1,
numeric_only=None,
**kwargs,
):
return NDFrame.sem(self, axis, skipna, level, ddof, numeric_only, **kwargs)
# pandas\core\generic.py:10758: error: Cannot assign to a method
# [assignment]
cls.sem = sem # type: ignore[assignment]
@doc(
_num_ddof_doc,
desc="Return unbiased variance over requested axis.\n\nNormalized by "
"N-1 by default. This can be changed using the ddof argument",
name1=name1,
name2=name2,
axis_descr=axis_descr,
)
def var(
self,
axis=None,
skipna=None,
level=None,
ddof=1,
numeric_only=None,
**kwargs,
):
return NDFrame.var(self, axis, skipna, level, ddof, numeric_only, **kwargs)
# pandas\core\generic.py:10779: error: Cannot assign to a method
# [assignment]
cls.var = var # type: ignore[assignment]
@doc(
_num_ddof_doc,
desc="Return sample standard deviation over requested axis."
"\n\nNormalized by N-1 by default. This can be changed using the "
"ddof argument",
name1=name1,
name2=name2,
axis_descr=axis_descr,
)
def std(
self,
axis=None,
skipna=None,
level=None,
ddof=1,
numeric_only=None,
**kwargs,
):
return NDFrame.std(self, axis, skipna, level, ddof, numeric_only, **kwargs)
# pandas\core\generic.py:10801: error: Cannot assign to a method
# [assignment]
cls.std = std # type: ignore[assignment]
@doc(
_cnum_doc,
desc="minimum",
name1=name1,
name2=name2,
axis_descr=axis_descr,
accum_func_name="min",
examples=_cummin_examples,
)
def cummin(self, axis=None, skipna=True, *args, **kwargs):
return NDFrame.cummin(self, axis, skipna, *args, **kwargs)
# pandas\core\generic.py:10815: error: Cannot assign to a method
# [assignment]
cls.cummin = cummin # type: ignore[assignment]
@doc(
_cnum_doc,
desc="maximum",
name1=name1,
name2=name2,
axis_descr=axis_descr,
accum_func_name="max",
examples=_cummax_examples,
)
def cummax(self, axis=None, skipna=True, *args, **kwargs):
return NDFrame.cummax(self, axis, skipna, *args, **kwargs)
# pandas\core\generic.py:10829: error: Cannot assign to a method
# [assignment]
cls.cummax = cummax # type: ignore[assignment]
@doc(
_cnum_doc,
desc="sum",
name1=name1,
name2=name2,
axis_descr=axis_descr,
accum_func_name="sum",
examples=_cumsum_examples,
)
def cumsum(self, axis=None, skipna=True, *args, **kwargs):
return NDFrame.cumsum(self, axis, skipna, *args, **kwargs)
# pandas\core\generic.py:10843: error: Cannot assign to a method
# [assignment]
cls.cumsum = cumsum # type: ignore[assignment]
@doc(
_cnum_doc,
desc="product",
name1=name1,
name2=name2,
axis_descr=axis_descr,
accum_func_name="prod",
examples=_cumprod_examples,
)
def cumprod(self, axis=None, skipna=True, *args, **kwargs):
return NDFrame.cumprod(self, axis, skipna, *args, **kwargs)
# pandas\core\generic.py:10857: error: Cannot assign to a method
# [assignment]
cls.cumprod = cumprod # type: ignore[assignment]
@doc(
_num_doc,
desc="Return the sum of the values over the requested axis.\n\n"
"This is equivalent to the method ``numpy.sum``.",
name1=name1,
name2=name2,
axis_descr=axis_descr,
min_count=_min_count_stub,
see_also=_stat_func_see_also,
examples=_sum_examples,
)
def sum(
self,
axis=None,
skipna=None,
level=None,
numeric_only=None,
min_count=0,
**kwargs,
):
return NDFrame.sum(
self, axis, skipna, level, numeric_only, min_count, **kwargs
)
# pandas\core\generic.py:10883: error: Cannot assign to a method
# [assignment]
cls.sum = sum # type: ignore[assignment]
@doc(
_num_doc,
desc="Return the product of the values over the requested axis.",
name1=name1,
name2=name2,
axis_descr=axis_descr,
min_count=_min_count_stub,
see_also=_stat_func_see_also,
examples=_prod_examples,
)
def prod(
self,
axis=None,
skipna=None,
level=None,
numeric_only=None,
min_count=0,
**kwargs,
):
return NDFrame.prod(
self, axis, skipna, level, numeric_only, min_count, **kwargs
)
# pandas\core\generic.py:10908: error: Cannot assign to a method
# [assignment]
cls.prod = prod # type: ignore[assignment]
cls.product = prod
@doc(
_num_doc,
desc="Return the mean of the values over the requested axis.",
name1=name1,
name2=name2,
axis_descr=axis_descr,
min_count="",
see_also="",
examples="",
)
def mean(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs):
return NDFrame.mean(self, axis, skipna, level, numeric_only, **kwargs)
# pandas\core\generic.py:10924: error: Cannot assign to a method
# [assignment]
cls.mean = mean # type: ignore[assignment]
@doc(
_num_doc,
desc="Return unbiased skew over requested axis.\n\nNormalized by N-1.",
name1=name1,
name2=name2,
axis_descr=axis_descr,
min_count="",
see_also="",
examples="",
)
def skew(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs):
return NDFrame.skew(self, axis, skipna, level, numeric_only, **kwargs)
# pandas\core\generic.py:10939: error: Cannot assign to a method
# [assignment]
cls.skew = skew # type: ignore[assignment]
@doc(
_num_doc,
desc="Return unbiased kurtosis over requested axis.\n\n"
"Kurtosis obtained using Fisher's definition of\n"
"kurtosis (kurtosis of normal == 0.0). Normalized "
"by N-1.",
name1=name1,
name2=name2,
axis_descr=axis_descr,
min_count="",
see_also="",
examples="",
)
def kurt(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs):
return NDFrame.kurt(self, axis, skipna, level, numeric_only, **kwargs)
# pandas\core\generic.py:10957: error: Cannot assign to a method
# [assignment]
cls.kurt = kurt # type: ignore[assignment]
cls.kurtosis = kurt
@doc(
_num_doc,
desc="Return the median of the values over the requested axis.",
name1=name1,
name2=name2,
axis_descr=axis_descr,
min_count="",
see_also="",
examples="",
)
def median(
self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs
):
return NDFrame.median(self, axis, skipna, level, numeric_only, **kwargs)
# pandas\core\generic.py:10975: error: Cannot assign to a method
# [assignment]
cls.median = median # type: ignore[assignment]
@doc(
_num_doc,
desc="Return the maximum of the values over the requested axis.\n\n"
"If you want the *index* of the maximum, use ``idxmax``. This is"
"the equivalent of the ``numpy.ndarray`` method ``argmax``.",
name1=name1,
name2=name2,
axis_descr=axis_descr,
min_count="",
see_also=_stat_func_see_also,
examples=_max_examples,
)
def max(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs):
return NDFrame.max(self, axis, skipna, level, numeric_only, **kwargs)
# pandas\core\generic.py:10992: error: Cannot assign to a method
# [assignment]
cls.max = max # type: ignore[assignment]
@doc(
_num_doc,
desc="Return the minimum of the values over the requested axis.\n\n"
"If you want the *index* of the minimum, use ``idxmin``. This is"
"the equivalent of the ``numpy.ndarray`` method ``argmin``.",
name1=name1,
name2=name2,
axis_descr=axis_descr,
min_count="",
see_also=_stat_func_see_also,
examples=_min_examples,
)
def min(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs):
return NDFrame.min(self, axis, skipna, level, numeric_only, **kwargs)
# pandas\core\generic.py:11009: error: Cannot assign to a method
# [assignment]
cls.min = min # type: ignore[assignment]
@final
@doc(Rolling)
def rolling(
self,
window: Union[int, timedelta, BaseOffset, BaseIndexer],
min_periods: Optional[int] = None,
center: bool_t = False,
win_type: Optional[str] = None,
on: Optional[str] = None,
axis: Axis = 0,
closed: Optional[str] = None,
):
axis = self._get_axis_number(axis)
if win_type is not None:
return Window(
self,
window=window,
min_periods=min_periods,
center=center,
win_type=win_type,
on=on,
axis=axis,
closed=closed,
)
return Rolling(
self,
window=window,
min_periods=min_periods,
center=center,
win_type=win_type,
on=on,
axis=axis,
closed=closed,
)
@final
@doc(Expanding)
def expanding(
self, min_periods: int = 1, center: Optional[bool_t] = None, axis: Axis = 0
) -> Expanding:
axis = self._get_axis_number(axis)
if center is not None:
warnings.warn(
"The `center` argument on `expanding` will be removed in the future",
FutureWarning,
stacklevel=2,
)
else:
center = False
return Expanding(self, min_periods=min_periods, center=center, axis=axis)
@final
@doc(ExponentialMovingWindow)
def ewm(
self,
com: Optional[float] = None,
span: Optional[float] = None,
halflife: Optional[Union[float, TimedeltaConvertibleTypes]] = None,
alpha: Optional[float] = None,
min_periods: int = 0,
adjust: bool_t = True,
ignore_na: bool_t = False,
axis: Axis = 0,
times: Optional[Union[str, np.ndarray, FrameOrSeries]] = None,
) -> ExponentialMovingWindow:
axis = self._get_axis_number(axis)
return ExponentialMovingWindow(
self,
com=com,
span=span,
halflife=halflife,
alpha=alpha,
min_periods=min_periods,
adjust=adjust,
ignore_na=ignore_na,
axis=axis,
times=times,
)
# ----------------------------------------------------------------------
# Arithmetic Methods
@final
def _inplace_method(self, other, op):
"""
Wrap arithmetic method to operate inplace.
"""
result = op(self, other)
if (
self.ndim == 1
and result._indexed_same(self)
and is_dtype_equal(result.dtype, self.dtype)
):
# GH#36498 this inplace op can _actually_ be inplace.
self._values[:] = result._values
return self
# Delete cacher
self._reset_cacher()
# this makes sure that we are aligned like the input
# we are updating inplace so we want to ignore is_copy
self._update_inplace(
result.reindex_like(self, copy=False), verify_is_copy=False
)
return self
def __iadd__(self, other):
return self._inplace_method(other, type(self).__add__) # type: ignore[operator]
def __isub__(self, other):
return self._inplace_method(other, type(self).__sub__) # type: ignore[operator]
def __imul__(self, other):
return self._inplace_method(other, type(self).__mul__) # type: ignore[operator]
def __itruediv__(self, other):
return self._inplace_method(
other, type(self).__truediv__ # type: ignore[operator]
)
def __ifloordiv__(self, other):
return self._inplace_method(
other, type(self).__floordiv__ # type: ignore[operator]
)
def __imod__(self, other):
return self._inplace_method(other, type(self).__mod__) # type: ignore[operator]
def __ipow__(self, other):
return self._inplace_method(other, type(self).__pow__) # type: ignore[operator]
def __iand__(self, other):
return self._inplace_method(other, type(self).__and__) # type: ignore[operator]
def __ior__(self, other):
return self._inplace_method(other, type(self).__or__) # type: ignore[operator]
def __ixor__(self, other):
return self._inplace_method(other, type(self).__xor__) # type: ignore[operator]
# ----------------------------------------------------------------------
# Misc methods
@final
def _find_valid_index(self, how: str):
"""
Retrieves the index of the first valid value.
Parameters
----------
how : {'first', 'last'}
Use this parameter to change between the first or last valid index.
Returns
-------
idx_first_valid : type of index
"""
idxpos = find_valid_index(self._values, how)
if idxpos is None:
return None
return self.index[idxpos]
@final
@doc(position="first", klass=_shared_doc_kwargs["klass"])
def first_valid_index(self):
"""
Return index for {position} non-NA/null value.
Returns
-------
scalar : type of index
Notes
-----
If all elements are non-NA/null, returns None.
Also returns None for empty {klass}.
"""
return self._find_valid_index("first")
@final
@doc(first_valid_index, position="last", klass=_shared_doc_kwargs["klass"])
def last_valid_index(self):
return self._find_valid_index("last")
def _doc_parms(cls):
"""Return a tuple of the doc parms."""
axis_descr = (
f"{{{', '.join(f'{a} ({i})' for i, a in enumerate(cls._AXIS_ORDERS))}}}"
)
name = cls._constructor_sliced.__name__ if cls._AXIS_LEN > 1 else "scalar"
name2 = cls.__name__
return axis_descr, name, name2
_num_doc = """
{desc}
Parameters
----------
axis : {axis_descr}
Axis for the function to be applied on.
skipna : bool, default True
Exclude NA/null values when computing the result.
level : int or level name, default None
If the axis is a MultiIndex (hierarchical), count along a
particular level, collapsing into a {name1}.
numeric_only : bool, default None
Include only float, int, boolean columns. If None, will attempt to use
everything, then use only numeric data. Not implemented for Series.
{min_count}\
**kwargs
Additional keyword arguments to be passed to the function.
Returns
-------
{name1} or {name2} (if level specified)\
{see_also}\
{examples}
"""
_num_ddof_doc = """
{desc}
Parameters
----------
axis : {axis_descr}
skipna : bool, default True
Exclude NA/null values. If an entire row/column is NA, the result
will be NA.
level : int or level name, default None
If the axis is a MultiIndex (hierarchical), count along a
particular level, collapsing into a {name1}.
ddof : int, default 1
Delta Degrees of Freedom. The divisor used in calculations is N - ddof,
where N represents the number of elements.
numeric_only : bool, default None
Include only float, int, boolean columns. If None, will attempt to use
everything, then use only numeric data. Not implemented for Series.
Returns
-------
{name1} or {name2} (if level specified)
Notes
-----
To have the same behaviour as `numpy.std`, use `ddof=0` (instead of the
default `ddof=1`)\n"""
_bool_doc = """
{desc}
Parameters
----------
axis : {{0 or 'index', 1 or 'columns', None}}, default 0
Indicate which axis or axes should be reduced.
* 0 / 'index' : reduce the index, return a Series whose index is the
original column labels.
* 1 / 'columns' : reduce the columns, return a Series whose index is the
original index.
* None : reduce all axes, return a scalar.
bool_only : bool, default None
Include only boolean columns. If None, will attempt to use everything,
then use only boolean data. Not implemented for Series.
skipna : bool, default True
Exclude NA/null values. If the entire row/column is NA and skipna is
True, then the result will be {empty_value}, as for an empty row/column.
If skipna is False, then NA are treated as True, because these are not
equal to zero.
level : int or level name, default None
If the axis is a MultiIndex (hierarchical), count along a
particular level, collapsing into a {name1}.
**kwargs : any, default None
Additional keywords have no effect but might be accepted for
compatibility with NumPy.
Returns
-------
{name1} or {name2}
If level is specified, then, {name2} is returned; otherwise, {name1}
is returned.
{see_also}
{examples}"""
_all_desc = """\
Return whether all elements are True, potentially over an axis.
Returns True unless there at least one element within a series or
along a Dataframe axis that is False or equivalent (e.g. zero or
empty)."""
_all_examples = """\
Examples
--------
**Series**
>>> pd.Series([True, True]).all()
True
>>> pd.Series([True, False]).all()
False
>>> pd.Series([]).all()
True
>>> pd.Series([np.nan]).all()
True
>>> pd.Series([np.nan]).all(skipna=False)
True
**DataFrames**
Create a dataframe from a dictionary.
>>> df = pd.DataFrame({'col1': [True, True], 'col2': [True, False]})
>>> df
col1 col2
0 True True
1 True False
Default behaviour checks if column-wise values all return True.
>>> df.all()
col1 True
col2 False
dtype: bool
Specify ``axis='columns'`` to check if row-wise values all return True.
>>> df.all(axis='columns')
0 True
1 False
dtype: bool
Or ``axis=None`` for whether every value is True.
>>> df.all(axis=None)
False
"""
_all_see_also = """\
See Also
--------
Series.all : Return True if all elements are True.
DataFrame.any : Return True if one (or more) elements are True.
"""
_cnum_doc = """
Return cumulative {desc} over a DataFrame or Series axis.
Returns a DataFrame or Series of the same size containing the cumulative
{desc}.
Parameters
----------
axis : {{0 or 'index', 1 or 'columns'}}, default 0
The index or the name of the axis. 0 is equivalent to None or 'index'.
skipna : bool, default True
Exclude NA/null values. If an entire row/column is NA, the result
will be NA.
*args, **kwargs
Additional keywords have no effect but might be accepted for
compatibility with NumPy.
Returns
-------
{name1} or {name2}
Return cumulative {desc} of {name1} or {name2}.
See Also
--------
core.window.Expanding.{accum_func_name} : Similar functionality
but ignores ``NaN`` values.
{name2}.{accum_func_name} : Return the {desc} over
{name2} axis.
{name2}.cummax : Return cumulative maximum over {name2} axis.
{name2}.cummin : Return cumulative minimum over {name2} axis.
{name2}.cumsum : Return cumulative sum over {name2} axis.
{name2}.cumprod : Return cumulative product over {name2} axis.
{examples}"""
_cummin_examples = """\
Examples
--------
**Series**
>>> s = pd.Series([2, np.nan, 5, -1, 0])
>>> s
0 2.0
1 NaN
2 5.0
3 -1.0
4 0.0
dtype: float64
By default, NA values are ignored.
>>> s.cummin()
0 2.0
1 NaN
2 2.0
3 -1.0
4 -1.0
dtype: float64
To include NA values in the operation, use ``skipna=False``
>>> s.cummin(skipna=False)
0 2.0
1 NaN
2 NaN
3 NaN
4 NaN
dtype: float64
**DataFrame**
>>> df = pd.DataFrame([[2.0, 1.0],
... [3.0, np.nan],
... [1.0, 0.0]],
... columns=list('AB'))
>>> df
A B
0 2.0 1.0
1 3.0 NaN
2 1.0 0.0
By default, iterates over rows and finds the minimum
in each column. This is equivalent to ``axis=None`` or ``axis='index'``.
>>> df.cummin()
A B
0 2.0 1.0
1 2.0 NaN
2 1.0 0.0
To iterate over columns and find the minimum in each row,
use ``axis=1``
>>> df.cummin(axis=1)
A B
0 2.0 1.0
1 3.0 NaN
2 1.0 0.0
"""
_cumsum_examples = """\
Examples
--------
**Series**
>>> s = pd.Series([2, np.nan, 5, -1, 0])
>>> s
0 2.0
1 NaN
2 5.0
3 -1.0
4 0.0
dtype: float64
By default, NA values are ignored.
>>> s.cumsum()
0 2.0
1 NaN
2 7.0
3 6.0
4 6.0
dtype: float64
To include NA values in the operation, use ``skipna=False``
>>> s.cumsum(skipna=False)
0 2.0
1 NaN
2 NaN
3 NaN
4 NaN
dtype: float64
**DataFrame**
>>> df = pd.DataFrame([[2.0, 1.0],
... [3.0, np.nan],
... [1.0, 0.0]],
... columns=list('AB'))
>>> df
A B
0 2.0 1.0
1 3.0 NaN
2 1.0 0.0
By default, iterates over rows and finds the sum
in each column. This is equivalent to ``axis=None`` or ``axis='index'``.
>>> df.cumsum()
A B
0 2.0 1.0
1 5.0 NaN
2 6.0 1.0
To iterate over columns and find the sum in each row,
use ``axis=1``
>>> df.cumsum(axis=1)
A B
0 2.0 3.0
1 3.0 NaN
2 1.0 1.0
"""
_cumprod_examples = """\
Examples
--------
**Series**
>>> s = pd.Series([2, np.nan, 5, -1, 0])
>>> s
0 2.0
1 NaN
2 5.0
3 -1.0
4 0.0
dtype: float64
By default, NA values are ignored.
>>> s.cumprod()
0 2.0
1 NaN
2 10.0
3 -10.0
4 -0.0
dtype: float64
To include NA values in the operation, use ``skipna=False``
>>> s.cumprod(skipna=False)
0 2.0
1 NaN
2 NaN
3 NaN
4 NaN
dtype: float64
**DataFrame**
>>> df = pd.DataFrame([[2.0, 1.0],
... [3.0, np.nan],
... [1.0, 0.0]],
... columns=list('AB'))
>>> df
A B
0 2.0 1.0
1 3.0 NaN
2 1.0 0.0
By default, iterates over rows and finds the product
in each column. This is equivalent to ``axis=None`` or ``axis='index'``.
>>> df.cumprod()
A B
0 2.0 1.0
1 6.0 NaN
2 6.0 0.0
To iterate over columns and find the product in each row,
use ``axis=1``
>>> df.cumprod(axis=1)
A B
0 2.0 2.0
1 3.0 NaN
2 1.0 0.0
"""
_cummax_examples = """\
Examples
--------
**Series**
>>> s = pd.Series([2, np.nan, 5, -1, 0])
>>> s
0 2.0
1 NaN
2 5.0
3 -1.0
4 0.0
dtype: float64
By default, NA values are ignored.
>>> s.cummax()
0 2.0
1 NaN
2 5.0
3 5.0
4 5.0
dtype: float64
To include NA values in the operation, use ``skipna=False``
>>> s.cummax(skipna=False)
0 2.0
1 NaN
2 NaN
3 NaN
4 NaN
dtype: float64
**DataFrame**
>>> df = pd.DataFrame([[2.0, 1.0],
... [3.0, np.nan],
... [1.0, 0.0]],
... columns=list('AB'))
>>> df
A B
0 2.0 1.0
1 3.0 NaN
2 1.0 0.0
By default, iterates over rows and finds the maximum
in each column. This is equivalent to ``axis=None`` or ``axis='index'``.
>>> df.cummax()
A B
0 2.0 1.0
1 3.0 NaN
2 3.0 1.0
To iterate over columns and find the maximum in each row,
use ``axis=1``
>>> df.cummax(axis=1)
A B
0 2.0 2.0
1 3.0 NaN
2 1.0 1.0
"""
_any_see_also = """\
See Also
--------
numpy.any : Numpy version of this method.
Series.any : Return whether any element is True.
Series.all : Return whether all elements are True.
DataFrame.any : Return whether any element is True over requested axis.
DataFrame.all : Return whether all elements are True over requested axis.
"""
_any_desc = """\
Return whether any element is True, potentially over an axis.
Returns False unless there is at least one element within a series or
along a Dataframe axis that is True or equivalent (e.g. non-zero or
non-empty)."""
_any_examples = """\
Examples
--------
**Series**
For Series input, the output is a scalar indicating whether any element
is True.
>>> pd.Series([False, False]).any()
False
>>> pd.Series([True, False]).any()
True
>>> pd.Series([]).any()
False
>>> pd.Series([np.nan]).any()
False
>>> pd.Series([np.nan]).any(skipna=False)
True
**DataFrame**
Whether each column contains at least one True element (the default).
>>> df = pd.DataFrame({"A": [1, 2], "B": [0, 2], "C": [0, 0]})
>>> df
A B C
0 1 0 0
1 2 2 0
>>> df.any()
A True
B True
C False
dtype: bool
Aggregating over the columns.
>>> df = pd.DataFrame({"A": [True, False], "B": [1, 2]})
>>> df
A B
0 True 1
1 False 2
>>> df.any(axis='columns')
0 True
1 True
dtype: bool
>>> df = pd.DataFrame({"A": [True, False], "B": [1, 0]})
>>> df
A B
0 True 1
1 False 0
>>> df.any(axis='columns')
0 True
1 False
dtype: bool
Aggregating over the entire DataFrame with ``axis=None``.
>>> df.any(axis=None)
True
`any` for an empty DataFrame is an empty Series.
>>> pd.DataFrame([]).any()
Series([], dtype: bool)
"""
_shared_docs[
"stat_func_example"
] = """
Examples
--------
>>> idx = pd.MultiIndex.from_arrays([
... ['warm', 'warm', 'cold', 'cold'],
... ['dog', 'falcon', 'fish', 'spider']],
... names=['blooded', 'animal'])
>>> s = pd.Series([4, 2, 0, 8], name='legs', index=idx)
>>> s
blooded animal
warm dog 4
falcon 2
cold fish 0
spider 8
Name: legs, dtype: int64
>>> s.{stat_func}()
{default_output}
{verb} using level names, as well as indices.
>>> s.{stat_func}(level='blooded')
blooded
warm {level_output_0}
cold {level_output_1}
Name: legs, dtype: int64
>>> s.{stat_func}(level=0)
blooded
warm {level_output_0}
cold {level_output_1}
Name: legs, dtype: int64"""
_sum_examples = _shared_docs["stat_func_example"].format(
stat_func="sum", verb="Sum", default_output=14, level_output_0=6, level_output_1=8
)
_sum_examples += """
By default, the sum of an empty or all-NA Series is ``0``.
>>> pd.Series([]).sum() # min_count=0 is the default
0.0
This can be controlled with the ``min_count`` parameter. For example, if
you'd like the sum of an empty series to be NaN, pass ``min_count=1``.
>>> pd.Series([]).sum(min_count=1)
nan
Thanks to the ``skipna`` parameter, ``min_count`` handles all-NA and
empty series identically.
>>> pd.Series([np.nan]).sum()
0.0
>>> pd.Series([np.nan]).sum(min_count=1)
nan"""
_max_examples = _shared_docs["stat_func_example"].format(
stat_func="max", verb="Max", default_output=8, level_output_0=4, level_output_1=8
)
_min_examples = _shared_docs["stat_func_example"].format(
stat_func="min", verb="Min", default_output=0, level_output_0=2, level_output_1=0
)
_stat_func_see_also = """
See Also
--------
Series.sum : Return the sum.
Series.min : Return the minimum.
Series.max : Return the maximum.
Series.idxmin : Return the index of the minimum.
Series.idxmax : Return the index of the maximum.
DataFrame.sum : Return the sum over the requested axis.
DataFrame.min : Return the minimum over the requested axis.
DataFrame.max : Return the maximum over the requested axis.
DataFrame.idxmin : Return the index of the minimum over the requested axis.
DataFrame.idxmax : Return the index of the maximum over the requested axis."""
_prod_examples = """
Examples
--------
By default, the product of an empty or all-NA Series is ``1``
>>> pd.Series([]).prod()
1.0
This can be controlled with the ``min_count`` parameter
>>> pd.Series([]).prod(min_count=1)
nan
Thanks to the ``skipna`` parameter, ``min_count`` handles all-NA and
empty series identically.
>>> pd.Series([np.nan]).prod()
1.0
>>> pd.Series([np.nan]).prod(min_count=1)
nan"""
_min_count_stub = """\
min_count : int, default 0
The required number of valid values to perform the operation. If fewer than
``min_count`` non-NA values are present the result will be NA.
"""
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