824 lines
25 KiB
Python
824 lines
25 KiB
Python
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"""
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Concat routines.
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"""
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from __future__ import annotations
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from collections import abc
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from typing import (
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TYPE_CHECKING,
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Callable,
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Hashable,
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Iterable,
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Literal,
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Mapping,
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cast,
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overload,
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)
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import numpy as np
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from pandas._config import using_copy_on_write
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from pandas._typing import (
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Axis,
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AxisInt,
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HashableT,
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)
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from pandas.util._decorators import cache_readonly
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from pandas.core.dtypes.concat import concat_compat
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from pandas.core.dtypes.generic import (
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ABCDataFrame,
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ABCSeries,
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)
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from pandas.core.dtypes.inference import is_bool
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from pandas.core.dtypes.missing import isna
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from pandas.core.arrays.categorical import (
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factorize_from_iterable,
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factorize_from_iterables,
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)
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import pandas.core.common as com
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from pandas.core.indexes.api import (
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Index,
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MultiIndex,
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all_indexes_same,
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default_index,
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ensure_index,
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get_objs_combined_axis,
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get_unanimous_names,
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)
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from pandas.core.internals import concatenate_managers
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if TYPE_CHECKING:
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from pandas import (
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DataFrame,
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Series,
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)
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from pandas.core.generic import NDFrame
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# ---------------------------------------------------------------------
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# Concatenate DataFrame objects
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@overload
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def concat(
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objs: Iterable[DataFrame] | Mapping[HashableT, DataFrame],
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*,
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axis: Literal[0, "index"] = ...,
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join: str = ...,
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ignore_index: bool = ...,
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keys=...,
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levels=...,
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names=...,
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verify_integrity: bool = ...,
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sort: bool = ...,
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copy: bool | None = ...,
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) -> DataFrame:
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...
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@overload
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def concat(
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objs: Iterable[Series] | Mapping[HashableT, Series],
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*,
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axis: Literal[0, "index"] = ...,
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join: str = ...,
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ignore_index: bool = ...,
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keys=...,
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levels=...,
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names=...,
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verify_integrity: bool = ...,
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sort: bool = ...,
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copy: bool | None = ...,
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) -> Series:
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...
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@overload
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def concat(
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objs: Iterable[NDFrame] | Mapping[HashableT, NDFrame],
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*,
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axis: Literal[0, "index"] = ...,
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join: str = ...,
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ignore_index: bool = ...,
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keys=...,
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levels=...,
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names=...,
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verify_integrity: bool = ...,
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sort: bool = ...,
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copy: bool | None = ...,
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) -> DataFrame | Series:
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...
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@overload
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def concat(
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objs: Iterable[NDFrame] | Mapping[HashableT, NDFrame],
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*,
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axis: Literal[1, "columns"],
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join: str = ...,
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ignore_index: bool = ...,
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keys=...,
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levels=...,
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names=...,
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verify_integrity: bool = ...,
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sort: bool = ...,
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copy: bool | None = ...,
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) -> DataFrame:
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...
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@overload
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def concat(
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objs: Iterable[NDFrame] | Mapping[HashableT, NDFrame],
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*,
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axis: Axis = ...,
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join: str = ...,
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ignore_index: bool = ...,
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keys=...,
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levels=...,
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names=...,
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verify_integrity: bool = ...,
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sort: bool = ...,
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copy: bool | None = ...,
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) -> DataFrame | Series:
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...
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def concat(
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objs: Iterable[NDFrame] | Mapping[HashableT, NDFrame],
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*,
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axis: Axis = 0,
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join: str = "outer",
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ignore_index: bool = False,
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keys=None,
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levels=None,
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names=None,
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verify_integrity: bool = False,
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sort: bool = False,
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copy: bool | None = None,
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) -> DataFrame | Series:
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"""
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Concatenate pandas objects along a particular axis.
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Allows optional set logic along the other axes.
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Can also add a layer of hierarchical indexing on the concatenation axis,
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which may be useful if the labels are the same (or overlapping) on
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the passed axis number.
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Parameters
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----------
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objs : a sequence or mapping of Series or DataFrame objects
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If a mapping is passed, the sorted keys will be used as the `keys`
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argument, unless it is passed, in which case the values will be
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selected (see below). Any None objects will be dropped silently unless
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they are all None in which case a ValueError will be raised.
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axis : {0/'index', 1/'columns'}, default 0
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The axis to concatenate along.
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join : {'inner', 'outer'}, default 'outer'
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How to handle indexes on other axis (or axes).
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ignore_index : bool, default False
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If True, do not use the index values along the concatenation axis. The
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resulting axis will be labeled 0, ..., n - 1. This is useful if you are
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concatenating objects where the concatenation axis does not have
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meaningful indexing information. Note the index values on the other
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axes are still respected in the join.
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keys : sequence, default None
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If multiple levels passed, should contain tuples. Construct
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hierarchical index using the passed keys as the outermost level.
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levels : list of sequences, default None
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Specific levels (unique values) to use for constructing a
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MultiIndex. Otherwise they will be inferred from the keys.
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names : list, default None
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Names for the levels in the resulting hierarchical index.
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verify_integrity : bool, default False
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Check whether the new concatenated axis contains duplicates. This can
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be very expensive relative to the actual data concatenation.
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sort : bool, default False
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Sort non-concatenation axis if it is not already aligned.
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copy : bool, default True
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If False, do not copy data unnecessarily.
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Returns
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-------
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object, type of objs
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When concatenating all ``Series`` along the index (axis=0), a
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``Series`` is returned. When ``objs`` contains at least one
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``DataFrame``, a ``DataFrame`` is returned. When concatenating along
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the columns (axis=1), a ``DataFrame`` is returned.
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See Also
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--------
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DataFrame.join : Join DataFrames using indexes.
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DataFrame.merge : Merge DataFrames by indexes or columns.
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Notes
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-----
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The keys, levels, and names arguments are all optional.
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A walkthrough of how this method fits in with other tools for combining
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pandas objects can be found `here
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<https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html>`__.
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It is not recommended to build DataFrames by adding single rows in a
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for loop. Build a list of rows and make a DataFrame in a single concat.
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Examples
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--------
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Combine two ``Series``.
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>>> s1 = pd.Series(['a', 'b'])
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>>> s2 = pd.Series(['c', 'd'])
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>>> pd.concat([s1, s2])
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0 a
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1 b
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0 c
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1 d
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dtype: object
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Clear the existing index and reset it in the result
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by setting the ``ignore_index`` option to ``True``.
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>>> pd.concat([s1, s2], ignore_index=True)
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0 a
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1 b
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2 c
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3 d
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dtype: object
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Add a hierarchical index at the outermost level of
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the data with the ``keys`` option.
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>>> pd.concat([s1, s2], keys=['s1', 's2'])
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s1 0 a
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1 b
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s2 0 c
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1 d
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dtype: object
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Label the index keys you create with the ``names`` option.
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>>> pd.concat([s1, s2], keys=['s1', 's2'],
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... names=['Series name', 'Row ID'])
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Series name Row ID
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s1 0 a
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1 b
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s2 0 c
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1 d
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dtype: object
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Combine two ``DataFrame`` objects with identical columns.
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>>> df1 = pd.DataFrame([['a', 1], ['b', 2]],
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... columns=['letter', 'number'])
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>>> df1
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letter number
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0 a 1
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1 b 2
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>>> df2 = pd.DataFrame([['c', 3], ['d', 4]],
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... columns=['letter', 'number'])
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>>> df2
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letter number
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0 c 3
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1 d 4
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>>> pd.concat([df1, df2])
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letter number
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0 a 1
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1 b 2
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0 c 3
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1 d 4
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Combine ``DataFrame`` objects with overlapping columns
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and return everything. Columns outside the intersection will
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be filled with ``NaN`` values.
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>>> df3 = pd.DataFrame([['c', 3, 'cat'], ['d', 4, 'dog']],
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... columns=['letter', 'number', 'animal'])
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>>> df3
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letter number animal
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0 c 3 cat
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1 d 4 dog
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>>> pd.concat([df1, df3], sort=False)
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letter number animal
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0 a 1 NaN
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1 b 2 NaN
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0 c 3 cat
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1 d 4 dog
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Combine ``DataFrame`` objects with overlapping columns
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and return only those that are shared by passing ``inner`` to
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the ``join`` keyword argument.
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>>> pd.concat([df1, df3], join="inner")
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letter number
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0 a 1
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1 b 2
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0 c 3
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1 d 4
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Combine ``DataFrame`` objects horizontally along the x axis by
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passing in ``axis=1``.
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>>> df4 = pd.DataFrame([['bird', 'polly'], ['monkey', 'george']],
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... columns=['animal', 'name'])
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>>> pd.concat([df1, df4], axis=1)
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letter number animal name
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0 a 1 bird polly
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1 b 2 monkey george
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Prevent the result from including duplicate index values with the
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``verify_integrity`` option.
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>>> df5 = pd.DataFrame([1], index=['a'])
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>>> df5
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0
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a 1
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>>> df6 = pd.DataFrame([2], index=['a'])
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>>> df6
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0
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a 2
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>>> pd.concat([df5, df6], verify_integrity=True)
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Traceback (most recent call last):
|
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...
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ValueError: Indexes have overlapping values: ['a']
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Append a single row to the end of a ``DataFrame`` object.
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>>> df7 = pd.DataFrame({'a': 1, 'b': 2}, index=[0])
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>>> df7
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a b
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0 1 2
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>>> new_row = pd.Series({'a': 3, 'b': 4})
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>>> new_row
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a 3
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b 4
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dtype: int64
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>>> pd.concat([df7, new_row.to_frame().T], ignore_index=True)
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a b
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0 1 2
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1 3 4
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"""
|
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|
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if copy is None:
|
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|
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if using_copy_on_write():
|
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|
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copy = False
|
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|
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else:
|
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|
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copy = True
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|
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elif copy and using_copy_on_write():
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|
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copy = False
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op = _Concatenator(
|
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objs,
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axis=axis,
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ignore_index=ignore_index,
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||
|
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join=join,
|
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keys=keys,
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||
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levels=levels,
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|
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names=names,
|
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|
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verify_integrity=verify_integrity,
|
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|
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copy=copy,
|
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|
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sort=sort,
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)
|
||
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return op.get_result()
|
||
|
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|
||
|
|
|
||
|
|
class _Concatenator:
|
||
|
|
"""
|
||
|
|
Orchestrates a concatenation operation for BlockManagers
|
||
|
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"""
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
objs: Iterable[NDFrame] | Mapping[HashableT, NDFrame],
|
||
|
|
axis: Axis = 0,
|
||
|
|
join: str = "outer",
|
||
|
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keys=None,
|
||
|
|
levels=None,
|
||
|
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names=None,
|
||
|
|
ignore_index: bool = False,
|
||
|
|
verify_integrity: bool = False,
|
||
|
|
copy: bool = True,
|
||
|
|
sort: bool = False,
|
||
|
|
) -> None:
|
||
|
|
if isinstance(objs, (ABCSeries, ABCDataFrame, str)):
|
||
|
|
raise TypeError(
|
||
|
|
"first argument must be an iterable of pandas "
|
||
|
|
f'objects, you passed an object of type "{type(objs).__name__}"'
|
||
|
|
)
|
||
|
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|
||
|
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if join == "outer":
|
||
|
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self.intersect = False
|
||
|
|
elif join == "inner":
|
||
|
|
self.intersect = True
|
||
|
|
else: # pragma: no cover
|
||
|
|
raise ValueError(
|
||
|
|
"Only can inner (intersect) or outer (union) join the other axis"
|
||
|
|
)
|
||
|
|
|
||
|
|
if isinstance(objs, abc.Mapping):
|
||
|
|
if keys is None:
|
||
|
|
keys = list(objs.keys())
|
||
|
|
objs = [objs[k] for k in keys]
|
||
|
|
else:
|
||
|
|
objs = list(objs)
|
||
|
|
|
||
|
|
if len(objs) == 0:
|
||
|
|
raise ValueError("No objects to concatenate")
|
||
|
|
|
||
|
|
if keys is None:
|
||
|
|
objs = list(com.not_none(*objs))
|
||
|
|
else:
|
||
|
|
# #1649
|
||
|
|
clean_keys = []
|
||
|
|
clean_objs = []
|
||
|
|
for k, v in zip(keys, objs):
|
||
|
|
if v is None:
|
||
|
|
continue
|
||
|
|
clean_keys.append(k)
|
||
|
|
clean_objs.append(v)
|
||
|
|
objs = clean_objs
|
||
|
|
|
||
|
|
if isinstance(keys, MultiIndex):
|
||
|
|
# TODO: retain levels?
|
||
|
|
keys = type(keys).from_tuples(clean_keys, names=keys.names)
|
||
|
|
else:
|
||
|
|
name = getattr(keys, "name", None)
|
||
|
|
keys = Index(clean_keys, name=name, dtype=getattr(keys, "dtype", None))
|
||
|
|
|
||
|
|
if len(objs) == 0:
|
||
|
|
raise ValueError("All objects passed were None")
|
||
|
|
|
||
|
|
# figure out what our result ndim is going to be
|
||
|
|
ndims = set()
|
||
|
|
for obj in objs:
|
||
|
|
if not isinstance(obj, (ABCSeries, ABCDataFrame)):
|
||
|
|
msg = (
|
||
|
|
f"cannot concatenate object of type '{type(obj)}'; "
|
||
|
|
"only Series and DataFrame objs are valid"
|
||
|
|
)
|
||
|
|
raise TypeError(msg)
|
||
|
|
|
||
|
|
ndims.add(obj.ndim)
|
||
|
|
|
||
|
|
# get the sample
|
||
|
|
# want the highest ndim that we have, and must be non-empty
|
||
|
|
# unless all objs are empty
|
||
|
|
sample: NDFrame | None = None
|
||
|
|
if len(ndims) > 1:
|
||
|
|
max_ndim = max(ndims)
|
||
|
|
for obj in objs:
|
||
|
|
if obj.ndim == max_ndim and np.sum(obj.shape):
|
||
|
|
sample = obj
|
||
|
|
break
|
||
|
|
|
||
|
|
else:
|
||
|
|
# filter out the empties if we have not multi-index possibilities
|
||
|
|
# note to keep empty Series as it affect to result columns / name
|
||
|
|
non_empties = [
|
||
|
|
obj for obj in objs if sum(obj.shape) > 0 or isinstance(obj, ABCSeries)
|
||
|
|
]
|
||
|
|
|
||
|
|
if len(non_empties) and (
|
||
|
|
keys is None and names is None and levels is None and not self.intersect
|
||
|
|
):
|
||
|
|
objs = non_empties
|
||
|
|
sample = objs[0]
|
||
|
|
|
||
|
|
if sample is None:
|
||
|
|
sample = objs[0]
|
||
|
|
self.objs = objs
|
||
|
|
|
||
|
|
# Standardize axis parameter to int
|
||
|
|
if isinstance(sample, ABCSeries):
|
||
|
|
from pandas import DataFrame
|
||
|
|
|
||
|
|
axis = DataFrame._get_axis_number(axis)
|
||
|
|
else:
|
||
|
|
axis = sample._get_axis_number(axis)
|
||
|
|
|
||
|
|
# Need to flip BlockManager axis in the DataFrame special case
|
||
|
|
self._is_frame = isinstance(sample, ABCDataFrame)
|
||
|
|
if self._is_frame:
|
||
|
|
axis = sample._get_block_manager_axis(axis)
|
||
|
|
|
||
|
|
self._is_series = isinstance(sample, ABCSeries)
|
||
|
|
if not 0 <= axis <= sample.ndim:
|
||
|
|
raise AssertionError(
|
||
|
|
f"axis must be between 0 and {sample.ndim}, input was {axis}"
|
||
|
|
)
|
||
|
|
|
||
|
|
# if we have mixed ndims, then convert to highest ndim
|
||
|
|
# creating column numbers as needed
|
||
|
|
if len(ndims) > 1:
|
||
|
|
current_column = 0
|
||
|
|
max_ndim = sample.ndim
|
||
|
|
self.objs, objs = [], self.objs
|
||
|
|
for obj in objs:
|
||
|
|
ndim = obj.ndim
|
||
|
|
if ndim == max_ndim:
|
||
|
|
pass
|
||
|
|
|
||
|
|
elif ndim != max_ndim - 1:
|
||
|
|
raise ValueError(
|
||
|
|
"cannot concatenate unaligned mixed "
|
||
|
|
"dimensional NDFrame objects"
|
||
|
|
)
|
||
|
|
|
||
|
|
else:
|
||
|
|
name = getattr(obj, "name", None)
|
||
|
|
if ignore_index or name is None:
|
||
|
|
name = current_column
|
||
|
|
current_column += 1
|
||
|
|
|
||
|
|
# doing a row-wise concatenation so need everything
|
||
|
|
# to line up
|
||
|
|
if self._is_frame and axis == 1:
|
||
|
|
name = 0
|
||
|
|
# mypy needs to know sample is not an NDFrame
|
||
|
|
sample = cast("DataFrame | Series", sample)
|
||
|
|
obj = sample._constructor({name: obj}, copy=False)
|
||
|
|
|
||
|
|
self.objs.append(obj)
|
||
|
|
|
||
|
|
# note: this is the BlockManager axis (since DataFrame is transposed)
|
||
|
|
self.bm_axis = axis
|
||
|
|
self.axis = 1 - self.bm_axis if self._is_frame else 0
|
||
|
|
self.keys = keys
|
||
|
|
self.names = names or getattr(keys, "names", None)
|
||
|
|
self.levels = levels
|
||
|
|
|
||
|
|
if not is_bool(sort):
|
||
|
|
raise ValueError(
|
||
|
|
f"The 'sort' keyword only accepts boolean values; {sort} was passed."
|
||
|
|
)
|
||
|
|
self.sort = sort
|
||
|
|
|
||
|
|
self.ignore_index = ignore_index
|
||
|
|
self.verify_integrity = verify_integrity
|
||
|
|
self.copy = copy
|
||
|
|
|
||
|
|
self.new_axes = self._get_new_axes()
|
||
|
|
|
||
|
|
def get_result(self):
|
||
|
|
cons: Callable[..., DataFrame | Series]
|
||
|
|
sample: DataFrame | Series
|
||
|
|
|
||
|
|
# series only
|
||
|
|
if self._is_series:
|
||
|
|
sample = cast("Series", self.objs[0])
|
||
|
|
|
||
|
|
# stack blocks
|
||
|
|
if self.bm_axis == 0:
|
||
|
|
name = com.consensus_name_attr(self.objs)
|
||
|
|
cons = sample._constructor
|
||
|
|
|
||
|
|
arrs = [ser._values for ser in self.objs]
|
||
|
|
|
||
|
|
res = concat_compat(arrs, axis=0)
|
||
|
|
result = cons(res, index=self.new_axes[0], name=name, dtype=res.dtype)
|
||
|
|
return result.__finalize__(self, method="concat")
|
||
|
|
|
||
|
|
# combine as columns in a frame
|
||
|
|
else:
|
||
|
|
data = dict(zip(range(len(self.objs)), self.objs))
|
||
|
|
|
||
|
|
# GH28330 Preserves subclassed objects through concat
|
||
|
|
cons = sample._constructor_expanddim
|
||
|
|
|
||
|
|
index, columns = self.new_axes
|
||
|
|
df = cons(data, index=index, copy=self.copy)
|
||
|
|
df.columns = columns
|
||
|
|
return df.__finalize__(self, method="concat")
|
||
|
|
|
||
|
|
# combine block managers
|
||
|
|
else:
|
||
|
|
sample = cast("DataFrame", self.objs[0])
|
||
|
|
|
||
|
|
mgrs_indexers = []
|
||
|
|
for obj in self.objs:
|
||
|
|
indexers = {}
|
||
|
|
for ax, new_labels in enumerate(self.new_axes):
|
||
|
|
# ::-1 to convert BlockManager ax to DataFrame ax
|
||
|
|
if ax == self.bm_axis:
|
||
|
|
# Suppress reindexing on concat axis
|
||
|
|
continue
|
||
|
|
|
||
|
|
# 1-ax to convert BlockManager axis to DataFrame axis
|
||
|
|
obj_labels = obj.axes[1 - ax]
|
||
|
|
if not new_labels.equals(obj_labels):
|
||
|
|
indexers[ax] = obj_labels.get_indexer(new_labels)
|
||
|
|
|
||
|
|
mgrs_indexers.append((obj._mgr, indexers))
|
||
|
|
|
||
|
|
new_data = concatenate_managers(
|
||
|
|
mgrs_indexers, self.new_axes, concat_axis=self.bm_axis, copy=self.copy
|
||
|
|
)
|
||
|
|
if not self.copy and not using_copy_on_write():
|
||
|
|
new_data._consolidate_inplace()
|
||
|
|
|
||
|
|
cons = sample._constructor
|
||
|
|
return cons(new_data).__finalize__(self, method="concat")
|
||
|
|
|
||
|
|
def _get_result_dim(self) -> int:
|
||
|
|
if self._is_series and self.bm_axis == 1:
|
||
|
|
return 2
|
||
|
|
else:
|
||
|
|
return self.objs[0].ndim
|
||
|
|
|
||
|
|
def _get_new_axes(self) -> list[Index]:
|
||
|
|
ndim = self._get_result_dim()
|
||
|
|
return [
|
||
|
|
self._get_concat_axis if i == self.bm_axis else self._get_comb_axis(i)
|
||
|
|
for i in range(ndim)
|
||
|
|
]
|
||
|
|
|
||
|
|
def _get_comb_axis(self, i: AxisInt) -> Index:
|
||
|
|
data_axis = self.objs[0]._get_block_manager_axis(i)
|
||
|
|
return get_objs_combined_axis(
|
||
|
|
self.objs,
|
||
|
|
axis=data_axis,
|
||
|
|
intersect=self.intersect,
|
||
|
|
sort=self.sort,
|
||
|
|
copy=self.copy,
|
||
|
|
)
|
||
|
|
|
||
|
|
@cache_readonly
|
||
|
|
def _get_concat_axis(self) -> Index:
|
||
|
|
"""
|
||
|
|
Return index to be used along concatenation axis.
|
||
|
|
"""
|
||
|
|
if self._is_series:
|
||
|
|
if self.bm_axis == 0:
|
||
|
|
indexes = [x.index for x in self.objs]
|
||
|
|
elif self.ignore_index:
|
||
|
|
idx = default_index(len(self.objs))
|
||
|
|
return idx
|
||
|
|
elif self.keys is None:
|
||
|
|
names: list[Hashable] = [None] * len(self.objs)
|
||
|
|
num = 0
|
||
|
|
has_names = False
|
||
|
|
for i, x in enumerate(self.objs):
|
||
|
|
if not isinstance(x, ABCSeries):
|
||
|
|
raise TypeError(
|
||
|
|
f"Cannot concatenate type 'Series' with "
|
||
|
|
f"object of type '{type(x).__name__}'"
|
||
|
|
)
|
||
|
|
if x.name is not None:
|
||
|
|
names[i] = x.name
|
||
|
|
has_names = True
|
||
|
|
else:
|
||
|
|
names[i] = num
|
||
|
|
num += 1
|
||
|
|
if has_names:
|
||
|
|
return Index(names)
|
||
|
|
else:
|
||
|
|
return default_index(len(self.objs))
|
||
|
|
else:
|
||
|
|
return ensure_index(self.keys).set_names(self.names)
|
||
|
|
else:
|
||
|
|
indexes = [x.axes[self.axis] for x in self.objs]
|
||
|
|
|
||
|
|
if self.ignore_index:
|
||
|
|
idx = default_index(sum(len(i) for i in indexes))
|
||
|
|
return idx
|
||
|
|
|
||
|
|
if self.keys is None:
|
||
|
|
if self.levels is not None:
|
||
|
|
raise ValueError("levels supported only when keys is not None")
|
||
|
|
concat_axis = _concat_indexes(indexes)
|
||
|
|
else:
|
||
|
|
concat_axis = _make_concat_multiindex(
|
||
|
|
indexes, self.keys, self.levels, self.names
|
||
|
|
)
|
||
|
|
|
||
|
|
self._maybe_check_integrity(concat_axis)
|
||
|
|
|
||
|
|
return concat_axis
|
||
|
|
|
||
|
|
def _maybe_check_integrity(self, concat_index: Index):
|
||
|
|
if self.verify_integrity:
|
||
|
|
if not concat_index.is_unique:
|
||
|
|
overlap = concat_index[concat_index.duplicated()].unique()
|
||
|
|
raise ValueError(f"Indexes have overlapping values: {overlap}")
|
||
|
|
|
||
|
|
|
||
|
|
def _concat_indexes(indexes) -> Index:
|
||
|
|
return indexes[0].append(indexes[1:])
|
||
|
|
|
||
|
|
|
||
|
|
def _make_concat_multiindex(indexes, keys, levels=None, names=None) -> MultiIndex:
|
||
|
|
if (levels is None and isinstance(keys[0], tuple)) or (
|
||
|
|
levels is not None and len(levels) > 1
|
||
|
|
):
|
||
|
|
zipped = list(zip(*keys))
|
||
|
|
if names is None:
|
||
|
|
names = [None] * len(zipped)
|
||
|
|
|
||
|
|
if levels is None:
|
||
|
|
_, levels = factorize_from_iterables(zipped)
|
||
|
|
else:
|
||
|
|
levels = [ensure_index(x) for x in levels]
|
||
|
|
else:
|
||
|
|
zipped = [keys]
|
||
|
|
if names is None:
|
||
|
|
names = [None]
|
||
|
|
|
||
|
|
if levels is None:
|
||
|
|
levels = [ensure_index(keys).unique()]
|
||
|
|
else:
|
||
|
|
levels = [ensure_index(x) for x in levels]
|
||
|
|
|
||
|
|
for level in levels:
|
||
|
|
if not level.is_unique:
|
||
|
|
raise ValueError(f"Level values not unique: {level.tolist()}")
|
||
|
|
|
||
|
|
if not all_indexes_same(indexes) or not all(level.is_unique for level in levels):
|
||
|
|
codes_list = []
|
||
|
|
|
||
|
|
# things are potentially different sizes, so compute the exact codes
|
||
|
|
# for each level and pass those to MultiIndex.from_arrays
|
||
|
|
|
||
|
|
for hlevel, level in zip(zipped, levels):
|
||
|
|
to_concat = []
|
||
|
|
if isinstance(hlevel, Index) and hlevel.equals(level):
|
||
|
|
lens = [len(idx) for idx in indexes]
|
||
|
|
codes_list.append(np.repeat(np.arange(len(hlevel)), lens))
|
||
|
|
else:
|
||
|
|
for key, index in zip(hlevel, indexes):
|
||
|
|
# Find matching codes, include matching nan values as equal.
|
||
|
|
mask = (isna(level) & isna(key)) | (level == key)
|
||
|
|
if not mask.any():
|
||
|
|
raise ValueError(f"Key {key} not in level {level}")
|
||
|
|
i = np.nonzero(mask)[0][0]
|
||
|
|
|
||
|
|
to_concat.append(np.repeat(i, len(index)))
|
||
|
|
codes_list.append(np.concatenate(to_concat))
|
||
|
|
|
||
|
|
concat_index = _concat_indexes(indexes)
|
||
|
|
|
||
|
|
# these go at the end
|
||
|
|
if isinstance(concat_index, MultiIndex):
|
||
|
|
levels.extend(concat_index.levels)
|
||
|
|
codes_list.extend(concat_index.codes)
|
||
|
|
else:
|
||
|
|
codes, categories = factorize_from_iterable(concat_index)
|
||
|
|
levels.append(categories)
|
||
|
|
codes_list.append(codes)
|
||
|
|
|
||
|
|
if len(names) == len(levels):
|
||
|
|
names = list(names)
|
||
|
|
else:
|
||
|
|
# make sure that all of the passed indices have the same nlevels
|
||
|
|
if not len({idx.nlevels for idx in indexes}) == 1:
|
||
|
|
raise AssertionError(
|
||
|
|
"Cannot concat indices that do not have the same number of levels"
|
||
|
|
)
|
||
|
|
|
||
|
|
# also copies
|
||
|
|
names = list(names) + list(get_unanimous_names(*indexes))
|
||
|
|
|
||
|
|
return MultiIndex(
|
||
|
|
levels=levels, codes=codes_list, names=names, verify_integrity=False
|
||
|
|
)
|
||
|
|
|
||
|
|
new_index = indexes[0]
|
||
|
|
n = len(new_index)
|
||
|
|
kpieces = len(indexes)
|
||
|
|
|
||
|
|
# also copies
|
||
|
|
new_names = list(names)
|
||
|
|
new_levels = list(levels)
|
||
|
|
|
||
|
|
# construct codes
|
||
|
|
new_codes = []
|
||
|
|
|
||
|
|
# do something a bit more speedy
|
||
|
|
|
||
|
|
for hlevel, level in zip(zipped, levels):
|
||
|
|
hlevel = ensure_index(hlevel)
|
||
|
|
mapped = level.get_indexer(hlevel)
|
||
|
|
|
||
|
|
mask = mapped == -1
|
||
|
|
if mask.any():
|
||
|
|
raise ValueError(f"Values not found in passed level: {hlevel[mask]!s}")
|
||
|
|
|
||
|
|
new_codes.append(np.repeat(mapped, n))
|
||
|
|
|
||
|
|
if isinstance(new_index, MultiIndex):
|
||
|
|
new_levels.extend(new_index.levels)
|
||
|
|
new_codes.extend([np.tile(lab, kpieces) for lab in new_index.codes])
|
||
|
|
else:
|
||
|
|
new_levels.append(new_index.unique())
|
||
|
|
single_codes = new_index.unique().get_indexer(new_index)
|
||
|
|
new_codes.append(np.tile(single_codes, kpieces))
|
||
|
|
|
||
|
|
if len(new_names) < len(new_levels):
|
||
|
|
new_names.extend(new_index.names)
|
||
|
|
|
||
|
|
return MultiIndex(
|
||
|
|
levels=new_levels, codes=new_codes, names=new_names, verify_integrity=False
|
||
|
|
)
|