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"""
DataFrame
---------
An efficient 2D container for potentially mixed-type time series or other
labeled data series.
Similar to its R counterpart, data.frame, except providing automatic data
alignment and a host of useful data manipulation methods having to do with the
labeling information
"""
from __future__ import annotations
import collections
from collections import abc
import datetime
import functools
from io import StringIO
import itertools
import sys
from textwrap import dedent
from typing import (
TYPE_CHECKING,
Any,
Callable,
Hashable,
Iterable,
Iterator,
Literal,
Mapping,
Sequence,
cast,
overload,
)
import warnings
import numpy as np
from numpy import ma
from pandas._config import (
get_option,
using_copy_on_write,
)
from pandas._libs import (
algos as libalgos,
lib,
properties,
)
from pandas._libs.hashtable import duplicated
from pandas._libs.lib import (
NoDefault,
is_range_indexer,
no_default,
)
from pandas._typing import (
AggFuncType,
AlignJoin,
AnyAll,
AnyArrayLike,
ArrayLike,
Axes,
Axis,
AxisInt,
ColspaceArgType,
CompressionOptions,
CorrelationMethod,
DropKeep,
Dtype,
DtypeObj,
FilePath,
FillnaOptions,
FloatFormatType,
FormattersType,
Frequency,
IgnoreRaise,
IndexKeyFunc,
IndexLabel,
Level,
MergeHow,
NaPosition,
PythonFuncType,
QuantileInterpolation,
ReadBuffer,
Renamer,
Scalar,
SortKind,
StorageOptions,
Suffixes,
TimedeltaConvertibleTypes,
TimestampConvertibleTypes,
ValueKeyFunc,
WriteBuffer,
npt,
)
from pandas.compat import PYPY
from pandas.compat._optional import import_optional_dependency
from pandas.compat.numpy import (
function as nv,
np_percentile_argname,
)
from pandas.errors import (
ChainedAssignmentError,
InvalidIndexError,
_chained_assignment_msg,
)
from pandas.util._decorators import (
Appender,
Substitution,
doc,
)
from pandas.util._exceptions import find_stack_level
from pandas.util._validators import (
validate_ascending,
validate_bool_kwarg,
validate_percentile,
)
from pandas.core.dtypes.cast import (
LossySetitemError,
can_hold_element,
construct_1d_arraylike_from_scalar,
construct_2d_arraylike_from_scalar,
find_common_type,
infer_dtype_from_scalar,
invalidate_string_dtypes,
maybe_box_native,
maybe_downcast_to_dtype,
)
from pandas.core.dtypes.common import (
infer_dtype_from_object,
is_1d_only_ea_dtype,
is_bool_dtype,
is_dataclass,
is_dict_like,
is_dtype_equal,
is_extension_array_dtype,
is_float,
is_float_dtype,
is_hashable,
is_integer,
is_integer_dtype,
is_iterator,
is_list_like,
is_scalar,
is_sequence,
needs_i8_conversion,
pandas_dtype,
)
from pandas.core.dtypes.dtypes import ExtensionDtype
from pandas.core.dtypes.missing import (
isna,
notna,
)
from pandas.core import (
algorithms,
common as com,
nanops,
ops,
)
from pandas.core.accessor import CachedAccessor
from pandas.core.apply import (
reconstruct_func,
relabel_result,
)
from pandas.core.array_algos.take import take_2d_multi
from pandas.core.arraylike import OpsMixin
from pandas.core.arrays import (
DatetimeArray,
ExtensionArray,
PeriodArray,
TimedeltaArray,
)
from pandas.core.arrays.arrow import ArrowDtype
from pandas.core.arrays.sparse import SparseFrameAccessor
from pandas.core.construction import (
ensure_wrapped_if_datetimelike,
extract_array,
sanitize_array,
sanitize_masked_array,
)
from pandas.core.generic import NDFrame
from pandas.core.indexers import check_key_length
from pandas.core.indexes.api import (
DatetimeIndex,
Index,
PeriodIndex,
default_index,
ensure_index,
ensure_index_from_sequences,
)
from pandas.core.indexes.multi import (
MultiIndex,
maybe_droplevels,
)
from pandas.core.indexing import (
check_bool_indexer,
check_dict_or_set_indexers,
)
from pandas.core.internals import (
ArrayManager,
BlockManager,
)
from pandas.core.internals.construction import (
arrays_to_mgr,
dataclasses_to_dicts,
dict_to_mgr,
mgr_to_mgr,
ndarray_to_mgr,
nested_data_to_arrays,
rec_array_to_mgr,
reorder_arrays,
to_arrays,
treat_as_nested,
)
from pandas.core.methods import selectn
from pandas.core.reshape.melt import melt
from pandas.core.series import Series
from pandas.core.shared_docs import _shared_docs
from pandas.core.sorting import (
get_group_index,
lexsort_indexer,
nargsort,
)
from pandas.io.common import get_handle
from pandas.io.formats import (
console,
format as fmt,
)
from pandas.io.formats.info import (
INFO_DOCSTRING,
DataFrameInfo,
frame_sub_kwargs,
)
import pandas.plotting
if TYPE_CHECKING:
from pandas.core.groupby.generic import DataFrameGroupBy
from pandas.core.interchange.dataframe_protocol import DataFrame as DataFrameXchg
from pandas.core.internals import SingleDataManager
from pandas.core.resample import Resampler
from pandas.io.formats.style import Styler
# ---------------------------------------------------------------------
# Docstring templates
_shared_doc_kwargs = {
"axes": "index, columns",
"klass": "DataFrame",
"axes_single_arg": "{0 or 'index', 1 or 'columns'}",
"axis": """axis : {0 or 'index', 1 or 'columns'}, default 0
If 0 or 'index': apply function to each column.
If 1 or 'columns': apply function to each row.""",
"inplace": """
inplace : bool, default False
Whether to modify the DataFrame rather than creating a new one.""",
"optional_by": """
by : str or list of str
Name or list of names to sort by.
- if `axis` is 0 or `'index'` then `by` may contain index
levels and/or column labels.
- if `axis` is 1 or `'columns'` then `by` may contain column
levels and/or index labels.""",
"optional_reindex": """
labels : array-like, optional
New labels / index to conform the axis specified by 'axis' to.
index : array-like, optional
New labels for the index. Preferably an Index object to avoid
duplicating data.
columns : array-like, optional
New labels for the columns. Preferably an Index object to avoid
duplicating data.
axis : int or str, optional
Axis to target. Can be either the axis name ('index', 'columns')
or number (0, 1).""",
"replace_iloc": """
This differs from updating with ``.loc`` or ``.iloc``, which require
you to specify a location to update with some value.""",
}
_numeric_only_doc = """numeric_only : bool, default False
Include only float, int, boolean data.
"""
_merge_doc = """
Merge DataFrame or named Series objects with a database-style join.
A named Series object is treated as a DataFrame with a single named column.
The join is done on columns or indexes. If joining columns on
columns, the DataFrame indexes *will be ignored*. Otherwise if joining indexes
on indexes or indexes on a column or columns, the index will be passed on.
When performing a cross merge, no column specifications to merge on are
allowed.
.. warning::
If both key columns contain rows where the key is a null value, those
rows will be matched against each other. This is different from usual SQL
join behaviour and can lead to unexpected results.
Parameters
----------%s
right : DataFrame or named Series
Object to merge with.
how : {'left', 'right', 'outer', 'inner', 'cross'}, default 'inner'
Type of merge to be performed.
* left: use only keys from left frame, similar to a SQL left outer join;
preserve key order.
* right: use only keys from right frame, similar to a SQL right outer join;
preserve key order.
* outer: use union of keys from both frames, similar to a SQL full outer
join; sort keys lexicographically.
* inner: use intersection of keys from both frames, similar to a SQL inner
join; preserve the order of the left keys.
* cross: creates the cartesian product from both frames, preserves the order
of the left keys.
.. versionadded:: 1.2.0
on : label or list
Column or index level names to join on. These must be found in both
DataFrames. If `on` is None and not merging on indexes then this defaults
to the intersection of the columns in both DataFrames.
left_on : label or list, or array-like
Column or index level names to join on in the left DataFrame. Can also
be an array or list of arrays of the length of the left DataFrame.
These arrays are treated as if they are columns.
right_on : label or list, or array-like
Column or index level names to join on in the right DataFrame. Can also
be an array or list of arrays of the length of the right DataFrame.
These arrays are treated as if they are columns.
left_index : bool, default False
Use the index from the left DataFrame as the join key(s). If it is a
MultiIndex, the number of keys in the other DataFrame (either the index
or a number of columns) must match the number of levels.
right_index : bool, default False
Use the index from the right DataFrame as the join key. Same caveats as
left_index.
sort : bool, default False
Sort the join keys lexicographically in the result DataFrame. If False,
the order of the join keys depends on the join type (how keyword).
suffixes : list-like, default is ("_x", "_y")
A length-2 sequence where each element is optionally a string
indicating the suffix to add to overlapping column names in
`left` and `right` respectively. Pass a value of `None` instead
of a string to indicate that the column name from `left` or
`right` should be left as-is, with no suffix. At least one of the
values must not be None.
copy : bool, default True
If False, avoid copy if possible.
indicator : bool or str, default False
If True, adds a column to the output DataFrame called "_merge" with
information on the source of each row. The column can be given a different
name by providing a string argument. The column will have a Categorical
type with the value of "left_only" for observations whose merge key only
appears in the left DataFrame, "right_only" for observations
whose merge key only appears in the right DataFrame, and "both"
if the observation's merge key is found in both DataFrames.
validate : str, optional
If specified, checks if merge is of specified type.
* "one_to_one" or "1:1": check if merge keys are unique in both
left and right datasets.
* "one_to_many" or "1:m": check if merge keys are unique in left
dataset.
* "many_to_one" or "m:1": check if merge keys are unique in right
dataset.
* "many_to_many" or "m:m": allowed, but does not result in checks.
Returns
-------
DataFrame
A DataFrame of the two merged objects.
See Also
--------
merge_ordered : Merge with optional filling/interpolation.
merge_asof : Merge on nearest keys.
DataFrame.join : Similar method using indices.
Notes
-----
Support for specifying index levels as the `on`, `left_on`, and
`right_on` parameters was added in version 0.23.0
Support for merging named Series objects was added in version 0.24.0
Examples
--------
>>> df1 = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],
... 'value': [1, 2, 3, 5]})
>>> df2 = pd.DataFrame({'rkey': ['foo', 'bar', 'baz', 'foo'],
... 'value': [5, 6, 7, 8]})
>>> df1
lkey value
0 foo 1
1 bar 2
2 baz 3
3 foo 5
>>> df2
rkey value
0 foo 5
1 bar 6
2 baz 7
3 foo 8
Merge df1 and df2 on the lkey and rkey columns. The value columns have
the default suffixes, _x and _y, appended.
>>> df1.merge(df2, left_on='lkey', right_on='rkey')
lkey value_x rkey value_y
0 foo 1 foo 5
1 foo 1 foo 8
2 foo 5 foo 5
3 foo 5 foo 8
4 bar 2 bar 6
5 baz 3 baz 7
Merge DataFrames df1 and df2 with specified left and right suffixes
appended to any overlapping columns.
>>> df1.merge(df2, left_on='lkey', right_on='rkey',
... suffixes=('_left', '_right'))
lkey value_left rkey value_right
0 foo 1 foo 5
1 foo 1 foo 8
2 foo 5 foo 5
3 foo 5 foo 8
4 bar 2 bar 6
5 baz 3 baz 7
Merge DataFrames df1 and df2, but raise an exception if the DataFrames have
any overlapping columns.
>>> df1.merge(df2, left_on='lkey', right_on='rkey', suffixes=(False, False))
Traceback (most recent call last):
...
ValueError: columns overlap but no suffix specified:
Index(['value'], dtype='object')
>>> df1 = pd.DataFrame({'a': ['foo', 'bar'], 'b': [1, 2]})
>>> df2 = pd.DataFrame({'a': ['foo', 'baz'], 'c': [3, 4]})
>>> df1
a b
0 foo 1
1 bar 2
>>> df2
a c
0 foo 3
1 baz 4
>>> df1.merge(df2, how='inner', on='a')
a b c
0 foo 1 3
>>> df1.merge(df2, how='left', on='a')
a b c
0 foo 1 3.0
1 bar 2 NaN
>>> df1 = pd.DataFrame({'left': ['foo', 'bar']})
>>> df2 = pd.DataFrame({'right': [7, 8]})
>>> df1
left
0 foo
1 bar
>>> df2
right
0 7
1 8
>>> df1.merge(df2, how='cross')
left right
0 foo 7
1 foo 8
2 bar 7
3 bar 8
"""
# -----------------------------------------------------------------------
# DataFrame class
class DataFrame(NDFrame, OpsMixin):
"""
Two-dimensional, size-mutable, potentially heterogeneous tabular data.
Data structure also contains labeled axes (rows and columns).
Arithmetic operations align on both row and column labels. Can be
thought of as a dict-like container for Series objects. The primary
pandas data structure.
Parameters
----------
data : ndarray (structured or homogeneous), Iterable, dict, or DataFrame
Dict can contain Series, arrays, constants, dataclass or list-like objects. If
data is a dict, column order follows insertion-order. If a dict contains Series
which have an index defined, it is aligned by its index. This alignment also
occurs if data is a Series or a DataFrame itself. Alignment is done on
Series/DataFrame inputs.
If data is a list of dicts, column order follows insertion-order.
index : Index or array-like
Index to use for resulting frame. Will default to RangeIndex if
no indexing information part of input data and no index provided.
columns : Index or array-like
Column labels to use for resulting frame when data does not have them,
defaulting to RangeIndex(0, 1, 2, ..., n). If data contains column labels,
will perform column selection instead.
dtype : dtype, default None
Data type to force. Only a single dtype is allowed. If None, infer.
copy : bool or None, default None
Copy data from inputs.
For dict data, the default of None behaves like ``copy=True``. For DataFrame
or 2d ndarray input, the default of None behaves like ``copy=False``.
If data is a dict containing one or more Series (possibly of different dtypes),
``copy=False`` will ensure that these inputs are not copied.
.. versionchanged:: 1.3.0
See Also
--------
DataFrame.from_records : Constructor from tuples, also record arrays.
DataFrame.from_dict : From dicts of Series, arrays, or dicts.
read_csv : Read a comma-separated values (csv) file into DataFrame.
read_table : Read general delimited file into DataFrame.
read_clipboard : Read text from clipboard into DataFrame.
Notes
-----
Please reference the :ref:`User Guide <basics.dataframe>` for more information.
Examples
--------
Constructing DataFrame from a dictionary.
>>> d = {'col1': [1, 2], 'col2': [3, 4]}
>>> df = pd.DataFrame(data=d)
>>> df
col1 col2
0 1 3
1 2 4
Notice that the inferred dtype is int64.
>>> df.dtypes
col1 int64
col2 int64
dtype: object
To enforce a single dtype:
>>> df = pd.DataFrame(data=d, dtype=np.int8)
>>> df.dtypes
col1 int8
col2 int8
dtype: object
Constructing DataFrame from a dictionary including Series:
>>> d = {'col1': [0, 1, 2, 3], 'col2': pd.Series([2, 3], index=[2, 3])}
>>> pd.DataFrame(data=d, index=[0, 1, 2, 3])
col1 col2
0 0 NaN
1 1 NaN
2 2 2.0
3 3 3.0
Constructing DataFrame from numpy ndarray:
>>> df2 = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]),
... columns=['a', 'b', 'c'])
>>> df2
a b c
0 1 2 3
1 4 5 6
2 7 8 9
Constructing DataFrame from a numpy ndarray that has labeled columns:
>>> data = np.array([(1, 2, 3), (4, 5, 6), (7, 8, 9)],
... dtype=[("a", "i4"), ("b", "i4"), ("c", "i4")])
>>> df3 = pd.DataFrame(data, columns=['c', 'a'])
...
>>> df3
c a
0 3 1
1 6 4
2 9 7
Constructing DataFrame from dataclass:
>>> from dataclasses import make_dataclass
>>> Point = make_dataclass("Point", [("x", int), ("y", int)])
>>> pd.DataFrame([Point(0, 0), Point(0, 3), Point(2, 3)])
x y
0 0 0
1 0 3
2 2 3
Constructing DataFrame from Series/DataFrame:
>>> ser = pd.Series([1, 2, 3], index=["a", "b", "c"])
>>> df = pd.DataFrame(data=ser, index=["a", "c"])
>>> df
0
a 1
c 3
>>> df1 = pd.DataFrame([1, 2, 3], index=["a", "b", "c"], columns=["x"])
>>> df2 = pd.DataFrame(data=df1, index=["a", "c"])
>>> df2
x
a 1
c 3
"""
_internal_names_set = {"columns", "index"} | NDFrame._internal_names_set
_typ = "dataframe"
_HANDLED_TYPES = (Series, Index, ExtensionArray, np.ndarray)
_accessors: set[str] = {"sparse"}
_hidden_attrs: frozenset[str] = NDFrame._hidden_attrs | frozenset([])
_mgr: BlockManager | ArrayManager
@property
def _constructor(self) -> Callable[..., DataFrame]:
return DataFrame
_constructor_sliced: Callable[..., Series] = Series
# ----------------------------------------------------------------------
# Constructors
def __init__(
self,
data=None,
index: Axes | None = None,
columns: Axes | None = None,
dtype: Dtype | None = None,
copy: bool | None = None,
) -> None:
if dtype is not None:
dtype = self._validate_dtype(dtype)
if isinstance(data, DataFrame):
data = data._mgr
if not copy:
# if not copying data, ensure to still return a shallow copy
# to avoid the result sharing the same Manager
data = data.copy(deep=False)
if isinstance(data, (BlockManager, ArrayManager)):
if using_copy_on_write():
data = data.copy(deep=False)
# first check if a Manager is passed without any other arguments
# -> use fastpath (without checking Manager type)
if index is None and columns is None and dtype is None and not copy:
# GH#33357 fastpath
NDFrame.__init__(self, data)
return
manager = get_option("mode.data_manager")
# GH47215
if index is not None and isinstance(index, set):
raise ValueError("index cannot be a set")
if columns is not None and isinstance(columns, set):
raise ValueError("columns cannot be a set")
if copy is None:
if isinstance(data, dict):
# retain pre-GH#38939 default behavior
copy = True
elif (
manager == "array"
and isinstance(data, (np.ndarray, ExtensionArray))
and data.ndim == 2
):
# INFO(ArrayManager) by default copy the 2D input array to get
# contiguous 1D arrays
copy = True
elif using_copy_on_write() and not isinstance(
data, (Index, DataFrame, Series)
):
copy = True
else:
copy = False
if data is None:
index = index if index is not None else default_index(0)
columns = columns if columns is not None else default_index(0)
dtype = dtype if dtype is not None else pandas_dtype(object)
data = []
if isinstance(data, (BlockManager, ArrayManager)):
mgr = self._init_mgr(
data, axes={"index": index, "columns": columns}, dtype=dtype, copy=copy
)
elif isinstance(data, dict):
# GH#38939 de facto copy defaults to False only in non-dict cases
mgr = dict_to_mgr(data, index, columns, dtype=dtype, copy=copy, typ=manager)
elif isinstance(data, ma.MaskedArray):
from numpy.ma import mrecords
# masked recarray
if isinstance(data, mrecords.MaskedRecords):
raise TypeError(
"MaskedRecords are not supported. Pass "
"{name: data[name] for name in data.dtype.names} "
"instead"
)
# a masked array
data = sanitize_masked_array(data)
mgr = ndarray_to_mgr(
data,
index,
columns,
dtype=dtype,
copy=copy,
typ=manager,
)
elif isinstance(data, (np.ndarray, Series, Index, ExtensionArray)):
if data.dtype.names:
# i.e. numpy structured array
data = cast(np.ndarray, data)
mgr = rec_array_to_mgr(
data,
index,
columns,
dtype,
copy,
typ=manager,
)
elif getattr(data, "name", None) is not None:
# i.e. Series/Index with non-None name
_copy = copy if using_copy_on_write() else True
mgr = dict_to_mgr(
# error: Item "ndarray" of "Union[ndarray, Series, Index]" has no
# attribute "name"
{data.name: data}, # type: ignore[union-attr]
index,
columns,
dtype=dtype,
typ=manager,
copy=_copy,
)
else:
mgr = ndarray_to_mgr(
data,
index,
columns,
dtype=dtype,
copy=copy,
typ=manager,
)
# For data is list-like, or Iterable (will consume into list)
elif is_list_like(data):
if not isinstance(data, abc.Sequence):
if hasattr(data, "__array__"):
# GH#44616 big perf improvement for e.g. pytorch tensor
data = np.asarray(data)
else:
data = list(data)
if len(data) > 0:
if is_dataclass(data[0]):
data = dataclasses_to_dicts(data)
if not isinstance(data, np.ndarray) and treat_as_nested(data):
# exclude ndarray as we may have cast it a few lines above
if columns is not None:
columns = ensure_index(columns)
arrays, columns, index = nested_data_to_arrays(
# error: Argument 3 to "nested_data_to_arrays" has incompatible
# type "Optional[Collection[Any]]"; expected "Optional[Index]"
data,
columns,
index, # type: ignore[arg-type]
dtype,
)
mgr = arrays_to_mgr(
arrays,
columns,
index,
dtype=dtype,
typ=manager,
)
else:
mgr = ndarray_to_mgr(
data,
index,
columns,
dtype=dtype,
copy=copy,
typ=manager,
)
else:
mgr = dict_to_mgr(
{},
index,
columns if columns is not None else default_index(0),
dtype=dtype,
typ=manager,
)
# For data is scalar
else:
if index is None or columns is None:
raise ValueError("DataFrame constructor not properly called!")
index = ensure_index(index)
columns = ensure_index(columns)
if not dtype:
dtype, _ = infer_dtype_from_scalar(data, pandas_dtype=True)
# For data is a scalar extension dtype
if isinstance(dtype, ExtensionDtype):
# TODO(EA2D): special case not needed with 2D EAs
values = [
construct_1d_arraylike_from_scalar(data, len(index), dtype)
for _ in range(len(columns))
]
mgr = arrays_to_mgr(values, columns, index, dtype=None, typ=manager)
else:
arr2d = construct_2d_arraylike_from_scalar(
data,
len(index),
len(columns),
dtype,
copy,
)
mgr = ndarray_to_mgr(
arr2d,
index,
columns,
dtype=arr2d.dtype,
copy=False,
typ=manager,
)
# ensure correct Manager type according to settings
mgr = mgr_to_mgr(mgr, typ=manager)
NDFrame.__init__(self, mgr)
# ----------------------------------------------------------------------
def __dataframe__(
self, nan_as_null: bool = False, allow_copy: bool = True
) -> DataFrameXchg:
"""
Return the dataframe interchange object implementing the interchange protocol.
Parameters
----------
nan_as_null : bool, default False
Whether to tell the DataFrame to overwrite null values in the data
with ``NaN`` (or ``NaT``).
allow_copy : bool, default True
Whether to allow memory copying when exporting. If set to False
it would cause non-zero-copy exports to fail.
Returns
-------
DataFrame interchange object
The object which consuming library can use to ingress the dataframe.
Notes
-----
Details on the interchange protocol:
https://data-apis.org/dataframe-protocol/latest/index.html
`nan_as_null` currently has no effect; once support for nullable extension
dtypes is added, this value should be propagated to columns.
"""
from pandas.core.interchange.dataframe import PandasDataFrameXchg
return PandasDataFrameXchg(self, nan_as_null, allow_copy)
# ----------------------------------------------------------------------
@property
def axes(self) -> list[Index]:
"""
Return a list representing the axes of the DataFrame.
It has the row axis labels and column axis labels as the only members.
They are returned in that order.
Examples
--------
>>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> df.axes
[RangeIndex(start=0, stop=2, step=1), Index(['col1', 'col2'],
dtype='object')]
"""
return [self.index, self.columns]
@property
def shape(self) -> tuple[int, int]:
"""
Return a tuple representing the dimensionality of the DataFrame.
See Also
--------
ndarray.shape : Tuple of array dimensions.
Examples
--------
>>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> df.shape
(2, 2)
>>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4],
... 'col3': [5, 6]})
>>> df.shape
(2, 3)
"""
return len(self.index), len(self.columns)
@property
def _is_homogeneous_type(self) -> bool:
"""
Whether all the columns in a DataFrame have the same type.
Returns
-------
bool
See Also
--------
Index._is_homogeneous_type : Whether the object has a single
dtype.
MultiIndex._is_homogeneous_type : Whether all the levels of a
MultiIndex have the same dtype.
Examples
--------
>>> DataFrame({"A": [1, 2], "B": [3, 4]})._is_homogeneous_type
True
>>> DataFrame({"A": [1, 2], "B": [3.0, 4.0]})._is_homogeneous_type
False
Items with the same type but different sizes are considered
different types.
>>> DataFrame({
... "A": np.array([1, 2], dtype=np.int32),
... "B": np.array([1, 2], dtype=np.int64)})._is_homogeneous_type
False
"""
if isinstance(self._mgr, ArrayManager):
return len({arr.dtype for arr in self._mgr.arrays}) == 1
if self._mgr.any_extension_types:
return len({block.dtype for block in self._mgr.blocks}) == 1
else:
return not self._is_mixed_type
@property
def _can_fast_transpose(self) -> bool:
"""
Can we transpose this DataFrame without creating any new array objects.
"""
if isinstance(self._mgr, ArrayManager):
return False
blocks = self._mgr.blocks
if len(blocks) != 1:
return False
dtype = blocks[0].dtype
# TODO(EA2D) special case would be unnecessary with 2D EAs
return not is_1d_only_ea_dtype(dtype)
@property
def _values(self) -> np.ndarray | DatetimeArray | TimedeltaArray | PeriodArray:
"""
Analogue to ._values that may return a 2D ExtensionArray.
"""
mgr = self._mgr
if isinstance(mgr, ArrayManager):
if len(mgr.arrays) == 1 and not is_1d_only_ea_dtype(mgr.arrays[0].dtype):
# error: Item "ExtensionArray" of "Union[ndarray, ExtensionArray]"
# has no attribute "reshape"
return mgr.arrays[0].reshape(-1, 1) # type: ignore[union-attr]
return ensure_wrapped_if_datetimelike(self.values)
blocks = mgr.blocks
if len(blocks) != 1:
return ensure_wrapped_if_datetimelike(self.values)
arr = blocks[0].values
if arr.ndim == 1:
# non-2D ExtensionArray
return self.values
# more generally, whatever we allow in NDArrayBackedExtensionBlock
arr = cast("np.ndarray | DatetimeArray | TimedeltaArray | PeriodArray", arr)
return arr.T
# ----------------------------------------------------------------------
# Rendering Methods
def _repr_fits_vertical_(self) -> bool:
"""
Check length against max_rows.
"""
max_rows = get_option("display.max_rows")
return len(self) <= max_rows
def _repr_fits_horizontal_(self, ignore_width: bool = False) -> bool:
"""
Check if full repr fits in horizontal boundaries imposed by the display
options width and max_columns.
In case of non-interactive session, no boundaries apply.
`ignore_width` is here so ipynb+HTML output can behave the way
users expect. display.max_columns remains in effect.
GH3541, GH3573
"""
width, height = console.get_console_size()
max_columns = get_option("display.max_columns")
nb_columns = len(self.columns)
# exceed max columns
if (max_columns and nb_columns > max_columns) or (
(not ignore_width) and width and nb_columns > (width // 2)
):
return False
# used by repr_html under IPython notebook or scripts ignore terminal
# dims
if ignore_width or width is None or not console.in_interactive_session():
return True
if get_option("display.width") is not None or console.in_ipython_frontend():
# check at least the column row for excessive width
max_rows = 1
else:
max_rows = get_option("display.max_rows")
# when auto-detecting, so width=None and not in ipython front end
# check whether repr fits horizontal by actually checking
# the width of the rendered repr
buf = StringIO()
# only care about the stuff we'll actually print out
# and to_string on entire frame may be expensive
d = self
if max_rows is not None: # unlimited rows
# min of two, where one may be None
d = d.iloc[: min(max_rows, len(d))]
else:
return True
d.to_string(buf=buf)
value = buf.getvalue()
repr_width = max(len(line) for line in value.split("\n"))
return repr_width < width
def _info_repr(self) -> bool:
"""
True if the repr should show the info view.
"""
info_repr_option = get_option("display.large_repr") == "info"
return info_repr_option and not (
self._repr_fits_horizontal_() and self._repr_fits_vertical_()
)
def __repr__(self) -> str:
"""
Return a string representation for a particular DataFrame.
"""
if self._info_repr():
buf = StringIO()
self.info(buf=buf)
return buf.getvalue()
repr_params = fmt.get_dataframe_repr_params()
return self.to_string(**repr_params)
def _repr_html_(self) -> str | None:
"""
Return a html representation for a particular DataFrame.
Mainly for IPython notebook.
"""
if self._info_repr():
buf = StringIO()
self.info(buf=buf)
# need to escape the <class>, should be the first line.
val = buf.getvalue().replace("<", r"&lt;", 1)
val = val.replace(">", r"&gt;", 1)
return f"<pre>{val}</pre>"
if get_option("display.notebook_repr_html"):
max_rows = get_option("display.max_rows")
min_rows = get_option("display.min_rows")
max_cols = get_option("display.max_columns")
show_dimensions = get_option("display.show_dimensions")
formatter = fmt.DataFrameFormatter(
self,
columns=None,
col_space=None,
na_rep="NaN",
formatters=None,
float_format=None,
sparsify=None,
justify=None,
index_names=True,
header=True,
index=True,
bold_rows=True,
escape=True,
max_rows=max_rows,
min_rows=min_rows,
max_cols=max_cols,
show_dimensions=show_dimensions,
decimal=".",
)
return fmt.DataFrameRenderer(formatter).to_html(notebook=True)
else:
return None
@overload
def to_string(
self,
buf: None = ...,
columns: Sequence[str] | None = ...,
col_space: int | list[int] | dict[Hashable, int] | None = ...,
header: bool | Sequence[str] = ...,
index: bool = ...,
na_rep: str = ...,
formatters: fmt.FormattersType | None = ...,
float_format: fmt.FloatFormatType | None = ...,
sparsify: bool | None = ...,
index_names: bool = ...,
justify: str | None = ...,
max_rows: int | None = ...,
max_cols: int | None = ...,
show_dimensions: bool = ...,
decimal: str = ...,
line_width: int | None = ...,
min_rows: int | None = ...,
max_colwidth: int | None = ...,
encoding: str | None = ...,
) -> str:
...
@overload
def to_string(
self,
buf: FilePath | WriteBuffer[str],
columns: Sequence[str] | None = ...,
col_space: int | list[int] | dict[Hashable, int] | None = ...,
header: bool | Sequence[str] = ...,
index: bool = ...,
na_rep: str = ...,
formatters: fmt.FormattersType | None = ...,
float_format: fmt.FloatFormatType | None = ...,
sparsify: bool | None = ...,
index_names: bool = ...,
justify: str | None = ...,
max_rows: int | None = ...,
max_cols: int | None = ...,
show_dimensions: bool = ...,
decimal: str = ...,
line_width: int | None = ...,
min_rows: int | None = ...,
max_colwidth: int | None = ...,
encoding: str | None = ...,
) -> None:
...
@Substitution(
header_type="bool or sequence of str",
header="Write out the column names. If a list of strings "
"is given, it is assumed to be aliases for the "
"column names",
col_space_type="int, list or dict of int",
col_space="The minimum width of each column. If a list of ints is given "
"every integers corresponds with one column. If a dict is given, the key "
"references the column, while the value defines the space to use.",
)
@Substitution(shared_params=fmt.common_docstring, returns=fmt.return_docstring)
def to_string(
self,
buf: FilePath | WriteBuffer[str] | None = None,
columns: Sequence[str] | None = None,
col_space: int | list[int] | dict[Hashable, int] | None = None,
header: bool | Sequence[str] = True,
index: bool = True,
na_rep: str = "NaN",
formatters: fmt.FormattersType | None = None,
float_format: fmt.FloatFormatType | None = None,
sparsify: bool | None = None,
index_names: bool = True,
justify: str | None = None,
max_rows: int | None = None,
max_cols: int | None = None,
show_dimensions: bool = False,
decimal: str = ".",
line_width: int | None = None,
min_rows: int | None = None,
max_colwidth: int | None = None,
encoding: str | None = None,
) -> str | None:
"""
Render a DataFrame to a console-friendly tabular output.
%(shared_params)s
line_width : int, optional
Width to wrap a line in characters.
min_rows : int, optional
The number of rows to display in the console in a truncated repr
(when number of rows is above `max_rows`).
max_colwidth : int, optional
Max width to truncate each column in characters. By default, no limit.
encoding : str, default "utf-8"
Set character encoding.
%(returns)s
See Also
--------
to_html : Convert DataFrame to HTML.
Examples
--------
>>> d = {'col1': [1, 2, 3], 'col2': [4, 5, 6]}
>>> df = pd.DataFrame(d)
>>> print(df.to_string())
col1 col2
0 1 4
1 2 5
2 3 6
"""
from pandas import option_context
with option_context("display.max_colwidth", max_colwidth):
formatter = fmt.DataFrameFormatter(
self,
columns=columns,
col_space=col_space,
na_rep=na_rep,
formatters=formatters,
float_format=float_format,
sparsify=sparsify,
justify=justify,
index_names=index_names,
header=header,
index=index,
min_rows=min_rows,
max_rows=max_rows,
max_cols=max_cols,
show_dimensions=show_dimensions,
decimal=decimal,
)
return fmt.DataFrameRenderer(formatter).to_string(
buf=buf,
encoding=encoding,
line_width=line_width,
)
# ----------------------------------------------------------------------
@property
def style(self) -> Styler:
"""
Returns a Styler object.
Contains methods for building a styled HTML representation of the DataFrame.
See Also
--------
io.formats.style.Styler : Helps style a DataFrame or Series according to the
data with HTML and CSS.
"""
from pandas.io.formats.style import Styler
return Styler(self)
_shared_docs[
"items"
] = r"""
Iterate over (column name, Series) pairs.
Iterates over the DataFrame columns, returning a tuple with
the column name and the content as a Series.
Yields
------
label : object
The column names for the DataFrame being iterated over.
content : Series
The column entries belonging to each label, as a Series.
See Also
--------
DataFrame.iterrows : Iterate over DataFrame rows as
(index, Series) pairs.
DataFrame.itertuples : Iterate over DataFrame rows as namedtuples
of the values.
Examples
--------
>>> df = pd.DataFrame({'species': ['bear', 'bear', 'marsupial'],
... 'population': [1864, 22000, 80000]},
... index=['panda', 'polar', 'koala'])
>>> df
species population
panda bear 1864
polar bear 22000
koala marsupial 80000
>>> for label, content in df.items():
... print(f'label: {label}')
... print(f'content: {content}', sep='\n')
...
label: species
content:
panda bear
polar bear
koala marsupial
Name: species, dtype: object
label: population
content:
panda 1864
polar 22000
koala 80000
Name: population, dtype: int64
"""
@Appender(_shared_docs["items"])
def items(self) -> Iterable[tuple[Hashable, Series]]:
if self.columns.is_unique and hasattr(self, "_item_cache"):
for k in self.columns:
yield k, self._get_item_cache(k)
else:
for i, k in enumerate(self.columns):
yield k, self._ixs(i, axis=1)
def iterrows(self) -> Iterable[tuple[Hashable, Series]]:
"""
Iterate over DataFrame rows as (index, Series) pairs.
Yields
------
index : label or tuple of label
The index of the row. A tuple for a `MultiIndex`.
data : Series
The data of the row as a Series.
See Also
--------
DataFrame.itertuples : Iterate over DataFrame rows as namedtuples of the values.
DataFrame.items : Iterate over (column name, Series) pairs.
Notes
-----
1. Because ``iterrows`` returns a Series for each row,
it does **not** preserve dtypes across the rows (dtypes are
preserved across columns for DataFrames). For example,
>>> df = pd.DataFrame([[1, 1.5]], columns=['int', 'float'])
>>> row = next(df.iterrows())[1]
>>> row
int 1.0
float 1.5
Name: 0, dtype: float64
>>> print(row['int'].dtype)
float64
>>> print(df['int'].dtype)
int64
To preserve dtypes while iterating over the rows, it is better
to use :meth:`itertuples` which returns namedtuples of the values
and which is generally faster than ``iterrows``.
2. You should **never modify** something you are iterating over.
This is not guaranteed to work in all cases. Depending on the
data types, the iterator returns a copy and not a view, and writing
to it will have no effect.
"""
columns = self.columns
klass = self._constructor_sliced
using_cow = using_copy_on_write()
for k, v in zip(self.index, self.values):
s = klass(v, index=columns, name=k).__finalize__(self)
if using_cow and self._mgr.is_single_block:
s._mgr.add_references(self._mgr) # type: ignore[arg-type]
yield k, s
def itertuples(
self, index: bool = True, name: str | None = "Pandas"
) -> Iterable[tuple[Any, ...]]:
"""
Iterate over DataFrame rows as namedtuples.
Parameters
----------
index : bool, default True
If True, return the index as the first element of the tuple.
name : str or None, default "Pandas"
The name of the returned namedtuples or None to return regular
tuples.
Returns
-------
iterator
An object to iterate over namedtuples for each row in the
DataFrame with the first field possibly being the index and
following fields being the column values.
See Also
--------
DataFrame.iterrows : Iterate over DataFrame rows as (index, Series)
pairs.
DataFrame.items : Iterate over (column name, Series) pairs.
Notes
-----
The column names will be renamed to positional names if they are
invalid Python identifiers, repeated, or start with an underscore.
Examples
--------
>>> df = pd.DataFrame({'num_legs': [4, 2], 'num_wings': [0, 2]},
... index=['dog', 'hawk'])
>>> df
num_legs num_wings
dog 4 0
hawk 2 2
>>> for row in df.itertuples():
... print(row)
...
Pandas(Index='dog', num_legs=4, num_wings=0)
Pandas(Index='hawk', num_legs=2, num_wings=2)
By setting the `index` parameter to False we can remove the index
as the first element of the tuple:
>>> for row in df.itertuples(index=False):
... print(row)
...
Pandas(num_legs=4, num_wings=0)
Pandas(num_legs=2, num_wings=2)
With the `name` parameter set we set a custom name for the yielded
namedtuples:
>>> for row in df.itertuples(name='Animal'):
... print(row)
...
Animal(Index='dog', num_legs=4, num_wings=0)
Animal(Index='hawk', num_legs=2, num_wings=2)
"""
arrays = []
fields = list(self.columns)
if index:
arrays.append(self.index)
fields.insert(0, "Index")
# use integer indexing because of possible duplicate column names
arrays.extend(self.iloc[:, k] for k in range(len(self.columns)))
if name is not None:
# https://github.com/python/mypy/issues/9046
# error: namedtuple() expects a string literal as the first argument
itertuple = collections.namedtuple( # type: ignore[misc]
name, fields, rename=True
)
return map(itertuple._make, zip(*arrays))
# fallback to regular tuples
return zip(*arrays)
def __len__(self) -> int:
"""
Returns length of info axis, but here we use the index.
"""
return len(self.index)
@overload
def dot(self, other: Series) -> Series:
...
@overload
def dot(self, other: DataFrame | Index | ArrayLike) -> DataFrame:
...
def dot(self, other: AnyArrayLike | DataFrame) -> DataFrame | Series:
"""
Compute the matrix multiplication between the DataFrame and other.
This method computes the matrix product between the DataFrame and the
values of an other Series, DataFrame or a numpy array.
It can also be called using ``self @ other`` in Python >= 3.5.
Parameters
----------
other : Series, DataFrame or array-like
The other object to compute the matrix product with.
Returns
-------
Series or DataFrame
If other is a Series, return the matrix product between self and
other as a Series. If other is a DataFrame or a numpy.array, return
the matrix product of self and other in a DataFrame of a np.array.
See Also
--------
Series.dot: Similar method for Series.
Notes
-----
The dimensions of DataFrame and other must be compatible in order to
compute the matrix multiplication. In addition, the column names of
DataFrame and the index of other must contain the same values, as they
will be aligned prior to the multiplication.
The dot method for Series computes the inner product, instead of the
matrix product here.
Examples
--------
Here we multiply a DataFrame with a Series.
>>> df = pd.DataFrame([[0, 1, -2, -1], [1, 1, 1, 1]])
>>> s = pd.Series([1, 1, 2, 1])
>>> df.dot(s)
0 -4
1 5
dtype: int64
Here we multiply a DataFrame with another DataFrame.
>>> other = pd.DataFrame([[0, 1], [1, 2], [-1, -1], [2, 0]])
>>> df.dot(other)
0 1
0 1 4
1 2 2
Note that the dot method give the same result as @
>>> df @ other
0 1
0 1 4
1 2 2
The dot method works also if other is an np.array.
>>> arr = np.array([[0, 1], [1, 2], [-1, -1], [2, 0]])
>>> df.dot(arr)
0 1
0 1 4
1 2 2
Note how shuffling of the objects does not change the result.
>>> s2 = s.reindex([1, 0, 2, 3])
>>> df.dot(s2)
0 -4
1 5
dtype: int64
"""
if isinstance(other, (Series, DataFrame)):
common = self.columns.union(other.index)
if len(common) > len(self.columns) or len(common) > len(other.index):
raise ValueError("matrices are not aligned")
left = self.reindex(columns=common, copy=False)
right = other.reindex(index=common, copy=False)
lvals = left.values
rvals = right._values
else:
left = self
lvals = self.values
rvals = np.asarray(other)
if lvals.shape[1] != rvals.shape[0]:
raise ValueError(
f"Dot product shape mismatch, {lvals.shape} vs {rvals.shape}"
)
if isinstance(other, DataFrame):
return self._constructor(
np.dot(lvals, rvals),
index=left.index,
columns=other.columns,
copy=False,
)
elif isinstance(other, Series):
return self._constructor_sliced(
np.dot(lvals, rvals), index=left.index, copy=False
)
elif isinstance(rvals, (np.ndarray, Index)):
result = np.dot(lvals, rvals)
if result.ndim == 2:
return self._constructor(result, index=left.index, copy=False)
else:
return self._constructor_sliced(result, index=left.index, copy=False)
else: # pragma: no cover
raise TypeError(f"unsupported type: {type(other)}")
@overload
def __matmul__(self, other: Series) -> Series:
...
@overload
def __matmul__(self, other: AnyArrayLike | DataFrame) -> DataFrame | Series:
...
def __matmul__(self, other: AnyArrayLike | DataFrame) -> DataFrame | Series:
"""
Matrix multiplication using binary `@` operator in Python>=3.5.
"""
return self.dot(other)
def __rmatmul__(self, other) -> DataFrame:
"""
Matrix multiplication using binary `@` operator in Python>=3.5.
"""
try:
return self.T.dot(np.transpose(other)).T
except ValueError as err:
if "shape mismatch" not in str(err):
raise
# GH#21581 give exception message for original shapes
msg = f"shapes {np.shape(other)} and {self.shape} not aligned"
raise ValueError(msg) from err
# ----------------------------------------------------------------------
# IO methods (to / from other formats)
@classmethod
def from_dict(
cls,
data: dict,
orient: str = "columns",
dtype: Dtype | None = None,
columns: Axes | None = None,
) -> DataFrame:
"""
Construct DataFrame from dict of array-like or dicts.
Creates DataFrame object from dictionary by columns or by index
allowing dtype specification.
Parameters
----------
data : dict
Of the form {field : array-like} or {field : dict}.
orient : {'columns', 'index', 'tight'}, default 'columns'
The "orientation" of the data. If the keys of the passed dict
should be the columns of the resulting DataFrame, pass 'columns'
(default). Otherwise if the keys should be rows, pass 'index'.
If 'tight', assume a dict with keys ['index', 'columns', 'data',
'index_names', 'column_names'].
.. versionadded:: 1.4.0
'tight' as an allowed value for the ``orient`` argument
dtype : dtype, default None
Data type to force after DataFrame construction, otherwise infer.
columns : list, default None
Column labels to use when ``orient='index'``. Raises a ValueError
if used with ``orient='columns'`` or ``orient='tight'``.
Returns
-------
DataFrame
See Also
--------
DataFrame.from_records : DataFrame from structured ndarray, sequence
of tuples or dicts, or DataFrame.
DataFrame : DataFrame object creation using constructor.
DataFrame.to_dict : Convert the DataFrame to a dictionary.
Examples
--------
By default the keys of the dict become the DataFrame columns:
>>> data = {'col_1': [3, 2, 1, 0], 'col_2': ['a', 'b', 'c', 'd']}
>>> pd.DataFrame.from_dict(data)
col_1 col_2
0 3 a
1 2 b
2 1 c
3 0 d
Specify ``orient='index'`` to create the DataFrame using dictionary
keys as rows:
>>> data = {'row_1': [3, 2, 1, 0], 'row_2': ['a', 'b', 'c', 'd']}
>>> pd.DataFrame.from_dict(data, orient='index')
0 1 2 3
row_1 3 2 1 0
row_2 a b c d
When using the 'index' orientation, the column names can be
specified manually:
>>> pd.DataFrame.from_dict(data, orient='index',
... columns=['A', 'B', 'C', 'D'])
A B C D
row_1 3 2 1 0
row_2 a b c d
Specify ``orient='tight'`` to create the DataFrame using a 'tight'
format:
>>> data = {'index': [('a', 'b'), ('a', 'c')],
... 'columns': [('x', 1), ('y', 2)],
... 'data': [[1, 3], [2, 4]],
... 'index_names': ['n1', 'n2'],
... 'column_names': ['z1', 'z2']}
>>> pd.DataFrame.from_dict(data, orient='tight')
z1 x y
z2 1 2
n1 n2
a b 1 3
c 2 4
"""
index = None
orient = orient.lower()
if orient == "index":
if len(data) > 0:
# TODO speed up Series case
if isinstance(list(data.values())[0], (Series, dict)):
data = _from_nested_dict(data)
else:
index = list(data.keys())
# error: Incompatible types in assignment (expression has type
# "List[Any]", variable has type "Dict[Any, Any]")
data = list(data.values()) # type: ignore[assignment]
elif orient in ("columns", "tight"):
if columns is not None:
raise ValueError(f"cannot use columns parameter with orient='{orient}'")
else: # pragma: no cover
raise ValueError(
f"Expected 'index', 'columns' or 'tight' for orient parameter. "
f"Got '{orient}' instead"
)
if orient != "tight":
return cls(data, index=index, columns=columns, dtype=dtype)
else:
realdata = data["data"]
def create_index(indexlist, namelist):
index: Index
if len(namelist) > 1:
index = MultiIndex.from_tuples(indexlist, names=namelist)
else:
index = Index(indexlist, name=namelist[0])
return index
index = create_index(data["index"], data["index_names"])
columns = create_index(data["columns"], data["column_names"])
return cls(realdata, index=index, columns=columns, dtype=dtype)
def to_numpy(
self,
dtype: npt.DTypeLike | None = None,
copy: bool = False,
na_value: object = lib.no_default,
) -> np.ndarray:
"""
Convert the DataFrame to a NumPy array.
By default, the dtype of the returned array will be the common NumPy
dtype of all types in the DataFrame. For example, if the dtypes are
``float16`` and ``float32``, the results dtype will be ``float32``.
This may require copying data and coercing values, which may be
expensive.
Parameters
----------
dtype : str or numpy.dtype, optional
The dtype to pass to :meth:`numpy.asarray`.
copy : bool, default False
Whether to ensure that the returned value is not a view on
another array. Note that ``copy=False`` does not *ensure* that
``to_numpy()`` is no-copy. Rather, ``copy=True`` ensure that
a copy is made, even if not strictly necessary.
na_value : Any, optional
The value to use for missing values. The default value depends
on `dtype` and the dtypes of the DataFrame columns.
.. versionadded:: 1.1.0
Returns
-------
numpy.ndarray
See Also
--------
Series.to_numpy : Similar method for Series.
Examples
--------
>>> pd.DataFrame({"A": [1, 2], "B": [3, 4]}).to_numpy()
array([[1, 3],
[2, 4]])
With heterogeneous data, the lowest common type will have to
be used.
>>> df = pd.DataFrame({"A": [1, 2], "B": [3.0, 4.5]})
>>> df.to_numpy()
array([[1. , 3. ],
[2. , 4.5]])
For a mix of numeric and non-numeric types, the output array will
have object dtype.
>>> df['C'] = pd.date_range('2000', periods=2)
>>> df.to_numpy()
array([[1, 3.0, Timestamp('2000-01-01 00:00:00')],
[2, 4.5, Timestamp('2000-01-02 00:00:00')]], dtype=object)
"""
if dtype is not None:
dtype = np.dtype(dtype)
result = self._mgr.as_array(dtype=dtype, copy=copy, na_value=na_value)
if result.dtype is not dtype:
result = np.array(result, dtype=dtype, copy=False)
return result
def _create_data_for_split_and_tight_to_dict(
self, are_all_object_dtype_cols: bool, object_dtype_indices: list[int]
) -> list:
"""
Simple helper method to create data for to ``to_dict(orient="split")`` and
``to_dict(orient="tight")`` to create the main output data
"""
if are_all_object_dtype_cols:
data = [
list(map(maybe_box_native, t))
for t in self.itertuples(index=False, name=None)
]
else:
data = [list(t) for t in self.itertuples(index=False, name=None)]
if object_dtype_indices:
# If we have object_dtype_cols, apply maybe_box_naive after list
# comprehension for perf
for row in data:
for i in object_dtype_indices:
row[i] = maybe_box_native(row[i])
return data
@overload
def to_dict(
self,
orient: Literal["dict", "list", "series", "split", "tight", "index"] = ...,
into: type[dict] = ...,
) -> dict:
...
@overload
def to_dict(self, orient: Literal["records"], into: type[dict] = ...) -> list[dict]:
...
def to_dict(
self,
orient: Literal[
"dict", "list", "series", "split", "tight", "records", "index"
] = "dict",
into: type[dict] = dict,
index: bool = True,
) -> dict | list[dict]:
"""
Convert the DataFrame to a dictionary.
The type of the key-value pairs can be customized with the parameters
(see below).
Parameters
----------
orient : str {'dict', 'list', 'series', 'split', 'tight', 'records', 'index'}
Determines the type of the values of the dictionary.
- 'dict' (default) : dict like {column -> {index -> value}}
- 'list' : dict like {column -> [values]}
- 'series' : dict like {column -> Series(values)}
- 'split' : dict like
{'index' -> [index], 'columns' -> [columns], 'data' -> [values]}
- 'tight' : dict like
{'index' -> [index], 'columns' -> [columns], 'data' -> [values],
'index_names' -> [index.names], 'column_names' -> [column.names]}
- 'records' : list like
[{column -> value}, ... , {column -> value}]
- 'index' : dict like {index -> {column -> value}}
.. versionadded:: 1.4.0
'tight' as an allowed value for the ``orient`` argument
into : class, default dict
The collections.abc.Mapping subclass used for all Mappings
in the return value. Can be the actual class or an empty
instance of the mapping type you want. If you want a
collections.defaultdict, you must pass it initialized.
index : bool, default True
Whether to include the index item (and index_names item if `orient`
is 'tight') in the returned dictionary. Can only be ``False``
when `orient` is 'split' or 'tight'.
.. versionadded:: 2.0.0
Returns
-------
dict, list or collections.abc.Mapping
Return a collections.abc.Mapping object representing the DataFrame.
The resulting transformation depends on the `orient` parameter.
See Also
--------
DataFrame.from_dict: Create a DataFrame from a dictionary.
DataFrame.to_json: Convert a DataFrame to JSON format.
Examples
--------
>>> df = pd.DataFrame({'col1': [1, 2],
... 'col2': [0.5, 0.75]},
... index=['row1', 'row2'])
>>> df
col1 col2
row1 1 0.50
row2 2 0.75
>>> df.to_dict()
{'col1': {'row1': 1, 'row2': 2}, 'col2': {'row1': 0.5, 'row2': 0.75}}
You can specify the return orientation.
>>> df.to_dict('series')
{'col1': row1 1
row2 2
Name: col1, dtype: int64,
'col2': row1 0.50
row2 0.75
Name: col2, dtype: float64}
>>> df.to_dict('split')
{'index': ['row1', 'row2'], 'columns': ['col1', 'col2'],
'data': [[1, 0.5], [2, 0.75]]}
>>> df.to_dict('records')
[{'col1': 1, 'col2': 0.5}, {'col1': 2, 'col2': 0.75}]
>>> df.to_dict('index')
{'row1': {'col1': 1, 'col2': 0.5}, 'row2': {'col1': 2, 'col2': 0.75}}
>>> df.to_dict('tight')
{'index': ['row1', 'row2'], 'columns': ['col1', 'col2'],
'data': [[1, 0.5], [2, 0.75]], 'index_names': [None], 'column_names': [None]}
You can also specify the mapping type.
>>> from collections import OrderedDict, defaultdict
>>> df.to_dict(into=OrderedDict)
OrderedDict([('col1', OrderedDict([('row1', 1), ('row2', 2)])),
('col2', OrderedDict([('row1', 0.5), ('row2', 0.75)]))])
If you want a `defaultdict`, you need to initialize it:
>>> dd = defaultdict(list)
>>> df.to_dict('records', into=dd)
[defaultdict(<class 'list'>, {'col1': 1, 'col2': 0.5}),
defaultdict(<class 'list'>, {'col1': 2, 'col2': 0.75})]
"""
from pandas.core.methods.to_dict import to_dict
return to_dict(self, orient, into, index)
def to_gbq(
self,
destination_table: str,
project_id: str | None = None,
chunksize: int | None = None,
reauth: bool = False,
if_exists: str = "fail",
auth_local_webserver: bool = True,
table_schema: list[dict[str, str]] | None = None,
location: str | None = None,
progress_bar: bool = True,
credentials=None,
) -> None:
"""
Write a DataFrame to a Google BigQuery table.
This function requires the `pandas-gbq package
<https://pandas-gbq.readthedocs.io>`__.
See the `How to authenticate with Google BigQuery
<https://pandas-gbq.readthedocs.io/en/latest/howto/authentication.html>`__
guide for authentication instructions.
Parameters
----------
destination_table : str
Name of table to be written, in the form ``dataset.tablename``.
project_id : str, optional
Google BigQuery Account project ID. Optional when available from
the environment.
chunksize : int, optional
Number of rows to be inserted in each chunk from the dataframe.
Set to ``None`` to load the whole dataframe at once.
reauth : bool, default False
Force Google BigQuery to re-authenticate the user. This is useful
if multiple accounts are used.
if_exists : str, default 'fail'
Behavior when the destination table exists. Value can be one of:
``'fail'``
If table exists raise pandas_gbq.gbq.TableCreationError.
``'replace'``
If table exists, drop it, recreate it, and insert data.
``'append'``
If table exists, insert data. Create if does not exist.
auth_local_webserver : bool, default True
Use the `local webserver flow`_ instead of the `console flow`_
when getting user credentials.
.. _local webserver flow:
https://google-auth-oauthlib.readthedocs.io/en/latest/reference/google_auth_oauthlib.flow.html#google_auth_oauthlib.flow.InstalledAppFlow.run_local_server
.. _console flow:
https://google-auth-oauthlib.readthedocs.io/en/latest/reference/google_auth_oauthlib.flow.html#google_auth_oauthlib.flow.InstalledAppFlow.run_console
*New in version 0.2.0 of pandas-gbq*.
.. versionchanged:: 1.5.0
Default value is changed to ``True``. Google has deprecated the
``auth_local_webserver = False`` `"out of band" (copy-paste)
flow
<https://developers.googleblog.com/2022/02/making-oauth-flows-safer.html?m=1#disallowed-oob>`_.
table_schema : list of dicts, optional
List of BigQuery table fields to which according DataFrame
columns conform to, e.g. ``[{'name': 'col1', 'type':
'STRING'},...]``. If schema is not provided, it will be
generated according to dtypes of DataFrame columns. See
BigQuery API documentation on available names of a field.
*New in version 0.3.1 of pandas-gbq*.
location : str, optional
Location where the load job should run. See the `BigQuery locations
documentation
<https://cloud.google.com/bigquery/docs/dataset-locations>`__ for a
list of available locations. The location must match that of the
target dataset.
*New in version 0.5.0 of pandas-gbq*.
progress_bar : bool, default True
Use the library `tqdm` to show the progress bar for the upload,
chunk by chunk.
*New in version 0.5.0 of pandas-gbq*.
credentials : google.auth.credentials.Credentials, optional
Credentials for accessing Google APIs. Use this parameter to
override default credentials, such as to use Compute Engine
:class:`google.auth.compute_engine.Credentials` or Service
Account :class:`google.oauth2.service_account.Credentials`
directly.
*New in version 0.8.0 of pandas-gbq*.
See Also
--------
pandas_gbq.to_gbq : This function in the pandas-gbq library.
read_gbq : Read a DataFrame from Google BigQuery.
"""
from pandas.io import gbq
gbq.to_gbq(
self,
destination_table,
project_id=project_id,
chunksize=chunksize,
reauth=reauth,
if_exists=if_exists,
auth_local_webserver=auth_local_webserver,
table_schema=table_schema,
location=location,
progress_bar=progress_bar,
credentials=credentials,
)
@classmethod
def from_records(
cls,
data,
index=None,
exclude=None,
columns=None,
coerce_float: bool = False,
nrows: int | None = None,
) -> DataFrame:
"""
Convert structured or record ndarray to DataFrame.
Creates a DataFrame object from a structured ndarray, sequence of
tuples or dicts, or DataFrame.
Parameters
----------
data : structured ndarray, sequence of tuples or dicts, or DataFrame
Structured input data.
index : str, list of fields, array-like
Field of array to use as the index, alternately a specific set of
input labels to use.
exclude : sequence, default None
Columns or fields to exclude.
columns : sequence, default None
Column names to use. If the passed data do not have names
associated with them, this argument provides names for the
columns. Otherwise this argument indicates the order of the columns
in the result (any names not found in the data will become all-NA
columns).
coerce_float : bool, default False
Attempt to convert values of non-string, non-numeric objects (like
decimal.Decimal) to floating point, useful for SQL result sets.
nrows : int, default None
Number of rows to read if data is an iterator.
Returns
-------
DataFrame
See Also
--------
DataFrame.from_dict : DataFrame from dict of array-like or dicts.
DataFrame : DataFrame object creation using constructor.
Examples
--------
Data can be provided as a structured ndarray:
>>> data = np.array([(3, 'a'), (2, 'b'), (1, 'c'), (0, 'd')],
... dtype=[('col_1', 'i4'), ('col_2', 'U1')])
>>> pd.DataFrame.from_records(data)
col_1 col_2
0 3 a
1 2 b
2 1 c
3 0 d
Data can be provided as a list of dicts:
>>> data = [{'col_1': 3, 'col_2': 'a'},
... {'col_1': 2, 'col_2': 'b'},
... {'col_1': 1, 'col_2': 'c'},
... {'col_1': 0, 'col_2': 'd'}]
>>> pd.DataFrame.from_records(data)
col_1 col_2
0 3 a
1 2 b
2 1 c
3 0 d
Data can be provided as a list of tuples with corresponding columns:
>>> data = [(3, 'a'), (2, 'b'), (1, 'c'), (0, 'd')]
>>> pd.DataFrame.from_records(data, columns=['col_1', 'col_2'])
col_1 col_2
0 3 a
1 2 b
2 1 c
3 0 d
"""
if isinstance(data, DataFrame):
if columns is not None:
if is_scalar(columns):
columns = [columns]
data = data[columns]
if index is not None:
data = data.set_index(index)
if exclude is not None:
data = data.drop(columns=exclude)
return data.copy(deep=False)
result_index = None
# Make a copy of the input columns so we can modify it
if columns is not None:
columns = ensure_index(columns)
def maybe_reorder(
arrays: list[ArrayLike], arr_columns: Index, columns: Index, index
) -> tuple[list[ArrayLike], Index, Index | None]:
"""
If our desired 'columns' do not match the data's pre-existing 'arr_columns',
we re-order our arrays. This is like a pre-emptive (cheap) reindex.
"""
if len(arrays):
length = len(arrays[0])
else:
length = 0
result_index = None
if len(arrays) == 0 and index is None and length == 0:
result_index = default_index(0)
arrays, arr_columns = reorder_arrays(arrays, arr_columns, columns, length)
return arrays, arr_columns, result_index
if is_iterator(data):
if nrows == 0:
return cls()
try:
first_row = next(data)
except StopIteration:
return cls(index=index, columns=columns)
dtype = None
if hasattr(first_row, "dtype") and first_row.dtype.names:
dtype = first_row.dtype
values = [first_row]
if nrows is None:
values += data
else:
values.extend(itertools.islice(data, nrows - 1))
if dtype is not None:
data = np.array(values, dtype=dtype)
else:
data = values
if isinstance(data, dict):
if columns is None:
columns = arr_columns = ensure_index(sorted(data))
arrays = [data[k] for k in columns]
else:
arrays = []
arr_columns_list = []
for k, v in data.items():
if k in columns:
arr_columns_list.append(k)
arrays.append(v)
arr_columns = Index(arr_columns_list)
arrays, arr_columns, result_index = maybe_reorder(
arrays, arr_columns, columns, index
)
elif isinstance(data, (np.ndarray, DataFrame)):
arrays, columns = to_arrays(data, columns)
arr_columns = columns
else:
arrays, arr_columns = to_arrays(data, columns)
if coerce_float:
for i, arr in enumerate(arrays):
if arr.dtype == object:
# error: Argument 1 to "maybe_convert_objects" has
# incompatible type "Union[ExtensionArray, ndarray]";
# expected "ndarray"
arrays[i] = lib.maybe_convert_objects(
arr, # type: ignore[arg-type]
try_float=True,
)
arr_columns = ensure_index(arr_columns)
if columns is None:
columns = arr_columns
else:
arrays, arr_columns, result_index = maybe_reorder(
arrays, arr_columns, columns, index
)
if exclude is None:
exclude = set()
else:
exclude = set(exclude)
if index is not None:
if isinstance(index, str) or not hasattr(index, "__iter__"):
i = columns.get_loc(index)
exclude.add(index)
if len(arrays) > 0:
result_index = Index(arrays[i], name=index)
else:
result_index = Index([], name=index)
else:
try:
index_data = [arrays[arr_columns.get_loc(field)] for field in index]
except (KeyError, TypeError):
# raised by get_loc, see GH#29258
result_index = index
else:
result_index = ensure_index_from_sequences(index_data, names=index)
exclude.update(index)
if any(exclude):
arr_exclude = [x for x in exclude if x in arr_columns]
to_remove = [arr_columns.get_loc(col) for col in arr_exclude]
arrays = [v for i, v in enumerate(arrays) if i not in to_remove]
columns = columns.drop(exclude)
manager = get_option("mode.data_manager")
mgr = arrays_to_mgr(arrays, columns, result_index, typ=manager)
return cls(mgr)
def to_records(
self, index: bool = True, column_dtypes=None, index_dtypes=None
) -> np.recarray:
"""
Convert DataFrame to a NumPy record array.
Index will be included as the first field of the record array if
requested.
Parameters
----------
index : bool, default True
Include index in resulting record array, stored in 'index'
field or using the index label, if set.
column_dtypes : str, type, dict, default None
If a string or type, the data type to store all columns. If
a dictionary, a mapping of column names and indices (zero-indexed)
to specific data types.
index_dtypes : str, type, dict, default None
If a string or type, the data type to store all index levels. If
a dictionary, a mapping of index level names and indices
(zero-indexed) to specific data types.
This mapping is applied only if `index=True`.
Returns
-------
numpy.recarray
NumPy ndarray with the DataFrame labels as fields and each row
of the DataFrame as entries.
See Also
--------
DataFrame.from_records: Convert structured or record ndarray
to DataFrame.
numpy.recarray: An ndarray that allows field access using
attributes, analogous to typed columns in a
spreadsheet.
Examples
--------
>>> df = pd.DataFrame({'A': [1, 2], 'B': [0.5, 0.75]},
... index=['a', 'b'])
>>> df
A B
a 1 0.50
b 2 0.75
>>> df.to_records()
rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
dtype=[('index', 'O'), ('A', '<i8'), ('B', '<f8')])
If the DataFrame index has no label then the recarray field name
is set to 'index'. If the index has a label then this is used as the
field name:
>>> df.index = df.index.rename("I")
>>> df.to_records()
rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
dtype=[('I', 'O'), ('A', '<i8'), ('B', '<f8')])
The index can be excluded from the record array:
>>> df.to_records(index=False)
rec.array([(1, 0.5 ), (2, 0.75)],
dtype=[('A', '<i8'), ('B', '<f8')])
Data types can be specified for the columns:
>>> df.to_records(column_dtypes={"A": "int32"})
rec.array([('a', 1, 0.5 ), ('b', 2, 0.75)],
dtype=[('I', 'O'), ('A', '<i4'), ('B', '<f8')])
As well as for the index:
>>> df.to_records(index_dtypes="<S2")
rec.array([(b'a', 1, 0.5 ), (b'b', 2, 0.75)],
dtype=[('I', 'S2'), ('A', '<i8'), ('B', '<f8')])
>>> index_dtypes = f"<S{df.index.str.len().max()}"
>>> df.to_records(index_dtypes=index_dtypes)
rec.array([(b'a', 1, 0.5 ), (b'b', 2, 0.75)],
dtype=[('I', 'S1'), ('A', '<i8'), ('B', '<f8')])
"""
if index:
ix_vals = [
np.asarray(self.index.get_level_values(i))
for i in range(self.index.nlevels)
]
arrays = ix_vals + [
np.asarray(self.iloc[:, i]) for i in range(len(self.columns))
]
index_names = list(self.index.names)
if isinstance(self.index, MultiIndex):
index_names = com.fill_missing_names(index_names)
elif index_names[0] is None:
index_names = ["index"]
names = [str(name) for name in itertools.chain(index_names, self.columns)]
else:
arrays = [np.asarray(self.iloc[:, i]) for i in range(len(self.columns))]
names = [str(c) for c in self.columns]
index_names = []
index_len = len(index_names)
formats = []
for i, v in enumerate(arrays):
index_int = i
# When the names and arrays are collected, we
# first collect those in the DataFrame's index,
# followed by those in its columns.
#
# Thus, the total length of the array is:
# len(index_names) + len(DataFrame.columns).
#
# This check allows us to see whether we are
# handling a name / array in the index or column.
if index_int < index_len:
dtype_mapping = index_dtypes
name = index_names[index_int]
else:
index_int -= index_len
dtype_mapping = column_dtypes
name = self.columns[index_int]
# We have a dictionary, so we get the data type
# associated with the index or column (which can
# be denoted by its name in the DataFrame or its
# position in DataFrame's array of indices or
# columns, whichever is applicable.
if is_dict_like(dtype_mapping):
if name in dtype_mapping:
dtype_mapping = dtype_mapping[name]
elif index_int in dtype_mapping:
dtype_mapping = dtype_mapping[index_int]
else:
dtype_mapping = None
# If no mapping can be found, use the array's
# dtype attribute for formatting.
#
# A valid dtype must either be a type or
# string naming a type.
if dtype_mapping is None:
formats.append(v.dtype)
elif isinstance(dtype_mapping, (type, np.dtype, str)):
# error: Argument 1 to "append" of "list" has incompatible
# type "Union[type, dtype[Any], str]"; expected "dtype[Any]"
formats.append(dtype_mapping) # type: ignore[arg-type]
else:
element = "row" if i < index_len else "column"
msg = f"Invalid dtype {dtype_mapping} specified for {element} {name}"
raise ValueError(msg)
return np.rec.fromarrays(arrays, dtype={"names": names, "formats": formats})
@classmethod
def _from_arrays(
cls,
arrays,
columns,
index,
dtype: Dtype | None = None,
verify_integrity: bool = True,
) -> DataFrame:
"""
Create DataFrame from a list of arrays corresponding to the columns.
Parameters
----------
arrays : list-like of arrays
Each array in the list corresponds to one column, in order.
columns : list-like, Index
The column names for the resulting DataFrame.
index : list-like, Index
The rows labels for the resulting DataFrame.
dtype : dtype, optional
Optional dtype to enforce for all arrays.
verify_integrity : bool, default True
Validate and homogenize all input. If set to False, it is assumed
that all elements of `arrays` are actual arrays how they will be
stored in a block (numpy ndarray or ExtensionArray), have the same
length as and are aligned with the index, and that `columns` and
`index` are ensured to be an Index object.
Returns
-------
DataFrame
"""
if dtype is not None:
dtype = pandas_dtype(dtype)
manager = get_option("mode.data_manager")
columns = ensure_index(columns)
if len(columns) != len(arrays):
raise ValueError("len(columns) must match len(arrays)")
mgr = arrays_to_mgr(
arrays,
columns,
index,
dtype=dtype,
verify_integrity=verify_integrity,
typ=manager,
)
return cls(mgr)
@doc(
storage_options=_shared_docs["storage_options"],
compression_options=_shared_docs["compression_options"] % "path",
)
def to_stata(
self,
path: FilePath | WriteBuffer[bytes],
*,
convert_dates: dict[Hashable, str] | None = None,
write_index: bool = True,
byteorder: str | None = None,
time_stamp: datetime.datetime | None = None,
data_label: str | None = None,
variable_labels: dict[Hashable, str] | None = None,
version: int | None = 114,
convert_strl: Sequence[Hashable] | None = None,
compression: CompressionOptions = "infer",
storage_options: StorageOptions = None,
value_labels: dict[Hashable, dict[float, str]] | None = None,
) -> None:
"""
Export DataFrame object to Stata dta format.
Writes the DataFrame to a Stata dataset file.
"dta" files contain a Stata dataset.
Parameters
----------
path : str, path object, or buffer
String, path object (implementing ``os.PathLike[str]``), or file-like
object implementing a binary ``write()`` function.
convert_dates : dict
Dictionary mapping columns containing datetime types to stata
internal format to use when writing the dates. Options are 'tc',
'td', 'tm', 'tw', 'th', 'tq', 'ty'. Column can be either an integer
or a name. Datetime columns that do not have a conversion type
specified will be converted to 'tc'. Raises NotImplementedError if
a datetime column has timezone information.
write_index : bool
Write the index to Stata dataset.
byteorder : str
Can be ">", "<", "little", or "big". default is `sys.byteorder`.
time_stamp : datetime
A datetime to use as file creation date. Default is the current
time.
data_label : str, optional
A label for the data set. Must be 80 characters or smaller.
variable_labels : dict
Dictionary containing columns as keys and variable labels as
values. Each label must be 80 characters or smaller.
version : {{114, 117, 118, 119, None}}, default 114
Version to use in the output dta file. Set to None to let pandas
decide between 118 or 119 formats depending on the number of
columns in the frame. Version 114 can be read by Stata 10 and
later. Version 117 can be read by Stata 13 or later. Version 118
is supported in Stata 14 and later. Version 119 is supported in
Stata 15 and later. Version 114 limits string variables to 244
characters or fewer while versions 117 and later allow strings
with lengths up to 2,000,000 characters. Versions 118 and 119
support Unicode characters, and version 119 supports more than
32,767 variables.
Version 119 should usually only be used when the number of
variables exceeds the capacity of dta format 118. Exporting
smaller datasets in format 119 may have unintended consequences,
and, as of November 2020, Stata SE cannot read version 119 files.
convert_strl : list, optional
List of column names to convert to string columns to Stata StrL
format. Only available if version is 117. Storing strings in the
StrL format can produce smaller dta files if strings have more than
8 characters and values are repeated.
{compression_options}
.. versionadded:: 1.1.0
.. versionchanged:: 1.4.0 Zstandard support.
{storage_options}
.. versionadded:: 1.2.0
value_labels : dict of dicts
Dictionary containing columns as keys and dictionaries of column value
to labels as values. Labels for a single variable must be 32,000
characters or smaller.
.. versionadded:: 1.4.0
Raises
------
NotImplementedError
* If datetimes contain timezone information
* Column dtype is not representable in Stata
ValueError
* Columns listed in convert_dates are neither datetime64[ns]
or datetime.datetime
* Column listed in convert_dates is not in DataFrame
* Categorical label contains more than 32,000 characters
See Also
--------
read_stata : Import Stata data files.
io.stata.StataWriter : Low-level writer for Stata data files.
io.stata.StataWriter117 : Low-level writer for version 117 files.
Examples
--------
>>> df = pd.DataFrame({{'animal': ['falcon', 'parrot', 'falcon',
... 'parrot'],
... 'speed': [350, 18, 361, 15]}})
>>> df.to_stata('animals.dta') # doctest: +SKIP
"""
if version not in (114, 117, 118, 119, None):
raise ValueError("Only formats 114, 117, 118 and 119 are supported.")
if version == 114:
if convert_strl is not None:
raise ValueError("strl is not supported in format 114")
from pandas.io.stata import StataWriter as statawriter
elif version == 117:
# Incompatible import of "statawriter" (imported name has type
# "Type[StataWriter117]", local name has type "Type[StataWriter]")
from pandas.io.stata import ( # type: ignore[assignment]
StataWriter117 as statawriter,
)
else: # versions 118 and 119
# Incompatible import of "statawriter" (imported name has type
# "Type[StataWriter117]", local name has type "Type[StataWriter]")
from pandas.io.stata import ( # type: ignore[assignment]
StataWriterUTF8 as statawriter,
)
kwargs: dict[str, Any] = {}
if version is None or version >= 117:
# strl conversion is only supported >= 117
kwargs["convert_strl"] = convert_strl
if version is None or version >= 118:
# Specifying the version is only supported for UTF8 (118 or 119)
kwargs["version"] = version
writer = statawriter(
path,
self,
convert_dates=convert_dates,
byteorder=byteorder,
time_stamp=time_stamp,
data_label=data_label,
write_index=write_index,
variable_labels=variable_labels,
compression=compression,
storage_options=storage_options,
value_labels=value_labels,
**kwargs,
)
writer.write_file()
def to_feather(self, path: FilePath | WriteBuffer[bytes], **kwargs) -> None:
"""
Write a DataFrame to the binary Feather format.
Parameters
----------
path : str, path object, file-like object
String, path object (implementing ``os.PathLike[str]``), or file-like
object implementing a binary ``write()`` function. If a string or a path,
it will be used as Root Directory path when writing a partitioned dataset.
**kwargs :
Additional keywords passed to :func:`pyarrow.feather.write_feather`.
Starting with pyarrow 0.17, this includes the `compression`,
`compression_level`, `chunksize` and `version` keywords.
.. versionadded:: 1.1.0
Notes
-----
This function writes the dataframe as a `feather file
<https://arrow.apache.org/docs/python/feather.html>`_. Requires a default
index. For saving the DataFrame with your custom index use a method that
supports custom indices e.g. `to_parquet`.
"""
from pandas.io.feather_format import to_feather
to_feather(self, path, **kwargs)
@doc(
Series.to_markdown,
klass=_shared_doc_kwargs["klass"],
storage_options=_shared_docs["storage_options"],
examples="""Examples
--------
>>> df = pd.DataFrame(
... data={"animal_1": ["elk", "pig"], "animal_2": ["dog", "quetzal"]}
... )
>>> print(df.to_markdown())
| | animal_1 | animal_2 |
|---:|:-----------|:-----------|
| 0 | elk | dog |
| 1 | pig | quetzal |
Output markdown with a tabulate option.
>>> print(df.to_markdown(tablefmt="grid"))
+----+------------+------------+
| | animal_1 | animal_2 |
+====+============+============+
| 0 | elk | dog |
+----+------------+------------+
| 1 | pig | quetzal |
+----+------------+------------+""",
)
def to_markdown(
self,
buf: FilePath | WriteBuffer[str] | None = None,
mode: str = "wt",
index: bool = True,
storage_options: StorageOptions = None,
**kwargs,
) -> str | None:
if "showindex" in kwargs:
raise ValueError("Pass 'index' instead of 'showindex")
kwargs.setdefault("headers", "keys")
kwargs.setdefault("tablefmt", "pipe")
kwargs.setdefault("showindex", index)
tabulate = import_optional_dependency("tabulate")
result = tabulate.tabulate(self, **kwargs)
if buf is None:
return result
with get_handle(buf, mode, storage_options=storage_options) as handles:
handles.handle.write(result)
return None
@overload
def to_parquet(
self,
path: None = ...,
engine: str = ...,
compression: str | None = ...,
index: bool | None = ...,
partition_cols: list[str] | None = ...,
storage_options: StorageOptions = ...,
**kwargs,
) -> bytes:
...
@overload
def to_parquet(
self,
path: FilePath | WriteBuffer[bytes],
engine: str = ...,
compression: str | None = ...,
index: bool | None = ...,
partition_cols: list[str] | None = ...,
storage_options: StorageOptions = ...,
**kwargs,
) -> None:
...
@doc(storage_options=_shared_docs["storage_options"])
def to_parquet(
self,
path: FilePath | WriteBuffer[bytes] | None = None,
engine: str = "auto",
compression: str | None = "snappy",
index: bool | None = None,
partition_cols: list[str] | None = None,
storage_options: StorageOptions = None,
**kwargs,
) -> bytes | None:
"""
Write a DataFrame to the binary parquet format.
This function writes the dataframe as a `parquet file
<https://parquet.apache.org/>`_. You can choose different parquet
backends, and have the option of compression. See
:ref:`the user guide <io.parquet>` for more details.
Parameters
----------
path : str, path object, file-like object, or None, default None
String, path object (implementing ``os.PathLike[str]``), or file-like
object implementing a binary ``write()`` function. If None, the result is
returned as bytes. If a string or path, it will be used as Root Directory
path when writing a partitioned dataset.
.. versionchanged:: 1.2.0
Previously this was "fname"
engine : {{'auto', 'pyarrow', 'fastparquet'}}, default 'auto'
Parquet library to use. If 'auto', then the option
``io.parquet.engine`` is used. The default ``io.parquet.engine``
behavior is to try 'pyarrow', falling back to 'fastparquet' if
'pyarrow' is unavailable.
compression : {{'snappy', 'gzip', 'brotli', None}}, default 'snappy'
Name of the compression to use. Use ``None`` for no compression.
index : bool, default None
If ``True``, include the dataframe's index(es) in the file output.
If ``False``, they will not be written to the file.
If ``None``, similar to ``True`` the dataframe's index(es)
will be saved. However, instead of being saved as values,
the RangeIndex will be stored as a range in the metadata so it
doesn't require much space and is faster. Other indexes will
be included as columns in the file output.
partition_cols : list, optional, default None
Column names by which to partition the dataset.
Columns are partitioned in the order they are given.
Must be None if path is not a string.
{storage_options}
.. versionadded:: 1.2.0
**kwargs
Additional arguments passed to the parquet library. See
:ref:`pandas io <io.parquet>` for more details.
Returns
-------
bytes if no path argument is provided else None
See Also
--------
read_parquet : Read a parquet file.
DataFrame.to_orc : Write an orc file.
DataFrame.to_csv : Write a csv file.
DataFrame.to_sql : Write to a sql table.
DataFrame.to_hdf : Write to hdf.
Notes
-----
This function requires either the `fastparquet
<https://pypi.org/project/fastparquet>`_ or `pyarrow
<https://arrow.apache.org/docs/python/>`_ library.
Examples
--------
>>> df = pd.DataFrame(data={{'col1': [1, 2], 'col2': [3, 4]}})
>>> df.to_parquet('df.parquet.gzip',
... compression='gzip') # doctest: +SKIP
>>> pd.read_parquet('df.parquet.gzip') # doctest: +SKIP
col1 col2
0 1 3
1 2 4
If you want to get a buffer to the parquet content you can use a io.BytesIO
object, as long as you don't use partition_cols, which creates multiple files.
>>> import io
>>> f = io.BytesIO()
>>> df.to_parquet(f)
>>> f.seek(0)
0
>>> content = f.read()
"""
from pandas.io.parquet import to_parquet
return to_parquet(
self,
path,
engine,
compression=compression,
index=index,
partition_cols=partition_cols,
storage_options=storage_options,
**kwargs,
)
def to_orc(
self,
path: FilePath | WriteBuffer[bytes] | None = None,
*,
engine: Literal["pyarrow"] = "pyarrow",
index: bool | None = None,
engine_kwargs: dict[str, Any] | None = None,
) -> bytes | None:
"""
Write a DataFrame to the ORC format.
.. versionadded:: 1.5.0
Parameters
----------
path : str, file-like object or None, default None
If a string, it will be used as Root Directory path
when writing a partitioned dataset. By file-like object,
we refer to objects with a write() method, such as a file handle
(e.g. via builtin open function). If path is None,
a bytes object is returned.
engine : str, default 'pyarrow'
ORC library to use. Pyarrow must be >= 7.0.0.
index : bool, optional
If ``True``, include the dataframe's index(es) in the file output.
If ``False``, they will not be written to the file.
If ``None``, similar to ``infer`` the dataframe's index(es)
will be saved. However, instead of being saved as values,
the RangeIndex will be stored as a range in the metadata so it
doesn't require much space and is faster. Other indexes will
be included as columns in the file output.
engine_kwargs : dict[str, Any] or None, default None
Additional keyword arguments passed to :func:`pyarrow.orc.write_table`.
Returns
-------
bytes if no path argument is provided else None
Raises
------
NotImplementedError
Dtype of one or more columns is category, unsigned integers, interval,
period or sparse.
ValueError
engine is not pyarrow.
See Also
--------
read_orc : Read a ORC file.
DataFrame.to_parquet : Write a parquet file.
DataFrame.to_csv : Write a csv file.
DataFrame.to_sql : Write to a sql table.
DataFrame.to_hdf : Write to hdf.
Notes
-----
* Before using this function you should read the :ref:`user guide about
ORC <io.orc>` and :ref:`install optional dependencies <install.warn_orc>`.
* This function requires `pyarrow <https://arrow.apache.org/docs/python/>`_
library.
* For supported dtypes please refer to `supported ORC features in Arrow
<https://arrow.apache.org/docs/cpp/orc.html#data-types>`__.
* Currently timezones in datetime columns are not preserved when a
dataframe is converted into ORC files.
Examples
--------
>>> df = pd.DataFrame(data={'col1': [1, 2], 'col2': [4, 3]})
>>> df.to_orc('df.orc') # doctest: +SKIP
>>> pd.read_orc('df.orc') # doctest: +SKIP
col1 col2
0 1 4
1 2 3
If you want to get a buffer to the orc content you can write it to io.BytesIO
>>> import io
>>> b = io.BytesIO(df.to_orc()) # doctest: +SKIP
>>> b.seek(0) # doctest: +SKIP
0
>>> content = b.read() # doctest: +SKIP
"""
from pandas.io.orc import to_orc
return to_orc(
self, path, engine=engine, index=index, engine_kwargs=engine_kwargs
)
@overload
def to_html(
self,
buf: FilePath | WriteBuffer[str],
columns: Sequence[Level] | None = ...,
col_space: ColspaceArgType | None = ...,
header: bool | Sequence[str] = ...,
index: bool = ...,
na_rep: str = ...,
formatters: FormattersType | None = ...,
float_format: FloatFormatType | None = ...,
sparsify: bool | None = ...,
index_names: bool = ...,
justify: str | None = ...,
max_rows: int | None = ...,
max_cols: int | None = ...,
show_dimensions: bool | str = ...,
decimal: str = ...,
bold_rows: bool = ...,
classes: str | list | tuple | None = ...,
escape: bool = ...,
notebook: bool = ...,
border: int | bool | None = ...,
table_id: str | None = ...,
render_links: bool = ...,
encoding: str | None = ...,
) -> None:
...
@overload
def to_html(
self,
buf: None = ...,
columns: Sequence[Level] | None = ...,
col_space: ColspaceArgType | None = ...,
header: bool | Sequence[str] = ...,
index: bool = ...,
na_rep: str = ...,
formatters: FormattersType | None = ...,
float_format: FloatFormatType | None = ...,
sparsify: bool | None = ...,
index_names: bool = ...,
justify: str | None = ...,
max_rows: int | None = ...,
max_cols: int | None = ...,
show_dimensions: bool | str = ...,
decimal: str = ...,
bold_rows: bool = ...,
classes: str | list | tuple | None = ...,
escape: bool = ...,
notebook: bool = ...,
border: int | bool | None = ...,
table_id: str | None = ...,
render_links: bool = ...,
encoding: str | None = ...,
) -> str:
...
@Substitution(
header_type="bool",
header="Whether to print column labels, default True",
col_space_type="str or int, list or dict of int or str",
col_space="The minimum width of each column in CSS length "
"units. An int is assumed to be px units.",
)
@Substitution(shared_params=fmt.common_docstring, returns=fmt.return_docstring)
def to_html(
self,
buf: FilePath | WriteBuffer[str] | None = None,
columns: Sequence[Level] | None = None,
col_space: ColspaceArgType | None = None,
header: bool | Sequence[str] = True,
index: bool = True,
na_rep: str = "NaN",
formatters: FormattersType | None = None,
float_format: FloatFormatType | None = None,
sparsify: bool | None = None,
index_names: bool = True,
justify: str | None = None,
max_rows: int | None = None,
max_cols: int | None = None,
show_dimensions: bool | str = False,
decimal: str = ".",
bold_rows: bool = True,
classes: str | list | tuple | None = None,
escape: bool = True,
notebook: bool = False,
border: int | bool | None = None,
table_id: str | None = None,
render_links: bool = False,
encoding: str | None = None,
) -> str | None:
"""
Render a DataFrame as an HTML table.
%(shared_params)s
bold_rows : bool, default True
Make the row labels bold in the output.
classes : str or list or tuple, default None
CSS class(es) to apply to the resulting html table.
escape : bool, default True
Convert the characters <, >, and & to HTML-safe sequences.
notebook : {True, False}, default False
Whether the generated HTML is for IPython Notebook.
border : int
A ``border=border`` attribute is included in the opening
`<table>` tag. Default ``pd.options.display.html.border``.
table_id : str, optional
A css id is included in the opening `<table>` tag if specified.
render_links : bool, default False
Convert URLs to HTML links.
encoding : str, default "utf-8"
Set character encoding.
.. versionadded:: 1.0
%(returns)s
See Also
--------
to_string : Convert DataFrame to a string.
"""
if justify is not None and justify not in fmt._VALID_JUSTIFY_PARAMETERS:
raise ValueError("Invalid value for justify parameter")
formatter = fmt.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,
justify=justify,
index_names=index_names,
escape=escape,
decimal=decimal,
max_rows=max_rows,
max_cols=max_cols,
show_dimensions=show_dimensions,
)
# TODO: a generic formatter wld b in DataFrameFormatter
return fmt.DataFrameRenderer(formatter).to_html(
buf=buf,
classes=classes,
notebook=notebook,
border=border,
encoding=encoding,
table_id=table_id,
render_links=render_links,
)
@doc(
storage_options=_shared_docs["storage_options"],
compression_options=_shared_docs["compression_options"] % "path_or_buffer",
)
def to_xml(
self,
path_or_buffer: FilePath | WriteBuffer[bytes] | WriteBuffer[str] | None = None,
index: bool = True,
root_name: str | None = "data",
row_name: str | None = "row",
na_rep: str | None = None,
attr_cols: list[str] | None = None,
elem_cols: list[str] | None = None,
namespaces: dict[str | None, str] | None = None,
prefix: str | None = None,
encoding: str = "utf-8",
xml_declaration: bool | None = True,
pretty_print: bool | None = True,
parser: str | None = "lxml",
stylesheet: FilePath | ReadBuffer[str] | ReadBuffer[bytes] | None = None,
compression: CompressionOptions = "infer",
storage_options: StorageOptions = None,
) -> str | None:
"""
Render a DataFrame to an XML document.
.. versionadded:: 1.3.0
Parameters
----------
path_or_buffer : str, path object, file-like object, or None, default None
String, path object (implementing ``os.PathLike[str]``), or file-like
object implementing a ``write()`` function. If None, the result is returned
as a string.
index : bool, default True
Whether to include index in XML document.
root_name : str, default 'data'
The name of root element in XML document.
row_name : str, default 'row'
The name of row element in XML document.
na_rep : str, optional
Missing data representation.
attr_cols : list-like, optional
List of columns to write as attributes in row element.
Hierarchical columns will be flattened with underscore
delimiting the different levels.
elem_cols : list-like, optional
List of columns to write as children in row element. By default,
all columns output as children of row element. Hierarchical
columns will be flattened with underscore delimiting the
different levels.
namespaces : dict, optional
All namespaces to be defined in root element. Keys of dict
should be prefix names and values of dict corresponding URIs.
Default namespaces should be given empty string key. For
example, ::
namespaces = {{"": "https://example.com"}}
prefix : str, optional
Namespace prefix to be used for every element and/or attribute
in document. This should be one of the keys in ``namespaces``
dict.
encoding : str, default 'utf-8'
Encoding of the resulting document.
xml_declaration : bool, default True
Whether to include the XML declaration at start of document.
pretty_print : bool, default True
Whether output should be pretty printed with indentation and
line breaks.
parser : {{'lxml','etree'}}, default 'lxml'
Parser module to use for building of tree. Only 'lxml' and
'etree' are supported. With 'lxml', the ability to use XSLT
stylesheet is supported.
stylesheet : str, path object or file-like object, optional
A URL, file-like object, or a raw string containing an XSLT
script used to transform the raw XML output. Script should use
layout of elements and attributes from original output. This
argument requires ``lxml`` to be installed. Only XSLT 1.0
scripts and not later versions is currently supported.
{compression_options}
.. versionchanged:: 1.4.0 Zstandard support.
{storage_options}
Returns
-------
None or str
If ``io`` is None, returns the resulting XML format as a
string. Otherwise returns None.
See Also
--------
to_json : Convert the pandas object to a JSON string.
to_html : Convert DataFrame to a html.
Examples
--------
>>> df = pd.DataFrame({{'shape': ['square', 'circle', 'triangle'],
... 'degrees': [360, 360, 180],
... 'sides': [4, np.nan, 3]}})
>>> df.to_xml() # doctest: +SKIP
<?xml version='1.0' encoding='utf-8'?>
<data>
<row>
<index>0</index>
<shape>square</shape>
<degrees>360</degrees>
<sides>4.0</sides>
</row>
<row>
<index>1</index>
<shape>circle</shape>
<degrees>360</degrees>
<sides/>
</row>
<row>
<index>2</index>
<shape>triangle</shape>
<degrees>180</degrees>
<sides>3.0</sides>
</row>
</data>
>>> df.to_xml(attr_cols=[
... 'index', 'shape', 'degrees', 'sides'
... ]) # doctest: +SKIP
<?xml version='1.0' encoding='utf-8'?>
<data>
<row index="0" shape="square" degrees="360" sides="4.0"/>
<row index="1" shape="circle" degrees="360"/>
<row index="2" shape="triangle" degrees="180" sides="3.0"/>
</data>
>>> df.to_xml(namespaces={{"doc": "https://example.com"}},
... prefix="doc") # doctest: +SKIP
<?xml version='1.0' encoding='utf-8'?>
<doc:data xmlns:doc="https://example.com">
<doc:row>
<doc:index>0</doc:index>
<doc:shape>square</doc:shape>
<doc:degrees>360</doc:degrees>
<doc:sides>4.0</doc:sides>
</doc:row>
<doc:row>
<doc:index>1</doc:index>
<doc:shape>circle</doc:shape>
<doc:degrees>360</doc:degrees>
<doc:sides/>
</doc:row>
<doc:row>
<doc:index>2</doc:index>
<doc:shape>triangle</doc:shape>
<doc:degrees>180</doc:degrees>
<doc:sides>3.0</doc:sides>
</doc:row>
</doc:data>
"""
from pandas.io.formats.xml import (
EtreeXMLFormatter,
LxmlXMLFormatter,
)
lxml = import_optional_dependency("lxml.etree", errors="ignore")
TreeBuilder: type[EtreeXMLFormatter] | type[LxmlXMLFormatter]
if parser == "lxml":
if lxml is not None:
TreeBuilder = LxmlXMLFormatter
else:
raise ImportError(
"lxml not found, please install or use the etree parser."
)
elif parser == "etree":
TreeBuilder = EtreeXMLFormatter
else:
raise ValueError("Values for parser can only be lxml or etree.")
xml_formatter = TreeBuilder(
self,
path_or_buffer=path_or_buffer,
index=index,
root_name=root_name,
row_name=row_name,
na_rep=na_rep,
attr_cols=attr_cols,
elem_cols=elem_cols,
namespaces=namespaces,
prefix=prefix,
encoding=encoding,
xml_declaration=xml_declaration,
pretty_print=pretty_print,
stylesheet=stylesheet,
compression=compression,
storage_options=storage_options,
)
return xml_formatter.write_output()
# ----------------------------------------------------------------------
@doc(INFO_DOCSTRING, **frame_sub_kwargs)
def info(
self,
verbose: bool | None = None,
buf: WriteBuffer[str] | None = None,
max_cols: int | None = None,
memory_usage: bool | str | None = None,
show_counts: bool | None = None,
) -> None:
info = DataFrameInfo(
data=self,
memory_usage=memory_usage,
)
info.render(
buf=buf,
max_cols=max_cols,
verbose=verbose,
show_counts=show_counts,
)
def memory_usage(self, index: bool = True, deep: bool = False) -> Series:
"""
Return the memory usage of each column in bytes.
The memory usage can optionally include the contribution of
the index and elements of `object` dtype.
This value is displayed in `DataFrame.info` by default. This can be
suppressed by setting ``pandas.options.display.memory_usage`` to False.
Parameters
----------
index : bool, default True
Specifies whether to include the memory usage of the DataFrame's
index in returned Series. If ``index=True``, the memory usage of
the index is the first item in the output.
deep : bool, default False
If True, introspect the data deeply by interrogating
`object` dtypes for system-level memory consumption, and include
it in the returned values.
Returns
-------
Series
A Series whose index is the original column names and whose values
is the memory usage of each column in bytes.
See Also
--------
numpy.ndarray.nbytes : Total bytes consumed by the elements of an
ndarray.
Series.memory_usage : Bytes consumed by a Series.
Categorical : Memory-efficient array for string values with
many repeated values.
DataFrame.info : Concise summary of a DataFrame.
Notes
-----
See the :ref:`Frequently Asked Questions <df-memory-usage>` for more
details.
Examples
--------
>>> dtypes = ['int64', 'float64', 'complex128', 'object', 'bool']
>>> data = dict([(t, np.ones(shape=5000, dtype=int).astype(t))
... for t in dtypes])
>>> df = pd.DataFrame(data)
>>> df.head()
int64 float64 complex128 object bool
0 1 1.0 1.0+0.0j 1 True
1 1 1.0 1.0+0.0j 1 True
2 1 1.0 1.0+0.0j 1 True
3 1 1.0 1.0+0.0j 1 True
4 1 1.0 1.0+0.0j 1 True
>>> df.memory_usage()
Index 128
int64 40000
float64 40000
complex128 80000
object 40000
bool 5000
dtype: int64
>>> df.memory_usage(index=False)
int64 40000
float64 40000
complex128 80000
object 40000
bool 5000
dtype: int64
The memory footprint of `object` dtype columns is ignored by default:
>>> df.memory_usage(deep=True)
Index 128
int64 40000
float64 40000
complex128 80000
object 180000
bool 5000
dtype: int64
Use a Categorical for efficient storage of an object-dtype column with
many repeated values.
>>> df['object'].astype('category').memory_usage(deep=True)
5244
"""
result = self._constructor_sliced(
[c.memory_usage(index=False, deep=deep) for col, c in self.items()],
index=self.columns,
dtype=np.intp,
)
if index:
index_memory_usage = self._constructor_sliced(
self.index.memory_usage(deep=deep), index=["Index"]
)
result = index_memory_usage._append(result)
return result
def transpose(self, *args, copy: bool = False) -> DataFrame:
"""
Transpose index and columns.
Reflect the DataFrame over its main diagonal by writing rows as columns
and vice-versa. The property :attr:`.T` is an accessor to the method
:meth:`transpose`.
Parameters
----------
*args : tuple, optional
Accepted for compatibility with NumPy.
copy : bool, default False
Whether to copy the data after transposing, even for DataFrames
with a single dtype.
Note that a copy is always required for mixed dtype DataFrames,
or for DataFrames with any extension types.
Returns
-------
DataFrame
The transposed DataFrame.
See Also
--------
numpy.transpose : Permute the dimensions of a given array.
Notes
-----
Transposing a DataFrame with mixed dtypes will result in a homogeneous
DataFrame with the `object` dtype. In such a case, a copy of the data
is always made.
Examples
--------
**Square DataFrame with homogeneous dtype**
>>> d1 = {'col1': [1, 2], 'col2': [3, 4]}
>>> df1 = pd.DataFrame(data=d1)
>>> df1
col1 col2
0 1 3
1 2 4
>>> df1_transposed = df1.T # or df1.transpose()
>>> df1_transposed
0 1
col1 1 2
col2 3 4
When the dtype is homogeneous in the original DataFrame, we get a
transposed DataFrame with the same dtype:
>>> df1.dtypes
col1 int64
col2 int64
dtype: object
>>> df1_transposed.dtypes
0 int64
1 int64
dtype: object
**Non-square DataFrame with mixed dtypes**
>>> d2 = {'name': ['Alice', 'Bob'],
... 'score': [9.5, 8],
... 'employed': [False, True],
... 'kids': [0, 0]}
>>> df2 = pd.DataFrame(data=d2)
>>> df2
name score employed kids
0 Alice 9.5 False 0
1 Bob 8.0 True 0
>>> df2_transposed = df2.T # or df2.transpose()
>>> df2_transposed
0 1
name Alice Bob
score 9.5 8.0
employed False True
kids 0 0
When the DataFrame has mixed dtypes, we get a transposed DataFrame with
the `object` dtype:
>>> df2.dtypes
name object
score float64
employed bool
kids int64
dtype: object
>>> df2_transposed.dtypes
0 object
1 object
dtype: object
"""
nv.validate_transpose(args, {})
# construct the args
dtypes = list(self.dtypes)
if self._can_fast_transpose:
# Note: tests pass without this, but this improves perf quite a bit.
new_vals = self._values.T
if copy and not using_copy_on_write():
new_vals = new_vals.copy()
result = self._constructor(
new_vals, index=self.columns, columns=self.index, copy=False
)
if using_copy_on_write() and len(self) > 0:
result._mgr.add_references(self._mgr) # type: ignore[arg-type]
elif (
self._is_homogeneous_type and dtypes and is_extension_array_dtype(dtypes[0])
):
# We have EAs with the same dtype. We can preserve that dtype in transpose.
dtype = dtypes[0]
arr_type = dtype.construct_array_type()
values = self.values
new_values = [arr_type._from_sequence(row, dtype=dtype) for row in values]
result = type(self)._from_arrays(
new_values, index=self.columns, columns=self.index
)
else:
new_arr = self.values.T
if copy and not using_copy_on_write():
new_arr = new_arr.copy()
result = self._constructor(
new_arr,
index=self.columns,
columns=self.index,
# We already made a copy (more than one block)
copy=False,
)
return result.__finalize__(self, method="transpose")
@property
def T(self) -> DataFrame:
"""
The transpose of the DataFrame.
Returns
-------
DataFrame
The transposed DataFrame.
See Also
--------
DataFrame.transpose : Transpose index and columns.
Examples
--------
>>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> df
col1 col2
0 1 3
1 2 4
>>> df.T
0 1
col1 1 2
col2 3 4
"""
return self.transpose()
# ----------------------------------------------------------------------
# Indexing Methods
def _ixs(self, i: int, axis: AxisInt = 0) -> Series:
"""
Parameters
----------
i : int
axis : int
Returns
-------
Series
"""
# irow
if axis == 0:
new_mgr = self._mgr.fast_xs(i)
# if we are a copy, mark as such
copy = isinstance(new_mgr.array, np.ndarray) and new_mgr.array.base is None
result = self._constructor_sliced(new_mgr, name=self.index[i]).__finalize__(
self
)
result._set_is_copy(self, copy=copy)
return result
# icol
else:
label = self.columns[i]
col_mgr = self._mgr.iget(i)
result = self._box_col_values(col_mgr, i)
# this is a cached value, mark it so
result._set_as_cached(label, self)
return result
def _get_column_array(self, i: int) -> ArrayLike:
"""
Get the values of the i'th column (ndarray or ExtensionArray, as stored
in the Block)
Warning! The returned array is a view but doesn't handle Copy-on-Write,
so this should be used with caution (for read-only purposes).
"""
return self._mgr.iget_values(i)
def _iter_column_arrays(self) -> Iterator[ArrayLike]:
"""
Iterate over the arrays of all columns in order.
This returns the values as stored in the Block (ndarray or ExtensionArray).
Warning! The returned array is a view but doesn't handle Copy-on-Write,
so this should be used with caution (for read-only purposes).
"""
for i in range(len(self.columns)):
yield self._get_column_array(i)
def _getitem_nocopy(self, key: list):
"""
Behaves like __getitem__, but returns a view in cases where __getitem__
would make a copy.
"""
# TODO(CoW): can be removed if/when we are always Copy-on-Write
indexer = self.columns._get_indexer_strict(key, "columns")[1]
new_axis = self.columns[indexer]
new_mgr = self._mgr.reindex_indexer(
new_axis,
indexer,
axis=0,
allow_dups=True,
copy=False,
only_slice=True,
)
return self._constructor(new_mgr)
def __getitem__(self, key):
check_dict_or_set_indexers(key)
key = lib.item_from_zerodim(key)
key = com.apply_if_callable(key, self)
if is_hashable(key) and not is_iterator(key):
# is_iterator to exclude generator e.g. test_getitem_listlike
# shortcut if the key is in columns
is_mi = isinstance(self.columns, MultiIndex)
# GH#45316 Return view if key is not duplicated
# Only use drop_duplicates with duplicates for performance
if not is_mi and (
self.columns.is_unique
and key in self.columns
or key in self.columns.drop_duplicates(keep=False)
):
return self._get_item_cache(key)
elif is_mi and self.columns.is_unique and key in self.columns:
return self._getitem_multilevel(key)
# Do we have a slicer (on rows)?
if isinstance(key, slice):
indexer = self.index._convert_slice_indexer(key, kind="getitem")
if isinstance(indexer, np.ndarray):
# reachable with DatetimeIndex
indexer = lib.maybe_indices_to_slice(
indexer.astype(np.intp, copy=False), len(self)
)
if isinstance(indexer, np.ndarray):
# GH#43223 If we can not convert, use take
return self.take(indexer, axis=0)
return self._slice(indexer, axis=0)
# Do we have a (boolean) DataFrame?
if isinstance(key, DataFrame):
return self.where(key)
# Do we have a (boolean) 1d indexer?
if com.is_bool_indexer(key):
return self._getitem_bool_array(key)
# We are left with two options: a single key, and a collection of keys,
# We interpret tuples as collections only for non-MultiIndex
is_single_key = isinstance(key, tuple) or not is_list_like(key)
if is_single_key:
if self.columns.nlevels > 1:
return self._getitem_multilevel(key)
indexer = self.columns.get_loc(key)
if is_integer(indexer):
indexer = [indexer]
else:
if is_iterator(key):
key = list(key)
indexer = self.columns._get_indexer_strict(key, "columns")[1]
# take() does not accept boolean indexers
if getattr(indexer, "dtype", None) == bool:
indexer = np.where(indexer)[0]
data = self._take_with_is_copy(indexer, axis=1)
if is_single_key:
# What does looking for a single key in a non-unique index return?
# The behavior is inconsistent. It returns a Series, except when
# - the key itself is repeated (test on data.shape, #9519), or
# - we have a MultiIndex on columns (test on self.columns, #21309)
if data.shape[1] == 1 and not isinstance(self.columns, MultiIndex):
# GH#26490 using data[key] can cause RecursionError
return data._get_item_cache(key)
return data
def _getitem_bool_array(self, key):
# also raises Exception if object array with NA values
# warning here just in case -- previously __setitem__ was
# reindexing but __getitem__ was not; it seems more reasonable to
# go with the __setitem__ behavior since that is more consistent
# with all other indexing behavior
if isinstance(key, Series) and not key.index.equals(self.index):
warnings.warn(
"Boolean Series key will be reindexed to match DataFrame index.",
UserWarning,
stacklevel=find_stack_level(),
)
elif len(key) != len(self.index):
raise ValueError(
f"Item wrong length {len(key)} instead of {len(self.index)}."
)
# check_bool_indexer will throw exception if Series key cannot
# be reindexed to match DataFrame rows
key = check_bool_indexer(self.index, key)
if key.all():
return self.copy(deep=None)
indexer = key.nonzero()[0]
return self._take_with_is_copy(indexer, axis=0)
def _getitem_multilevel(self, key):
# self.columns is a MultiIndex
loc = self.columns.get_loc(key)
if isinstance(loc, (slice, np.ndarray)):
new_columns = self.columns[loc]
result_columns = maybe_droplevels(new_columns, key)
result = self.iloc[:, loc]
result.columns = result_columns
# If there is only one column being returned, and its name is
# either an empty string, or a tuple with an empty string as its
# first element, then treat the empty string as a placeholder
# and return the column as if the user had provided that empty
# string in the key. If the result is a Series, exclude the
# implied empty string from its name.
if len(result.columns) == 1:
# e.g. test_frame_getitem_multicolumn_empty_level,
# test_frame_mixed_depth_get, test_loc_setitem_single_column_slice
top = result.columns[0]
if isinstance(top, tuple):
top = top[0]
if top == "":
result = result[""]
if isinstance(result, Series):
result = self._constructor_sliced(
result, index=self.index, name=key
)
result._set_is_copy(self)
return result
else:
# loc is neither a slice nor ndarray, so must be an int
return self._ixs(loc, axis=1)
def _get_value(self, index, col, takeable: bool = False) -> Scalar:
"""
Quickly retrieve single value at passed column and index.
Parameters
----------
index : row label
col : column label
takeable : interpret the index/col as indexers, default False
Returns
-------
scalar
Notes
-----
Assumes that both `self.index._index_as_unique` and
`self.columns._index_as_unique`; Caller is responsible for checking.
"""
if takeable:
series = self._ixs(col, axis=1)
return series._values[index]
series = self._get_item_cache(col)
engine = self.index._engine
if not isinstance(self.index, MultiIndex):
# CategoricalIndex: Trying to use the engine fastpath may give incorrect
# results if our categories are integers that dont match our codes
# IntervalIndex: IntervalTree has no get_loc
row = self.index.get_loc(index)
return series._values[row]
# For MultiIndex going through engine effectively restricts us to
# same-length tuples; see test_get_set_value_no_partial_indexing
loc = engine.get_loc(index)
return series._values[loc]
def isetitem(self, loc, value) -> None:
"""
Set the given value in the column with position `loc`.
This is a positional analogue to ``__setitem__``.
Parameters
----------
loc : int or sequence of ints
Index position for the column.
value : scalar or arraylike
Value(s) for the column.
Notes
-----
``frame.isetitem(loc, value)`` is an in-place method as it will
modify the DataFrame in place (not returning a new object). In contrast to
``frame.iloc[:, i] = value`` which will try to update the existing values in
place, ``frame.isetitem(loc, value)`` will not update the values of the column
itself in place, it will instead insert a new array.
In cases where ``frame.columns`` is unique, this is equivalent to
``frame[frame.columns[i]] = value``.
"""
if isinstance(value, DataFrame):
if is_scalar(loc):
loc = [loc]
for i, idx in enumerate(loc):
arraylike = self._sanitize_column(value.iloc[:, i])
self._iset_item_mgr(idx, arraylike, inplace=False)
return
arraylike = self._sanitize_column(value)
self._iset_item_mgr(loc, arraylike, inplace=False)
def __setitem__(self, key, value):
if not PYPY and using_copy_on_write():
if sys.getrefcount(self) <= 3:
warnings.warn(
_chained_assignment_msg, ChainedAssignmentError, stacklevel=2
)
key = com.apply_if_callable(key, self)
# see if we can slice the rows
if isinstance(key, slice):
slc = self.index._convert_slice_indexer(key, kind="getitem")
return self._setitem_slice(slc, value)
if isinstance(key, DataFrame) or getattr(key, "ndim", None) == 2:
self._setitem_frame(key, value)
elif isinstance(key, (Series, np.ndarray, list, Index)):
self._setitem_array(key, value)
elif isinstance(value, DataFrame):
self._set_item_frame_value(key, value)
elif (
is_list_like(value)
and not self.columns.is_unique
and 1 < len(self.columns.get_indexer_for([key])) == len(value)
):
# Column to set is duplicated
self._setitem_array([key], value)
else:
# set column
self._set_item(key, value)
def _setitem_slice(self, key: slice, value) -> None:
# NB: we can't just use self.loc[key] = value because that
# operates on labels and we need to operate positional for
# backwards-compat, xref GH#31469
self._check_setitem_copy()
self.iloc[key] = value
def _setitem_array(self, key, value):
# also raises Exception if object array with NA values
if com.is_bool_indexer(key):
# bool indexer is indexing along rows
if len(key) != len(self.index):
raise ValueError(
f"Item wrong length {len(key)} instead of {len(self.index)}!"
)
key = check_bool_indexer(self.index, key)
indexer = key.nonzero()[0]
self._check_setitem_copy()
if isinstance(value, DataFrame):
# GH#39931 reindex since iloc does not align
value = value.reindex(self.index.take(indexer))
self.iloc[indexer] = value
else:
# Note: unlike self.iloc[:, indexer] = value, this will
# never try to overwrite values inplace
if isinstance(value, DataFrame):
check_key_length(self.columns, key, value)
for k1, k2 in zip(key, value.columns):
self[k1] = value[k2]
elif not is_list_like(value):
for col in key:
self[col] = value
elif isinstance(value, np.ndarray) and value.ndim == 2:
self._iset_not_inplace(key, value)
elif np.ndim(value) > 1:
# list of lists
value = DataFrame(value).values
return self._setitem_array(key, value)
else:
self._iset_not_inplace(key, value)
def _iset_not_inplace(self, key, value):
# GH#39510 when setting with df[key] = obj with a list-like key and
# list-like value, we iterate over those listlikes and set columns
# one at a time. This is different from dispatching to
# `self.loc[:, key]= value` because loc.__setitem__ may overwrite
# data inplace, whereas this will insert new arrays.
def igetitem(obj, i: int):
# Note: we catch DataFrame obj before getting here, but
# hypothetically would return obj.iloc[:, i]
if isinstance(obj, np.ndarray):
return obj[..., i]
else:
return obj[i]
if self.columns.is_unique:
if np.shape(value)[-1] != len(key):
raise ValueError("Columns must be same length as key")
for i, col in enumerate(key):
self[col] = igetitem(value, i)
else:
ilocs = self.columns.get_indexer_non_unique(key)[0]
if (ilocs < 0).any():
# key entries not in self.columns
raise NotImplementedError
if np.shape(value)[-1] != len(ilocs):
raise ValueError("Columns must be same length as key")
assert np.ndim(value) <= 2
orig_columns = self.columns
# Using self.iloc[:, i] = ... may set values inplace, which
# by convention we do not do in __setitem__
try:
self.columns = Index(range(len(self.columns)))
for i, iloc in enumerate(ilocs):
self[iloc] = igetitem(value, i)
finally:
self.columns = orig_columns
def _setitem_frame(self, key, value):
# support boolean setting with DataFrame input, e.g.
# df[df > df2] = 0
if isinstance(key, np.ndarray):
if key.shape != self.shape:
raise ValueError("Array conditional must be same shape as self")
key = self._constructor(key, **self._construct_axes_dict(), copy=False)
if key.size and not all(is_bool_dtype(dtype) for dtype in key.dtypes):
raise TypeError(
"Must pass DataFrame or 2-d ndarray with boolean values only"
)
self._check_inplace_setting(value)
self._check_setitem_copy()
self._where(-key, value, inplace=True)
def _set_item_frame_value(self, key, value: DataFrame) -> None:
self._ensure_valid_index(value)
# align columns
if key in self.columns:
loc = self.columns.get_loc(key)
cols = self.columns[loc]
len_cols = 1 if is_scalar(cols) or isinstance(cols, tuple) else len(cols)
if len_cols != len(value.columns):
raise ValueError("Columns must be same length as key")
# align right-hand-side columns if self.columns
# is multi-index and self[key] is a sub-frame
if isinstance(self.columns, MultiIndex) and isinstance(
loc, (slice, Series, np.ndarray, Index)
):
cols_droplevel = maybe_droplevels(cols, key)
if len(cols_droplevel) and not cols_droplevel.equals(value.columns):
value = value.reindex(cols_droplevel, axis=1)
for col, col_droplevel in zip(cols, cols_droplevel):
self[col] = value[col_droplevel]
return
if is_scalar(cols):
self[cols] = value[value.columns[0]]
return
# now align rows
arraylike = _reindex_for_setitem(value, self.index)
self._set_item_mgr(key, arraylike)
return
if len(value.columns) != 1:
raise ValueError(
"Cannot set a DataFrame with multiple columns to the single "
f"column {key}"
)
self[key] = value[value.columns[0]]
def _iset_item_mgr(
self, loc: int | slice | np.ndarray, value, inplace: bool = False
) -> None:
# when called from _set_item_mgr loc can be anything returned from get_loc
self._mgr.iset(loc, value, inplace=inplace)
self._clear_item_cache()
def _set_item_mgr(self, key, value: ArrayLike) -> 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)
else:
self._iset_item_mgr(loc, value)
# check if we are modifying a copy
# try to set first as we want an invalid
# value exception to occur first
if len(self):
self._check_setitem_copy()
def _iset_item(self, loc: int, value) -> None:
arraylike = self._sanitize_column(value)
self._iset_item_mgr(loc, arraylike, inplace=True)
# check if we are modifying a copy
# try to set first as we want an invalid
# value exception to occur first
if len(self):
self._check_setitem_copy()
def _set_item(self, key, value) -> None:
"""
Add series to DataFrame in specified column.
If series is a numpy-array (not a Series/TimeSeries), it must be the
same length as the DataFrames index or an error will be thrown.
Series/TimeSeries will be conformed to the DataFrames index to
ensure homogeneity.
"""
value = self._sanitize_column(value)
if (
key in self.columns
and value.ndim == 1
and not is_extension_array_dtype(value)
):
# broadcast across multiple columns if necessary
if not self.columns.is_unique or isinstance(self.columns, MultiIndex):
existing_piece = self[key]
if isinstance(existing_piece, DataFrame):
value = np.tile(value, (len(existing_piece.columns), 1)).T
self._set_item_mgr(key, value)
def _set_value(
self, index: IndexLabel, col, value: Scalar, takeable: bool = False
) -> None:
"""
Put single value at passed column and index.
Parameters
----------
index : Label
row label
col : Label
column label
value : scalar
takeable : bool, default False
Sets whether or not index/col interpreted as indexers
"""
try:
if takeable:
icol = col
iindex = cast(int, index)
else:
icol = self.columns.get_loc(col)
iindex = self.index.get_loc(index)
self._mgr.column_setitem(icol, iindex, value, inplace_only=True)
self._clear_item_cache()
except (KeyError, TypeError, ValueError, LossySetitemError):
# get_loc might raise a KeyError for missing labels (falling back
# to (i)loc will do expansion of the index)
# column_setitem will do validation that may raise TypeError,
# ValueError, or LossySetitemError
# set using a non-recursive method & reset the cache
if takeable:
self.iloc[index, col] = value
else:
self.loc[index, col] = value
self._item_cache.pop(col, None)
except InvalidIndexError as ii_err:
# GH48729: Seems like you are trying to assign a value to a
# row when only scalar options are permitted
raise InvalidIndexError(
f"You can only assign a scalar value not a {type(value)}"
) from ii_err
def _ensure_valid_index(self, value) -> None:
"""
Ensure that if we don't have an index, that we can create one from the
passed value.
"""
# GH5632, make sure that we are a Series convertible
if not len(self.index) and is_list_like(value) and len(value):
if not isinstance(value, DataFrame):
try:
value = Series(value)
except (ValueError, NotImplementedError, TypeError) as err:
raise ValueError(
"Cannot set a frame with no defined index "
"and a value that cannot be converted to a Series"
) from err
# GH31368 preserve name of index
index_copy = value.index.copy()
if self.index.name is not None:
index_copy.name = self.index.name
self._mgr = self._mgr.reindex_axis(index_copy, axis=1, fill_value=np.nan)
def _box_col_values(self, values: SingleDataManager, loc: int) -> Series:
"""
Provide boxed values for a column.
"""
# Lookup in columns so that if e.g. a str datetime was passed
# we attach the Timestamp object as the name.
name = self.columns[loc]
klass = self._constructor_sliced
# We get index=self.index bc values is a SingleDataManager
return klass(values, name=name, fastpath=True).__finalize__(self)
# ----------------------------------------------------------------------
# Lookup Caching
def _clear_item_cache(self) -> None:
self._item_cache.clear()
def _get_item_cache(self, item: Hashable) -> Series:
"""Return the cached item, item represents a label indexer."""
if using_copy_on_write():
loc = self.columns.get_loc(item)
return self._ixs(loc, axis=1)
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)
res = self._ixs(loc, axis=1)
cache[item] = res
# for a chain
res._is_copy = self._is_copy
return res
def _reset_cacher(self) -> None:
# no-op for DataFrame
pass
def _maybe_cache_changed(self, item, value: Series, inplace: bool) -> None:
"""
The object has called back to us saying maybe it has changed.
"""
loc = self._info_axis.get_loc(item)
arraylike = value._values
old = self._ixs(loc, axis=1)
if old._values is value._values and inplace:
# GH#46149 avoid making unnecessary copies/block-splitting
return
self._mgr.iset(loc, arraylike, inplace=inplace)
# ----------------------------------------------------------------------
# Unsorted
@overload
def query(self, expr: str, *, inplace: Literal[False] = ..., **kwargs) -> DataFrame:
...
@overload
def query(self, expr: str, *, inplace: Literal[True], **kwargs) -> None:
...
@overload
def query(self, expr: str, *, inplace: bool = ..., **kwargs) -> DataFrame | None:
...
def query(self, expr: str, *, inplace: bool = False, **kwargs) -> DataFrame | None:
"""
Query the columns of a DataFrame with a boolean expression.
Parameters
----------
expr : str
The query string to evaluate.
You can refer to variables
in the environment by prefixing them with an '@' character like
``@a + b``.
You can refer to column names that are not valid Python variable names
by surrounding them in backticks. Thus, column names containing spaces
or punctuations (besides underscores) or starting with digits must be
surrounded by backticks. (For example, a column named "Area (cm^2)" would
be referenced as ```Area (cm^2)```). Column names which are Python keywords
(like "list", "for", "import", etc) cannot be used.
For example, if one of your columns is called ``a a`` and you want
to sum it with ``b``, your query should be ```a a` + b``.
inplace : bool
Whether to modify the DataFrame rather than creating a new one.
**kwargs
See the documentation for :func:`eval` for complete details
on the keyword arguments accepted by :meth:`DataFrame.query`.
Returns
-------
DataFrame or None
DataFrame resulting from the provided query expression or
None if ``inplace=True``.
See Also
--------
eval : Evaluate a string describing operations on
DataFrame columns.
DataFrame.eval : Evaluate a string describing operations on
DataFrame columns.
Notes
-----
The result of the evaluation of this expression is first passed to
:attr:`DataFrame.loc` and if that fails because of a
multidimensional key (e.g., a DataFrame) then the result will be passed
to :meth:`DataFrame.__getitem__`.
This method uses the top-level :func:`eval` function to
evaluate the passed query.
The :meth:`~pandas.DataFrame.query` method uses a slightly
modified Python syntax by default. For example, the ``&`` and ``|``
(bitwise) operators have the precedence of their boolean cousins,
:keyword:`and` and :keyword:`or`. This *is* syntactically valid Python,
however the semantics are different.
You can change the semantics of the expression by passing the keyword
argument ``parser='python'``. This enforces the same semantics as
evaluation in Python space. Likewise, you can pass ``engine='python'``
to evaluate an expression using Python itself as a backend. This is not
recommended as it is inefficient compared to using ``numexpr`` as the
engine.
The :attr:`DataFrame.index` and
:attr:`DataFrame.columns` attributes of the
:class:`~pandas.DataFrame` instance are placed in the query namespace
by default, which allows you to treat both the index and columns of the
frame as a column in the frame.
The identifier ``index`` is used for the frame index; you can also
use the name of the index to identify it in a query. Please note that
Python keywords may not be used as identifiers.
For further details and examples see the ``query`` documentation in
:ref:`indexing <indexing.query>`.
*Backtick quoted variables*
Backtick quoted variables are parsed as literal Python code and
are converted internally to a Python valid identifier.
This can lead to the following problems.
During parsing a number of disallowed characters inside the backtick
quoted string are replaced by strings that are allowed as a Python identifier.
These characters include all operators in Python, the space character, the
question mark, the exclamation mark, the dollar sign, and the euro sign.
For other characters that fall outside the ASCII range (U+0001..U+007F)
and those that are not further specified in PEP 3131,
the query parser will raise an error.
This excludes whitespace different than the space character,
but also the hashtag (as it is used for comments) and the backtick
itself (backtick can also not be escaped).
In a special case, quotes that make a pair around a backtick can
confuse the parser.
For example, ```it's` > `that's``` will raise an error,
as it forms a quoted string (``'s > `that'``) with a backtick inside.
See also the Python documentation about lexical analysis
(https://docs.python.org/3/reference/lexical_analysis.html)
in combination with the source code in :mod:`pandas.core.computation.parsing`.
Examples
--------
>>> df = pd.DataFrame({'A': range(1, 6),
... 'B': range(10, 0, -2),
... 'C C': range(10, 5, -1)})
>>> df
A B C C
0 1 10 10
1 2 8 9
2 3 6 8
3 4 4 7
4 5 2 6
>>> df.query('A > B')
A B C C
4 5 2 6
The previous expression is equivalent to
>>> df[df.A > df.B]
A B C C
4 5 2 6
For columns with spaces in their name, you can use backtick quoting.
>>> df.query('B == `C C`')
A B C C
0 1 10 10
The previous expression is equivalent to
>>> df[df.B == df['C C']]
A B C C
0 1 10 10
"""
inplace = validate_bool_kwarg(inplace, "inplace")
if not isinstance(expr, str):
msg = f"expr must be a string to be evaluated, {type(expr)} given"
raise ValueError(msg)
kwargs["level"] = kwargs.pop("level", 0) + 1
kwargs["target"] = None
res = self.eval(expr, **kwargs)
try:
result = self.loc[res]
except ValueError:
# when res is multi-dimensional loc raises, but this is sometimes a
# valid query
result = self[res]
if inplace:
self._update_inplace(result)
return None
else:
return result
@overload
def eval(self, expr: str, *, inplace: Literal[False] = ..., **kwargs) -> Any:
...
@overload
def eval(self, expr: str, *, inplace: Literal[True], **kwargs) -> None:
...
def eval(self, expr: str, *, inplace: bool = False, **kwargs) -> Any | None:
"""
Evaluate a string describing operations on DataFrame columns.
Operates on columns only, not specific rows or elements. This allows
`eval` to run arbitrary code, which can make you vulnerable to code
injection if you pass user input to this function.
Parameters
----------
expr : str
The expression string to evaluate.
inplace : bool, default False
If the expression contains an assignment, whether to perform the
operation inplace and mutate the existing DataFrame. Otherwise,
a new DataFrame is returned.
**kwargs
See the documentation for :func:`eval` for complete details
on the keyword arguments accepted by
:meth:`~pandas.DataFrame.query`.
Returns
-------
ndarray, scalar, pandas object, or None
The result of the evaluation or None if ``inplace=True``.
See Also
--------
DataFrame.query : Evaluates a boolean expression to query the columns
of a frame.
DataFrame.assign : Can evaluate an expression or function to create new
values for a column.
eval : Evaluate a Python expression as a string using various
backends.
Notes
-----
For more details see the API documentation for :func:`~eval`.
For detailed examples see :ref:`enhancing performance with eval
<enhancingperf.eval>`.
Examples
--------
>>> df = pd.DataFrame({'A': range(1, 6), 'B': range(10, 0, -2)})
>>> df
A B
0 1 10
1 2 8
2 3 6
3 4 4
4 5 2
>>> df.eval('A + B')
0 11
1 10
2 9
3 8
4 7
dtype: int64
Assignment is allowed though by default the original DataFrame is not
modified.
>>> df.eval('C = A + B')
A B C
0 1 10 11
1 2 8 10
2 3 6 9
3 4 4 8
4 5 2 7
>>> df
A B
0 1 10
1 2 8
2 3 6
3 4 4
4 5 2
Multiple columns can be assigned to using multi-line expressions:
>>> df.eval(
... '''
... C = A + B
... D = A - B
... '''
... )
A B C D
0 1 10 11 -9
1 2 8 10 -6
2 3 6 9 -3
3 4 4 8 0
4 5 2 7 3
"""
from pandas.core.computation.eval import eval as _eval
inplace = validate_bool_kwarg(inplace, "inplace")
kwargs["level"] = kwargs.pop("level", 0) + 1
index_resolvers = self._get_index_resolvers()
column_resolvers = self._get_cleaned_column_resolvers()
resolvers = column_resolvers, index_resolvers
if "target" not in kwargs:
kwargs["target"] = self
kwargs["resolvers"] = tuple(kwargs.get("resolvers", ())) + resolvers
return _eval(expr, inplace=inplace, **kwargs)
def select_dtypes(self, include=None, exclude=None) -> DataFrame:
"""
Return a subset of the DataFrame's columns based on the column dtypes.
Parameters
----------
include, exclude : scalar or list-like
A selection of dtypes or strings to be included/excluded. At least
one of these parameters must be supplied.
Returns
-------
DataFrame
The subset of the frame including the dtypes in ``include`` and
excluding the dtypes in ``exclude``.
Raises
------
ValueError
* If both of ``include`` and ``exclude`` are empty
* If ``include`` and ``exclude`` have overlapping elements
* If any kind of string dtype is passed in.
See Also
--------
DataFrame.dtypes: Return Series with the data type of each column.
Notes
-----
* To select all *numeric* types, use ``np.number`` or ``'number'``
* To select strings you must use the ``object`` dtype, but note that
this will return *all* object dtype columns
* See the `numpy dtype hierarchy
<https://numpy.org/doc/stable/reference/arrays.scalars.html>`__
* To select datetimes, use ``np.datetime64``, ``'datetime'`` or
``'datetime64'``
* To select timedeltas, use ``np.timedelta64``, ``'timedelta'`` or
``'timedelta64'``
* To select Pandas categorical dtypes, use ``'category'``
* To select Pandas datetimetz dtypes, use ``'datetimetz'`` (new in
0.20.0) or ``'datetime64[ns, tz]'``
Examples
--------
>>> df = pd.DataFrame({'a': [1, 2] * 3,
... 'b': [True, False] * 3,
... 'c': [1.0, 2.0] * 3})
>>> df
a b c
0 1 True 1.0
1 2 False 2.0
2 1 True 1.0
3 2 False 2.0
4 1 True 1.0
5 2 False 2.0
>>> df.select_dtypes(include='bool')
b
0 True
1 False
2 True
3 False
4 True
5 False
>>> df.select_dtypes(include=['float64'])
c
0 1.0
1 2.0
2 1.0
3 2.0
4 1.0
5 2.0
>>> df.select_dtypes(exclude=['int64'])
b c
0 True 1.0
1 False 2.0
2 True 1.0
3 False 2.0
4 True 1.0
5 False 2.0
"""
if not is_list_like(include):
include = (include,) if include is not None else ()
if not is_list_like(exclude):
exclude = (exclude,) if exclude is not None else ()
selection = (frozenset(include), frozenset(exclude))
if not any(selection):
raise ValueError("at least one of include or exclude must be nonempty")
# convert the myriad valid dtypes object to a single representation
def check_int_infer_dtype(dtypes):
converted_dtypes: list[type] = []
for dtype in dtypes:
# Numpy maps int to different types (int32, in64) on Windows and Linux
# see https://github.com/numpy/numpy/issues/9464
if (isinstance(dtype, str) and dtype == "int") or (dtype is int):
converted_dtypes.append(np.int32)
converted_dtypes.append(np.int64)
elif dtype == "float" or dtype is float:
# GH#42452 : np.dtype("float") coerces to np.float64 from Numpy 1.20
converted_dtypes.extend([np.float64, np.float32])
else:
converted_dtypes.append(infer_dtype_from_object(dtype))
return frozenset(converted_dtypes)
include = check_int_infer_dtype(include)
exclude = check_int_infer_dtype(exclude)
for dtypes in (include, exclude):
invalidate_string_dtypes(dtypes)
# can't both include AND exclude!
if not include.isdisjoint(exclude):
raise ValueError(f"include and exclude overlap on {(include & exclude)}")
def dtype_predicate(dtype: DtypeObj, dtypes_set) -> bool:
# GH 46870: BooleanDtype._is_numeric == True but should be excluded
dtype = dtype if not isinstance(dtype, ArrowDtype) else dtype.numpy_dtype
return issubclass(dtype.type, tuple(dtypes_set)) or (
np.number in dtypes_set
and getattr(dtype, "_is_numeric", False)
and not is_bool_dtype(dtype)
)
def predicate(arr: ArrayLike) -> bool:
dtype = arr.dtype
if include:
if not dtype_predicate(dtype, include):
return False
if exclude:
if dtype_predicate(dtype, exclude):
return False
return True
mgr = self._mgr._get_data_subset(predicate).copy(deep=None)
return type(self)(mgr).__finalize__(self)
def insert(
self,
loc: int,
column: Hashable,
value: Scalar | AnyArrayLike,
allow_duplicates: bool | lib.NoDefault = lib.no_default,
) -> None:
"""
Insert column into DataFrame at specified location.
Raises a ValueError if `column` is already contained in the DataFrame,
unless `allow_duplicates` is set to True.
Parameters
----------
loc : int
Insertion index. Must verify 0 <= loc <= len(columns).
column : str, number, or hashable object
Label of the inserted column.
value : Scalar, Series, or array-like
allow_duplicates : bool, optional, default lib.no_default
See Also
--------
Index.insert : Insert new item by index.
Examples
--------
>>> df = pd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> df
col1 col2
0 1 3
1 2 4
>>> df.insert(1, "newcol", [99, 99])
>>> df
col1 newcol col2
0 1 99 3
1 2 99 4
>>> df.insert(0, "col1", [100, 100], allow_duplicates=True)
>>> df
col1 col1 newcol col2
0 100 1 99 3
1 100 2 99 4
Notice that pandas uses index alignment in case of `value` from type `Series`:
>>> df.insert(0, "col0", pd.Series([5, 6], index=[1, 2]))
>>> df
col0 col1 col1 newcol col2
0 NaN 100 1 99 3
1 5.0 100 2 99 4
"""
if allow_duplicates is lib.no_default:
allow_duplicates = False
if allow_duplicates and not self.flags.allows_duplicate_labels:
raise ValueError(
"Cannot specify 'allow_duplicates=True' when "
"'self.flags.allows_duplicate_labels' is False."
)
if not allow_duplicates and column in self.columns:
# Should this be a different kind of error??
raise ValueError(f"cannot insert {column}, already exists")
if not isinstance(loc, int):
raise TypeError("loc must be int")
value = self._sanitize_column(value)
self._mgr.insert(loc, column, value)
def assign(self, **kwargs) -> DataFrame:
r"""
Assign new columns to a DataFrame.
Returns a new object with all original columns in addition to new ones.
Existing columns that are re-assigned will be overwritten.
Parameters
----------
**kwargs : dict of {str: callable or Series}
The column names are keywords. If the values are
callable, they are computed on the DataFrame and
assigned to the new columns. The callable must not
change input DataFrame (though pandas doesn't check it).
If the values are not callable, (e.g. a Series, scalar, or array),
they are simply assigned.
Returns
-------
DataFrame
A new DataFrame with the new columns in addition to
all the existing columns.
Notes
-----
Assigning multiple columns within the same ``assign`` is possible.
Later items in '\*\*kwargs' may refer to newly created or modified
columns in 'df'; items are computed and assigned into 'df' in order.
Examples
--------
>>> df = pd.DataFrame({'temp_c': [17.0, 25.0]},
... index=['Portland', 'Berkeley'])
>>> df
temp_c
Portland 17.0
Berkeley 25.0
Where the value is a callable, evaluated on `df`:
>>> df.assign(temp_f=lambda x: x.temp_c * 9 / 5 + 32)
temp_c temp_f
Portland 17.0 62.6
Berkeley 25.0 77.0
Alternatively, the same behavior can be achieved by directly
referencing an existing Series or sequence:
>>> df.assign(temp_f=df['temp_c'] * 9 / 5 + 32)
temp_c temp_f
Portland 17.0 62.6
Berkeley 25.0 77.0
You can create multiple columns within the same assign where one
of the columns depends on another one defined within the same assign:
>>> df.assign(temp_f=lambda x: x['temp_c'] * 9 / 5 + 32,
... temp_k=lambda x: (x['temp_f'] + 459.67) * 5 / 9)
temp_c temp_f temp_k
Portland 17.0 62.6 290.15
Berkeley 25.0 77.0 298.15
"""
data = self.copy(deep=None)
for k, v in kwargs.items():
data[k] = com.apply_if_callable(v, data)
return data
def _sanitize_column(self, value) -> ArrayLike:
"""
Ensures new columns (which go into the BlockManager as new blocks) are
always copied and converted into an array.
Parameters
----------
value : scalar, Series, or array-like
Returns
-------
numpy.ndarray or ExtensionArray
"""
self._ensure_valid_index(value)
# We can get there through isetitem with a DataFrame
# or through loc single_block_path
if isinstance(value, DataFrame):
return _reindex_for_setitem(value, self.index)
elif is_dict_like(value):
return _reindex_for_setitem(Series(value), self.index)
if is_list_like(value):
com.require_length_match(value, self.index)
return sanitize_array(value, self.index, copy=True, allow_2d=True)
@property
def _series(self):
return {
item: Series(
self._mgr.iget(idx), index=self.index, name=item, fastpath=True
)
for idx, item in enumerate(self.columns)
}
# ----------------------------------------------------------------------
# Reindexing and alignment
def _reindex_axes(self, axes, level, limit, tolerance, method, fill_value, copy):
frame = self
columns = axes["columns"]
if columns is not None:
frame = frame._reindex_columns(
columns, method, copy, level, fill_value, limit, tolerance
)
index = axes["index"]
if index is not None:
frame = frame._reindex_index(
index, method, copy, level, fill_value, limit, tolerance
)
return frame
def _reindex_index(
self,
new_index,
method,
copy: bool,
level: Level,
fill_value=np.nan,
limit=None,
tolerance=None,
):
new_index, indexer = self.index.reindex(
new_index, method=method, level=level, limit=limit, tolerance=tolerance
)
return self._reindex_with_indexers(
{0: [new_index, indexer]},
copy=copy,
fill_value=fill_value,
allow_dups=False,
)
def _reindex_columns(
self,
new_columns,
method,
copy: bool,
level: Level,
fill_value=None,
limit=None,
tolerance=None,
):
new_columns, indexer = self.columns.reindex(
new_columns, method=method, level=level, limit=limit, tolerance=tolerance
)
return self._reindex_with_indexers(
{1: [new_columns, indexer]},
copy=copy,
fill_value=fill_value,
allow_dups=False,
)
def _reindex_multi(
self, axes: dict[str, Index], copy: bool, fill_value
) -> DataFrame:
"""
We are guaranteed non-Nones in the axes.
"""
new_index, row_indexer = self.index.reindex(axes["index"])
new_columns, col_indexer = self.columns.reindex(axes["columns"])
if row_indexer is not None and col_indexer is not None:
# Fastpath. By doing two 'take's at once we avoid making an
# unnecessary copy.
# We only get here with `not self._is_mixed_type`, which (almost)
# ensures that self.values is cheap. It may be worth making this
# condition more specific.
indexer = row_indexer, col_indexer
new_values = take_2d_multi(self.values, indexer, fill_value=fill_value)
return self._constructor(
new_values, index=new_index, columns=new_columns, copy=False
)
else:
return self._reindex_with_indexers(
{0: [new_index, row_indexer], 1: [new_columns, col_indexer]},
copy=copy,
fill_value=fill_value,
)
@doc(NDFrame.align, **_shared_doc_kwargs)
def align(
self,
other: DataFrame,
join: AlignJoin = "outer",
axis: Axis | None = None,
level: Level = None,
copy: bool | None = None,
fill_value=None,
method: FillnaOptions | None = None,
limit: int | None = None,
fill_axis: Axis = 0,
broadcast_axis: Axis | None = None,
) -> DataFrame:
return super().align(
other,
join=join,
axis=axis,
level=level,
copy=copy,
fill_value=fill_value,
method=method,
limit=limit,
fill_axis=fill_axis,
broadcast_axis=broadcast_axis,
)
@Appender(
"""
Examples
--------
>>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
Change the row labels.
>>> df.set_axis(['a', 'b', 'c'], axis='index')
A B
a 1 4
b 2 5
c 3 6
Change the column labels.
>>> df.set_axis(['I', 'II'], axis='columns')
I II
0 1 4
1 2 5
2 3 6
"""
)
@Substitution(
**_shared_doc_kwargs,
extended_summary_sub=" column or",
axis_description_sub=", and 1 identifies the columns",
see_also_sub=" or columns",
)
@Appender(NDFrame.set_axis.__doc__)
def set_axis(
self,
labels,
*,
axis: Axis = 0,
copy: bool | None = None,
) -> DataFrame:
return super().set_axis(labels, axis=axis, copy=copy)
@doc(
NDFrame.reindex,
klass=_shared_doc_kwargs["klass"],
optional_reindex=_shared_doc_kwargs["optional_reindex"],
)
def reindex( # type: ignore[override]
self,
labels=None,
*,
index=None,
columns=None,
axis: Axis | None = None,
method: str | None = None,
copy: bool | None = None,
level: Level | None = None,
fill_value: Scalar | None = np.nan,
limit: int | None = None,
tolerance=None,
) -> DataFrame:
return super().reindex(
labels=labels,
index=index,
columns=columns,
axis=axis,
method=method,
copy=copy,
level=level,
fill_value=fill_value,
limit=limit,
tolerance=tolerance,
)
@overload
def drop(
self,
labels: IndexLabel = ...,
*,
axis: Axis = ...,
index: IndexLabel = ...,
columns: IndexLabel = ...,
level: Level = ...,
inplace: Literal[True],
errors: IgnoreRaise = ...,
) -> None:
...
@overload
def drop(
self,
labels: IndexLabel = ...,
*,
axis: Axis = ...,
index: IndexLabel = ...,
columns: IndexLabel = ...,
level: Level = ...,
inplace: Literal[False] = ...,
errors: IgnoreRaise = ...,
) -> DataFrame:
...
@overload
def drop(
self,
labels: IndexLabel = ...,
*,
axis: Axis = ...,
index: IndexLabel = ...,
columns: IndexLabel = ...,
level: Level = ...,
inplace: bool = ...,
errors: IgnoreRaise = ...,
) -> DataFrame | None:
...
def drop(
self,
labels: IndexLabel = None,
*,
axis: Axis = 0,
index: IndexLabel = None,
columns: IndexLabel = None,
level: Level = None,
inplace: bool = False,
errors: IgnoreRaise = "raise",
) -> DataFrame | None:
"""
Drop specified labels from rows or columns.
Remove rows or columns by specifying label names and corresponding
axis, or by specifying directly index or column names. When using a
multi-index, labels on different levels can be removed by specifying
the level. See the :ref:`user guide <advanced.shown_levels>`
for more information about the now unused levels.
Parameters
----------
labels : single label or list-like
Index or column labels to drop. A tuple will be used as a single
label and not treated as a list-like.
axis : {0 or 'index', 1 or 'columns'}, default 0
Whether to drop labels from the index (0 or 'index') or
columns (1 or 'columns').
index : single label or list-like
Alternative to specifying axis (``labels, axis=0``
is equivalent to ``index=labels``).
columns : single label or list-like
Alternative to specifying axis (``labels, axis=1``
is equivalent to ``columns=labels``).
level : int or level name, optional
For MultiIndex, level from which the labels will be removed.
inplace : bool, default False
If False, return a copy. Otherwise, do operation
inplace and return None.
errors : {'ignore', 'raise'}, default 'raise'
If 'ignore', suppress error and only existing labels are
dropped.
Returns
-------
DataFrame or None
DataFrame without the removed index or column labels or
None if ``inplace=True``.
Raises
------
KeyError
If any of the labels is not found in the selected axis.
See Also
--------
DataFrame.loc : Label-location based indexer for selection by label.
DataFrame.dropna : Return DataFrame with labels on given axis omitted
where (all or any) data are missing.
DataFrame.drop_duplicates : Return DataFrame with duplicate rows
removed, optionally only considering certain columns.
Series.drop : Return Series with specified index labels removed.
Examples
--------
>>> df = pd.DataFrame(np.arange(12).reshape(3, 4),
... columns=['A', 'B', 'C', 'D'])
>>> df
A B C D
0 0 1 2 3
1 4 5 6 7
2 8 9 10 11
Drop columns
>>> df.drop(['B', 'C'], axis=1)
A D
0 0 3
1 4 7
2 8 11
>>> df.drop(columns=['B', 'C'])
A D
0 0 3
1 4 7
2 8 11
Drop a row by index
>>> df.drop([0, 1])
A B C D
2 8 9 10 11
Drop columns and/or rows of MultiIndex DataFrame
>>> midx = pd.MultiIndex(levels=[['lama', 'cow', 'falcon'],
... ['speed', 'weight', 'length']],
... codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2],
... [0, 1, 2, 0, 1, 2, 0, 1, 2]])
>>> df = pd.DataFrame(index=midx, columns=['big', 'small'],
... data=[[45, 30], [200, 100], [1.5, 1], [30, 20],
... [250, 150], [1.5, 0.8], [320, 250],
... [1, 0.8], [0.3, 0.2]])
>>> df
big small
lama speed 45.0 30.0
weight 200.0 100.0
length 1.5 1.0
cow speed 30.0 20.0
weight 250.0 150.0
length 1.5 0.8
falcon speed 320.0 250.0
weight 1.0 0.8
length 0.3 0.2
Drop a specific index combination from the MultiIndex
DataFrame, i.e., drop the combination ``'falcon'`` and
``'weight'``, which deletes only the corresponding row
>>> df.drop(index=('falcon', 'weight'))
big small
lama speed 45.0 30.0
weight 200.0 100.0
length 1.5 1.0
cow speed 30.0 20.0
weight 250.0 150.0
length 1.5 0.8
falcon speed 320.0 250.0
length 0.3 0.2
>>> df.drop(index='cow', columns='small')
big
lama speed 45.0
weight 200.0
length 1.5
falcon speed 320.0
weight 1.0
length 0.3
>>> df.drop(index='length', level=1)
big small
lama speed 45.0 30.0
weight 200.0 100.0
cow speed 30.0 20.0
weight 250.0 150.0
falcon speed 320.0 250.0
weight 1.0 0.8
"""
return super().drop(
labels=labels,
axis=axis,
index=index,
columns=columns,
level=level,
inplace=inplace,
errors=errors,
)
@overload
def rename(
self,
mapper: Renamer | None = ...,
*,
index: Renamer | None = ...,
columns: Renamer | None = ...,
axis: Axis | None = ...,
copy: bool | None = ...,
inplace: Literal[True],
level: Level = ...,
errors: IgnoreRaise = ...,
) -> None:
...
@overload
def rename(
self,
mapper: Renamer | None = ...,
*,
index: Renamer | None = ...,
columns: Renamer | None = ...,
axis: Axis | None = ...,
copy: bool | None = ...,
inplace: Literal[False] = ...,
level: Level = ...,
errors: IgnoreRaise = ...,
) -> DataFrame:
...
@overload
def rename(
self,
mapper: Renamer | None = ...,
*,
index: Renamer | None = ...,
columns: Renamer | None = ...,
axis: Axis | None = ...,
copy: bool | None = ...,
inplace: bool = ...,
level: Level = ...,
errors: IgnoreRaise = ...,
) -> DataFrame | None:
...
def rename(
self,
mapper: Renamer | None = None,
*,
index: Renamer | None = None,
columns: Renamer | None = None,
axis: Axis | None = None,
copy: bool | None = None,
inplace: bool = False,
level: Level = None,
errors: IgnoreRaise = "ignore",
) -> DataFrame | None:
"""
Rename columns or index labels.
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.
See the :ref:`user guide <basics.rename>` for more.
Parameters
----------
mapper : dict-like or function
Dict-like or function transformations to apply to
that axis' values. Use either ``mapper`` and ``axis`` to
specify the axis to target with ``mapper``, or ``index`` and
``columns``.
index : dict-like or function
Alternative to specifying axis (``mapper, axis=0``
is equivalent to ``index=mapper``).
columns : dict-like or function
Alternative to specifying axis (``mapper, axis=1``
is equivalent to ``columns=mapper``).
axis : {0 or 'index', 1 or 'columns'}, default 0
Axis to target with ``mapper``. Can be either the axis name
('index', 'columns') or number (0, 1). The default is 'index'.
copy : bool, default True
Also copy underlying data.
inplace : bool, default False
Whether to modify the DataFrame rather than creating a new one.
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
-------
DataFrame or None
DataFrame with the renamed axis labels or None if ``inplace=True``.
Raises
------
KeyError
If any of the labels is not found in the selected axis and
"errors='raise'".
See Also
--------
DataFrame.rename_axis : Set the name of the axis.
Examples
--------
``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.
Rename columns using a mapping:
>>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
>>> df.rename(columns={"A": "a", "B": "c"})
a c
0 1 4
1 2 5
2 3 6
Rename index using a mapping:
>>> df.rename(index={0: "x", 1: "y", 2: "z"})
A B
x 1 4
y 2 5
z 3 6
Cast index labels to a different type:
>>> df.index
RangeIndex(start=0, stop=3, step=1)
>>> df.rename(index=str).index
Index(['0', '1', '2'], dtype='object')
>>> df.rename(columns={"A": "a", "B": "b", "C": "c"}, errors="raise")
Traceback (most recent call last):
KeyError: ['C'] not found in axis
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
"""
return super()._rename(
mapper=mapper,
index=index,
columns=columns,
axis=axis,
copy=copy,
inplace=inplace,
level=level,
errors=errors,
)
@overload
def fillna(
self,
value: Hashable | Mapping | Series | DataFrame = ...,
*,
method: FillnaOptions | None = ...,
axis: Axis | None = ...,
inplace: Literal[False] = ...,
limit: int | None = ...,
downcast: dict | None = ...,
) -> DataFrame:
...
@overload
def fillna(
self,
value: Hashable | Mapping | Series | DataFrame = ...,
*,
method: FillnaOptions | None = ...,
axis: Axis | None = ...,
inplace: Literal[True],
limit: int | None = ...,
downcast: dict | None = ...,
) -> None:
...
@overload
def fillna(
self,
value: Hashable | Mapping | Series | DataFrame = ...,
*,
method: FillnaOptions | None = ...,
axis: Axis | None = ...,
inplace: bool = ...,
limit: int | None = ...,
downcast: dict | None = ...,
) -> DataFrame | None:
...
@doc(NDFrame.fillna, **_shared_doc_kwargs)
def fillna(
self,
value: Hashable | Mapping | Series | DataFrame = None,
*,
method: FillnaOptions | None = None,
axis: Axis | None = None,
inplace: bool = False,
limit: int | None = None,
downcast: dict | None = None,
) -> DataFrame | None:
return super().fillna(
value=value,
method=method,
axis=axis,
inplace=inplace,
limit=limit,
downcast=downcast,
)
def pop(self, item: Hashable) -> Series:
"""
Return item and drop from frame. Raise KeyError if not found.
Parameters
----------
item : label
Label of column to be popped.
Returns
-------
Series
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'))
>>> df
name class max_speed
0 falcon bird 389.0
1 parrot bird 24.0
2 lion mammal 80.5
3 monkey mammal NaN
>>> df.pop('class')
0 bird
1 bird
2 mammal
3 mammal
Name: class, dtype: object
>>> df
name max_speed
0 falcon 389.0
1 parrot 24.0
2 lion 80.5
3 monkey NaN
"""
return super().pop(item=item)
@overload
def replace(
self,
to_replace=...,
value=...,
*,
inplace: Literal[False] = ...,
limit: int | None = ...,
regex: bool = ...,
method: Literal["pad", "ffill", "bfill"] | lib.NoDefault = ...,
) -> DataFrame:
...
@overload
def replace(
self,
to_replace=...,
value=...,
*,
inplace: Literal[True],
limit: int | None = ...,
regex: bool = ...,
method: Literal["pad", "ffill", "bfill"] | lib.NoDefault = ...,
) -> None:
...
@doc(NDFrame.replace, **_shared_doc_kwargs)
def replace(
self,
to_replace=None,
value=lib.no_default,
*,
inplace: bool = False,
limit: int | None = None,
regex: bool = False,
method: Literal["pad", "ffill", "bfill"] | lib.NoDefault = lib.no_default,
) -> DataFrame | None:
return super().replace(
to_replace=to_replace,
value=value,
inplace=inplace,
limit=limit,
regex=regex,
method=method,
)
def _replace_columnwise(
self, mapping: dict[Hashable, tuple[Any, Any]], inplace: bool, regex
):
"""
Dispatch to Series.replace column-wise.
Parameters
----------
mapping : dict
of the form {col: (target, value)}
inplace : bool
regex : bool or same types as `to_replace` in DataFrame.replace
Returns
-------
DataFrame or None
"""
# Operate column-wise
res = self if inplace else self.copy(deep=None)
ax = self.columns
for i, ax_value in enumerate(ax):
if ax_value in mapping:
ser = self.iloc[:, i]
target, value = mapping[ax_value]
newobj = ser.replace(target, value, regex=regex)
res._iset_item(i, newobj)
if inplace:
return
return res.__finalize__(self)
@doc(NDFrame.shift, klass=_shared_doc_kwargs["klass"])
def shift(
self,
periods: int = 1,
freq: Frequency | None = None,
axis: Axis = 0,
fill_value: Hashable = lib.no_default,
) -> DataFrame:
axis = self._get_axis_number(axis)
ncols = len(self.columns)
if (
axis == 1
and periods != 0
and freq is None
and fill_value is lib.no_default
and ncols > 0
):
# We will infer fill_value to match the closest column
# Use a column that we know is valid for our column's dtype GH#38434
label = self.columns[0]
if periods > 0:
result = self.iloc[:, :-periods]
for col in range(min(ncols, abs(periods))):
# TODO(EA2D): doing this in a loop unnecessary with 2D EAs
# Define filler inside loop so we get a copy
filler = self.iloc[:, 0].shift(len(self))
result.insert(0, label, filler, allow_duplicates=True)
else:
result = self.iloc[:, -periods:]
for col in range(min(ncols, abs(periods))):
# Define filler inside loop so we get a copy
filler = self.iloc[:, -1].shift(len(self))
result.insert(
len(result.columns), label, filler, allow_duplicates=True
)
result.columns = self.columns.copy()
return result
elif (
axis == 1
and periods != 0
and fill_value is not lib.no_default
and ncols > 0
):
arrays = self._mgr.arrays
if len(arrays) > 1 or (
# If we only have one block and we know that we can't
# keep the same dtype (i.e. the _can_hold_element check)
# then we can go through the reindex_indexer path
# (and avoid casting logic in the Block method).
not can_hold_element(arrays[0], fill_value)
):
# GH#35488 we need to watch out for multi-block cases
# We only get here with fill_value not-lib.no_default
nper = abs(periods)
nper = min(nper, ncols)
if periods > 0:
indexer = np.array(
[-1] * nper + list(range(ncols - periods)), dtype=np.intp
)
else:
indexer = np.array(
list(range(nper, ncols)) + [-1] * nper, dtype=np.intp
)
mgr = self._mgr.reindex_indexer(
self.columns,
indexer,
axis=0,
fill_value=fill_value,
allow_dups=True,
)
res_df = self._constructor(mgr)
return res_df.__finalize__(self, method="shift")
return super().shift(
periods=periods, freq=freq, axis=axis, fill_value=fill_value
)
@overload
def set_index(
self,
keys,
*,
drop: bool = ...,
append: bool = ...,
inplace: Literal[False] = ...,
verify_integrity: bool = ...,
) -> DataFrame:
...
@overload
def set_index(
self,
keys,
*,
drop: bool = ...,
append: bool = ...,
inplace: Literal[True],
verify_integrity: bool = ...,
) -> None:
...
def set_index(
self,
keys,
*,
drop: bool = True,
append: bool = False,
inplace: bool = False,
verify_integrity: bool = False,
) -> DataFrame | None:
"""
Set the DataFrame index using existing columns.
Set the DataFrame index (row labels) using one or more existing
columns or arrays (of the correct length). The index can replace the
existing index or expand on it.
Parameters
----------
keys : label or array-like or list of labels/arrays
This parameter can be either a single column key, a single array of
the same length as the calling DataFrame, or a list containing an
arbitrary combination of column keys and arrays. Here, "array"
encompasses :class:`Series`, :class:`Index`, ``np.ndarray``, and
instances of :class:`~collections.abc.Iterator`.
drop : bool, default True
Delete columns to be used as the new index.
append : bool, default False
Whether to append columns to existing index.
inplace : bool, default False
Whether to modify the DataFrame rather than creating a new one.
verify_integrity : bool, default False
Check the new index for duplicates. Otherwise defer the check until
necessary. Setting to False will improve the performance of this
method.
Returns
-------
DataFrame or None
Changed row labels or None if ``inplace=True``.
See Also
--------
DataFrame.reset_index : Opposite of set_index.
DataFrame.reindex : Change to new indices or expand indices.
DataFrame.reindex_like : Change to same indices as other DataFrame.
Examples
--------
>>> df = pd.DataFrame({'month': [1, 4, 7, 10],
... 'year': [2012, 2014, 2013, 2014],
... 'sale': [55, 40, 84, 31]})
>>> df
month year sale
0 1 2012 55
1 4 2014 40
2 7 2013 84
3 10 2014 31
Set the index to become the 'month' column:
>>> df.set_index('month')
year sale
month
1 2012 55
4 2014 40
7 2013 84
10 2014 31
Create a MultiIndex using columns 'year' and 'month':
>>> df.set_index(['year', 'month'])
sale
year month
2012 1 55
2014 4 40
2013 7 84
2014 10 31
Create a MultiIndex using an Index and a column:
>>> df.set_index([pd.Index([1, 2, 3, 4]), 'year'])
month sale
year
1 2012 1 55
2 2014 4 40
3 2013 7 84
4 2014 10 31
Create a MultiIndex using two Series:
>>> s = pd.Series([1, 2, 3, 4])
>>> df.set_index([s, s**2])
month year sale
1 1 1 2012 55
2 4 4 2014 40
3 9 7 2013 84
4 16 10 2014 31
"""
inplace = validate_bool_kwarg(inplace, "inplace")
self._check_inplace_and_allows_duplicate_labels(inplace)
if not isinstance(keys, list):
keys = [keys]
err_msg = (
'The parameter "keys" may be a column key, one-dimensional '
"array, or a list containing only valid column keys and "
"one-dimensional arrays."
)
missing: list[Hashable] = []
for col in keys:
if isinstance(col, (Index, Series, np.ndarray, list, abc.Iterator)):
# arrays are fine as long as they are one-dimensional
# iterators get converted to list below
if getattr(col, "ndim", 1) != 1:
raise ValueError(err_msg)
else:
# everything else gets tried as a key; see GH 24969
try:
found = col in self.columns
except TypeError as err:
raise TypeError(
f"{err_msg}. Received column of type {type(col)}"
) from err
else:
if not found:
missing.append(col)
if missing:
raise KeyError(f"None of {missing} are in the columns")
if inplace:
frame = self
else:
# GH 49473 Use "lazy copy" with Copy-on-Write
frame = self.copy(deep=None)
arrays = []
names: list[Hashable] = []
if append:
names = list(self.index.names)
if isinstance(self.index, MultiIndex):
for i in range(self.index.nlevels):
arrays.append(self.index._get_level_values(i))
else:
arrays.append(self.index)
to_remove: list[Hashable] = []
for col in keys:
if isinstance(col, MultiIndex):
for n in range(col.nlevels):
arrays.append(col._get_level_values(n))
names.extend(col.names)
elif isinstance(col, (Index, Series)):
# if Index then not MultiIndex (treated above)
# error: Argument 1 to "append" of "list" has incompatible type
# "Union[Index, Series]"; expected "Index"
arrays.append(col) # type:ignore[arg-type]
names.append(col.name)
elif isinstance(col, (list, np.ndarray)):
# error: Argument 1 to "append" of "list" has incompatible type
# "Union[List[Any], ndarray]"; expected "Index"
arrays.append(col) # type: ignore[arg-type]
names.append(None)
elif isinstance(col, abc.Iterator):
# error: Argument 1 to "append" of "list" has incompatible type
# "List[Any]"; expected "Index"
arrays.append(list(col)) # type: ignore[arg-type]
names.append(None)
# from here, col can only be a column label
else:
arrays.append(frame[col])
names.append(col)
if drop:
to_remove.append(col)
if len(arrays[-1]) != len(self):
# check newest element against length of calling frame, since
# ensure_index_from_sequences would not raise for append=False.
raise ValueError(
f"Length mismatch: Expected {len(self)} rows, "
f"received array of length {len(arrays[-1])}"
)
index = ensure_index_from_sequences(arrays, names)
if verify_integrity and not index.is_unique:
duplicates = index[index.duplicated()].unique()
raise ValueError(f"Index has duplicate keys: {duplicates}")
# use set to handle duplicate column names gracefully in case of drop
for c in set(to_remove):
del frame[c]
# clear up memory usage
index._cleanup()
frame.index = index
if not inplace:
return frame
return None
@overload
def reset_index(
self,
level: IndexLabel = ...,
*,
drop: bool = ...,
inplace: Literal[False] = ...,
col_level: Hashable = ...,
col_fill: Hashable = ...,
allow_duplicates: bool | lib.NoDefault = ...,
names: Hashable | Sequence[Hashable] = None,
) -> DataFrame:
...
@overload
def reset_index(
self,
level: IndexLabel = ...,
*,
drop: bool = ...,
inplace: Literal[True],
col_level: Hashable = ...,
col_fill: Hashable = ...,
allow_duplicates: bool | lib.NoDefault = ...,
names: Hashable | Sequence[Hashable] = None,
) -> None:
...
@overload
def reset_index(
self,
level: IndexLabel = ...,
*,
drop: bool = ...,
inplace: bool = ...,
col_level: Hashable = ...,
col_fill: Hashable = ...,
allow_duplicates: bool | lib.NoDefault = ...,
names: Hashable | Sequence[Hashable] = None,
) -> DataFrame | None:
...
def reset_index(
self,
level: IndexLabel = None,
*,
drop: bool = False,
inplace: bool = False,
col_level: Hashable = 0,
col_fill: Hashable = "",
allow_duplicates: bool | lib.NoDefault = lib.no_default,
names: Hashable | Sequence[Hashable] = None,
) -> DataFrame | None:
"""
Reset the index, or a level of it.
Reset the index of the DataFrame, and use the default one instead.
If the DataFrame has a MultiIndex, this method can remove one or more
levels.
Parameters
----------
level : int, str, tuple, or list, default None
Only remove the given levels from the index. Removes all levels by
default.
drop : bool, default False
Do not try to insert index into dataframe columns. This resets
the index to the default integer index.
inplace : bool, default False
Whether to modify the DataFrame rather than creating a new one.
col_level : int or str, default 0
If the columns have multiple levels, determines which level the
labels are inserted into. By default it is inserted into the first
level.
col_fill : object, default ''
If the columns have multiple levels, determines how the other
levels are named. If None then the index name is repeated.
allow_duplicates : bool, optional, default lib.no_default
Allow duplicate column labels to be created.
.. versionadded:: 1.5.0
names : int, str or 1-dimensional list, default None
Using the given string, rename the DataFrame column which contains the
index data. If the DataFrame has a MultiIndex, this has to be a list or
tuple with length equal to the number of levels.
.. versionadded:: 1.5.0
Returns
-------
DataFrame or None
DataFrame with the new index or None if ``inplace=True``.
See Also
--------
DataFrame.set_index : Opposite of reset_index.
DataFrame.reindex : Change to new indices or expand indices.
DataFrame.reindex_like : Change to same indices as other DataFrame.
Examples
--------
>>> df = pd.DataFrame([('bird', 389.0),
... ('bird', 24.0),
... ('mammal', 80.5),
... ('mammal', np.nan)],
... index=['falcon', 'parrot', 'lion', 'monkey'],
... columns=('class', 'max_speed'))
>>> df
class max_speed
falcon bird 389.0
parrot bird 24.0
lion mammal 80.5
monkey mammal NaN
When we reset the index, the old index is added as a column, and a
new sequential index is used:
>>> df.reset_index()
index class max_speed
0 falcon bird 389.0
1 parrot bird 24.0
2 lion mammal 80.5
3 monkey mammal NaN
We can use the `drop` parameter to avoid the old index being added as
a column:
>>> df.reset_index(drop=True)
class max_speed
0 bird 389.0
1 bird 24.0
2 mammal 80.5
3 mammal NaN
You can also use `reset_index` with `MultiIndex`.
>>> index = pd.MultiIndex.from_tuples([('bird', 'falcon'),
... ('bird', 'parrot'),
... ('mammal', 'lion'),
... ('mammal', 'monkey')],
... names=['class', 'name'])
>>> columns = pd.MultiIndex.from_tuples([('speed', 'max'),
... ('species', 'type')])
>>> df = pd.DataFrame([(389.0, 'fly'),
... (24.0, 'fly'),
... (80.5, 'run'),
... (np.nan, 'jump')],
... index=index,
... columns=columns)
>>> df
speed species
max type
class name
bird falcon 389.0 fly
parrot 24.0 fly
mammal lion 80.5 run
monkey NaN jump
Using the `names` parameter, choose a name for the index column:
>>> df.reset_index(names=['classes', 'names'])
classes names speed species
max type
0 bird falcon 389.0 fly
1 bird parrot 24.0 fly
2 mammal lion 80.5 run
3 mammal monkey NaN jump
If the index has multiple levels, we can reset a subset of them:
>>> df.reset_index(level='class')
class speed species
max type
name
falcon bird 389.0 fly
parrot bird 24.0 fly
lion mammal 80.5 run
monkey mammal NaN jump
If we are not dropping the index, by default, it is placed in the top
level. We can place it in another level:
>>> df.reset_index(level='class', col_level=1)
speed species
class max type
name
falcon bird 389.0 fly
parrot bird 24.0 fly
lion mammal 80.5 run
monkey mammal NaN jump
When the index is inserted under another level, we can specify under
which one with the parameter `col_fill`:
>>> df.reset_index(level='class', col_level=1, col_fill='species')
species speed species
class max type
name
falcon bird 389.0 fly
parrot bird 24.0 fly
lion mammal 80.5 run
monkey mammal NaN jump
If we specify a nonexistent level for `col_fill`, it is created:
>>> df.reset_index(level='class', col_level=1, col_fill='genus')
genus speed species
class max type
name
falcon bird 389.0 fly
parrot bird 24.0 fly
lion mammal 80.5 run
monkey mammal NaN jump
"""
inplace = validate_bool_kwarg(inplace, "inplace")
self._check_inplace_and_allows_duplicate_labels(inplace)
if inplace:
new_obj = self
else:
new_obj = self.copy(deep=None)
if allow_duplicates is not lib.no_default:
allow_duplicates = validate_bool_kwarg(allow_duplicates, "allow_duplicates")
new_index = default_index(len(new_obj))
if level is not None:
if not isinstance(level, (tuple, list)):
level = [level]
level = [self.index._get_level_number(lev) for lev in level]
if len(level) < self.index.nlevels:
new_index = self.index.droplevel(level)
if not drop:
to_insert: Iterable[tuple[Any, Any | None]]
default = "index" if "index" not in self else "level_0"
names = self.index._get_default_index_names(names, default)
if isinstance(self.index, MultiIndex):
to_insert = zip(self.index.levels, self.index.codes)
else:
to_insert = ((self.index, None),)
multi_col = isinstance(self.columns, MultiIndex)
for i, (lev, lab) in reversed(list(enumerate(to_insert))):
if level is not None and i not in level:
continue
name = names[i]
if multi_col:
col_name = list(name) if isinstance(name, tuple) else [name]
if col_fill is None:
if len(col_name) not in (1, self.columns.nlevels):
raise ValueError(
"col_fill=None is incompatible "
f"with incomplete column name {name}"
)
col_fill = col_name[0]
lev_num = self.columns._get_level_number(col_level)
name_lst = [col_fill] * lev_num + col_name
missing = self.columns.nlevels - len(name_lst)
name_lst += [col_fill] * missing
name = tuple(name_lst)
# to ndarray and maybe infer different dtype
level_values = lev._values
if level_values.dtype == np.object_:
level_values = lib.maybe_convert_objects(level_values)
if lab is not None:
# if we have the codes, extract the values with a mask
level_values = algorithms.take(
level_values, lab, allow_fill=True, fill_value=lev._na_value
)
new_obj.insert(
0,
name,
level_values,
allow_duplicates=allow_duplicates,
)
new_obj.index = new_index
if not inplace:
return new_obj
return None
# ----------------------------------------------------------------------
# Reindex-based selection methods
@doc(NDFrame.isna, klass=_shared_doc_kwargs["klass"])
def isna(self) -> DataFrame:
result = self._constructor(self._mgr.isna(func=isna))
return result.__finalize__(self, method="isna")
@doc(NDFrame.isna, klass=_shared_doc_kwargs["klass"])
def isnull(self) -> DataFrame:
"""
DataFrame.isnull is an alias for DataFrame.isna.
"""
return self.isna()
@doc(NDFrame.notna, klass=_shared_doc_kwargs["klass"])
def notna(self) -> DataFrame:
return ~self.isna()
@doc(NDFrame.notna, klass=_shared_doc_kwargs["klass"])
def notnull(self) -> DataFrame:
"""
DataFrame.notnull is an alias for DataFrame.notna.
"""
return ~self.isna()
@overload
def dropna(
self,
*,
axis: Axis = ...,
how: AnyAll | NoDefault = ...,
thresh: int | NoDefault = ...,
subset: IndexLabel = ...,
inplace: Literal[False] = ...,
ignore_index: bool = ...,
) -> DataFrame:
...
@overload
def dropna(
self,
*,
axis: Axis = ...,
how: AnyAll | NoDefault = ...,
thresh: int | NoDefault = ...,
subset: IndexLabel = ...,
inplace: Literal[True],
ignore_index: bool = ...,
) -> None:
...
def dropna(
self,
*,
axis: Axis = 0,
how: AnyAll | NoDefault = no_default,
thresh: int | NoDefault = no_default,
subset: IndexLabel = None,
inplace: bool = False,
ignore_index: bool = False,
) -> DataFrame | None:
"""
Remove missing values.
See the :ref:`User Guide <missing_data>` for more on which values are
considered missing, and how to work with missing data.
Parameters
----------
axis : {0 or 'index', 1 or 'columns'}, default 0
Determine if rows or columns which contain missing values are
removed.
* 0, or 'index' : Drop rows which contain missing values.
* 1, or 'columns' : Drop columns which contain missing value.
Pass tuple or list to drop on multiple axes.
Only a single axis is allowed.
how : {'any', 'all'}, default 'any'
Determine if row or column is removed from DataFrame, when we have
at least one NA or all NA.
* 'any' : If any NA values are present, drop that row or column.
* 'all' : If all values are NA, drop that row or column.
thresh : int, optional
Require that many non-NA values. Cannot be combined with how.
subset : column label or sequence of labels, optional
Labels along other axis to consider, e.g. if you are dropping rows
these would be a list of columns to include.
inplace : bool, default False
Whether to modify the DataFrame rather than creating a new one.
ignore_index : bool, default ``False``
If ``True``, the resulting axis will be labeled 0, 1, …, n - 1.
.. versionadded:: 2.0.0
Returns
-------
DataFrame or None
DataFrame with NA entries dropped from it or None if ``inplace=True``.
See Also
--------
DataFrame.isna: Indicate missing values.
DataFrame.notna : Indicate existing (non-missing) values.
DataFrame.fillna : Replace missing values.
Series.dropna : Drop missing values.
Index.dropna : Drop missing indices.
Examples
--------
>>> df = pd.DataFrame({"name": ['Alfred', 'Batman', 'Catwoman'],
... "toy": [np.nan, 'Batmobile', 'Bullwhip'],
... "born": [pd.NaT, pd.Timestamp("1940-04-25"),
... pd.NaT]})
>>> df
name toy born
0 Alfred NaN NaT
1 Batman Batmobile 1940-04-25
2 Catwoman Bullwhip NaT
Drop the rows where at least one element is missing.
>>> df.dropna()
name toy born
1 Batman Batmobile 1940-04-25
Drop the columns where at least one element is missing.
>>> df.dropna(axis='columns')
name
0 Alfred
1 Batman
2 Catwoman
Drop the rows where all elements are missing.
>>> df.dropna(how='all')
name toy born
0 Alfred NaN NaT
1 Batman Batmobile 1940-04-25
2 Catwoman Bullwhip NaT
Keep only the rows with at least 2 non-NA values.
>>> df.dropna(thresh=2)
name toy born
1 Batman Batmobile 1940-04-25
2 Catwoman Bullwhip NaT
Define in which columns to look for missing values.
>>> df.dropna(subset=['name', 'toy'])
name toy born
1 Batman Batmobile 1940-04-25
2 Catwoman Bullwhip NaT
"""
if (how is not no_default) and (thresh is not no_default):
raise TypeError(
"You cannot set both the how and thresh arguments at the same time."
)
if how is no_default:
how = "any"
inplace = validate_bool_kwarg(inplace, "inplace")
if isinstance(axis, (tuple, list)):
# GH20987
raise TypeError("supplying multiple axes to axis is no longer supported.")
axis = self._get_axis_number(axis)
agg_axis = 1 - axis
agg_obj = self
if subset is not None:
# subset needs to be list
if not is_list_like(subset):
subset = [subset]
ax = self._get_axis(agg_axis)
indices = ax.get_indexer_for(subset)
check = indices == -1
if check.any():
raise KeyError(np.array(subset)[check].tolist())
agg_obj = self.take(indices, axis=agg_axis)
if thresh is not no_default:
count = agg_obj.count(axis=agg_axis)
mask = count >= thresh
elif how == "any":
# faster equivalent to 'agg_obj.count(agg_axis) == self.shape[agg_axis]'
mask = notna(agg_obj).all(axis=agg_axis, bool_only=False)
elif how == "all":
# faster equivalent to 'agg_obj.count(agg_axis) > 0'
mask = notna(agg_obj).any(axis=agg_axis, bool_only=False)
else:
raise ValueError(f"invalid how option: {how}")
if np.all(mask):
result = self.copy(deep=None)
else:
result = self.loc(axis=axis)[mask]
if ignore_index:
result.index = default_index(len(result))
if not inplace:
return result
self._update_inplace(result)
return None
def drop_duplicates(
self,
subset: Hashable | Sequence[Hashable] | None = None,
*,
keep: DropKeep = "first",
inplace: bool = False,
ignore_index: bool = False,
) -> DataFrame | None:
"""
Return DataFrame with duplicate rows removed.
Considering certain columns is optional. Indexes, including time indexes
are ignored.
Parameters
----------
subset : column label or sequence of labels, optional
Only consider certain columns for identifying duplicates, by
default use all of the columns.
keep : {'first', 'last', ``False``}, default 'first'
Determines which duplicates (if any) to keep.
- 'first' : Drop duplicates except for the first occurrence.
- 'last' : Drop duplicates except for the last occurrence.
- ``False`` : Drop all duplicates.
inplace : bool, default ``False``
Whether to modify the DataFrame rather than creating a new one.
ignore_index : bool, default ``False``
If ``True``, the resulting axis will be labeled 0, 1, …, n - 1.
Returns
-------
DataFrame or None
DataFrame with duplicates removed or None if ``inplace=True``.
See Also
--------
DataFrame.value_counts: Count unique combinations of columns.
Examples
--------
Consider dataset containing ramen rating.
>>> df = pd.DataFrame({
... 'brand': ['Yum Yum', 'Yum Yum', 'Indomie', 'Indomie', 'Indomie'],
... 'style': ['cup', 'cup', 'cup', 'pack', 'pack'],
... 'rating': [4, 4, 3.5, 15, 5]
... })
>>> df
brand style rating
0 Yum Yum cup 4.0
1 Yum Yum cup 4.0
2 Indomie cup 3.5
3 Indomie pack 15.0
4 Indomie pack 5.0
By default, it removes duplicate rows based on all columns.
>>> df.drop_duplicates()
brand style rating
0 Yum Yum cup 4.0
2 Indomie cup 3.5
3 Indomie pack 15.0
4 Indomie pack 5.0
To remove duplicates on specific column(s), use ``subset``.
>>> df.drop_duplicates(subset=['brand'])
brand style rating
0 Yum Yum cup 4.0
2 Indomie cup 3.5
To remove duplicates and keep last occurrences, use ``keep``.
>>> df.drop_duplicates(subset=['brand', 'style'], keep='last')
brand style rating
1 Yum Yum cup 4.0
2 Indomie cup 3.5
4 Indomie pack 5.0
"""
if self.empty:
return self.copy(deep=None)
inplace = validate_bool_kwarg(inplace, "inplace")
ignore_index = validate_bool_kwarg(ignore_index, "ignore_index")
result = self[-self.duplicated(subset, keep=keep)]
if ignore_index:
result.index = default_index(len(result))
if inplace:
self._update_inplace(result)
return None
else:
return result
def duplicated(
self,
subset: Hashable | Sequence[Hashable] | None = None,
keep: DropKeep = "first",
) -> Series:
"""
Return boolean Series denoting duplicate rows.
Considering certain columns is optional.
Parameters
----------
subset : column label or sequence of labels, optional
Only consider certain columns for identifying duplicates, by
default use all of the columns.
keep : {'first', 'last', False}, default 'first'
Determines which duplicates (if any) to mark.
- ``first`` : Mark duplicates as ``True`` except for the first occurrence.
- ``last`` : Mark duplicates as ``True`` except for the last occurrence.
- False : Mark all duplicates as ``True``.
Returns
-------
Series
Boolean series for each duplicated rows.
See Also
--------
Index.duplicated : Equivalent method on index.
Series.duplicated : Equivalent method on Series.
Series.drop_duplicates : Remove duplicate values from Series.
DataFrame.drop_duplicates : Remove duplicate values from DataFrame.
Examples
--------
Consider dataset containing ramen rating.
>>> df = pd.DataFrame({
... 'brand': ['Yum Yum', 'Yum Yum', 'Indomie', 'Indomie', 'Indomie'],
... 'style': ['cup', 'cup', 'cup', 'pack', 'pack'],
... 'rating': [4, 4, 3.5, 15, 5]
... })
>>> df
brand style rating
0 Yum Yum cup 4.0
1 Yum Yum cup 4.0
2 Indomie cup 3.5
3 Indomie pack 15.0
4 Indomie pack 5.0
By default, for each set of duplicated values, the first occurrence
is set on False and all others on True.
>>> df.duplicated()
0 False
1 True
2 False
3 False
4 False
dtype: bool
By using 'last', the last occurrence of each set of duplicated values
is set on False and all others on True.
>>> df.duplicated(keep='last')
0 True
1 False
2 False
3 False
4 False
dtype: bool
By setting ``keep`` on False, all duplicates are True.
>>> df.duplicated(keep=False)
0 True
1 True
2 False
3 False
4 False
dtype: bool
To find duplicates on specific column(s), use ``subset``.
>>> df.duplicated(subset=['brand'])
0 False
1 True
2 False
3 True
4 True
dtype: bool
"""
if self.empty:
return self._constructor_sliced(dtype=bool)
def f(vals) -> tuple[np.ndarray, int]:
labels, shape = algorithms.factorize(vals, size_hint=len(self))
return labels.astype("i8", copy=False), len(shape)
if subset is None:
# https://github.com/pandas-dev/pandas/issues/28770
# Incompatible types in assignment (expression has type "Index", variable
# has type "Sequence[Any]")
subset = self.columns # type: ignore[assignment]
elif (
not np.iterable(subset)
or isinstance(subset, str)
or isinstance(subset, tuple)
and subset in self.columns
):
subset = (subset,)
# needed for mypy since can't narrow types using np.iterable
subset = cast(Sequence, subset)
# Verify all columns in subset exist in the queried dataframe
# Otherwise, raise a KeyError, same as if you try to __getitem__ with a
# key that doesn't exist.
diff = set(subset) - set(self.columns)
if diff:
raise KeyError(Index(diff))
if len(subset) == 1 and self.columns.is_unique:
# GH#45236 This is faster than get_group_index below
result = self[subset[0]].duplicated(keep)
result.name = None
else:
vals = (col.values for name, col in self.items() if name in subset)
labels, shape = map(list, zip(*map(f, vals)))
ids = get_group_index(
labels,
# error: Argument 1 to "tuple" has incompatible type "List[_T]";
# expected "Iterable[int]"
tuple(shape), # type: ignore[arg-type]
sort=False,
xnull=False,
)
result = self._constructor_sliced(duplicated(ids, keep), index=self.index)
return result.__finalize__(self, method="duplicated")
# ----------------------------------------------------------------------
# Sorting
# error: Signature of "sort_values" incompatible with supertype "NDFrame"
@overload # type: ignore[override]
def sort_values(
self,
by: IndexLabel,
*,
axis: Axis = ...,
ascending=...,
inplace: Literal[False] = ...,
kind: str = ...,
na_position: str = ...,
ignore_index: bool = ...,
key: ValueKeyFunc = ...,
) -> DataFrame:
...
@overload
def sort_values(
self,
by: IndexLabel,
*,
axis: Axis = ...,
ascending=...,
inplace: Literal[True],
kind: str = ...,
na_position: str = ...,
ignore_index: bool = ...,
key: ValueKeyFunc = ...,
) -> None:
...
# TODO: Just move the sort_values doc here.
@Substitution(**_shared_doc_kwargs)
@Appender(NDFrame.sort_values.__doc__)
def sort_values(
self,
by: IndexLabel,
*,
axis: Axis = 0,
ascending: bool | list[bool] | tuple[bool, ...] = True,
inplace: bool = False,
kind: str = "quicksort",
na_position: str = "last",
ignore_index: bool = False,
key: ValueKeyFunc = None,
) -> DataFrame | None:
inplace = validate_bool_kwarg(inplace, "inplace")
axis = self._get_axis_number(axis)
ascending = validate_ascending(ascending)
if not isinstance(by, list):
by = [by]
# error: Argument 1 to "len" has incompatible type "Union[bool, List[bool]]";
# expected "Sized"
if is_sequence(ascending) and (
len(by) != len(ascending) # type: ignore[arg-type]
):
# error: Argument 1 to "len" has incompatible type "Union[bool,
# List[bool]]"; expected "Sized"
raise ValueError(
f"Length of ascending ({len(ascending)})" # type: ignore[arg-type]
f" != length of by ({len(by)})"
)
if len(by) > 1:
keys = [self._get_label_or_level_values(x, axis=axis) for x in by]
# need to rewrap columns in Series to apply key function
if key is not None:
# error: List comprehension has incompatible type List[Series];
# expected List[ndarray]
keys = [
Series(k, name=name) # type: ignore[misc]
for (k, name) in zip(keys, by)
]
indexer = lexsort_indexer(
keys, orders=ascending, na_position=na_position, key=key
)
elif len(by):
# len(by) == 1
by = by[0]
k = self._get_label_or_level_values(by, axis=axis)
# need to rewrap column in Series to apply key function
if key is not None:
# error: Incompatible types in assignment (expression has type
# "Series", variable has type "ndarray")
k = Series(k, name=by) # type: ignore[assignment]
if isinstance(ascending, (tuple, list)):
ascending = ascending[0]
indexer = nargsort(
k, kind=kind, ascending=ascending, na_position=na_position, key=key
)
else:
if inplace:
return self._update_inplace(self)
else:
return self.copy(deep=None)
if is_range_indexer(indexer, len(indexer)):
result = self.copy(deep=(not inplace and not using_copy_on_write()))
if ignore_index:
result.index = default_index(len(result))
if inplace:
return self._update_inplace(result)
else:
return result
new_data = self._mgr.take(
indexer, axis=self._get_block_manager_axis(axis), verify=False
)
if ignore_index:
new_data.set_axis(
self._get_block_manager_axis(axis), default_index(len(indexer))
)
result = self._constructor(new_data)
if inplace:
return self._update_inplace(result)
else:
return result.__finalize__(self, method="sort_values")
@overload
def sort_index(
self,
*,
axis: Axis = ...,
level: IndexLabel = ...,
ascending: bool | Sequence[bool] = ...,
inplace: Literal[True],
kind: SortKind = ...,
na_position: NaPosition = ...,
sort_remaining: bool = ...,
ignore_index: bool = ...,
key: IndexKeyFunc = ...,
) -> None:
...
@overload
def sort_index(
self,
*,
axis: Axis = ...,
level: IndexLabel = ...,
ascending: bool | Sequence[bool] = ...,
inplace: Literal[False] = ...,
kind: SortKind = ...,
na_position: NaPosition = ...,
sort_remaining: bool = ...,
ignore_index: bool = ...,
key: IndexKeyFunc = ...,
) -> DataFrame:
...
@overload
def sort_index(
self,
*,
axis: Axis = ...,
level: IndexLabel = ...,
ascending: bool | Sequence[bool] = ...,
inplace: bool = ...,
kind: SortKind = ...,
na_position: NaPosition = ...,
sort_remaining: bool = ...,
ignore_index: bool = ...,
key: IndexKeyFunc = ...,
) -> DataFrame | None:
...
def sort_index(
self,
*,
axis: Axis = 0,
level: IndexLabel = None,
ascending: bool | Sequence[bool] = True,
inplace: bool = False,
kind: SortKind = "quicksort",
na_position: NaPosition = "last",
sort_remaining: bool = True,
ignore_index: bool = False,
key: IndexKeyFunc = None,
) -> DataFrame | None:
"""
Sort object by labels (along an axis).
Returns a new DataFrame sorted by label if `inplace` argument is
``False``, otherwise updates the original DataFrame and returns None.
Parameters
----------
axis : {0 or 'index', 1 or 'columns'}, default 0
The axis along which to sort. The value 0 identifies the rows,
and 1 identifies the columns.
level : int or level name or list of ints or list of level names
If not None, sort on values in specified index level(s).
ascending : bool or list-like of bools, default True
Sort ascending vs. descending. When the index is a MultiIndex the
sort direction can be controlled for each level individually.
inplace : bool, default False
Whether to modify the DataFrame rather than creating a new one.
kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, default 'quicksort'
Choice of sorting algorithm. See also :func:`numpy.sort` for more
information. `mergesort` and `stable` are the only stable algorithms. 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.
Not implemented for MultiIndex.
sort_remaining : bool, default True
If True and sorting by level and index is multilevel, sort by other
levels too (in order) after sorting by specified level.
ignore_index : bool, default False
If True, the resulting axis will be labeled 0, 1, …, n - 1.
key : callable, optional
If not None, apply the key function to the index 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 an
``Index`` and return an ``Index`` of the same shape. For MultiIndex
inputs, the key is applied *per level*.
.. versionadded:: 1.1.0
Returns
-------
DataFrame or None
The original DataFrame sorted by the labels or None if ``inplace=True``.
See Also
--------
Series.sort_index : Sort Series by the index.
DataFrame.sort_values : Sort DataFrame by the value.
Series.sort_values : Sort Series by the value.
Examples
--------
>>> df = pd.DataFrame([1, 2, 3, 4, 5], index=[100, 29, 234, 1, 150],
... columns=['A'])
>>> df.sort_index()
A
1 4
29 2
100 1
150 5
234 3
By default, it sorts in ascending order, to sort in descending order,
use ``ascending=False``
>>> df.sort_index(ascending=False)
A
234 3
150 5
100 1
29 2
1 4
A key function can be specified which is applied to the index before
sorting. For a ``MultiIndex`` this is applied to each level separately.
>>> df = pd.DataFrame({"a": [1, 2, 3, 4]}, index=['A', 'b', 'C', 'd'])
>>> df.sort_index(key=lambda x: x.str.lower())
a
A 1
b 2
C 3
d 4
"""
return super().sort_index(
axis=axis,
level=level,
ascending=ascending,
inplace=inplace,
kind=kind,
na_position=na_position,
sort_remaining=sort_remaining,
ignore_index=ignore_index,
key=key,
)
def value_counts(
self,
subset: Sequence[Hashable] | None = None,
normalize: bool = False,
sort: bool = True,
ascending: bool = False,
dropna: bool = True,
) -> Series:
"""
Return a Series containing counts of unique rows in the DataFrame.
.. versionadded:: 1.1.0
Parameters
----------
subset : label or list of labels, optional
Columns to use when counting unique combinations.
normalize : bool, default False
Return proportions rather than frequencies.
sort : bool, default True
Sort by frequencies.
ascending : bool, default False
Sort in ascending order.
dropna : bool, default True
Don’t include counts of rows that contain NA values.
.. versionadded:: 1.3.0
Returns
-------
Series
See Also
--------
Series.value_counts: Equivalent method on Series.
Notes
-----
The returned Series will have a MultiIndex with one level per input
column but an Index (non-multi) for a single label. By default, rows
that contain any NA values are omitted from the result. By default,
the resulting Series will be in descending order so that the first
element is the most frequently-occurring row.
Examples
--------
>>> df = pd.DataFrame({'num_legs': [2, 4, 4, 6],
... 'num_wings': [2, 0, 0, 0]},
... index=['falcon', 'dog', 'cat', 'ant'])
>>> df
num_legs num_wings
falcon 2 2
dog 4 0
cat 4 0
ant 6 0
>>> df.value_counts()
num_legs num_wings
4 0 2
2 2 1
6 0 1
Name: count, dtype: int64
>>> df.value_counts(sort=False)
num_legs num_wings
2 2 1
4 0 2
6 0 1
Name: count, dtype: int64
>>> df.value_counts(ascending=True)
num_legs num_wings
2 2 1
6 0 1
4 0 2
Name: count, dtype: int64
>>> df.value_counts(normalize=True)
num_legs num_wings
4 0 0.50
2 2 0.25
6 0 0.25
Name: proportion, dtype: float64
With `dropna` set to `False` we can also count rows with NA values.
>>> df = pd.DataFrame({'first_name': ['John', 'Anne', 'John', 'Beth'],
... 'middle_name': ['Smith', pd.NA, pd.NA, 'Louise']})
>>> df
first_name middle_name
0 John Smith
1 Anne <NA>
2 John <NA>
3 Beth Louise
>>> df.value_counts()
first_name middle_name
Beth Louise 1
John Smith 1
Name: count, dtype: int64
>>> df.value_counts(dropna=False)
first_name middle_name
Anne NaN 1
Beth Louise 1
John Smith 1
NaN 1
Name: count, dtype: int64
>>> df.value_counts("first_name")
first_name
John 2
Anne 1
Beth 1
Name: count, dtype: int64
"""
if subset is None:
subset = self.columns.tolist()
name = "proportion" if normalize else "count"
counts = self.groupby(subset, dropna=dropna).grouper.size()
counts.name = name
if sort:
counts = counts.sort_values(ascending=ascending)
if normalize:
counts /= counts.sum()
# Force MultiIndex for single column
if is_list_like(subset) and len(subset) == 1:
counts.index = MultiIndex.from_arrays(
[counts.index], names=[counts.index.name]
)
return counts
def nlargest(self, n: int, columns: IndexLabel, keep: str = "first") -> DataFrame:
"""
Return the first `n` rows ordered by `columns` in descending order.
Return the first `n` rows with the largest values in `columns`, in
descending order. The columns that are not specified are returned as
well, but not used for ordering.
This method is equivalent to
``df.sort_values(columns, ascending=False).head(n)``, but more
performant.
Parameters
----------
n : int
Number of rows to return.
columns : label or list of labels
Column label(s) to order by.
keep : {'first', 'last', 'all'}, default 'first'
Where there are duplicate values:
- ``first`` : prioritize the first occurrence(s)
- ``last`` : prioritize the last occurrence(s)
- ``all`` : do not drop any duplicates, even it means
selecting more than `n` items.
Returns
-------
DataFrame
The first `n` rows ordered by the given columns in descending
order.
See Also
--------
DataFrame.nsmallest : Return the first `n` rows ordered by `columns` in
ascending order.
DataFrame.sort_values : Sort DataFrame by the values.
DataFrame.head : Return the first `n` rows without re-ordering.
Notes
-----
This function cannot be used with all column types. For example, when
specifying columns with `object` or `category` dtypes, ``TypeError`` is
raised.
Examples
--------
>>> df = pd.DataFrame({'population': [59000000, 65000000, 434000,
... 434000, 434000, 337000, 11300,
... 11300, 11300],
... 'GDP': [1937894, 2583560 , 12011, 4520, 12128,
... 17036, 182, 38, 311],
... 'alpha-2': ["IT", "FR", "MT", "MV", "BN",
... "IS", "NR", "TV", "AI"]},
... index=["Italy", "France", "Malta",
... "Maldives", "Brunei", "Iceland",
... "Nauru", "Tuvalu", "Anguilla"])
>>> df
population GDP alpha-2
Italy 59000000 1937894 IT
France 65000000 2583560 FR
Malta 434000 12011 MT
Maldives 434000 4520 MV
Brunei 434000 12128 BN
Iceland 337000 17036 IS
Nauru 11300 182 NR
Tuvalu 11300 38 TV
Anguilla 11300 311 AI
In the following example, we will use ``nlargest`` to select the three
rows having the largest values in column "population".
>>> df.nlargest(3, 'population')
population GDP alpha-2
France 65000000 2583560 FR
Italy 59000000 1937894 IT
Malta 434000 12011 MT
When using ``keep='last'``, ties are resolved in reverse order:
>>> df.nlargest(3, 'population', keep='last')
population GDP alpha-2
France 65000000 2583560 FR
Italy 59000000 1937894 IT
Brunei 434000 12128 BN
When using ``keep='all'``, all duplicate items are maintained:
>>> df.nlargest(3, 'population', keep='all')
population GDP alpha-2
France 65000000 2583560 FR
Italy 59000000 1937894 IT
Malta 434000 12011 MT
Maldives 434000 4520 MV
Brunei 434000 12128 BN
To order by the largest values in column "population" and then "GDP",
we can specify multiple columns like in the next example.
>>> df.nlargest(3, ['population', 'GDP'])
population GDP alpha-2
France 65000000 2583560 FR
Italy 59000000 1937894 IT
Brunei 434000 12128 BN
"""
return selectn.SelectNFrame(self, n=n, keep=keep, columns=columns).nlargest()
def nsmallest(self, n: int, columns: IndexLabel, keep: str = "first") -> DataFrame:
"""
Return the first `n` rows ordered by `columns` in ascending order.
Return the first `n` rows with the smallest values in `columns`, in
ascending order. The columns that are not specified are returned as
well, but not used for ordering.
This method is equivalent to
``df.sort_values(columns, ascending=True).head(n)``, but more
performant.
Parameters
----------
n : int
Number of items to retrieve.
columns : list or str
Column name or names to order by.
keep : {'first', 'last', 'all'}, default 'first'
Where there are duplicate values:
- ``first`` : take the first occurrence.
- ``last`` : take the last occurrence.
- ``all`` : do not drop any duplicates, even it means
selecting more than `n` items.
Returns
-------
DataFrame
See Also
--------
DataFrame.nlargest : Return the first `n` rows ordered by `columns` in
descending order.
DataFrame.sort_values : Sort DataFrame by the values.
DataFrame.head : Return the first `n` rows without re-ordering.
Examples
--------
>>> df = pd.DataFrame({'population': [59000000, 65000000, 434000,
... 434000, 434000, 337000, 337000,
... 11300, 11300],
... 'GDP': [1937894, 2583560 , 12011, 4520, 12128,
... 17036, 182, 38, 311],
... 'alpha-2': ["IT", "FR", "MT", "MV", "BN",
... "IS", "NR", "TV", "AI"]},
... index=["Italy", "France", "Malta",
... "Maldives", "Brunei", "Iceland",
... "Nauru", "Tuvalu", "Anguilla"])
>>> df
population GDP alpha-2
Italy 59000000 1937894 IT
France 65000000 2583560 FR
Malta 434000 12011 MT
Maldives 434000 4520 MV
Brunei 434000 12128 BN
Iceland 337000 17036 IS
Nauru 337000 182 NR
Tuvalu 11300 38 TV
Anguilla 11300 311 AI
In the following example, we will use ``nsmallest`` to select the
three rows having the smallest values in column "population".
>>> df.nsmallest(3, 'population')
population GDP alpha-2
Tuvalu 11300 38 TV
Anguilla 11300 311 AI
Iceland 337000 17036 IS
When using ``keep='last'``, ties are resolved in reverse order:
>>> df.nsmallest(3, 'population', keep='last')
population GDP alpha-2
Anguilla 11300 311 AI
Tuvalu 11300 38 TV
Nauru 337000 182 NR
When using ``keep='all'``, all duplicate items are maintained:
>>> df.nsmallest(3, 'population', keep='all')
population GDP alpha-2
Tuvalu 11300 38 TV
Anguilla 11300 311 AI
Iceland 337000 17036 IS
Nauru 337000 182 NR
To order by the smallest values in column "population" and then "GDP", we can
specify multiple columns like in the next example.
>>> df.nsmallest(3, ['population', 'GDP'])
population GDP alpha-2
Tuvalu 11300 38 TV
Anguilla 11300 311 AI
Nauru 337000 182 NR
"""
return selectn.SelectNFrame(self, n=n, keep=keep, columns=columns).nsmallest()
@doc(
Series.swaplevel,
klass=_shared_doc_kwargs["klass"],
extra_params=dedent(
"""axis : {0 or 'index', 1 or 'columns'}, default 0
The axis to swap levels on. 0 or 'index' for row-wise, 1 or
'columns' for column-wise."""
),
examples=dedent(
"""\
Examples
--------
>>> df = pd.DataFrame(
... {"Grade": ["A", "B", "A", "C"]},
... index=[
... ["Final exam", "Final exam", "Coursework", "Coursework"],
... ["History", "Geography", "History", "Geography"],
... ["January", "February", "March", "April"],
... ],
... )
>>> df
Grade
Final exam History January A
Geography February B
Coursework History March A
Geography April C
In the following example, we will swap the levels of the indices.
Here, we will swap the levels column-wise, but levels can be swapped row-wise
in a similar manner. Note that column-wise is the default behaviour.
By not supplying any arguments for i and j, we swap the last and second to
last indices.
>>> df.swaplevel()
Grade
Final exam January History A
February Geography B
Coursework March History A
April Geography C
By supplying one argument, we can choose which index to swap the last
index with. We can for example swap the first index with the last one as
follows.
>>> df.swaplevel(0)
Grade
January History Final exam A
February Geography Final exam B
March History Coursework A
April Geography Coursework C
We can also define explicitly which indices we want to swap by supplying values
for both i and j. Here, we for example swap the first and second indices.
>>> df.swaplevel(0, 1)
Grade
History Final exam January A
Geography Final exam February B
History Coursework March A
Geography Coursework April C"""
),
)
def swaplevel(self, i: Axis = -2, j: Axis = -1, axis: Axis = 0) -> DataFrame:
result = self.copy(deep=None)
axis = self._get_axis_number(axis)
if not isinstance(result._get_axis(axis), MultiIndex): # pragma: no cover
raise TypeError("Can only swap levels on a hierarchical axis.")
if axis == 0:
assert isinstance(result.index, MultiIndex)
result.index = result.index.swaplevel(i, j)
else:
assert isinstance(result.columns, MultiIndex)
result.columns = result.columns.swaplevel(i, j)
return result
def reorder_levels(self, order: Sequence[int | str], axis: Axis = 0) -> DataFrame:
"""
Rearrange index levels using input order. May not drop or duplicate levels.
Parameters
----------
order : list of int or list of str
List representing new level order. Reference level by number
(position) or by key (label).
axis : {0 or 'index', 1 or 'columns'}, default 0
Where to reorder levels.
Returns
-------
DataFrame
Examples
--------
>>> data = {
... "class": ["Mammals", "Mammals", "Reptiles"],
... "diet": ["Omnivore", "Carnivore", "Carnivore"],
... "species": ["Humans", "Dogs", "Snakes"],
... }
>>> df = pd.DataFrame(data, columns=["class", "diet", "species"])
>>> df = df.set_index(["class", "diet"])
>>> df
species
class diet
Mammals Omnivore Humans
Carnivore Dogs
Reptiles Carnivore Snakes
Let's reorder the levels of the index:
>>> df.reorder_levels(["diet", "class"])
species
diet class
Omnivore Mammals Humans
Carnivore Mammals Dogs
Reptiles Snakes
"""
axis = self._get_axis_number(axis)
if not isinstance(self._get_axis(axis), MultiIndex): # pragma: no cover
raise TypeError("Can only reorder levels on a hierarchical axis.")
result = self.copy(deep=None)
if axis == 0:
assert isinstance(result.index, MultiIndex)
result.index = result.index.reorder_levels(order)
else:
assert isinstance(result.columns, MultiIndex)
result.columns = result.columns.reorder_levels(order)
return result
# ----------------------------------------------------------------------
# Arithmetic Methods
def _cmp_method(self, other, op):
axis: Literal[1] = 1 # only relevant for Series other case
self, other = ops.align_method_FRAME(self, other, axis, flex=False, level=None)
# See GH#4537 for discussion of scalar op behavior
new_data = self._dispatch_frame_op(other, op, axis=axis)
return self._construct_result(new_data)
def _arith_method(self, other, op):
if ops.should_reindex_frame_op(self, other, op, 1, None, None):
return ops.frame_arith_method_with_reindex(self, other, op)
axis: Literal[1] = 1 # only relevant for Series other case
other = ops.maybe_prepare_scalar_for_op(other, (self.shape[axis],))
self, other = ops.align_method_FRAME(self, other, axis, flex=True, level=None)
new_data = self._dispatch_frame_op(other, op, axis=axis)
return self._construct_result(new_data)
_logical_method = _arith_method
def _dispatch_frame_op(self, right, func: Callable, axis: AxisInt | None = None):
"""
Evaluate the frame operation func(left, right) by evaluating
column-by-column, dispatching to the Series implementation.
Parameters
----------
right : scalar, Series, or DataFrame
func : arithmetic or comparison operator
axis : {None, 0, 1}
Returns
-------
DataFrame
"""
# Get the appropriate array-op to apply to each column/block's values.
array_op = ops.get_array_op(func)
right = lib.item_from_zerodim(right)
if not is_list_like(right):
# i.e. scalar, faster than checking np.ndim(right) == 0
with np.errstate(all="ignore"):
bm = self._mgr.apply(array_op, right=right)
return self._constructor(bm)
elif isinstance(right, DataFrame):
assert self.index.equals(right.index)
assert self.columns.equals(right.columns)
# TODO: The previous assertion `assert right._indexed_same(self)`
# fails in cases with empty columns reached via
# _frame_arith_method_with_reindex
# TODO operate_blockwise expects a manager of the same type
with np.errstate(all="ignore"):
bm = self._mgr.operate_blockwise(
# error: Argument 1 to "operate_blockwise" of "ArrayManager" has
# incompatible type "Union[ArrayManager, BlockManager]"; expected
# "ArrayManager"
# error: Argument 1 to "operate_blockwise" of "BlockManager" has
# incompatible type "Union[ArrayManager, BlockManager]"; expected
# "BlockManager"
right._mgr, # type: ignore[arg-type]
array_op,
)
return self._constructor(bm)
elif isinstance(right, Series) and axis == 1:
# axis=1 means we want to operate row-by-row
assert right.index.equals(self.columns)
right = right._values
# maybe_align_as_frame ensures we do not have an ndarray here
assert not isinstance(right, np.ndarray)
with np.errstate(all="ignore"):
arrays = [
array_op(_left, _right)
for _left, _right in zip(self._iter_column_arrays(), right)
]
elif isinstance(right, Series):
assert right.index.equals(self.index) # Handle other cases later
right = right._values
with np.errstate(all="ignore"):
arrays = [array_op(left, right) for left in self._iter_column_arrays()]
else:
# Remaining cases have less-obvious dispatch rules
raise NotImplementedError(right)
return type(self)._from_arrays(
arrays, self.columns, self.index, verify_integrity=False
)
def _combine_frame(self, other: DataFrame, func, fill_value=None):
# at this point we have `self._indexed_same(other)`
if fill_value is None:
# since _arith_op may be called in a loop, avoid function call
# overhead if possible by doing this check once
_arith_op = func
else:
def _arith_op(left, right):
# for the mixed_type case where we iterate over columns,
# _arith_op(left, right) is equivalent to
# left._binop(right, func, fill_value=fill_value)
left, right = ops.fill_binop(left, right, fill_value)
return func(left, right)
new_data = self._dispatch_frame_op(other, _arith_op)
return new_data
def _construct_result(self, result) -> DataFrame:
"""
Wrap the result of an arithmetic, comparison, or logical operation.
Parameters
----------
result : DataFrame
Returns
-------
DataFrame
"""
out = self._constructor(result, copy=False).__finalize__(self)
# Pin columns instead of passing to constructor for compat with
# non-unique columns case
out.columns = self.columns
out.index = self.index
return out
def __divmod__(self, other) -> tuple[DataFrame, DataFrame]:
# Naive implementation, room for optimization
div = self // other
mod = self - div * other
return div, mod
def __rdivmod__(self, other) -> tuple[DataFrame, DataFrame]:
# Naive implementation, room for optimization
div = other // self
mod = other - div * self
return div, mod
# ----------------------------------------------------------------------
# Combination-Related
@doc(
_shared_docs["compare"],
"""
Returns
-------
DataFrame
DataFrame that shows the differences stacked side by side.
The resulting index will be a MultiIndex with 'self' and 'other'
stacked alternately at the inner level.
Raises
------
ValueError
When the two DataFrames don't have identical labels or shape.
See Also
--------
Series.compare : Compare with another Series and show differences.
DataFrame.equals : Test whether two objects contain the same elements.
Notes
-----
Matching NaNs will not appear as a difference.
Can only compare identically-labeled
(i.e. same shape, identical row and column labels) DataFrames
Examples
--------
>>> df = pd.DataFrame(
... {{
... "col1": ["a", "a", "b", "b", "a"],
... "col2": [1.0, 2.0, 3.0, np.nan, 5.0],
... "col3": [1.0, 2.0, 3.0, 4.0, 5.0]
... }},
... columns=["col1", "col2", "col3"],
... )
>>> df
col1 col2 col3
0 a 1.0 1.0
1 a 2.0 2.0
2 b 3.0 3.0
3 b NaN 4.0
4 a 5.0 5.0
>>> df2 = df.copy()
>>> df2.loc[0, 'col1'] = 'c'
>>> df2.loc[2, 'col3'] = 4.0
>>> df2
col1 col2 col3
0 c 1.0 1.0
1 a 2.0 2.0
2 b 3.0 4.0
3 b NaN 4.0
4 a 5.0 5.0
Align the differences on columns
>>> df.compare(df2)
col1 col3
self other self other
0 a c NaN NaN
2 NaN NaN 3.0 4.0
Assign result_names
>>> df.compare(df2, result_names=("left", "right"))
col1 col3
left right left right
0 a c NaN NaN
2 NaN NaN 3.0 4.0
Stack the differences on rows
>>> df.compare(df2, align_axis=0)
col1 col3
0 self a NaN
other c NaN
2 self NaN 3.0
other NaN 4.0
Keep the equal values
>>> df.compare(df2, keep_equal=True)
col1 col3
self other self other
0 a c 1.0 1.0
2 b b 3.0 4.0
Keep all original rows and columns
>>> df.compare(df2, keep_shape=True)
col1 col2 col3
self other self other self other
0 a c NaN NaN NaN NaN
1 NaN NaN NaN NaN NaN NaN
2 NaN NaN NaN NaN 3.0 4.0
3 NaN NaN NaN NaN NaN NaN
4 NaN NaN NaN NaN NaN NaN
Keep all original rows and columns and also all original values
>>> df.compare(df2, keep_shape=True, keep_equal=True)
col1 col2 col3
self other self other self other
0 a c 1.0 1.0 1.0 1.0
1 a a 2.0 2.0 2.0 2.0
2 b b 3.0 3.0 3.0 4.0
3 b b NaN NaN 4.0 4.0
4 a a 5.0 5.0 5.0 5.0
""",
klass=_shared_doc_kwargs["klass"],
)
def compare(
self,
other: DataFrame,
align_axis: Axis = 1,
keep_shape: bool = False,
keep_equal: bool = False,
result_names: Suffixes = ("self", "other"),
) -> DataFrame:
return super().compare(
other=other,
align_axis=align_axis,
keep_shape=keep_shape,
keep_equal=keep_equal,
result_names=result_names,
)
def combine(
self,
other: DataFrame,
func: Callable[[Series, Series], Series | Hashable],
fill_value=None,
overwrite: bool = True,
) -> DataFrame:
"""
Perform column-wise combine with another DataFrame.
Combines a DataFrame with `other` DataFrame using `func`
to element-wise combine columns. The row and column indexes of the
resulting DataFrame will be the union of the two.
Parameters
----------
other : DataFrame
The DataFrame to merge column-wise.
func : function
Function that takes two series as inputs and return a Series or a
scalar. Used to merge the two dataframes column by columns.
fill_value : scalar value, default None
The value to fill NaNs with prior to passing any column to the
merge func.
overwrite : bool, default True
If True, columns in `self` that do not exist in `other` will be
overwritten with NaNs.
Returns
-------
DataFrame
Combination of the provided DataFrames.
See Also
--------
DataFrame.combine_first : Combine two DataFrame objects and default to
non-null values in frame calling the method.
Examples
--------
Combine using a simple function that chooses the smaller column.
>>> df1 = pd.DataFrame({'A': [0, 0], 'B': [4, 4]})
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
>>> take_smaller = lambda s1, s2: s1 if s1.sum() < s2.sum() else s2
>>> df1.combine(df2, take_smaller)
A B
0 0 3
1 0 3
Example using a true element-wise combine function.
>>> df1 = pd.DataFrame({'A': [5, 0], 'B': [2, 4]})
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
>>> df1.combine(df2, np.minimum)
A B
0 1 2
1 0 3
Using `fill_value` fills Nones prior to passing the column to the
merge function.
>>> df1 = pd.DataFrame({'A': [0, 0], 'B': [None, 4]})
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
>>> df1.combine(df2, take_smaller, fill_value=-5)
A B
0 0 -5.0
1 0 4.0
However, if the same element in both dataframes is None, that None
is preserved
>>> df1 = pd.DataFrame({'A': [0, 0], 'B': [None, 4]})
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [None, 3]})
>>> df1.combine(df2, take_smaller, fill_value=-5)
A B
0 0 -5.0
1 0 3.0
Example that demonstrates the use of `overwrite` and behavior when
the axis differ between the dataframes.
>>> df1 = pd.DataFrame({'A': [0, 0], 'B': [4, 4]})
>>> df2 = pd.DataFrame({'B': [3, 3], 'C': [-10, 1], }, index=[1, 2])
>>> df1.combine(df2, take_smaller)
A B C
0 NaN NaN NaN
1 NaN 3.0 -10.0
2 NaN 3.0 1.0
>>> df1.combine(df2, take_smaller, overwrite=False)
A B C
0 0.0 NaN NaN
1 0.0 3.0 -10.0
2 NaN 3.0 1.0
Demonstrating the preference of the passed in dataframe.
>>> df2 = pd.DataFrame({'B': [3, 3], 'C': [1, 1], }, index=[1, 2])
>>> df2.combine(df1, take_smaller)
A B C
0 0.0 NaN NaN
1 0.0 3.0 NaN
2 NaN 3.0 NaN
>>> df2.combine(df1, take_smaller, overwrite=False)
A B C
0 0.0 NaN NaN
1 0.0 3.0 1.0
2 NaN 3.0 1.0
"""
other_idxlen = len(other.index) # save for compare
this, other = self.align(other, copy=False)
new_index = this.index
if other.empty and len(new_index) == len(self.index):
return self.copy()
if self.empty and len(other) == other_idxlen:
return other.copy()
# sorts if possible; otherwise align above ensures that these are set-equal
new_columns = this.columns.union(other.columns)
do_fill = fill_value is not None
result = {}
for col in new_columns:
series = this[col]
other_series = other[col]
this_dtype = series.dtype
other_dtype = other_series.dtype
this_mask = isna(series)
other_mask = isna(other_series)
# don't overwrite columns unnecessarily
# DO propagate if this column is not in the intersection
if not overwrite and other_mask.all():
result[col] = this[col].copy()
continue
if do_fill:
series = series.copy()
other_series = other_series.copy()
series[this_mask] = fill_value
other_series[other_mask] = fill_value
if col not in self.columns:
# If self DataFrame does not have col in other DataFrame,
# try to promote series, which is all NaN, as other_dtype.
new_dtype = other_dtype
try:
series = series.astype(new_dtype, copy=False)
except ValueError:
# e.g. new_dtype is integer types
pass
else:
# if we have different dtypes, possibly promote
new_dtype = find_common_type([this_dtype, other_dtype])
series = series.astype(new_dtype, copy=False)
other_series = other_series.astype(new_dtype, copy=False)
arr = func(series, other_series)
if isinstance(new_dtype, np.dtype):
# if new_dtype is an EA Dtype, then `func` is expected to return
# the correct dtype without any additional casting
# error: No overload variant of "maybe_downcast_to_dtype" matches
# argument types "Union[Series, Hashable]", "dtype[Any]"
arr = maybe_downcast_to_dtype( # type: ignore[call-overload]
arr, new_dtype
)
result[col] = arr
# convert_objects just in case
return self._constructor(result, index=new_index, columns=new_columns)
def combine_first(self, other: DataFrame) -> DataFrame:
"""
Update null elements with value in the same location in `other`.
Combine two DataFrame objects by filling null values in one DataFrame
with non-null values from other DataFrame. The row and column indexes
of the resulting DataFrame will be the union of the two. The resulting
dataframe contains the 'first' dataframe values and overrides the
second one values where both first.loc[index, col] and
second.loc[index, col] are not missing values, upon calling
first.combine_first(second).
Parameters
----------
other : DataFrame
Provided DataFrame to use to fill null values.
Returns
-------
DataFrame
The result of combining the provided DataFrame with the other object.
See Also
--------
DataFrame.combine : Perform series-wise operation on two DataFrames
using a given function.
Examples
--------
>>> df1 = pd.DataFrame({'A': [None, 0], 'B': [None, 4]})
>>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
>>> df1.combine_first(df2)
A B
0 1.0 3.0
1 0.0 4.0
Null values still persist if the location of that null value
does not exist in `other`
>>> df1 = pd.DataFrame({'A': [None, 0], 'B': [4, None]})
>>> df2 = pd.DataFrame({'B': [3, 3], 'C': [1, 1]}, index=[1, 2])
>>> df1.combine_first(df2)
A B C
0 NaN 4.0 NaN
1 0.0 3.0 1.0
2 NaN 3.0 1.0
"""
from pandas.core.computation import expressions
def combiner(x, y):
mask = extract_array(isna(x))
x_values = extract_array(x, extract_numpy=True)
y_values = extract_array(y, extract_numpy=True)
# If the column y in other DataFrame is not in first DataFrame,
# just return y_values.
if y.name not in self.columns:
return y_values
return expressions.where(mask, y_values, x_values)
combined = self.combine(other, combiner, overwrite=False)
dtypes = {
col: find_common_type([self.dtypes[col], other.dtypes[col]])
for col in self.columns.intersection(other.columns)
if not is_dtype_equal(combined.dtypes[col], self.dtypes[col])
}
if dtypes:
combined = combined.astype(dtypes)
return combined
def update(
self,
other,
join: str = "left",
overwrite: bool = True,
filter_func=None,
errors: str = "ignore",
) -> None:
"""
Modify in place using non-NA values from another DataFrame.
Aligns on indices. There is no return value.
Parameters
----------
other : DataFrame, or object coercible into a DataFrame
Should have at least one matching index/column label
with the original DataFrame. If a Series is passed,
its name attribute must be set, and that will be
used as the column name to align with the original DataFrame.
join : {'left'}, default 'left'
Only left join is implemented, keeping the index and columns of the
original object.
overwrite : bool, default True
How to handle non-NA values for overlapping keys:
* True: overwrite original DataFrame's values
with values from `other`.
* False: only update values that are NA in
the original DataFrame.
filter_func : callable(1d-array) -> bool 1d-array, optional
Can choose to replace values other than NA. Return True for values
that should be updated.
errors : {'raise', 'ignore'}, default 'ignore'
If 'raise', will raise a ValueError if the DataFrame and `other`
both contain non-NA data in the same place.
Returns
-------
None
This method directly changes calling object.
Raises
------
ValueError
* When `errors='raise'` and there's overlapping non-NA data.
* When `errors` is not either `'ignore'` or `'raise'`
NotImplementedError
* If `join != 'left'`
See Also
--------
dict.update : Similar method for dictionaries.
DataFrame.merge : For column(s)-on-column(s) operations.
Examples
--------
>>> df = pd.DataFrame({'A': [1, 2, 3],
... 'B': [400, 500, 600]})
>>> new_df = pd.DataFrame({'B': [4, 5, 6],
... 'C': [7, 8, 9]})
>>> df.update(new_df)
>>> df
A B
0 1 4
1 2 5
2 3 6
The DataFrame's length does not increase as a result of the update,
only values at matching index/column labels are updated.
>>> df = pd.DataFrame({'A': ['a', 'b', 'c'],
... 'B': ['x', 'y', 'z']})
>>> new_df = pd.DataFrame({'B': ['d', 'e', 'f', 'g', 'h', 'i']})
>>> df.update(new_df)
>>> df
A B
0 a d
1 b e
2 c f
For Series, its name attribute must be set.
>>> df = pd.DataFrame({'A': ['a', 'b', 'c'],
... 'B': ['x', 'y', 'z']})
>>> new_column = pd.Series(['d', 'e'], name='B', index=[0, 2])
>>> df.update(new_column)
>>> df
A B
0 a d
1 b y
2 c e
>>> df = pd.DataFrame({'A': ['a', 'b', 'c'],
... 'B': ['x', 'y', 'z']})
>>> new_df = pd.DataFrame({'B': ['d', 'e']}, index=[1, 2])
>>> df.update(new_df)
>>> df
A B
0 a x
1 b d
2 c e
If `other` contains NaNs the corresponding values are not updated
in the original dataframe.
>>> df = pd.DataFrame({'A': [1, 2, 3],
... 'B': [400, 500, 600]})
>>> new_df = pd.DataFrame({'B': [4, np.nan, 6]})
>>> df.update(new_df)
>>> df
A B
0 1 4
1 2 500
2 3 6
"""
from pandas.core.computation import expressions
# TODO: Support other joins
if join != "left": # pragma: no cover
raise NotImplementedError("Only left join is supported")
if errors not in ["ignore", "raise"]:
raise ValueError("The parameter errors must be either 'ignore' or 'raise'")
if not isinstance(other, DataFrame):
other = DataFrame(other)
other = other.reindex(self.index)
for col in self.columns.intersection(other.columns):
this = self[col]._values
that = other[col]._values
if filter_func is not None:
with np.errstate(all="ignore"):
mask = ~filter_func(this) | isna(that)
else:
if errors == "raise":
mask_this = notna(that)
mask_that = notna(this)
if any(mask_this & mask_that):
raise ValueError("Data overlaps.")
if overwrite:
mask = isna(that)
else:
mask = notna(this)
# don't overwrite columns unnecessarily
if mask.all():
continue
self.loc[:, col] = expressions.where(mask, this, that)
# ----------------------------------------------------------------------
# Data reshaping
@Appender(
"""
Examples
--------
>>> df = pd.DataFrame({'Animal': ['Falcon', 'Falcon',
... 'Parrot', 'Parrot'],
... 'Max Speed': [380., 370., 24., 26.]})
>>> df
Animal Max Speed
0 Falcon 380.0
1 Falcon 370.0
2 Parrot 24.0
3 Parrot 26.0
>>> df.groupby(['Animal']).mean()
Max Speed
Animal
Falcon 375.0
Parrot 25.0
**Hierarchical Indexes**
We can groupby different levels of a hierarchical index
using the `level` parameter:
>>> arrays = [['Falcon', 'Falcon', 'Parrot', 'Parrot'],
... ['Captive', 'Wild', 'Captive', 'Wild']]
>>> index = pd.MultiIndex.from_arrays(arrays, names=('Animal', 'Type'))
>>> df = pd.DataFrame({'Max Speed': [390., 350., 30., 20.]},
... index=index)
>>> df
Max Speed
Animal Type
Falcon Captive 390.0
Wild 350.0
Parrot Captive 30.0
Wild 20.0
>>> df.groupby(level=0).mean()
Max Speed
Animal
Falcon 370.0
Parrot 25.0
>>> df.groupby(level="Type").mean()
Max Speed
Type
Captive 210.0
Wild 185.0
We can also choose to include NA in group keys or not by setting
`dropna` parameter, the default setting is `True`.
>>> l = [[1, 2, 3], [1, None, 4], [2, 1, 3], [1, 2, 2]]
>>> df = pd.DataFrame(l, columns=["a", "b", "c"])
>>> df.groupby(by=["b"]).sum()
a c
b
1.0 2 3
2.0 2 5
>>> df.groupby(by=["b"], dropna=False).sum()
a c
b
1.0 2 3
2.0 2 5
NaN 1 4
>>> l = [["a", 12, 12], [None, 12.3, 33.], ["b", 12.3, 123], ["a", 1, 1]]
>>> df = pd.DataFrame(l, columns=["a", "b", "c"])
>>> df.groupby(by="a").sum()
b c
a
a 13.0 13.0
b 12.3 123.0
>>> df.groupby(by="a", dropna=False).sum()
b c
a
a 13.0 13.0
b 12.3 123.0
NaN 12.3 33.0
When using ``.apply()``, use ``group_keys`` to include or exclude the group keys.
The ``group_keys`` argument defaults to ``True`` (include).
>>> df = pd.DataFrame({'Animal': ['Falcon', 'Falcon',
... 'Parrot', 'Parrot'],
... 'Max Speed': [380., 370., 24., 26.]})
>>> df.groupby("Animal", group_keys=True).apply(lambda x: x)
Animal Max Speed
Animal
Falcon 0 Falcon 380.0
1 Falcon 370.0
Parrot 2 Parrot 24.0
3 Parrot 26.0
>>> df.groupby("Animal", group_keys=False).apply(lambda x: x)
Animal Max Speed
0 Falcon 380.0
1 Falcon 370.0
2 Parrot 24.0
3 Parrot 26.0
"""
)
@Appender(_shared_docs["groupby"] % _shared_doc_kwargs)
def groupby(
self,
by=None,
axis: Axis = 0,
level: IndexLabel | None = None,
as_index: bool = True,
sort: bool = True,
group_keys: bool = True,
observed: bool = False,
dropna: bool = True,
) -> DataFrameGroupBy:
from pandas.core.groupby.generic import DataFrameGroupBy
if level is None and by is None:
raise TypeError("You have to supply one of 'by' and 'level'")
axis = self._get_axis_number(axis)
return DataFrameGroupBy(
obj=self,
keys=by,
axis=axis,
level=level,
as_index=as_index,
sort=sort,
group_keys=group_keys,
observed=observed,
dropna=dropna,
)
_shared_docs[
"pivot"
] = """
Return reshaped DataFrame organized by given index / column values.
Reshape data (produce a "pivot" table) based on column values. Uses
unique values from specified `index` / `columns` to form axes of the
resulting DataFrame. This function does not support data
aggregation, multiple values will result in a MultiIndex in the
columns. See the :ref:`User Guide <reshaping>` for more on reshaping.
Parameters
----------%s
columns : str or object or a list of str
Column to use to make new frame's columns.
.. versionchanged:: 1.1.0
Also accept list of columns names.
index : str or object or a list of str, optional
Column to use to make new frame's index. If not given, uses existing index.
.. versionchanged:: 1.1.0
Also accept list of index names.
values : str, object or a list of the previous, optional
Column(s) to use for populating new frame's values. If not
specified, all remaining columns will be used and the result will
have hierarchically indexed columns.
Returns
-------
DataFrame
Returns reshaped DataFrame.
Raises
------
ValueError:
When there are any `index`, `columns` combinations with multiple
values. `DataFrame.pivot_table` when you need to aggregate.
See Also
--------
DataFrame.pivot_table : Generalization of pivot that can handle
duplicate values for one index/column pair.
DataFrame.unstack : Pivot based on the index values instead of a
column.
wide_to_long : Wide panel to long format. Less flexible but more
user-friendly than melt.
Notes
-----
For finer-tuned control, see hierarchical indexing documentation along
with the related stack/unstack methods.
Reference :ref:`the user guide <reshaping.pivot>` for more examples.
Examples
--------
>>> df = pd.DataFrame({'foo': ['one', 'one', 'one', 'two', 'two',
... 'two'],
... 'bar': ['A', 'B', 'C', 'A', 'B', 'C'],
... 'baz': [1, 2, 3, 4, 5, 6],
... 'zoo': ['x', 'y', 'z', 'q', 'w', 't']})
>>> df
foo bar baz zoo
0 one A 1 x
1 one B 2 y
2 one C 3 z
3 two A 4 q
4 two B 5 w
5 two C 6 t
>>> df.pivot(index='foo', columns='bar', values='baz')
bar A B C
foo
one 1 2 3
two 4 5 6
>>> df.pivot(index='foo', columns='bar')['baz']
bar A B C
foo
one 1 2 3
two 4 5 6
>>> df.pivot(index='foo', columns='bar', values=['baz', 'zoo'])
baz zoo
bar A B C A B C
foo
one 1 2 3 x y z
two 4 5 6 q w t
You could also assign a list of column names or a list of index names.
>>> df = pd.DataFrame({
... "lev1": [1, 1, 1, 2, 2, 2],
... "lev2": [1, 1, 2, 1, 1, 2],
... "lev3": [1, 2, 1, 2, 1, 2],
... "lev4": [1, 2, 3, 4, 5, 6],
... "values": [0, 1, 2, 3, 4, 5]})
>>> df
lev1 lev2 lev3 lev4 values
0 1 1 1 1 0
1 1 1 2 2 1
2 1 2 1 3 2
3 2 1 2 4 3
4 2 1 1 5 4
5 2 2 2 6 5
>>> df.pivot(index="lev1", columns=["lev2", "lev3"], values="values")
lev2 1 2
lev3 1 2 1 2
lev1
1 0.0 1.0 2.0 NaN
2 4.0 3.0 NaN 5.0
>>> df.pivot(index=["lev1", "lev2"], columns=["lev3"], values="values")
lev3 1 2
lev1 lev2
1 1 0.0 1.0
2 2.0 NaN
2 1 4.0 3.0
2 NaN 5.0
A ValueError is raised if there are any duplicates.
>>> df = pd.DataFrame({"foo": ['one', 'one', 'two', 'two'],
... "bar": ['A', 'A', 'B', 'C'],
... "baz": [1, 2, 3, 4]})
>>> df
foo bar baz
0 one A 1
1 one A 2
2 two B 3
3 two C 4
Notice that the first two rows are the same for our `index`
and `columns` arguments.
>>> df.pivot(index='foo', columns='bar', values='baz')
Traceback (most recent call last):
...
ValueError: Index contains duplicate entries, cannot reshape
"""
@Substitution("")
@Appender(_shared_docs["pivot"])
def pivot(self, *, columns, index=lib.NoDefault, values=lib.NoDefault) -> DataFrame:
from pandas.core.reshape.pivot import pivot
return pivot(self, index=index, columns=columns, values=values)
_shared_docs[
"pivot_table"
] = """
Create a spreadsheet-style pivot table as a DataFrame.
The levels in the pivot table will be stored in MultiIndex objects
(hierarchical indexes) on the index and columns of the result DataFrame.
Parameters
----------%s
values : list-like or scalar, optional
Column or columns to aggregate.
index : column, Grouper, array, or list of the previous
If an array is passed, it must be the same length as the data. The
list can contain any of the other types (except list).
Keys to group by on the pivot table index. If an array is passed,
it is being used as the same manner as column values.
columns : column, Grouper, array, or list of the previous
If an array is passed, it must be the same length as the data. The
list can contain any of the other types (except list).
Keys to group by on the pivot table column. If an array is passed,
it is being used as the same manner as column values.
aggfunc : function, list of functions, dict, default numpy.mean
If list of functions passed, the resulting pivot table will have
hierarchical columns whose top level are the function names
(inferred from the function objects themselves)
If dict is passed, the key is column to aggregate and value
is function or list of functions. If ``margin=True``,
aggfunc will be used to calculate the partial aggregates.
fill_value : scalar, default None
Value to replace missing values with (in the resulting pivot table,
after aggregation).
margins : bool, default False
If ``margins=True``, special ``All`` columns and rows
will be added with partial group aggregates across the categories
on the rows and columns.
dropna : bool, default True
Do not include columns whose entries are all NaN. If True,
rows with a NaN value in any column will be omitted before
computing margins.
margins_name : str, default 'All'
Name of the row / column that will contain the totals
when margins is True.
observed : bool, default False
This only applies if any of the groupers are Categoricals.
If True: only show observed values for categorical groupers.
If False: show all values for categorical groupers.
sort : bool, default True
Specifies if the result should be sorted.
.. versionadded:: 1.3.0
Returns
-------
DataFrame
An Excel style pivot table.
See Also
--------
DataFrame.pivot : Pivot without aggregation that can handle
non-numeric data.
DataFrame.melt: Unpivot a DataFrame from wide to long format,
optionally leaving identifiers set.
wide_to_long : Wide panel to long format. Less flexible but more
user-friendly than melt.
Notes
-----
Reference :ref:`the user guide <reshaping.pivot>` for more examples.
Examples
--------
>>> df = pd.DataFrame({"A": ["foo", "foo", "foo", "foo", "foo",
... "bar", "bar", "bar", "bar"],
... "B": ["one", "one", "one", "two", "two",
... "one", "one", "two", "two"],
... "C": ["small", "large", "large", "small",
... "small", "large", "small", "small",
... "large"],
... "D": [1, 2, 2, 3, 3, 4, 5, 6, 7],
... "E": [2, 4, 5, 5, 6, 6, 8, 9, 9]})
>>> df
A B C D E
0 foo one small 1 2
1 foo one large 2 4
2 foo one large 2 5
3 foo two small 3 5
4 foo two small 3 6
5 bar one large 4 6
6 bar one small 5 8
7 bar two small 6 9
8 bar two large 7 9
This first example aggregates values by taking the sum.
>>> table = pd.pivot_table(df, values='D', index=['A', 'B'],
... columns=['C'], aggfunc=np.sum)
>>> table
C large small
A B
bar one 4.0 5.0
two 7.0 6.0
foo one 4.0 1.0
two NaN 6.0
We can also fill missing values using the `fill_value` parameter.
>>> table = pd.pivot_table(df, values='D', index=['A', 'B'],
... columns=['C'], aggfunc=np.sum, fill_value=0)
>>> table
C large small
A B
bar one 4 5
two 7 6
foo one 4 1
two 0 6
The next example aggregates by taking the mean across multiple columns.
>>> table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'],
... aggfunc={'D': np.mean, 'E': np.mean})
>>> table
D E
A C
bar large 5.500000 7.500000
small 5.500000 8.500000
foo large 2.000000 4.500000
small 2.333333 4.333333
We can also calculate multiple types of aggregations for any given
value column.
>>> table = pd.pivot_table(df, values=['D', 'E'], index=['A', 'C'],
... aggfunc={'D': np.mean,
... 'E': [min, max, np.mean]})
>>> table
D E
mean max mean min
A C
bar large 5.500000 9 7.500000 6
small 5.500000 9 8.500000 8
foo large 2.000000 5 4.500000 4
small 2.333333 6 4.333333 2
"""
@Substitution("")
@Appender(_shared_docs["pivot_table"])
def pivot_table(
self,
values=None,
index=None,
columns=None,
aggfunc: AggFuncType = "mean",
fill_value=None,
margins: bool = False,
dropna: bool = True,
margins_name: Level = "All",
observed: bool = False,
sort: bool = True,
) -> DataFrame:
from pandas.core.reshape.pivot import pivot_table
return pivot_table(
self,
values=values,
index=index,
columns=columns,
aggfunc=aggfunc,
fill_value=fill_value,
margins=margins,
dropna=dropna,
margins_name=margins_name,
observed=observed,
sort=sort,
)
def stack(self, level: Level = -1, dropna: bool = True):
"""
Stack the prescribed level(s) from columns to index.
Return a reshaped DataFrame or Series having a multi-level
index with one or more new inner-most levels compared to the current
DataFrame. The new inner-most levels are created by pivoting the
columns of the current dataframe:
- if the columns have a single level, the output is a Series;
- if the columns have multiple levels, the new index
level(s) is (are) taken from the prescribed level(s) and
the output is a DataFrame.
Parameters
----------
level : int, str, list, default -1
Level(s) to stack from the column axis onto the index
axis, defined as one index or label, or a list of indices
or labels.
dropna : bool, default True
Whether to drop rows in the resulting Frame/Series with
missing values. Stacking a column level onto the index
axis can create combinations of index and column values
that are missing from the original dataframe. See Examples
section.
Returns
-------
DataFrame or Series
Stacked dataframe or series.
See Also
--------
DataFrame.unstack : Unstack prescribed level(s) from index axis
onto column axis.
DataFrame.pivot : Reshape dataframe from long format to wide
format.
DataFrame.pivot_table : Create a spreadsheet-style pivot table
as a DataFrame.
Notes
-----
The function is named by analogy with a collection of books
being reorganized from being side by side on a horizontal
position (the columns of the dataframe) to being stacked
vertically on top of each other (in the index of the
dataframe).
Reference :ref:`the user guide <reshaping.stacking>` for more examples.
Examples
--------
**Single level columns**
>>> df_single_level_cols = pd.DataFrame([[0, 1], [2, 3]],
... index=['cat', 'dog'],
... columns=['weight', 'height'])
Stacking a dataframe with a single level column axis returns a Series:
>>> df_single_level_cols
weight height
cat 0 1
dog 2 3
>>> df_single_level_cols.stack()
cat weight 0
height 1
dog weight 2
height 3
dtype: int64
**Multi level columns: simple case**
>>> multicol1 = pd.MultiIndex.from_tuples([('weight', 'kg'),
... ('weight', 'pounds')])
>>> df_multi_level_cols1 = pd.DataFrame([[1, 2], [2, 4]],
... index=['cat', 'dog'],
... columns=multicol1)
Stacking a dataframe with a multi-level column axis:
>>> df_multi_level_cols1
weight
kg pounds
cat 1 2
dog 2 4
>>> df_multi_level_cols1.stack()
weight
cat kg 1
pounds 2
dog kg 2
pounds 4
**Missing values**
>>> multicol2 = pd.MultiIndex.from_tuples([('weight', 'kg'),
... ('height', 'm')])
>>> df_multi_level_cols2 = pd.DataFrame([[1.0, 2.0], [3.0, 4.0]],
... index=['cat', 'dog'],
... columns=multicol2)
It is common to have missing values when stacking a dataframe
with multi-level columns, as the stacked dataframe typically
has more values than the original dataframe. Missing values
are filled with NaNs:
>>> df_multi_level_cols2
weight height
kg m
cat 1.0 2.0
dog 3.0 4.0
>>> df_multi_level_cols2.stack()
height weight
cat kg NaN 1.0
m 2.0 NaN
dog kg NaN 3.0
m 4.0 NaN
**Prescribing the level(s) to be stacked**
The first parameter controls which level or levels are stacked:
>>> df_multi_level_cols2.stack(0)
kg m
cat height NaN 2.0
weight 1.0 NaN
dog height NaN 4.0
weight 3.0 NaN
>>> df_multi_level_cols2.stack([0, 1])
cat height m 2.0
weight kg 1.0
dog height m 4.0
weight kg 3.0
dtype: float64
**Dropping missing values**
>>> df_multi_level_cols3 = pd.DataFrame([[None, 1.0], [2.0, 3.0]],
... index=['cat', 'dog'],
... columns=multicol2)
Note that rows where all values are missing are dropped by
default but this behaviour can be controlled via the dropna
keyword parameter:
>>> df_multi_level_cols3
weight height
kg m
cat NaN 1.0
dog 2.0 3.0
>>> df_multi_level_cols3.stack(dropna=False)
height weight
cat kg NaN NaN
m 1.0 NaN
dog kg NaN 2.0
m 3.0 NaN
>>> df_multi_level_cols3.stack(dropna=True)
height weight
cat m 1.0 NaN
dog kg NaN 2.0
m 3.0 NaN
"""
from pandas.core.reshape.reshape import (
stack,
stack_multiple,
)
if isinstance(level, (tuple, list)):
result = stack_multiple(self, level, dropna=dropna)
else:
result = stack(self, level, dropna=dropna)
return result.__finalize__(self, method="stack")
def explode(
self,
column: IndexLabel,
ignore_index: bool = False,
) -> DataFrame:
"""
Transform each element of a list-like to a row, replicating index values.
Parameters
----------
column : IndexLabel
Column(s) to explode.
For multiple columns, specify a non-empty list with each element
be str or tuple, and all specified columns their list-like data
on same row of the frame must have matching length.
.. versionadded:: 1.3.0
Multi-column explode
ignore_index : bool, default False
If True, the resulting index will be labeled 0, 1, …, n - 1.
.. versionadded:: 1.1.0
Returns
-------
DataFrame
Exploded lists to rows of the subset columns;
index will be duplicated for these rows.
Raises
------
ValueError :
* If columns of the frame are not unique.
* If specified columns to explode is empty list.
* If specified columns to explode have not matching count of
elements rowwise in the frame.
See Also
--------
DataFrame.unstack : Pivot a level of the (necessarily hierarchical)
index labels.
DataFrame.melt : Unpivot a DataFrame from wide format to long format.
Series.explode : Explode a DataFrame from list-like columns to long format.
Notes
-----
This routine will explode list-likes including lists, tuples, sets,
Series, and np.ndarray. The result dtype of the subset rows will
be object. Scalars will be returned unchanged, and empty list-likes will
result in a np.nan for that row. In addition, the ordering of rows in the
output will be non-deterministic when exploding sets.
Reference :ref:`the user guide <reshaping.explode>` for more examples.
Examples
--------
>>> df = pd.DataFrame({'A': [[0, 1, 2], 'foo', [], [3, 4]],
... 'B': 1,
... 'C': [['a', 'b', 'c'], np.nan, [], ['d', 'e']]})
>>> df
A B C
0 [0, 1, 2] 1 [a, b, c]
1 foo 1 NaN
2 [] 1 []
3 [3, 4] 1 [d, e]
Single-column explode.
>>> df.explode('A')
A B C
0 0 1 [a, b, c]
0 1 1 [a, b, c]
0 2 1 [a, b, c]
1 foo 1 NaN
2 NaN 1 []
3 3 1 [d, e]
3 4 1 [d, e]
Multi-column explode.
>>> df.explode(list('AC'))
A B C
0 0 1 a
0 1 1 b
0 2 1 c
1 foo 1 NaN
2 NaN 1 NaN
3 3 1 d
3 4 1 e
"""
if not self.columns.is_unique:
duplicate_cols = self.columns[self.columns.duplicated()].tolist()
raise ValueError(
f"DataFrame columns must be unique. Duplicate columns: {duplicate_cols}"
)
columns: list[Hashable]
if is_scalar(column) or isinstance(column, tuple):
columns = [column]
elif isinstance(column, list) and all(
is_scalar(c) or isinstance(c, tuple) for c in column
):
if not column:
raise ValueError("column must be nonempty")
if len(column) > len(set(column)):
raise ValueError("column must be unique")
columns = column
else:
raise ValueError("column must be a scalar, tuple, or list thereof")
df = self.reset_index(drop=True)
if len(columns) == 1:
result = df[columns[0]].explode()
else:
mylen = lambda x: len(x) if (is_list_like(x) and len(x) > 0) else 1
counts0 = self[columns[0]].apply(mylen)
for c in columns[1:]:
if not all(counts0 == self[c].apply(mylen)):
raise ValueError("columns must have matching element counts")
result = DataFrame({c: df[c].explode() for c in columns})
result = df.drop(columns, axis=1).join(result)
if ignore_index:
result.index = default_index(len(result))
else:
result.index = self.index.take(result.index)
result = result.reindex(columns=self.columns, copy=False)
return result.__finalize__(self, method="explode")
def unstack(self, level: Level = -1, fill_value=None):
"""
Pivot a level of the (necessarily hierarchical) index labels.
Returns a DataFrame having a new level of column labels whose inner-most level
consists of the pivoted index labels.
If the index is not a MultiIndex, the output will be a Series
(the analogue of stack when the columns are not a MultiIndex).
Parameters
----------
level : int, str, or list of these, default -1 (last level)
Level(s) of index to unstack, can pass level name.
fill_value : int, str or dict
Replace NaN with this value if the unstack produces missing values.
Returns
-------
Series or DataFrame
See Also
--------
DataFrame.pivot : Pivot a table based on column values.
DataFrame.stack : Pivot a level of the column labels (inverse operation
from `unstack`).
Notes
-----
Reference :ref:`the user guide <reshaping.stacking>` for more examples.
Examples
--------
>>> index = pd.MultiIndex.from_tuples([('one', 'a'), ('one', 'b'),
... ('two', 'a'), ('two', 'b')])
>>> s = pd.Series(np.arange(1.0, 5.0), index=index)
>>> s
one a 1.0
b 2.0
two a 3.0
b 4.0
dtype: float64
>>> s.unstack(level=-1)
a b
one 1.0 2.0
two 3.0 4.0
>>> s.unstack(level=0)
one two
a 1.0 3.0
b 2.0 4.0
>>> df = s.unstack(level=0)
>>> df.unstack()
one a 1.0
b 2.0
two a 3.0
b 4.0
dtype: float64
"""
from pandas.core.reshape.reshape import unstack
result = unstack(self, level, fill_value)
return result.__finalize__(self, method="unstack")
@Appender(_shared_docs["melt"] % {"caller": "df.melt(", "other": "melt"})
def melt(
self,
id_vars=None,
value_vars=None,
var_name=None,
value_name: Hashable = "value",
col_level: Level = None,
ignore_index: bool = True,
) -> DataFrame:
return melt(
self,
id_vars=id_vars,
value_vars=value_vars,
var_name=var_name,
value_name=value_name,
col_level=col_level,
ignore_index=ignore_index,
).__finalize__(self, method="melt")
# ----------------------------------------------------------------------
# Time series-related
@doc(
Series.diff,
klass="DataFrame",
extra_params="axis : {0 or 'index', 1 or 'columns'}, default 0\n "
"Take difference over rows (0) or columns (1).\n",
other_klass="Series",
examples=dedent(
"""
Difference with previous row
>>> df = pd.DataFrame({'a': [1, 2, 3, 4, 5, 6],
... 'b': [1, 1, 2, 3, 5, 8],
... 'c': [1, 4, 9, 16, 25, 36]})
>>> df
a b c
0 1 1 1
1 2 1 4
2 3 2 9
3 4 3 16
4 5 5 25
5 6 8 36
>>> df.diff()
a b c
0 NaN NaN NaN
1 1.0 0.0 3.0
2 1.0 1.0 5.0
3 1.0 1.0 7.0
4 1.0 2.0 9.0
5 1.0 3.0 11.0
Difference with previous column
>>> df.diff(axis=1)
a b c
0 NaN 0 0
1 NaN -1 3
2 NaN -1 7
3 NaN -1 13
4 NaN 0 20
5 NaN 2 28
Difference with 3rd previous row
>>> df.diff(periods=3)
a b c
0 NaN NaN NaN
1 NaN NaN NaN
2 NaN NaN NaN
3 3.0 2.0 15.0
4 3.0 4.0 21.0
5 3.0 6.0 27.0
Difference with following row
>>> df.diff(periods=-1)
a b c
0 -1.0 0.0 -3.0
1 -1.0 -1.0 -5.0
2 -1.0 -1.0 -7.0
3 -1.0 -2.0 -9.0
4 -1.0 -3.0 -11.0
5 NaN NaN NaN
Overflow in input dtype
>>> df = pd.DataFrame({'a': [1, 0]}, dtype=np.uint8)
>>> df.diff()
a
0 NaN
1 255.0"""
),
)
def diff(self, periods: int = 1, axis: Axis = 0) -> DataFrame:
if not lib.is_integer(periods):
if not (
is_float(periods)
# error: "int" has no attribute "is_integer"
and periods.is_integer() # type: ignore[attr-defined]
):
raise ValueError("periods must be an integer")
periods = int(periods)
axis = self._get_axis_number(axis)
if axis == 1:
if periods != 0:
# in the periods == 0 case, this is equivalent diff of 0 periods
# along axis=0, and the Manager method may be somewhat more
# performant, so we dispatch in that case.
return self - self.shift(periods, axis=axis)
# With periods=0 this is equivalent to a diff with axis=0
axis = 0
new_data = self._mgr.diff(n=periods, axis=axis)
return self._constructor(new_data).__finalize__(self, "diff")
# ----------------------------------------------------------------------
# Function application
def _gotitem(
self,
key: IndexLabel,
ndim: int,
subset: DataFrame | Series | None = None,
) -> DataFrame | Series:
"""
Sub-classes to define. Return a sliced object.
Parameters
----------
key : string / list of selections
ndim : {1, 2}
requested ndim of result
subset : object, default None
subset to act on
"""
if subset is None:
subset = self
elif subset.ndim == 1: # is Series
return subset
# TODO: _shallow_copy(subset)?
return subset[key]
_agg_summary_and_see_also_doc = dedent(
"""
The aggregation operations are always performed over an axis, either the
index (default) or the column axis. This behavior is different from
`numpy` aggregation functions (`mean`, `median`, `prod`, `sum`, `std`,
`var`), where the default is to compute the aggregation of the flattened
array, e.g., ``numpy.mean(arr_2d)`` as opposed to
``numpy.mean(arr_2d, axis=0)``.
`agg` is an alias for `aggregate`. Use the alias.
See Also
--------
DataFrame.apply : Perform any type of operations.
DataFrame.transform : Perform transformation type operations.
core.groupby.GroupBy : Perform operations over groups.
core.resample.Resampler : Perform operations over resampled bins.
core.window.Rolling : Perform operations over rolling window.
core.window.Expanding : Perform operations over expanding window.
core.window.ExponentialMovingWindow : Perform operation over exponential weighted
window.
"""
)
_agg_examples_doc = dedent(
"""
Examples
--------
>>> df = pd.DataFrame([[1, 2, 3],
... [4, 5, 6],
... [7, 8, 9],
... [np.nan, np.nan, np.nan]],
... columns=['A', 'B', 'C'])
Aggregate these functions over the rows.
>>> df.agg(['sum', 'min'])
A B C
sum 12.0 15.0 18.0
min 1.0 2.0 3.0
Different aggregations per column.
>>> df.agg({'A' : ['sum', 'min'], 'B' : ['min', 'max']})
A B
sum 12.0 NaN
min 1.0 2.0
max NaN 8.0
Aggregate different functions over the columns and rename the index of the resulting
DataFrame.
>>> df.agg(x=('A', max), y=('B', 'min'), z=('C', np.mean))
A B C
x 7.0 NaN NaN
y NaN 2.0 NaN
z NaN NaN 6.0
Aggregate over the columns.
>>> df.agg("mean", axis="columns")
0 2.0
1 5.0
2 8.0
3 NaN
dtype: float64
"""
)
@doc(
_shared_docs["aggregate"],
klass=_shared_doc_kwargs["klass"],
axis=_shared_doc_kwargs["axis"],
see_also=_agg_summary_and_see_also_doc,
examples=_agg_examples_doc,
)
def aggregate(self, func=None, axis: Axis = 0, *args, **kwargs):
from pandas.core.apply import frame_apply
axis = self._get_axis_number(axis)
relabeling, func, columns, order = reconstruct_func(func, **kwargs)
op = frame_apply(self, func=func, axis=axis, args=args, kwargs=kwargs)
result = op.agg()
if relabeling:
# This is to keep the order to columns occurrence unchanged, and also
# keep the order of new columns occurrence unchanged
# For the return values of reconstruct_func, if relabeling is
# False, columns and order will be None.
assert columns is not None
assert order is not None
result_in_dict = relabel_result(result, func, columns, order)
result = DataFrame(result_in_dict, index=columns)
return result
agg = aggregate
# error: Signature of "any" incompatible with supertype "NDFrame" [override]
@overload # type: ignore[override]
def any(
self,
*,
axis: Axis = ...,
bool_only: bool | None = ...,
skipna: bool = ...,
level: None = ...,
**kwargs,
) -> Series:
...
@overload
def any(
self,
*,
axis: Axis = ...,
bool_only: bool | None = ...,
skipna: bool = ...,
level: Level,
**kwargs,
) -> DataFrame | Series:
...
# error: Missing return statement
@doc(NDFrame.any, **_shared_doc_kwargs)
def any( # type: ignore[empty-body]
self,
axis: Axis = 0,
bool_only: bool | None = None,
skipna: bool = True,
level: Level = None,
**kwargs,
) -> DataFrame | Series:
...
@doc(
_shared_docs["transform"],
klass=_shared_doc_kwargs["klass"],
axis=_shared_doc_kwargs["axis"],
)
def transform(
self, func: AggFuncType, axis: Axis = 0, *args, **kwargs
) -> DataFrame:
from pandas.core.apply import frame_apply
op = frame_apply(self, func=func, axis=axis, args=args, kwargs=kwargs)
result = op.transform()
assert isinstance(result, DataFrame)
return result
def apply(
self,
func: AggFuncType,
axis: Axis = 0,
raw: bool = False,
result_type: Literal["expand", "reduce", "broadcast"] | None = None,
args=(),
**kwargs,
):
"""
Apply a function along an axis of the DataFrame.
Objects passed to the function are Series objects whose index is
either the DataFrame's index (``axis=0``) or the DataFrame's columns
(``axis=1``). By default (``result_type=None``), the final return type
is inferred from the return type of the applied function. Otherwise,
it depends on the `result_type` argument.
Parameters
----------
func : function
Function to apply to each column or row.
axis : {0 or 'index', 1 or 'columns'}, default 0
Axis along which the function is applied:
* 0 or 'index': apply function to each column.
* 1 or 'columns': apply function to each row.
raw : bool, default False
Determines if row or column is passed as a Series or ndarray object:
* ``False`` : passes each row or column as a Series to the
function.
* ``True`` : the passed function will receive ndarray objects
instead.
If you are just applying a NumPy reduction function this will
achieve much better performance.
result_type : {'expand', 'reduce', 'broadcast', None}, default None
These only act when ``axis=1`` (columns):
* 'expand' : list-like results will be turned into columns.
* 'reduce' : returns a Series if possible rather than expanding
list-like results. This is the opposite of 'expand'.
* 'broadcast' : results will be broadcast to the original shape
of the DataFrame, the original index and columns will be
retained.
The default behaviour (None) depends on the return value of the
applied function: list-like results will be returned as a Series
of those. However if the apply function returns a Series these
are expanded to columns.
args : tuple
Positional arguments to pass to `func` in addition to the
array/series.
**kwargs
Additional keyword arguments to pass as keywords arguments to
`func`.
Returns
-------
Series or DataFrame
Result of applying ``func`` along the given axis of the
DataFrame.
See Also
--------
DataFrame.applymap: For elementwise operations.
DataFrame.aggregate: Only perform aggregating type operations.
DataFrame.transform: Only perform transforming type operations.
Notes
-----
Functions that mutate the passed object can produce unexpected
behavior or errors and are not supported. See :ref:`gotchas.udf-mutation`
for more details.
Examples
--------
>>> df = pd.DataFrame([[4, 9]] * 3, columns=['A', 'B'])
>>> df
A B
0 4 9
1 4 9
2 4 9
Using a numpy universal function (in this case the same as
``np.sqrt(df)``):
>>> df.apply(np.sqrt)
A B
0 2.0 3.0
1 2.0 3.0
2 2.0 3.0
Using a reducing function on either axis
>>> df.apply(np.sum, axis=0)
A 12
B 27
dtype: int64
>>> df.apply(np.sum, axis=1)
0 13
1 13
2 13
dtype: int64
Returning a list-like will result in a Series
>>> df.apply(lambda x: [1, 2], axis=1)
0 [1, 2]
1 [1, 2]
2 [1, 2]
dtype: object
Passing ``result_type='expand'`` will expand list-like results
to columns of a Dataframe
>>> df.apply(lambda x: [1, 2], axis=1, result_type='expand')
0 1
0 1 2
1 1 2
2 1 2
Returning a Series inside the function is similar to passing
``result_type='expand'``. The resulting column names
will be the Series index.
>>> df.apply(lambda x: pd.Series([1, 2], index=['foo', 'bar']), axis=1)
foo bar
0 1 2
1 1 2
2 1 2
Passing ``result_type='broadcast'`` will ensure the same shape
result, whether list-like or scalar is returned by the function,
and broadcast it along the axis. The resulting column names will
be the originals.
>>> df.apply(lambda x: [1, 2], axis=1, result_type='broadcast')
A B
0 1 2
1 1 2
2 1 2
"""
from pandas.core.apply import frame_apply
op = frame_apply(
self,
func=func,
axis=axis,
raw=raw,
result_type=result_type,
args=args,
kwargs=kwargs,
)
return op.apply().__finalize__(self, method="apply")
def applymap(
self, func: PythonFuncType, na_action: str | None = None, **kwargs
) -> DataFrame:
"""
Apply a function to a Dataframe elementwise.
This method applies a function that accepts and returns a scalar
to every element of a DataFrame.
Parameters
----------
func : callable
Python function, returns a single value from a single value.
na_action : {None, 'ignore'}, default None
If ‘ignore’, propagate NaN values, without passing them to func.
.. versionadded:: 1.2
**kwargs
Additional keyword arguments to pass as keywords arguments to
`func`.
.. versionadded:: 1.3.0
Returns
-------
DataFrame
Transformed DataFrame.
See Also
--------
DataFrame.apply : Apply a function along input axis of DataFrame.
Examples
--------
>>> df = pd.DataFrame([[1, 2.12], [3.356, 4.567]])
>>> df
0 1
0 1.000 2.120
1 3.356 4.567
>>> df.applymap(lambda x: len(str(x)))
0 1
0 3 4
1 5 5
Like Series.map, NA values can be ignored:
>>> df_copy = df.copy()
>>> df_copy.iloc[0, 0] = pd.NA
>>> df_copy.applymap(lambda x: len(str(x)), na_action='ignore')
0 1
0 NaN 4
1 5.0 5
Note that a vectorized version of `func` often exists, which will
be much faster. You could square each number elementwise.
>>> df.applymap(lambda x: x**2)
0 1
0 1.000000 4.494400
1 11.262736 20.857489
But it's better to avoid applymap in that case.
>>> df ** 2
0 1
0 1.000000 4.494400
1 11.262736 20.857489
"""
if na_action not in {"ignore", None}:
raise ValueError(
f"na_action must be 'ignore' or None. Got {repr(na_action)}"
)
ignore_na = na_action == "ignore"
func = functools.partial(func, **kwargs)
# if we have a dtype == 'M8[ns]', provide boxed values
def infer(x):
if x.empty:
return lib.map_infer(x, func, ignore_na=ignore_na)
return lib.map_infer(x.astype(object)._values, func, ignore_na=ignore_na)
return self.apply(infer).__finalize__(self, "applymap")
# ----------------------------------------------------------------------
# Merging / joining methods
def _append(
self,
other,
ignore_index: bool = False,
verify_integrity: bool = False,
sort: bool = False,
) -> DataFrame:
if isinstance(other, (Series, dict)):
if isinstance(other, dict):
if not ignore_index:
raise TypeError("Can only append a dict if ignore_index=True")
other = Series(other)
if other.name is None and not ignore_index:
raise TypeError(
"Can only append a Series if ignore_index=True "
"or if the Series has a name"
)
index = Index(
[other.name],
name=self.index.names
if isinstance(self.index, MultiIndex)
else self.index.name,
)
row_df = other.to_frame().T
# infer_objects is needed for
# test_append_empty_frame_to_series_with_dateutil_tz
other = row_df.infer_objects(copy=False).rename_axis(
index.names, copy=False
)
elif isinstance(other, list):
if not other:
pass
elif not isinstance(other[0], DataFrame):
other = DataFrame(other)
if self.index.name is not None and not ignore_index:
other.index.name = self.index.name
from pandas.core.reshape.concat import concat
if isinstance(other, (list, tuple)):
to_concat = [self, *other]
else:
to_concat = [self, other]
result = concat(
to_concat,
ignore_index=ignore_index,
verify_integrity=verify_integrity,
sort=sort,
)
return result.__finalize__(self, method="append")
def join(
self,
other: DataFrame | Series | Iterable[DataFrame | Series],
on: IndexLabel | None = None,
how: MergeHow = "left",
lsuffix: str = "",
rsuffix: str = "",
sort: bool = False,
validate: str | None = None,
) -> DataFrame:
"""
Join columns of another DataFrame.
Join columns with `other` DataFrame either on index or on a key
column. Efficiently join multiple DataFrame objects by index at once by
passing a list.
Parameters
----------
other : DataFrame, Series, or a list containing any combination of them
Index should be similar to one of the columns in this one. If a
Series is passed, its name attribute must be set, and that will be
used as the column name in the resulting joined DataFrame.
on : str, list of str, or array-like, optional
Column or index level name(s) in the caller to join on the index
in `other`, otherwise joins index-on-index. If multiple
values given, the `other` DataFrame must have a MultiIndex. Can
pass an array as the join key if it is not already contained in
the calling DataFrame. Like an Excel VLOOKUP operation.
how : {'left', 'right', 'outer', 'inner', 'cross'}, default 'left'
How to handle the operation of the two objects.
* left: use calling frame's index (or column if on is specified)
* right: use `other`'s index.
* outer: form union of calling frame's index (or column if on is
specified) with `other`'s index, and sort it.
lexicographically.
* inner: form intersection of calling frame's index (or column if
on is specified) with `other`'s index, preserving the order
of the calling's one.
* cross: creates the cartesian product from both frames, preserves the order
of the left keys.
.. versionadded:: 1.2.0
lsuffix : str, default ''
Suffix to use from left frame's overlapping columns.
rsuffix : str, default ''
Suffix to use from right frame's overlapping columns.
sort : bool, default False
Order result DataFrame lexicographically by the join key. If False,
the order of the join key depends on the join type (how keyword).
validate : str, optional
If specified, checks if join is of specified type.
* "one_to_one" or "1:1": check if join keys are unique in both left
and right datasets.
* "one_to_many" or "1:m": check if join keys are unique in left dataset.
* "many_to_one" or "m:1": check if join keys are unique in right dataset.
* "many_to_many" or "m:m": allowed, but does not result in checks.
.. versionadded:: 1.5.0
Returns
-------
DataFrame
A dataframe containing columns from both the caller and `other`.
See Also
--------
DataFrame.merge : For column(s)-on-column(s) operations.
Notes
-----
Parameters `on`, `lsuffix`, and `rsuffix` are not supported when
passing a list of `DataFrame` objects.
Support for specifying index levels as the `on` parameter was added
in version 0.23.0.
Examples
--------
>>> df = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3', 'K4', 'K5'],
... 'A': ['A0', 'A1', 'A2', 'A3', 'A4', 'A5']})
>>> df
key A
0 K0 A0
1 K1 A1
2 K2 A2
3 K3 A3
4 K4 A4
5 K5 A5
>>> other = pd.DataFrame({'key': ['K0', 'K1', 'K2'],
... 'B': ['B0', 'B1', 'B2']})
>>> other
key B
0 K0 B0
1 K1 B1
2 K2 B2
Join DataFrames using their indexes.
>>> df.join(other, lsuffix='_caller', rsuffix='_other')
key_caller A key_other B
0 K0 A0 K0 B0
1 K1 A1 K1 B1
2 K2 A2 K2 B2
3 K3 A3 NaN NaN
4 K4 A4 NaN NaN
5 K5 A5 NaN NaN
If we want to join using the key columns, we need to set key to be
the index in both `df` and `other`. The joined DataFrame will have
key as its index.
>>> df.set_index('key').join(other.set_index('key'))
A B
key
K0 A0 B0
K1 A1 B1
K2 A2 B2
K3 A3 NaN
K4 A4 NaN
K5 A5 NaN
Another option to join using the key columns is to use the `on`
parameter. DataFrame.join always uses `other`'s index but we can use
any column in `df`. This method preserves the original DataFrame's
index in the result.
>>> df.join(other.set_index('key'), on='key')
key A B
0 K0 A0 B0
1 K1 A1 B1
2 K2 A2 B2
3 K3 A3 NaN
4 K4 A4 NaN
5 K5 A5 NaN
Using non-unique key values shows how they are matched.
>>> df = pd.DataFrame({'key': ['K0', 'K1', 'K1', 'K3', 'K0', 'K1'],
... 'A': ['A0', 'A1', 'A2', 'A3', 'A4', 'A5']})
>>> df
key A
0 K0 A0
1 K1 A1
2 K1 A2
3 K3 A3
4 K0 A4
5 K1 A5
>>> df.join(other.set_index('key'), on='key', validate='m:1')
key A B
0 K0 A0 B0
1 K1 A1 B1
2 K1 A2 B1
3 K3 A3 NaN
4 K0 A4 B0
5 K1 A5 B1
"""
return self._join_compat(
other,
on=on,
how=how,
lsuffix=lsuffix,
rsuffix=rsuffix,
sort=sort,
validate=validate,
)
def _join_compat(
self,
other: DataFrame | Series | Iterable[DataFrame | Series],
on: IndexLabel | None = None,
how: MergeHow = "left",
lsuffix: str = "",
rsuffix: str = "",
sort: bool = False,
validate: str | None = None,
):
from pandas.core.reshape.concat import concat
from pandas.core.reshape.merge import merge
if isinstance(other, Series):
if other.name is None:
raise ValueError("Other Series must have a name")
other = DataFrame({other.name: other})
if isinstance(other, DataFrame):
if how == "cross":
return merge(
self,
other,
how=how,
on=on,
suffixes=(lsuffix, rsuffix),
sort=sort,
validate=validate,
)
return merge(
self,
other,
left_on=on,
how=how,
left_index=on is None,
right_index=True,
suffixes=(lsuffix, rsuffix),
sort=sort,
validate=validate,
)
else:
if on is not None:
raise ValueError(
"Joining multiple DataFrames only supported for joining on index"
)
if rsuffix or lsuffix:
raise ValueError(
"Suffixes not supported when joining multiple DataFrames"
)
# Mypy thinks the RHS is a
# "Union[DataFrame, Series, Iterable[Union[DataFrame, Series]]]" whereas
# the LHS is an "Iterable[DataFrame]", but in reality both types are
# "Iterable[Union[DataFrame, Series]]" due to the if statements
frames = [cast("DataFrame | Series", self)] + list(other)
can_concat = all(df.index.is_unique for df in frames)
# join indexes only using concat
if can_concat:
if how == "left":
res = concat(
frames, axis=1, join="outer", verify_integrity=True, sort=sort
)
return res.reindex(self.index, copy=False)
else:
return concat(
frames, axis=1, join=how, verify_integrity=True, sort=sort
)
joined = frames[0]
for frame in frames[1:]:
joined = merge(
joined,
frame,
how=how,
left_index=True,
right_index=True,
validate=validate,
)
return joined
@Substitution("")
@Appender(_merge_doc, indents=2)
def merge(
self,
right: DataFrame | Series,
how: MergeHow = "inner",
on: IndexLabel | None = None,
left_on: IndexLabel | None = None,
right_on: IndexLabel | None = None,
left_index: bool = False,
right_index: bool = False,
sort: bool = False,
suffixes: Suffixes = ("_x", "_y"),
copy: bool | None = None,
indicator: str | bool = False,
validate: str | None = None,
) -> DataFrame:
from pandas.core.reshape.merge import merge
return merge(
self,
right,
how=how,
on=on,
left_on=left_on,
right_on=right_on,
left_index=left_index,
right_index=right_index,
sort=sort,
suffixes=suffixes,
copy=copy,
indicator=indicator,
validate=validate,
)
def round(
self, decimals: int | dict[IndexLabel, int] | Series = 0, *args, **kwargs
) -> DataFrame:
"""
Round a DataFrame to a variable number of decimal places.
Parameters
----------
decimals : int, dict, Series
Number of decimal places to round each column to. If an int is
given, round each column to the same number of places.
Otherwise dict and Series round to variable numbers of places.
Column names should be in the keys if `decimals` is a
dict-like, or in the index if `decimals` is a Series. Any
columns not included in `decimals` will be left as is. Elements
of `decimals` which are not columns of the input will be
ignored.
*args
Additional keywords have no effect but might be accepted for
compatibility with numpy.
**kwargs
Additional keywords have no effect but might be accepted for
compatibility with numpy.
Returns
-------
DataFrame
A DataFrame with the affected columns rounded to the specified
number of decimal places.
See Also
--------
numpy.around : Round a numpy array to the given number of decimals.
Series.round : Round a Series to the given number of decimals.
Examples
--------
>>> df = pd.DataFrame([(.21, .32), (.01, .67), (.66, .03), (.21, .18)],
... columns=['dogs', 'cats'])
>>> df
dogs cats
0 0.21 0.32
1 0.01 0.67
2 0.66 0.03
3 0.21 0.18
By providing an integer each column is rounded to the same number
of decimal places
>>> df.round(1)
dogs cats
0 0.2 0.3
1 0.0 0.7
2 0.7 0.0
3 0.2 0.2
With a dict, the number of places for specific columns can be
specified with the column names as key and the number of decimal
places as value
>>> df.round({'dogs': 1, 'cats': 0})
dogs cats
0 0.2 0.0
1 0.0 1.0
2 0.7 0.0
3 0.2 0.0
Using a Series, the number of places for specific columns can be
specified with the column names as index and the number of
decimal places as value
>>> decimals = pd.Series([0, 1], index=['cats', 'dogs'])
>>> df.round(decimals)
dogs cats
0 0.2 0.0
1 0.0 1.0
2 0.7 0.0
3 0.2 0.0
"""
from pandas.core.reshape.concat import concat
def _dict_round(df: DataFrame, decimals):
for col, vals in df.items():
try:
yield _series_round(vals, decimals[col])
except KeyError:
yield vals
def _series_round(ser: Series, decimals: int) -> Series:
if is_integer_dtype(ser.dtype) or is_float_dtype(ser.dtype):
return ser.round(decimals)
return ser
nv.validate_round(args, kwargs)
if isinstance(decimals, (dict, Series)):
if isinstance(decimals, Series) and not decimals.index.is_unique:
raise ValueError("Index of decimals must be unique")
if is_dict_like(decimals) and not all(
is_integer(value) for _, value in decimals.items()
):
raise TypeError("Values in decimals must be integers")
new_cols = list(_dict_round(self, decimals))
elif is_integer(decimals):
# Dispatch to Block.round
return self._constructor(
self._mgr.round(decimals=decimals, using_cow=using_copy_on_write()),
).__finalize__(self, method="round")
else:
raise TypeError("decimals must be an integer, a dict-like or a Series")
if new_cols is not None and len(new_cols) > 0:
return self._constructor(
concat(new_cols, axis=1), index=self.index, columns=self.columns
).__finalize__(self, method="round")
else:
return self.copy(deep=False)
# ----------------------------------------------------------------------
# Statistical methods, etc.
def corr(
self,
method: CorrelationMethod = "pearson",
min_periods: int = 1,
numeric_only: bool = False,
) -> DataFrame:
"""
Compute pairwise correlation of columns, excluding NA/null values.
Parameters
----------
method : {'pearson', 'kendall', 'spearman'} or callable
Method of correlation:
* pearson : standard correlation coefficient
* kendall : Kendall Tau correlation coefficient
* spearman : Spearman rank correlation
* callable: callable with input two 1d ndarrays
and returning a float. Note that the returned matrix from corr
will have 1 along the diagonals and will be symmetric
regardless of the callable's behavior.
min_periods : int, optional
Minimum number of observations required per pair of columns
to have a valid result. Currently only available for Pearson
and Spearman correlation.
numeric_only : bool, default False
Include only `float`, `int` or `boolean` data.
.. versionadded:: 1.5.0
.. versionchanged:: 2.0.0
The default value of ``numeric_only`` is now ``False``.
Returns
-------
DataFrame
Correlation matrix.
See Also
--------
DataFrame.corrwith : Compute pairwise correlation with another
DataFrame or Series.
Series.corr : Compute the correlation between two Series.
Notes
-----
Pearson, Kendall and Spearman correlation are currently computed using pairwise complete observations.
* `Pearson correlation coefficient <https://en.wikipedia.org/wiki/Pearson_correlation_coefficient>`_
* `Kendall rank correlation coefficient <https://en.wikipedia.org/wiki/Kendall_rank_correlation_coefficient>`_
* `Spearman's rank correlation coefficient <https://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient>`_
Examples
--------
>>> def histogram_intersection(a, b):
... v = np.minimum(a, b).sum().round(decimals=1)
... return v
>>> df = pd.DataFrame([(.2, .3), (.0, .6), (.6, .0), (.2, .1)],
... columns=['dogs', 'cats'])
>>> df.corr(method=histogram_intersection)
dogs cats
dogs 1.0 0.3
cats 0.3 1.0
>>> df = pd.DataFrame([(1, 1), (2, np.nan), (np.nan, 3), (4, 4)],
... columns=['dogs', 'cats'])
>>> df.corr(min_periods=3)
dogs cats
dogs 1.0 NaN
cats NaN 1.0
""" # noqa:E501
data = self._get_numeric_data() if numeric_only else self
cols = data.columns
idx = cols.copy()
mat = data.to_numpy(dtype=float, na_value=np.nan, copy=False)
if method == "pearson":
correl = libalgos.nancorr(mat, minp=min_periods)
elif method == "spearman":
correl = libalgos.nancorr_spearman(mat, minp=min_periods)
elif method == "kendall" or callable(method):
if min_periods is None:
min_periods = 1
mat = mat.T
corrf = nanops.get_corr_func(method)
K = len(cols)
correl = np.empty((K, K), dtype=float)
mask = np.isfinite(mat)
for i, ac in enumerate(mat):
for j, bc in enumerate(mat):
if i > j:
continue
valid = mask[i] & mask[j]
if valid.sum() < min_periods:
c = np.nan
elif i == j:
c = 1.0
elif not valid.all():
c = corrf(ac[valid], bc[valid])
else:
c = corrf(ac, bc)
correl[i, j] = c
correl[j, i] = c
else:
raise ValueError(
"method must be either 'pearson', "
"'spearman', 'kendall', or a callable, "
f"'{method}' was supplied"
)
result = self._constructor(correl, index=idx, columns=cols, copy=False)
return result.__finalize__(self, method="corr")
def cov(
self,
min_periods: int | None = None,
ddof: int | None = 1,
numeric_only: bool = False,
) -> DataFrame:
"""
Compute pairwise covariance of columns, excluding NA/null values.
Compute the pairwise covariance among the series of a DataFrame.
The returned data frame is the `covariance matrix
<https://en.wikipedia.org/wiki/Covariance_matrix>`__ of the columns
of the DataFrame.
Both NA and null values are automatically excluded from the
calculation. (See the note below about bias from missing values.)
A threshold can be set for the minimum number of
observations for each value created. Comparisons with observations
below this threshold will be returned as ``NaN``.
This method is generally used for the analysis of time series data to
understand the relationship between different measures
across time.
Parameters
----------
min_periods : int, optional
Minimum number of observations required per pair of columns
to have a valid result.
ddof : int, default 1
Delta degrees of freedom. The divisor used in calculations
is ``N - ddof``, where ``N`` represents the number of elements.
.. versionadded:: 1.1.0
numeric_only : bool, default False
Include only `float`, `int` or `boolean` data.
.. versionadded:: 1.5.0
.. versionchanged:: 2.0.0
The default value of ``numeric_only`` is now ``False``.
Returns
-------
DataFrame
The covariance matrix of the series of the DataFrame.
See Also
--------
Series.cov : Compute covariance with another Series.
core.window.ewm.ExponentialMovingWindow.cov : Exponential weighted sample
covariance.
core.window.expanding.Expanding.cov : Expanding sample covariance.
core.window.rolling.Rolling.cov : Rolling sample covariance.
Notes
-----
Returns the covariance matrix of the DataFrame's time series.
The covariance is normalized by N-ddof.
For DataFrames that have Series that are missing data (assuming that
data is `missing at random
<https://en.wikipedia.org/wiki/Missing_data#Missing_at_random>`__)
the returned covariance matrix will be an unbiased estimate
of the variance and covariance between the member Series.
However, for many applications this estimate may not be acceptable
because the estimate covariance matrix is not guaranteed to be positive
semi-definite. This could lead to estimate correlations having
absolute values which are greater than one, and/or a non-invertible
covariance matrix. See `Estimation of covariance matrices
<https://en.wikipedia.org/w/index.php?title=Estimation_of_covariance_
matrices>`__ for more details.
Examples
--------
>>> df = pd.DataFrame([(1, 2), (0, 3), (2, 0), (1, 1)],
... columns=['dogs', 'cats'])
>>> df.cov()
dogs cats
dogs 0.666667 -1.000000
cats -1.000000 1.666667
>>> np.random.seed(42)
>>> df = pd.DataFrame(np.random.randn(1000, 5),
... columns=['a', 'b', 'c', 'd', 'e'])
>>> df.cov()
a b c d e
a 0.998438 -0.020161 0.059277 -0.008943 0.014144
b -0.020161 1.059352 -0.008543 -0.024738 0.009826
c 0.059277 -0.008543 1.010670 -0.001486 -0.000271
d -0.008943 -0.024738 -0.001486 0.921297 -0.013692
e 0.014144 0.009826 -0.000271 -0.013692 0.977795
**Minimum number of periods**
This method also supports an optional ``min_periods`` keyword
that specifies the required minimum number of non-NA observations for
each column pair in order to have a valid result:
>>> np.random.seed(42)
>>> df = pd.DataFrame(np.random.randn(20, 3),
... columns=['a', 'b', 'c'])
>>> df.loc[df.index[:5], 'a'] = np.nan
>>> df.loc[df.index[5:10], 'b'] = np.nan
>>> df.cov(min_periods=12)
a b c
a 0.316741 NaN -0.150812
b NaN 1.248003 0.191417
c -0.150812 0.191417 0.895202
"""
data = self._get_numeric_data() if numeric_only else self
cols = data.columns
idx = cols.copy()
mat = data.to_numpy(dtype=float, na_value=np.nan, copy=False)
if notna(mat).all():
if min_periods is not None and min_periods > len(mat):
base_cov = np.empty((mat.shape[1], mat.shape[1]))
base_cov.fill(np.nan)
else:
base_cov = np.cov(mat.T, ddof=ddof)
base_cov = base_cov.reshape((len(cols), len(cols)))
else:
base_cov = libalgos.nancorr(mat, cov=True, minp=min_periods)
result = self._constructor(base_cov, index=idx, columns=cols, copy=False)
return result.__finalize__(self, method="cov")
def corrwith(
self,
other: DataFrame | Series,
axis: Axis = 0,
drop: bool = False,
method: CorrelationMethod = "pearson",
numeric_only: bool = False,
) -> Series:
"""
Compute pairwise correlation.
Pairwise correlation is computed between rows or columns of
DataFrame with rows or columns of Series or DataFrame. DataFrames
are first aligned along both axes before computing the
correlations.
Parameters
----------
other : DataFrame, Series
Object with which to compute correlations.
axis : {0 or 'index', 1 or 'columns'}, default 0
The axis to use. 0 or 'index' to compute row-wise, 1 or 'columns' for
column-wise.
drop : bool, default False
Drop missing indices from result.
method : {'pearson', 'kendall', 'spearman'} or callable
Method of correlation:
* pearson : standard correlation coefficient
* kendall : Kendall Tau correlation coefficient
* spearman : Spearman rank correlation
* callable: callable with input two 1d ndarrays
and returning a float.
numeric_only : bool, default False
Include only `float`, `int` or `boolean` data.
.. versionadded:: 1.5.0
.. versionchanged:: 2.0.0
The default value of ``numeric_only`` is now ``False``.
Returns
-------
Series
Pairwise correlations.
See Also
--------
DataFrame.corr : Compute pairwise correlation of columns.
Examples
--------
>>> index = ["a", "b", "c", "d", "e"]
>>> columns = ["one", "two", "three", "four"]
>>> df1 = pd.DataFrame(np.arange(20).reshape(5, 4), index=index, columns=columns)
>>> df2 = pd.DataFrame(np.arange(16).reshape(4, 4), index=index[:4], columns=columns)
>>> df1.corrwith(df2)
one 1.0
two 1.0
three 1.0
four 1.0
dtype: float64
>>> df2.corrwith(df1, axis=1)
a 1.0
b 1.0
c 1.0
d 1.0
e NaN
dtype: float64
""" # noqa:E501
axis = self._get_axis_number(axis)
this = self._get_numeric_data() if numeric_only else self
if isinstance(other, Series):
return this.apply(lambda x: other.corr(x, method=method), axis=axis)
if numeric_only:
other = other._get_numeric_data()
left, right = this.align(other, join="inner", copy=False)
if axis == 1:
left = left.T
right = right.T
if method == "pearson":
# mask missing values
left = left + right * 0
right = right + left * 0
# demeaned data
ldem = left - left.mean(numeric_only=numeric_only)
rdem = right - right.mean(numeric_only=numeric_only)
num = (ldem * rdem).sum()
dom = (
(left.count() - 1)
* left.std(numeric_only=numeric_only)
* right.std(numeric_only=numeric_only)
)
correl = num / dom
elif method in ["kendall", "spearman"] or callable(method):
def c(x):
return nanops.nancorr(x[0], x[1], method=method)
correl = self._constructor_sliced(
map(c, zip(left.values.T, right.values.T)),
index=left.columns,
copy=False,
)
else:
raise ValueError(
f"Invalid method {method} was passed, "
"valid methods are: 'pearson', 'kendall', "
"'spearman', or callable"
)
if not drop:
# Find non-matching labels along the given axis
# and append missing correlations (GH 22375)
raxis: AxisInt = 1 if axis == 0 else 0
result_index = this._get_axis(raxis).union(other._get_axis(raxis))
idx_diff = result_index.difference(correl.index)
if len(idx_diff) > 0:
correl = correl._append(
Series([np.nan] * len(idx_diff), index=idx_diff)
)
return correl
# ----------------------------------------------------------------------
# ndarray-like stats methods
def count(self, axis: Axis = 0, numeric_only: bool = False):
"""
Count non-NA cells for each column or row.
The values `None`, `NaN`, `NaT`, and optionally `numpy.inf` (depending
on `pandas.options.mode.use_inf_as_na`) are considered NA.
Parameters
----------
axis : {0 or 'index', 1 or 'columns'}, default 0
If 0 or 'index' counts are generated for each column.
If 1 or 'columns' counts are generated for each row.
numeric_only : bool, default False
Include only `float`, `int` or `boolean` data.
Returns
-------
Series or DataFrame
For each column/row the number of non-NA/null entries.
If `level` is specified returns a `DataFrame`.
See Also
--------
Series.count: Number of non-NA elements in a Series.
DataFrame.value_counts: Count unique combinations of columns.
DataFrame.shape: Number of DataFrame rows and columns (including NA
elements).
DataFrame.isna: Boolean same-sized DataFrame showing places of NA
elements.
Examples
--------
Constructing DataFrame from a dictionary:
>>> df = pd.DataFrame({"Person":
... ["John", "Myla", "Lewis", "John", "Myla"],
... "Age": [24., np.nan, 21., 33, 26],
... "Single": [False, True, True, True, False]})
>>> df
Person Age Single
0 John 24.0 False
1 Myla NaN True
2 Lewis 21.0 True
3 John 33.0 True
4 Myla 26.0 False
Notice the uncounted NA values:
>>> df.count()
Person 5
Age 4
Single 5
dtype: int64
Counts for each **row**:
>>> df.count(axis='columns')
0 3
1 2
2 3
3 3
4 3
dtype: int64
"""
axis = self._get_axis_number(axis)
if numeric_only:
frame = self._get_numeric_data()
else:
frame = self
# GH #423
if len(frame._get_axis(axis)) == 0:
result = self._constructor_sliced(0, index=frame._get_agg_axis(axis))
else:
if frame._is_mixed_type or frame._mgr.any_extension_types:
# the or any_extension_types is really only hit for single-
# column frames with an extension array
result = notna(frame).sum(axis=axis)
else:
# GH13407
series_counts = notna(frame).sum(axis=axis)
counts = series_counts._values
result = self._constructor_sliced(
counts, index=frame._get_agg_axis(axis), copy=False
)
return result.astype("int64").__finalize__(self, method="count")
def _reduce(
self,
op,
name: str,
*,
axis: Axis = 0,
skipna: bool = True,
numeric_only: bool = False,
filter_type=None,
**kwds,
):
assert filter_type is None or filter_type == "bool", filter_type
out_dtype = "bool" if filter_type == "bool" else None
if axis is not None:
axis = self._get_axis_number(axis)
def func(values: np.ndarray):
# We only use this in the case that operates on self.values
return op(values, axis=axis, skipna=skipna, **kwds)
def blk_func(values, axis: Axis = 1):
if isinstance(values, ExtensionArray):
if not is_1d_only_ea_dtype(values.dtype) and not isinstance(
self._mgr, ArrayManager
):
return values._reduce(name, axis=1, skipna=skipna, **kwds)
return values._reduce(name, skipna=skipna, **kwds)
else:
return op(values, axis=axis, skipna=skipna, **kwds)
def _get_data() -> DataFrame:
if filter_type is None:
data = self._get_numeric_data()
else:
# GH#25101, GH#24434
assert filter_type == "bool"
data = self._get_bool_data()
return data
# Case with EAs see GH#35881
df = self
if numeric_only:
df = _get_data()
if axis is None:
return func(df.values)
elif axis == 1:
if len(df.index) == 0:
# Taking a transpose would result in no columns, losing the dtype.
# In the empty case, reducing along axis 0 or 1 gives the same
# result dtype, so reduce with axis=0 and ignore values
result = df._reduce(
op,
name,
axis=0,
skipna=skipna,
numeric_only=False,
filter_type=filter_type,
**kwds,
).iloc[:0]
result.index = df.index
return result
df = df.T
# After possibly _get_data and transposing, we are now in the
# simple case where we can use BlockManager.reduce
res = df._mgr.reduce(blk_func)
out = df._constructor(res).iloc[0]
if out_dtype is not None:
out = out.astype(out_dtype)
elif (df._mgr.get_dtypes() == object).any():
out = out.astype(object)
elif len(self) == 0 and name in ("sum", "prod"):
# Even if we are object dtype, follow numpy and return
# float64, see test_apply_funcs_over_empty
out = out.astype(np.float64)
return out
def _reduce_axis1(self, name: str, func, skipna: bool) -> Series:
"""
Special case for _reduce to try to avoid a potentially-expensive transpose.
Apply the reduction block-wise along axis=1 and then reduce the resulting
1D arrays.
"""
if name == "all":
result = np.ones(len(self), dtype=bool)
ufunc = np.logical_and
elif name == "any":
result = np.zeros(len(self), dtype=bool)
# error: Incompatible types in assignment
# (expression has type "_UFunc_Nin2_Nout1[Literal['logical_or'],
# Literal[20], Literal[False]]", variable has type
# "_UFunc_Nin2_Nout1[Literal['logical_and'], Literal[20],
# Literal[True]]")
ufunc = np.logical_or # type: ignore[assignment]
else:
raise NotImplementedError(name)
for arr in self._mgr.arrays:
middle = func(arr, axis=0, skipna=skipna)
result = ufunc(result, middle)
res_ser = self._constructor_sliced(result, index=self.index, copy=False)
return res_ser
def nunique(self, axis: Axis = 0, dropna: bool = True) -> Series:
"""
Count number of distinct elements in specified axis.
Return Series with number of distinct elements. Can ignore NaN
values.
Parameters
----------
axis : {0 or 'index', 1 or 'columns'}, default 0
The axis to use. 0 or 'index' for row-wise, 1 or 'columns' for
column-wise.
dropna : bool, default True
Don't include NaN in the counts.
Returns
-------
Series
See Also
--------
Series.nunique: Method nunique for Series.
DataFrame.count: Count non-NA cells for each column or row.
Examples
--------
>>> df = pd.DataFrame({'A': [4, 5, 6], 'B': [4, 1, 1]})
>>> df.nunique()
A 3
B 2
dtype: int64
>>> df.nunique(axis=1)
0 1
1 2
2 2
dtype: int64
"""
return self.apply(Series.nunique, axis=axis, dropna=dropna)
@doc(_shared_docs["idxmin"], numeric_only_default="False")
def idxmin(
self, axis: Axis = 0, skipna: bool = True, numeric_only: bool = False
) -> Series:
axis = self._get_axis_number(axis)
if numeric_only:
data = self._get_numeric_data()
else:
data = self
res = data._reduce(
nanops.nanargmin, "argmin", axis=axis, skipna=skipna, numeric_only=False
)
indices = res._values
# indices will always be np.ndarray since axis is not None and
# values is a 2d array for DataFrame
# error: Item "int" of "Union[int, Any]" has no attribute "__iter__"
assert isinstance(indices, np.ndarray) # for mypy
index = data._get_axis(axis)
result = [index[i] if i >= 0 else np.nan for i in indices]
final_result = data._constructor_sliced(result, index=data._get_agg_axis(axis))
return final_result.__finalize__(self, method="idxmin")
@doc(_shared_docs["idxmax"], numeric_only_default="False")
def idxmax(
self, axis: Axis = 0, skipna: bool = True, numeric_only: bool = False
) -> Series:
axis = self._get_axis_number(axis)
if numeric_only:
data = self._get_numeric_data()
else:
data = self
res = data._reduce(
nanops.nanargmax, "argmax", axis=axis, skipna=skipna, numeric_only=False
)
indices = res._values
# indices will always be np.ndarray since axis is not None and
# values is a 2d array for DataFrame
# error: Item "int" of "Union[int, Any]" has no attribute "__iter__"
assert isinstance(indices, np.ndarray) # for mypy
index = data._get_axis(axis)
result = [index[i] if i >= 0 else np.nan for i in indices]
final_result = data._constructor_sliced(result, index=data._get_agg_axis(axis))
return final_result.__finalize__(self, method="idxmax")
def _get_agg_axis(self, axis_num: int) -> Index:
"""
Let's be explicit about this.
"""
if axis_num == 0:
return self.columns
elif axis_num == 1:
return self.index
else:
raise ValueError(f"Axis must be 0 or 1 (got {repr(axis_num)})")
def mode(
self, axis: Axis = 0, numeric_only: bool = False, dropna: bool = True
) -> DataFrame:
"""
Get the mode(s) of each element along the selected axis.
The mode of a set of values is the value that appears most often.
It can be multiple values.
Parameters
----------
axis : {0 or 'index', 1 or 'columns'}, default 0
The axis to iterate over while searching for the mode:
* 0 or 'index' : get mode of each column
* 1 or 'columns' : get mode of each row.
numeric_only : bool, default False
If True, only apply to numeric columns.
dropna : bool, default True
Don't consider counts of NaN/NaT.
Returns
-------
DataFrame
The modes of each column or row.
See Also
--------
Series.mode : Return the highest frequency value in a Series.
Series.value_counts : Return the counts of values in a Series.
Examples
--------
>>> df = pd.DataFrame([('bird', 2, 2),
... ('mammal', 4, np.nan),
... ('arthropod', 8, 0),
... ('bird', 2, np.nan)],
... index=('falcon', 'horse', 'spider', 'ostrich'),
... columns=('species', 'legs', 'wings'))
>>> df
species legs wings
falcon bird 2 2.0
horse mammal 4 NaN
spider arthropod 8 0.0
ostrich bird 2 NaN
By default, missing values are not considered, and the mode of wings
are both 0 and 2. Because the resulting DataFrame has two rows,
the second row of ``species`` and ``legs`` contains ``NaN``.
>>> df.mode()
species legs wings
0 bird 2.0 0.0
1 NaN NaN 2.0
Setting ``dropna=False`` ``NaN`` values are considered and they can be
the mode (like for wings).
>>> df.mode(dropna=False)
species legs wings
0 bird 2 NaN
Setting ``numeric_only=True``, only the mode of numeric columns is
computed, and columns of other types are ignored.
>>> df.mode(numeric_only=True)
legs wings
0 2.0 0.0
1 NaN 2.0
To compute the mode over columns and not rows, use the axis parameter:
>>> df.mode(axis='columns', numeric_only=True)
0 1
falcon 2.0 NaN
horse 4.0 NaN
spider 0.0 8.0
ostrich 2.0 NaN
"""
data = self if not numeric_only else self._get_numeric_data()
def f(s):
return s.mode(dropna=dropna)
data = data.apply(f, axis=axis)
# Ensure index is type stable (should always use int index)
if data.empty:
data.index = default_index(0)
return data
@overload
def quantile(
self,
q: float = ...,
axis: Axis = ...,
numeric_only: bool = ...,
interpolation: QuantileInterpolation = ...,
) -> Series:
...
@overload
def quantile(
self,
q: AnyArrayLike | Sequence[float],
axis: Axis = ...,
numeric_only: bool = ...,
interpolation: QuantileInterpolation = ...,
) -> Series | DataFrame:
...
@overload
def quantile(
self,
q: float | AnyArrayLike | Sequence[float] = ...,
axis: Axis = ...,
numeric_only: bool = ...,
interpolation: QuantileInterpolation = ...,
) -> Series | DataFrame:
...
def quantile(
self,
q: float | AnyArrayLike | Sequence[float] = 0.5,
axis: Axis = 0,
numeric_only: bool = False,
interpolation: QuantileInterpolation = "linear",
method: Literal["single", "table"] = "single",
) -> Series | DataFrame:
"""
Return values at the given quantile over requested axis.
Parameters
----------
q : float or array-like, default 0.5 (50% quantile)
Value between 0 <= q <= 1, the quantile(s) to compute.
axis : {0 or 'index', 1 or 'columns'}, default 0
Equals 0 or 'index' for row-wise, 1 or 'columns' for column-wise.
numeric_only : bool, default False
Include only `float`, `int` or `boolean` data.
.. versionchanged:: 2.0.0
The default value of ``numeric_only`` is now ``False``.
interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}
This optional parameter specifies the interpolation method to use,
when the desired quantile lies between two data points `i` and `j`:
* linear: `i + (j - i) * fraction`, where `fraction` is the
fractional part of the index surrounded by `i` and `j`.
* lower: `i`.
* higher: `j`.
* nearest: `i` or `j` whichever is nearest.
* midpoint: (`i` + `j`) / 2.
method : {'single', 'table'}, default 'single'
Whether to compute quantiles per-column ('single') or over all columns
('table'). When 'table', the only allowed interpolation methods are
'nearest', 'lower', and 'higher'.
Returns
-------
Series or DataFrame
If ``q`` is an array, a DataFrame will be returned where the
index is ``q``, the columns are the columns of self, and the
values are the quantiles.
If ``q`` is a float, a Series will be returned where the
index is the columns of self and the values are the quantiles.
See Also
--------
core.window.rolling.Rolling.quantile: Rolling quantile.
numpy.percentile: Numpy function to compute the percentile.
Examples
--------
>>> df = pd.DataFrame(np.array([[1, 1], [2, 10], [3, 100], [4, 100]]),
... columns=['a', 'b'])
>>> df.quantile(.1)
a 1.3
b 3.7
Name: 0.1, dtype: float64
>>> df.quantile([.1, .5])
a b
0.1 1.3 3.7
0.5 2.5 55.0
Specifying `method='table'` will compute the quantile over all columns.
>>> df.quantile(.1, method="table", interpolation="nearest")
a 1
b 1
Name: 0.1, dtype: int64
>>> df.quantile([.1, .5], method="table", interpolation="nearest")
a b
0.1 1 1
0.5 3 100
Specifying `numeric_only=False` will also compute the quantile of
datetime and timedelta data.
>>> df = pd.DataFrame({'A': [1, 2],
... 'B': [pd.Timestamp('2010'),
... pd.Timestamp('2011')],
... 'C': [pd.Timedelta('1 days'),
... pd.Timedelta('2 days')]})
>>> df.quantile(0.5, numeric_only=False)
A 1.5
B 2010-07-02 12:00:00
C 1 days 12:00:00
Name: 0.5, dtype: object
"""
validate_percentile(q)
axis = self._get_axis_number(axis)
if not is_list_like(q):
# BlockManager.quantile expects listlike, so we wrap and unwrap here
# error: List item 0 has incompatible type "Union[float, Union[Union[
# ExtensionArray, ndarray[Any, Any]], Index, Series], Sequence[float]]";
# expected "float"
res_df = self.quantile( # type: ignore[call-overload]
[q],
axis=axis,
numeric_only=numeric_only,
interpolation=interpolation,
method=method,
)
if method == "single":
res = res_df.iloc[0]
else:
# cannot directly iloc over sparse arrays
res = res_df.T.iloc[:, 0]
if axis == 1 and len(self) == 0:
# GH#41544 try to get an appropriate dtype
dtype = find_common_type(list(self.dtypes))
if needs_i8_conversion(dtype):
return res.astype(dtype)
return res
q = Index(q, dtype=np.float64)
data = self._get_numeric_data() if numeric_only else self
if axis == 1:
data = data.T
if len(data.columns) == 0:
# GH#23925 _get_numeric_data may have dropped all columns
cols = Index([], name=self.columns.name)
dtype = np.float64
if axis == 1:
# GH#41544 try to get an appropriate dtype
cdtype = find_common_type(list(self.dtypes))
if needs_i8_conversion(cdtype):
dtype = cdtype
res = self._constructor([], index=q, columns=cols, dtype=dtype)
return res.__finalize__(self, method="quantile")
valid_method = {"single", "table"}
if method not in valid_method:
raise ValueError(
f"Invalid method: {method}. Method must be in {valid_method}."
)
if method == "single":
res = data._mgr.quantile(qs=q, axis=1, interpolation=interpolation)
elif method == "table":
valid_interpolation = {"nearest", "lower", "higher"}
if interpolation not in valid_interpolation:
raise ValueError(
f"Invalid interpolation: {interpolation}. "
f"Interpolation must be in {valid_interpolation}"
)
# handle degenerate case
if len(data) == 0:
if data.ndim == 2:
dtype = find_common_type(list(self.dtypes))
else:
dtype = self.dtype
return self._constructor([], index=q, columns=data.columns, dtype=dtype)
q_idx = np.quantile( # type: ignore[call-overload]
np.arange(len(data)), q, **{np_percentile_argname: interpolation}
)
by = data.columns
if len(by) > 1:
keys = [data._get_label_or_level_values(x) for x in by]
indexer = lexsort_indexer(keys)
else:
by = by[0]
k = data._get_label_or_level_values(by) # type: ignore[arg-type]
indexer = nargsort(k)
res = data._mgr.take(indexer[q_idx], verify=False)
res.axes[1] = q
result = self._constructor(res)
return result.__finalize__(self, method="quantile")
@doc(NDFrame.asfreq, **_shared_doc_kwargs)
def asfreq(
self,
freq: Frequency,
method: FillnaOptions | None = None,
how: str | None = None,
normalize: bool = False,
fill_value: Hashable = None,
) -> DataFrame:
return super().asfreq(
freq=freq,
method=method,
how=how,
normalize=normalize,
fill_value=fill_value,
)
@doc(NDFrame.resample, **_shared_doc_kwargs)
def resample(
self,
rule,
axis: Axis = 0,
closed: str | None = None,
label: str | None = None,
convention: str = "start",
kind: str | None = None,
on: Level = None,
level: Level = None,
origin: str | TimestampConvertibleTypes = "start_day",
offset: TimedeltaConvertibleTypes | None = None,
group_keys: bool = False,
) -> Resampler:
return super().resample(
rule=rule,
axis=axis,
closed=closed,
label=label,
convention=convention,
kind=kind,
on=on,
level=level,
origin=origin,
offset=offset,
group_keys=group_keys,
)
def to_timestamp(
self,
freq: Frequency | None = None,
how: str = "start",
axis: Axis = 0,
copy: bool | None = None,
) -> DataFrame:
"""
Cast to DatetimeIndex of timestamps, at *beginning* of period.
Parameters
----------
freq : str, default frequency of PeriodIndex
Desired frequency.
how : {'s', 'e', 'start', 'end'}
Convention for converting period to timestamp; start of period
vs. end.
axis : {0 or 'index', 1 or 'columns'}, default 0
The axis to convert (the index by default).
copy : bool, default True
If False then underlying input data is not copied.
Returns
-------
DataFrame
The DataFrame has a DatetimeIndex.
Examples
--------
>>> idx = pd.PeriodIndex(['2023', '2024'], freq='Y')
>>> d = {'col1': [1, 2], 'col2': [3, 4]}
>>> df1 = pd.DataFrame(data=d, index=idx)
>>> df1
col1 col2
2023 1 3
2024 2 4
The resulting timestamps will be at the beginning of the year in this case
>>> df1 = df1.to_timestamp()
>>> df1
col1 col2
2023-01-01 1 3
2024-01-01 2 4
>>> df1.index
DatetimeIndex(['2023-01-01', '2024-01-01'], dtype='datetime64[ns]', freq=None)
Using `freq` which is the offset that the Timestamps will have
>>> df2 = pd.DataFrame(data=d, index=idx)
>>> df2 = df2.to_timestamp(freq='M')
>>> df2
col1 col2
2023-01-31 1 3
2024-01-31 2 4
>>> df2.index
DatetimeIndex(['2023-01-31', '2024-01-31'], dtype='datetime64[ns]', freq=None)
"""
new_obj = self.copy(deep=copy and not using_copy_on_write())
axis_name = self._get_axis_name(axis)
old_ax = getattr(self, axis_name)
if not isinstance(old_ax, PeriodIndex):
raise TypeError(f"unsupported Type {type(old_ax).__name__}")
new_ax = old_ax.to_timestamp(freq=freq, how=how)
setattr(new_obj, axis_name, new_ax)
return new_obj
def to_period(
self, freq: Frequency | None = None, axis: Axis = 0, copy: bool | None = None
) -> DataFrame:
"""
Convert DataFrame from DatetimeIndex to PeriodIndex.
Convert DataFrame from DatetimeIndex to PeriodIndex with desired
frequency (inferred from index if not passed).
Parameters
----------
freq : str, default
Frequency of the PeriodIndex.
axis : {0 or 'index', 1 or 'columns'}, default 0
The axis to convert (the index by default).
copy : bool, default True
If False then underlying input data is not copied.
Returns
-------
DataFrame
The DataFrame has a PeriodIndex.
Examples
--------
>>> idx = pd.to_datetime(
... [
... "2001-03-31 00:00:00",
... "2002-05-31 00:00:00",
... "2003-08-31 00:00:00",
... ]
... )
>>> idx
DatetimeIndex(['2001-03-31', '2002-05-31', '2003-08-31'],
dtype='datetime64[ns]', freq=None)
>>> idx.to_period("M")
PeriodIndex(['2001-03', '2002-05', '2003-08'], dtype='period[M]')
For the yearly frequency
>>> idx.to_period("Y")
PeriodIndex(['2001', '2002', '2003'], dtype='period[A-DEC]')
"""
new_obj = self.copy(deep=copy and not using_copy_on_write())
axis_name = self._get_axis_name(axis)
old_ax = getattr(self, axis_name)
if not isinstance(old_ax, DatetimeIndex):
raise TypeError(f"unsupported Type {type(old_ax).__name__}")
new_ax = old_ax.to_period(freq=freq)
setattr(new_obj, axis_name, new_ax)
return new_obj
def isin(self, values: Series | DataFrame | Sequence | Mapping) -> DataFrame:
"""
Whether each element in the DataFrame is contained in values.
Parameters
----------
values : iterable, Series, DataFrame or dict
The result will only be true at a location if all the
labels match. If `values` is a Series, that's the index. If
`values` is a dict, the keys must be the column names,
which must match. If `values` is a DataFrame,
then both the index and column labels must match.
Returns
-------
DataFrame
DataFrame of booleans showing whether each element in the DataFrame
is contained in values.
See Also
--------
DataFrame.eq: Equality test for DataFrame.
Series.isin: Equivalent method on Series.
Series.str.contains: Test if pattern or regex is contained within a
string of a Series or Index.
Examples
--------
>>> df = pd.DataFrame({'num_legs': [2, 4], 'num_wings': [2, 0]},
... index=['falcon', 'dog'])
>>> df
num_legs num_wings
falcon 2 2
dog 4 0
When ``values`` is a list check whether every value in the DataFrame
is present in the list (which animals have 0 or 2 legs or wings)
>>> df.isin([0, 2])
num_legs num_wings
falcon True True
dog False True
To check if ``values`` is *not* in the DataFrame, use the ``~`` operator:
>>> ~df.isin([0, 2])
num_legs num_wings
falcon False False
dog True False
When ``values`` is a dict, we can pass values to check for each
column separately:
>>> df.isin({'num_wings': [0, 3]})
num_legs num_wings
falcon False False
dog False True
When ``values`` is a Series or DataFrame the index and column must
match. Note that 'falcon' does not match based on the number of legs
in other.
>>> other = pd.DataFrame({'num_legs': [8, 3], 'num_wings': [0, 2]},
... index=['spider', 'falcon'])
>>> df.isin(other)
num_legs num_wings
falcon False True
dog False False
"""
if isinstance(values, dict):
from pandas.core.reshape.concat import concat
values = collections.defaultdict(list, values)
result = concat(
(
self.iloc[:, [i]].isin(values[col])
for i, col in enumerate(self.columns)
),
axis=1,
)
elif isinstance(values, Series):
if not values.index.is_unique:
raise ValueError("cannot compute isin with a duplicate axis.")
result = self.eq(values.reindex_like(self), axis="index")
elif isinstance(values, DataFrame):
if not (values.columns.is_unique and values.index.is_unique):
raise ValueError("cannot compute isin with a duplicate axis.")
result = self.eq(values.reindex_like(self))
else:
if not is_list_like(values):
raise TypeError(
"only list-like or dict-like objects are allowed "
"to be passed to DataFrame.isin(), "
f"you passed a '{type(values).__name__}'"
)
# error: Argument 2 to "isin" has incompatible type "Union[Sequence[Any],
# Mapping[Any, Any]]"; expected "Union[Union[ExtensionArray,
# ndarray[Any, Any]], Index, Series]"
result = self._constructor(
algorithms.isin(
self.values.ravel(), values # type: ignore[arg-type]
).reshape(self.shape),
self.index,
self.columns,
copy=False,
)
return result.__finalize__(self, method="isin")
# ----------------------------------------------------------------------
# Add index and columns
_AXIS_ORDERS: list[Literal["index", "columns"]] = ["index", "columns"]
_AXIS_TO_AXIS_NUMBER: dict[Axis, int] = {
**NDFrame._AXIS_TO_AXIS_NUMBER,
1: 1,
"columns": 1,
}
_AXIS_LEN = len(_AXIS_ORDERS)
_info_axis_number: Literal[1] = 1
_info_axis_name: Literal["columns"] = "columns"
index = properties.AxisProperty(
axis=1, doc="The index (row labels) of the DataFrame."
)
columns = properties.AxisProperty(axis=0, doc="The column labels of the DataFrame.")
# ----------------------------------------------------------------------
# Add plotting methods to DataFrame
plot = CachedAccessor("plot", pandas.plotting.PlotAccessor)
hist = pandas.plotting.hist_frame
boxplot = pandas.plotting.boxplot_frame
sparse = CachedAccessor("sparse", SparseFrameAccessor)
# ----------------------------------------------------------------------
# Internal Interface Methods
def _to_dict_of_blocks(self, copy: bool = True):
"""
Return a dict of dtype -> Constructor Types that
each is a homogeneous dtype.
Internal ONLY - only works for BlockManager
"""
mgr = self._mgr
# convert to BlockManager if needed -> this way support ArrayManager as well
mgr = mgr_to_mgr(mgr, "block")
mgr = cast(BlockManager, mgr)
return {
k: self._constructor(v).__finalize__(self)
for k, v, in mgr.to_dict(copy=copy).items()
}
@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)
"""
return self._mgr.as_array()
@overload
def ffill(
self,
*,
axis: None | Axis = ...,
inplace: Literal[False] = ...,
limit: None | int = ...,
downcast: dict | None = ...,
) -> DataFrame:
...
@overload
def ffill(
self,
*,
axis: None | Axis = ...,
inplace: Literal[True],
limit: None | int = ...,
downcast: dict | None = ...,
) -> None:
...
@overload
def ffill(
self,
*,
axis: None | Axis = ...,
inplace: bool = ...,
limit: None | int = ...,
downcast: dict | None = ...,
) -> DataFrame | None:
...
def ffill(
self,
*,
axis: None | Axis = None,
inplace: bool = False,
limit: None | int = None,
downcast: dict | None = None,
) -> DataFrame | None:
return super().ffill(axis=axis, inplace=inplace, limit=limit, downcast=downcast)
@overload
def bfill(
self,
*,
axis: None | Axis = ...,
inplace: Literal[False] = ...,
limit: None | int = ...,
downcast=...,
) -> DataFrame:
...
@overload
def bfill(
self,
*,
axis: None | Axis = ...,
inplace: Literal[True],
limit: None | int = ...,
downcast=...,
) -> None:
...
@overload
def bfill(
self,
*,
axis: None | Axis = ...,
inplace: bool = ...,
limit: None | int = ...,
downcast=...,
) -> DataFrame | None:
...
def bfill(
self,
*,
axis: None | Axis = None,
inplace: bool = False,
limit: None | int = None,
downcast=None,
) -> DataFrame | None:
return super().bfill(axis=axis, inplace=inplace, limit=limit, downcast=downcast)
def clip(
self: DataFrame,
lower: float | None = None,
upper: float | None = None,
*,
axis: Axis | None = None,
inplace: bool = False,
**kwargs,
) -> DataFrame | None:
return super().clip(lower, upper, axis=axis, inplace=inplace, **kwargs)
def interpolate(
self: DataFrame,
method: str = "linear",
*,
axis: Axis = 0,
limit: int | None = None,
inplace: bool = False,
limit_direction: str | None = None,
limit_area: str | None = None,
downcast: str | None = None,
**kwargs,
) -> DataFrame | None:
return super().interpolate(
method=method,
axis=axis,
limit=limit,
inplace=inplace,
limit_direction=limit_direction,
limit_area=limit_area,
downcast=downcast,
**kwargs,
)
@overload
def where(
self,
cond,
other=...,
*,
inplace: Literal[False] = ...,
axis: Axis | None = ...,
level: Level = ...,
) -> DataFrame:
...
@overload
def where(
self,
cond,
other=...,
*,
inplace: Literal[True],
axis: Axis | None = ...,
level: Level = ...,
) -> None:
...
@overload
def where(
self,
cond,
other=...,
*,
inplace: bool = ...,
axis: Axis | None = ...,
level: Level = ...,
) -> DataFrame | None:
...
def where(
self,
cond,
other=lib.no_default,
*,
inplace: bool = False,
axis: Axis | None = None,
level: Level = None,
) -> DataFrame | None:
return super().where(
cond,
other,
inplace=inplace,
axis=axis,
level=level,
)
@overload
def mask(
self,
cond,
other=...,
*,
inplace: Literal[False] = ...,
axis: Axis | None = ...,
level: Level = ...,
) -> DataFrame:
...
@overload
def mask(
self,
cond,
other=...,
*,
inplace: Literal[True],
axis: Axis | None = ...,
level: Level = ...,
) -> None:
...
@overload
def mask(
self,
cond,
other=...,
*,
inplace: bool = ...,
axis: Axis | None = ...,
level: Level = ...,
) -> DataFrame | None:
...
def mask(
self,
cond,
other=lib.no_default,
*,
inplace: bool = False,
axis: Axis | None = None,
level: Level = None,
) -> DataFrame | None:
return super().mask(
cond,
other,
inplace=inplace,
axis=axis,
level=level,
)
DataFrame._add_numeric_operations()
ops.add_flex_arithmetic_methods(DataFrame)
def _from_nested_dict(data) -> collections.defaultdict:
new_data: collections.defaultdict = collections.defaultdict(dict)
for index, s in data.items():
for col, v in s.items():
new_data[col][index] = v
return new_data
def _reindex_for_setitem(value: DataFrame | Series, index: Index) -> ArrayLike:
# reindex if necessary
if value.index.equals(index) or not len(index):
return value._values.copy()
# GH#4107
try:
reindexed_value = value.reindex(index)._values
except ValueError as err:
# raised in MultiIndex.from_tuples, see test_insert_error_msmgs
if not value.index.is_unique:
# duplicate axis
raise err
raise TypeError(
"incompatible index of inserted column with frame index"
) from err
return reindexed_value
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