Sindbad~EG File Manager
"""
Concat routines.
"""
from __future__ import annotations
from collections import abc
from typing import (
TYPE_CHECKING,
Callable,
Literal,
cast,
overload,
)
import warnings
import numpy as np
from pandas._config import using_copy_on_write
from pandas.util._decorators import cache_readonly
from pandas.util._exceptions import find_stack_level
from pandas.core.dtypes.common import (
is_bool,
is_iterator,
)
from pandas.core.dtypes.concat import concat_compat
from pandas.core.dtypes.generic import (
ABCDataFrame,
ABCSeries,
)
from pandas.core.dtypes.missing import isna
from pandas.core.arrays.categorical import (
factorize_from_iterable,
factorize_from_iterables,
)
import pandas.core.common as com
from pandas.core.indexes.api import (
Index,
MultiIndex,
all_indexes_same,
default_index,
ensure_index,
get_objs_combined_axis,
get_unanimous_names,
)
from pandas.core.internals import concatenate_managers
if TYPE_CHECKING:
from collections.abc import (
Hashable,
Iterable,
Mapping,
)
from pandas._typing import (
Axis,
AxisInt,
HashableT,
)
from pandas import (
DataFrame,
Series,
)
# ---------------------------------------------------------------------
# Concatenate DataFrame objects
@overload
def concat(
objs: Iterable[DataFrame] | Mapping[HashableT, DataFrame],
*,
axis: Literal[0, "index"] = ...,
join: str = ...,
ignore_index: bool = ...,
keys: Iterable[Hashable] | None = ...,
levels=...,
names: list[HashableT] | None = ...,
verify_integrity: bool = ...,
sort: bool = ...,
copy: bool | None = ...,
) -> DataFrame:
...
@overload
def concat(
objs: Iterable[Series] | Mapping[HashableT, Series],
*,
axis: Literal[0, "index"] = ...,
join: str = ...,
ignore_index: bool = ...,
keys: Iterable[Hashable] | None = ...,
levels=...,
names: list[HashableT] | None = ...,
verify_integrity: bool = ...,
sort: bool = ...,
copy: bool | None = ...,
) -> Series:
...
@overload
def concat(
objs: Iterable[Series | DataFrame] | Mapping[HashableT, Series | DataFrame],
*,
axis: Literal[0, "index"] = ...,
join: str = ...,
ignore_index: bool = ...,
keys: Iterable[Hashable] | None = ...,
levels=...,
names: list[HashableT] | None = ...,
verify_integrity: bool = ...,
sort: bool = ...,
copy: bool | None = ...,
) -> DataFrame | Series:
...
@overload
def concat(
objs: Iterable[Series | DataFrame] | Mapping[HashableT, Series | DataFrame],
*,
axis: Literal[1, "columns"],
join: str = ...,
ignore_index: bool = ...,
keys: Iterable[Hashable] | None = ...,
levels=...,
names: list[HashableT] | None = ...,
verify_integrity: bool = ...,
sort: bool = ...,
copy: bool | None = ...,
) -> DataFrame:
...
@overload
def concat(
objs: Iterable[Series | DataFrame] | Mapping[HashableT, Series | DataFrame],
*,
axis: Axis = ...,
join: str = ...,
ignore_index: bool = ...,
keys: Iterable[Hashable] | None = ...,
levels=...,
names: list[HashableT] | None = ...,
verify_integrity: bool = ...,
sort: bool = ...,
copy: bool | None = ...,
) -> DataFrame | Series:
...
def concat(
objs: Iterable[Series | DataFrame] | Mapping[HashableT, Series | DataFrame],
*,
axis: Axis = 0,
join: str = "outer",
ignore_index: bool = False,
keys: Iterable[Hashable] | None = None,
levels=None,
names: list[HashableT] | None = None,
verify_integrity: bool = False,
sort: bool = False,
copy: bool | None = None,
) -> DataFrame | Series:
"""
Concatenate pandas objects along a particular axis.
Allows optional set logic along the other axes.
Can also add a layer of hierarchical indexing on the concatenation axis,
which may be useful if the labels are the same (or overlapping) on
the passed axis number.
Parameters
----------
objs : a sequence or mapping of Series or DataFrame objects
If a mapping is passed, the sorted keys will be used as the `keys`
argument, unless it is passed, in which case the values will be
selected (see below). Any None objects will be dropped silently unless
they are all None in which case a ValueError will be raised.
axis : {0/'index', 1/'columns'}, default 0
The axis to concatenate along.
join : {'inner', 'outer'}, default 'outer'
How to handle indexes on other axis (or axes).
ignore_index : bool, default False
If True, do not use the index values along the concatenation axis. The
resulting axis will be labeled 0, ..., n - 1. This is useful if you are
concatenating objects where the concatenation axis does not have
meaningful indexing information. Note the index values on the other
axes are still respected in the join.
keys : sequence, default None
If multiple levels passed, should contain tuples. Construct
hierarchical index using the passed keys as the outermost level.
levels : list of sequences, default None
Specific levels (unique values) to use for constructing a
MultiIndex. Otherwise they will be inferred from the keys.
names : list, default None
Names for the levels in the resulting hierarchical index.
verify_integrity : bool, default False
Check whether the new concatenated axis contains duplicates. This can
be very expensive relative to the actual data concatenation.
sort : bool, default False
Sort non-concatenation axis if it is not already aligned. One exception to
this is when the non-concatentation axis is a DatetimeIndex and join='outer'
and the axis is not already aligned. In that case, the non-concatenation
axis is always sorted lexicographically.
copy : bool, default True
If False, do not copy data unnecessarily.
Returns
-------
object, type of objs
When concatenating all ``Series`` along the index (axis=0), a
``Series`` is returned. When ``objs`` contains at least one
``DataFrame``, a ``DataFrame`` is returned. When concatenating along
the columns (axis=1), a ``DataFrame`` is returned.
See Also
--------
DataFrame.join : Join DataFrames using indexes.
DataFrame.merge : Merge DataFrames by indexes or columns.
Notes
-----
The keys, levels, and names arguments are all optional.
A walkthrough of how this method fits in with other tools for combining
pandas objects can be found `here
<https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html>`__.
It is not recommended to build DataFrames by adding single rows in a
for loop. Build a list of rows and make a DataFrame in a single concat.
Examples
--------
Combine two ``Series``.
>>> s1 = pd.Series(['a', 'b'])
>>> s2 = pd.Series(['c', 'd'])
>>> pd.concat([s1, s2])
0 a
1 b
0 c
1 d
dtype: object
Clear the existing index and reset it in the result
by setting the ``ignore_index`` option to ``True``.
>>> pd.concat([s1, s2], ignore_index=True)
0 a
1 b
2 c
3 d
dtype: object
Add a hierarchical index at the outermost level of
the data with the ``keys`` option.
>>> pd.concat([s1, s2], keys=['s1', 's2'])
s1 0 a
1 b
s2 0 c
1 d
dtype: object
Label the index keys you create with the ``names`` option.
>>> pd.concat([s1, s2], keys=['s1', 's2'],
... names=['Series name', 'Row ID'])
Series name Row ID
s1 0 a
1 b
s2 0 c
1 d
dtype: object
Combine two ``DataFrame`` objects with identical columns.
>>> df1 = pd.DataFrame([['a', 1], ['b', 2]],
... columns=['letter', 'number'])
>>> df1
letter number
0 a 1
1 b 2
>>> df2 = pd.DataFrame([['c', 3], ['d', 4]],
... columns=['letter', 'number'])
>>> df2
letter number
0 c 3
1 d 4
>>> pd.concat([df1, df2])
letter number
0 a 1
1 b 2
0 c 3
1 d 4
Combine ``DataFrame`` objects with overlapping columns
and return everything. Columns outside the intersection will
be filled with ``NaN`` values.
>>> df3 = pd.DataFrame([['c', 3, 'cat'], ['d', 4, 'dog']],
... columns=['letter', 'number', 'animal'])
>>> df3
letter number animal
0 c 3 cat
1 d 4 dog
>>> pd.concat([df1, df3], sort=False)
letter number animal
0 a 1 NaN
1 b 2 NaN
0 c 3 cat
1 d 4 dog
Combine ``DataFrame`` objects with overlapping columns
and return only those that are shared by passing ``inner`` to
the ``join`` keyword argument.
>>> pd.concat([df1, df3], join="inner")
letter number
0 a 1
1 b 2
0 c 3
1 d 4
Combine ``DataFrame`` objects horizontally along the x axis by
passing in ``axis=1``.
>>> df4 = pd.DataFrame([['bird', 'polly'], ['monkey', 'george']],
... columns=['animal', 'name'])
>>> pd.concat([df1, df4], axis=1)
letter number animal name
0 a 1 bird polly
1 b 2 monkey george
Prevent the result from including duplicate index values with the
``verify_integrity`` option.
>>> df5 = pd.DataFrame([1], index=['a'])
>>> df5
0
a 1
>>> df6 = pd.DataFrame([2], index=['a'])
>>> df6
0
a 2
>>> pd.concat([df5, df6], verify_integrity=True)
Traceback (most recent call last):
...
ValueError: Indexes have overlapping values: ['a']
Append a single row to the end of a ``DataFrame`` object.
>>> df7 = pd.DataFrame({'a': 1, 'b': 2}, index=[0])
>>> df7
a b
0 1 2
>>> new_row = pd.Series({'a': 3, 'b': 4})
>>> new_row
a 3
b 4
dtype: int64
>>> pd.concat([df7, new_row.to_frame().T], ignore_index=True)
a b
0 1 2
1 3 4
"""
if copy is None:
if using_copy_on_write():
copy = False
else:
copy = True
elif copy and using_copy_on_write():
copy = False
op = _Concatenator(
objs,
axis=axis,
ignore_index=ignore_index,
join=join,
keys=keys,
levels=levels,
names=names,
verify_integrity=verify_integrity,
copy=copy,
sort=sort,
)
return op.get_result()
class _Concatenator:
"""
Orchestrates a concatenation operation for BlockManagers
"""
sort: bool
def __init__(
self,
objs: Iterable[Series | DataFrame] | Mapping[HashableT, Series | DataFrame],
axis: Axis = 0,
join: str = "outer",
keys: Iterable[Hashable] | None = None,
levels=None,
names: list[HashableT] | None = 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__}"'
)
if join == "outer":
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 not is_bool(sort):
raise ValueError(
f"The 'sort' keyword only accepts boolean values; {sort} was passed."
)
# Incompatible types in assignment (expression has type "Union[bool, bool_]",
# variable has type "bool")
self.sort = sort # type: ignore[assignment]
self.ignore_index = ignore_index
self.verify_integrity = verify_integrity
self.copy = copy
objs, keys = self._clean_keys_and_objs(objs, keys)
# figure out what our result ndim is going to be
ndims = self._get_ndims(objs)
sample, objs = self._get_sample_object(objs, ndims, keys, names, levels)
# Standardize axis parameter to int
if sample.ndim == 1:
from pandas import DataFrame
axis = DataFrame._get_axis_number(axis)
self._is_frame = False
self._is_series = True
else:
axis = sample._get_axis_number(axis)
self._is_frame = True
self._is_series = False
# Need to flip BlockManager axis in the DataFrame special case
axis = sample._get_block_manager_axis(axis)
# if we have mixed ndims, then convert to highest ndim
# creating column numbers as needed
if len(ndims) > 1:
objs = self._sanitize_mixed_ndim(objs, sample, ignore_index, axis)
self.objs = objs
# 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
def _get_ndims(self, objs: list[Series | DataFrame]) -> set[int]:
# 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)
return ndims
def _clean_keys_and_objs(
self,
objs: Iterable[Series | DataFrame] | Mapping[HashableT, Series | DataFrame],
keys,
) -> tuple[list[Series | DataFrame], Index | None]:
if isinstance(objs, abc.Mapping):
if keys is None:
keys = list(objs.keys())
objs_list = [objs[k] for k in keys]
else:
objs_list = list(objs)
if len(objs_list) == 0:
raise ValueError("No objects to concatenate")
if keys is None:
objs_list = list(com.not_none(*objs_list))
else:
# GH#1649
clean_keys = []
clean_objs = []
if is_iterator(keys):
keys = list(keys)
if len(keys) != len(objs_list):
# GH#43485
warnings.warn(
"The behavior of pd.concat with len(keys) != len(objs) is "
"deprecated. In a future version this will raise instead of "
"truncating to the smaller of the two sequences",
FutureWarning,
stacklevel=find_stack_level(),
)
for k, v in zip(keys, objs_list):
if v is None:
continue
clean_keys.append(k)
clean_objs.append(v)
objs_list = 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_list) == 0:
raise ValueError("All objects passed were None")
return objs_list, keys
def _get_sample_object(
self,
objs: list[Series | DataFrame],
ndims: set[int],
keys,
names,
levels,
) -> tuple[Series | DataFrame, list[Series | DataFrame]]:
# get the sample
# want the highest ndim that we have, and must be non-empty
# unless all objs are empty
sample: Series | DataFrame | 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 obj.ndim == 1]
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]
return sample, objs
def _sanitize_mixed_ndim(
self,
objs: list[Series | DataFrame],
sample: Series | DataFrame,
ignore_index: bool,
axis: AxisInt,
) -> list[Series | DataFrame]:
# if we have mixed ndims, then convert to highest ndim
# creating column numbers as needed
new_objs = []
current_column = 0
max_ndim = sample.ndim
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:
if axis == 1:
# doing a row-wise concatenation so need everything
# to line up
name = 0
else:
# doing a column-wise concatenation so need series
# to have unique names
name = current_column
current_column += 1
obj = sample._constructor({name: obj}, copy=False)
new_objs.append(obj)
return new_objs
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)
new_index: Index
if self.ignore_index:
# We can avoid surprisingly-expensive _get_concat_axis
new_index = default_index(len(res))
else:
new_index = self.new_axes[0]
mgr = type(sample._mgr).from_array(res, index=new_index)
result = sample._constructor_from_mgr(mgr, axes=mgr.axes)
result._name = name
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()
out = sample._constructor_from_mgr(new_data, axes=new_data.axes)
return out.__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
@cache_readonly
def 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 x.ndim != 1:
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_index = ensure_index(hlevel)
mapped = level.get_indexer(hlevel_index)
mask = mapped == -1
if mask.any():
raise ValueError(
f"Values not found in passed level: {hlevel_index[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
)
Sindbad File Manager Version 1.0, Coded By Sindbad EG ~ The Terrorists