""" 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 `__. 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 )