""" concat routines """ import numpy as np from pandas import compat, DataFrame, Series, Index, MultiIndex from pandas.core.index import (_get_objs_combined_axis, _ensure_index, _get_consensus_names, _all_indexes_same) from pandas.core.categorical import (_factorize_from_iterable, _factorize_from_iterables) from pandas.core.internals import concatenate_block_managers from pandas.core import common as com from pandas.core.generic import NDFrame import pandas.core.dtypes.concat as _concat # --------------------------------------------------------------------- # Concatenate DataFrame objects def concat(objs, axis=0, join='outer', join_axes=None, ignore_index=False, keys=None, levels=None, names=None, verify_integrity=False, copy=True): """ Concatenate pandas objects along a particular axis with 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, DataFrame, or Panel objects If a dict 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(es) join_axes : list of Index objects Specific indexes to use for the other n - 1 axes instead of performing inner/outer set logic ignore_index : boolean, 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 : boolean, default False Check whether the new concatenated axis contains duplicates. This can be very expensive relative to the actual data concatenation copy : boolean, default True If False, do not copy data unnecessarily Returns ------- concatenated : 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. 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 `__. See Also -------- Series.append DataFrame.append DataFrame.join DataFrame.merge 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]) animal letter number 0 NaN a 1 1 NaN b 2 0 cat c 3 1 dog d 4 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'] """ op = _Concatenator(objs, axis=axis, join_axes=join_axes, ignore_index=ignore_index, join=join, keys=keys, levels=levels, names=names, verify_integrity=verify_integrity, copy=copy) return op.get_result() class _Concatenator(object): """ Orchestrates a concatenation operation for BlockManagers """ def __init__(self, objs, axis=0, join='outer', join_axes=None, keys=None, levels=None, names=None, ignore_index=False, verify_integrity=False, copy=True): if isinstance(objs, (NDFrame, compat.string_types)): raise TypeError('first argument must be an iterable of pandas ' 'objects, you passed an object of type ' '"{name}"'.format(name=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 isinstance(objs, dict): if keys is None: keys = sorted(objs) 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 name = getattr(keys, 'name', None) keys = Index(clean_keys, name=name) if len(objs) == 0: raise ValueError('All objects passed were None') # consolidate data & figure out what our result ndim is going to be ndims = set() for obj in objs: if not isinstance(obj, NDFrame): msg = ('cannot concatenate object of type "{0}";' ' only pd.Series, pd.DataFrame, and pd.Panel' ' (deprecated) objs are valid'.format(type(obj))) raise TypeError(msg) # consolidate obj._consolidate(inplace=True) ndims.add(obj.ndim) # get the sample # want the higest ndim that we have, and must be non-empty # unless all objs are empty sample = 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, Series)] if (len(non_empties) and (keys is None and names is None and levels is None and join_axes 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, Series): 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, DataFrame) if self._is_frame: axis = 1 if axis == 0 else 0 self._is_series = isinstance(sample, Series) if not 0 <= axis <= sample.ndim: raise AssertionError("axis must be between 0 and {ndim}, input was" " {axis}".format(ndim=sample.ndim, axis=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 obj = sample._constructor({name: obj}) self.objs.append(obj) # note: this is the BlockManager axis (since DataFrame is transposed) self.axis = axis self.join_axes = join_axes self.keys = keys self.names = names or getattr(keys, 'names', None) self.levels = levels self.ignore_index = ignore_index self.verify_integrity = verify_integrity self.copy = copy self.new_axes = self._get_new_axes() def get_result(self): # series only if self._is_series: # stack blocks if self.axis == 0: name = com._consensus_name_attr(self.objs) mgr = self.objs[0]._data.concat([x._data for x in self.objs], self.new_axes) cons = _concat._get_series_result_type(mgr, self.objs) return cons(mgr, name=name).__finalize__(self, method='concat') # combine as columns in a frame else: data = dict(zip(range(len(self.objs)), self.objs)) cons = _concat._get_series_result_type(data) index, columns = self.new_axes df = cons(data, index=index) df.columns = columns return df.__finalize__(self, method='concat') # combine block managers else: mgrs_indexers = [] for obj in self.objs: mgr = obj._data indexers = {} for ax, new_labels in enumerate(self.new_axes): if ax == self.axis: # Suppress reindexing on concat axis continue obj_labels = mgr.axes[ax] if not new_labels.equals(obj_labels): indexers[ax] = obj_labels.reindex(new_labels)[1] mgrs_indexers.append((obj._data, indexers)) new_data = concatenate_block_managers( mgrs_indexers, self.new_axes, concat_axis=self.axis, copy=self.copy) if not self.copy: new_data._consolidate_inplace() cons = _concat._get_frame_result_type(new_data, self.objs) return (cons._from_axes(new_data, self.new_axes) .__finalize__(self, method='concat')) def _get_result_dim(self): if self._is_series and self.axis == 1: return 2 else: return self.objs[0].ndim def _get_new_axes(self): ndim = self._get_result_dim() new_axes = [None] * ndim if self.join_axes is None: for i in range(ndim): if i == self.axis: continue new_axes[i] = self._get_comb_axis(i) else: if len(self.join_axes) != ndim - 1: raise AssertionError("length of join_axes must not be equal " "to {length}".format(length=ndim - 1)) # ufff... indices = compat.lrange(ndim) indices.remove(self.axis) for i, ax in zip(indices, self.join_axes): new_axes[i] = ax new_axes[self.axis] = self._get_concat_axis() return new_axes def _get_comb_axis(self, i): data_axis = self.objs[0]._get_block_manager_axis(i) try: return _get_objs_combined_axis(self.objs, axis=data_axis, intersect=self.intersect) except IndexError: types = [type(x).__name__ for x in self.objs] raise TypeError("Cannot concatenate list of {types}" .format(types=types)) def _get_concat_axis(self): """ Return index to be used along concatenation axis. """ if self._is_series: if self.axis == 0: indexes = [x.index for x in self.objs] elif self.ignore_index: idx = com._default_index(len(self.objs)) return idx elif self.keys is None: names = [None] * len(self.objs) num = 0 has_names = False for i, x in enumerate(self.objs): if not isinstance(x, Series): raise TypeError("Cannot concatenate type 'Series' " "with object of type {type!r}" .format(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 com._default_index(len(self.objs)) else: return _ensure_index(self.keys) else: indexes = [x._data.axes[self.axis] for x in self.objs] if self.ignore_index: idx = com._default_index(sum(len(i) for i in indexes)) return idx if self.keys is 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): if self.verify_integrity: if not concat_index.is_unique: overlap = concat_index.get_duplicates() raise ValueError('Indexes have overlapping values: ' '{overlap!s}'.format(overlap=overlap)) def _concat_indexes(indexes): return indexes[0].append(indexes[1:]) def _make_concat_multiindex(indexes, keys, levels=None, names=None): if ((levels is None and isinstance(keys[0], tuple)) or (levels is not None and len(levels) > 1)): zipped = compat.lzip(*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)] else: levels = [_ensure_index(x) for x in levels] if not _all_indexes_same(indexes): label_list = [] # things are potentially different sizes, so compute the exact labels # for each level and pass those to MultiIndex.from_arrays for hlevel, level in zip(zipped, levels): to_concat = [] for key, index in zip(hlevel, indexes): try: i = level.get_loc(key) except KeyError: raise ValueError('Key {key!s} not in level {level!s}' .format(key=key, level=level)) to_concat.append(np.repeat(i, len(index))) label_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) label_list.extend(concat_index.labels) else: codes, categories = _factorize_from_iterable(concat_index) levels.append(categories) label_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(set([idx.nlevels for idx in indexes])) == 1: raise AssertionError("Cannot concat indices that do" " not have the same number of levels") # also copies names = names + _get_consensus_names(indexes) return MultiIndex(levels=levels, labels=label_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 labels new_labels = [] # 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('Values not found in passed level: {hlevel!s}' .format(hlevel=hlevel[mask])) new_labels.append(np.repeat(mapped, n)) if isinstance(new_index, MultiIndex): new_levels.extend(new_index.levels) new_labels.extend([np.tile(lab, kpieces) for lab in new_index.labels]) else: new_levels.append(new_index) new_labels.append(np.tile(np.arange(n), kpieces)) if len(new_names) < len(new_levels): new_names.extend(new_index.names) return MultiIndex(levels=new_levels, labels=new_labels, names=new_names, verify_integrity=False)