DOC: use shared docs on Index._convert_list_indexer (#15678) · pandas-dev/pandas@05d70f4 (original) (raw)

`@@ -7,7 +7,6 @@

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`from pandas.types.generic import ABCDataFrame, ABCPanel, ABCSeries

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`from pandas.types.common import (is_integer_dtype,

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`is_integer, is_float,

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is_categorical_dtype,

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`is_list_like,

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`is_sequence,

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`is_iterator,

`@@ -1087,51 +1086,24 @@ def _getitem_iterable(self, key, axis=0):

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`inds, = key.nonzero()

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`return self.obj.take(inds, axis=axis, convert=False)

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`else:

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if isinstance(key, Index):

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keyarr = labels._convert_index_indexer(key)

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else:

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keyarr = _asarray_tuplesafe(key)

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keyarr = labels._convert_arr_indexer(keyarr)

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-

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if is_categorical_dtype(labels):

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keyarr = labels._shallow_copy(keyarr)

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-

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have the index handle the indexer and possibly return

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an indexer or raising

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indexer = labels._convert_list_indexer(keyarr, kind=self.name)

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Have the index compute an indexer or return None

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if it cannot handle

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indexer, keyarr = labels._convert_listlike_indexer(

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key, kind=self.name)

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`if indexer is not None:

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`return self.obj.take(indexer, axis=axis)

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this is not the most robust, but...

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if (isinstance(labels, MultiIndex) and len(keyarr) and

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not isinstance(keyarr[0], tuple)):

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level = 0

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else:

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level = None

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-

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`# existing labels are unique and indexer are unique

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`if labels.is_unique and Index(keyarr).is_unique:

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`try:

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result = self.obj.reindex_axis(keyarr, axis=axis,

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level=level)

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-

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this is an error as we are trying to find

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keys in a multi-index that don't exist

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if isinstance(labels, MultiIndex) and level is not None:

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if (hasattr(result, 'ndim') and

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not np.prod(result.shape) and len(keyarr)):

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raise KeyError("cannot index a multi-index axis "

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"with these keys")

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-

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return result

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-

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return self.obj.reindex_axis(keyarr, axis=axis)

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`except AttributeError:

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`# Series

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`if axis != 0:

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`raise AssertionError('axis must be 0')

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return self.obj.reindex(keyarr, level=level)

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return self.obj.reindex(keyarr)

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`# existing labels are non-unique

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`else:

`@@ -1225,49 +1197,33 @@ def _convert_to_indexer(self, obj, axis=0, is_setter=False):

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`if is_nested_tuple(obj, labels):

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`return labels.get_locs(obj)

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+

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`elif is_list_like_indexer(obj):

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+

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`if is_bool_indexer(obj):

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`obj = check_bool_indexer(labels, obj)

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`inds, = obj.nonzero()

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`return inds

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`else:

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if isinstance(obj, Index):

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want Index objects to pass through untouched

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objarr = obj

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else:

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objarr = _asarray_tuplesafe(obj)

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The index may want to handle a list indexer differently

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by returning an indexer or raising

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indexer = labels._convert_list_indexer(objarr, kind=self.name)

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Have the index compute an indexer or return None

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if it cannot handle

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indexer, objarr = labels._convert_listlike_indexer(

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obj, kind=self.name)

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`if indexer is not None:

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`return indexer

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this is not the most robust, but...

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if (isinstance(labels, MultiIndex) and

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not isinstance(objarr[0], tuple)):

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level = 0

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_, indexer = labels.reindex(objarr, level=level)

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unique index

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if labels.is_unique:

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indexer = check = labels.get_indexer(objarr)

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take all

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if indexer is None:

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indexer = np.arange(len(labels))

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-

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check = labels.levels[0].get_indexer(objarr)

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non-unique (dups)

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`else:

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level = None

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-

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unique index

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if labels.is_unique:

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indexer = check = labels.get_indexer(objarr)

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-

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non-unique (dups)

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else:

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(indexer,

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missing) = labels.get_indexer_non_unique(objarr)

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'indexer' has dupes, create 'check' using 'missing'

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check = np.zeros_like(objarr)

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check[missing] = -1

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(indexer,

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missing) = labels.get_indexer_non_unique(objarr)

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'indexer' has dupes, create 'check' using 'missing'

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check = np.zeros_like(objarr)

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check[missing] = -1

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`mask = check == -1

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`if mask.any():