Allow indices to be mapped through through dictionaries or series

asv_bench/benchmarks/series_methods.py

@@ -123,6 +123,30 @@ def time_series_dropna_datetime(self):
         self.s.dropna()
 
 
+class series_map_dict(object):
+    goal_time = 0.2
+
+    def setup(self):
+        map_size = 1000
+        self.s = Series(np.random.randint(0, map_size, 10000))
+        self.map_dict = {i: map_size - i for i in range(map_size)}
+
+    def time_series_map_dict(self):
+        self.s.map(self.map_dict)
+
+
+class series_map_series(object):
+    goal_time = 0.2
+
+    def setup(self):
+        map_size = 1000
+        self.s = Series(np.random.randint(0, map_size, 10000))
+        self.map_series = Series(map_size - np.arange(map_size))
+
+    def time_series_map_series(self):
+        self.s.map(self.map_series)
+
+
 class series_clip(object):
     goal_time = 0.2
 

doc/source/whatsnew/v0.22.0.txt

@@ -75,6 +75,7 @@ Other API Changes
 - :class:`CacheableOffset` and :class:`WeekDay` are no longer available in the ``pandas.tseries.offsets`` module (:issue:`17830`)
 - `tseries.frequencies.get_freq_group()` and `tseries.frequencies.DAYS` are removed from the public API (:issue:`18034`)
 - :func:`Series.truncate` and :func:`DataFrame.truncate` will raise a ``ValueError`` if the index is not sorted instead of an unhelpful ``KeyError`` (:issue:`17935`)
+- :func:`Index.map` can now accept ``Series`` and dictionary input objects (:issue:`12756`).
 - :func:`Dataframe.unstack` will now default to filling with ``np.nan`` for ``object`` columns. (:issue:`12815`)
 - :class:`IntervalIndex` constructor will raise if the ``closed`` parameter conflicts with how the input data is inferred to be closed (:issue:`18421`)
 
@@ -108,6 +109,7 @@ Performance Improvements
 - Added a keyword argument, ``cache``, to :func:`to_datetime` that improved the performance of converting duplicate datetime arguments (:issue:`11665`)
 - :class`DateOffset` arithmetic performance is improved (:issue:`18218`)
 - Converting a ``Series`` of ``Timedelta`` objects to days, seconds, etc... sped up through vectorization of underlying methods (:issue:`18092`)
+- Improved performance of ``.map()`` with a ``Series/dict`` input (:issue:`15081`)
 - The overriden ``Timedelta`` properties of days, seconds and microseconds have been removed, leveraging their built-in Python versions instead (:issue:`18242`)
 - ``Series`` construction will reduce the number of copies made of the input data in certain cases (:issue:`17449`)
 - Improved performance of :func:`Series.dt.date` and :func:`DatetimeIndex.date` (:issue:`18058`)

pandas/core/base.py

@@ -12,11 +12,12 @@
     is_object_dtype,
     is_list_like,
     is_scalar,
-    is_datetimelike)
+    is_datetimelike,
+    is_extension_type)
 
 from pandas.util._validators import validate_bool_kwarg
 
-from pandas.core import common as com
+from pandas.core import common as com, algorithms
 import pandas.core.nanops as nanops
 import pandas._libs.lib as lib
 from pandas.compat.numpy import function as nv
@@ -838,6 +839,78 @@ def _reduce(self, op, name, axis=0, skipna=True, numeric_only=None,
                             klass=self.__class__.__name__, op=name))
         return func(**kwds)
 
+    def _map_values(self, mapper, na_action=None):
+        """An internal function that maps values using the input
+        correspondence (which can be a dict, Series, or function).
+
+        Parameters
+        ----------
+        mapper : function, dict, or Series
+            The input correspondence object
+        na_action : {None, 'ignore'}
+            If 'ignore', propagate NA values, without passing them to the
+            mapping function
+
+        Returns
+        -------
+        applied : Union[Index, MultiIndex], inferred
+            The output of the mapping function applied to the index.
+            If the function returns a tuple with more than one element
+            a MultiIndex will be returned.
+
+        """
+
+        # we can fastpath dict/Series to an efficient map
+        # as we know that we are not going to have to yield
+        # python types
+        if isinstance(mapper, dict):
+            if hasattr(mapper, '__missing__'):
+                # If a dictionary subclass defines a default value method,
+                # convert mapper to a lookup function (GH #15999).
+                dict_with_default = mapper
+                mapper = lambda x: dict_with_default[x]
+            else:
+                # Dictionary does not have a default. Thus it's safe to
+                # convert to an Series for efficiency.
+                # we specify the keys here to handle the
+                # possibility that they are tuples
+                from pandas import Series
+                mapper = Series(mapper, index=mapper.keys())
+
+        if isinstance(mapper, ABCSeries):
+            # Since values were input this means we came from either
+            # a dict or a series and mapper should be an index
+            if is_extension_type(self.dtype):
+                values = self._values
+            else:
+                values = self.values
+
+            indexer = mapper.index.get_indexer(values)
+            new_values = algorithms.take_1d(mapper._values, indexer)
+
+            return new_values
+
+        # we must convert to python types
+        if is_extension_type(self.dtype):
+            values = self._values
+            if na_action is not None:
+                raise NotImplementedError
+            map_f = lambda values, f: values.map(f)
+        else:
+            values = self.astype(object)
+            values = getattr(values, 'values', values)
+            if na_action == 'ignore':
+                def map_f(values, f):
+                    return lib.map_infer_mask(values, f,
+                                              isna(values).view(np.uint8))
+            else:
+                map_f = lib.map_infer
+
+        # mapper is a function
+        new_values = map_f(values, mapper)
+
+        return new_values
+
     def value_counts(self, normalize=False, sort=True, ascending=False,
                      bins=None, dropna=True):
         """

pandas/core/dtypes/cast.py

@@ -1127,3 +1127,38 @@ def cast_scalar_to_array(shape, value, dtype=None):
     values.fill(fill_value)
 
     return values
+
+
+def construct_1d_arraylike_from_scalar(value, length, dtype):
+    """
+    create a np.ndarray / pandas type of specified shape and dtype
+    filled with values
+
+    Parameters
+    ----------
+    value : scalar value
+    length : int
+    dtype : pandas_dtype / np.dtype
+
+    Returns
+    -------
+    np.ndarray / pandas type of length, filled with value
+
+    """
+    if is_datetimetz(dtype):
+        from pandas import DatetimeIndex
+        subarr = DatetimeIndex([value] * length, dtype=dtype)
+    elif is_categorical_dtype(dtype):
+        from pandas import Categorical
+        subarr = Categorical([value] * length)
+    else:
+        if not isinstance(dtype, (np.dtype, type(np.dtype))):
+            dtype = dtype.dtype
+
+        # coerce if we have nan for an integer dtype
+        if is_integer_dtype(dtype) and isna(value):
+            dtype = np.float64
+        subarr = np.empty(length, dtype=dtype)
+        subarr.fill(value)
+
+    return subarr

pandas/core/dtypes/missing.py

@@ -369,13 +369,14 @@ def _maybe_fill(arr, fill_value=np.nan):
     return arr
 
 
-def na_value_for_dtype(dtype):
+def na_value_for_dtype(dtype, compat=True):
     """
     Return a dtype compat na value
 
     Parameters
     ----------
     dtype : string / dtype
+    compat : boolean, default True
 
     Returns
     -------
@@ -389,7 +390,9 @@ def na_value_for_dtype(dtype):
     elif is_float_dtype(dtype):
         return np.nan
     elif is_integer_dtype(dtype):
-        return 0
+        if compat:
+            return 0
+        return np.nan
     elif is_bool_dtype(dtype):
         return False
     return np.nan

pandas/core/indexes/base.py

@@ -13,7 +13,6 @@
 from pandas.compat.numpy import function as nv
 from pandas import compat
 
-
 from pandas.core.dtypes.generic import (
     ABCSeries,
     ABCMultiIndex,
@@ -2827,6 +2826,27 @@ def get_indexer_for(self, target, **kwargs):
         indexer, _ = self.get_indexer_non_unique(target, **kwargs)
         return indexer
 
+    _index_shared_docs['_get_values_from_dict'] = """
+        Return the values of the input dictionary in the order the keys are
+        in the index. np.nan is returned for index values not in the
+        dictionary.
+
+        Parameters
+        ----------
+        data : dict
+            The dictionary from which to extract the values
+
+        Returns
+        -------
+        np.array
+
+        """
+
+    @Appender(_index_shared_docs['_get_values_from_dict'])
+    def _get_values_from_dict(self, data):
+        return lib.fast_multiget(data, self.values,
+                                 default=np.nan)
+
     def _maybe_promote(self, other):
         # A hack, but it works
         from pandas.core.indexes.datetimes import DatetimeIndex
@@ -2865,13 +2885,15 @@ def groupby(self, values):
 
         return result
 
-    def map(self, mapper):
-        """Apply mapper function to an index.
+    def map(self, mapper, na_action=None):
+        """Map values of Series using input correspondence
 
         Parameters
         ----------
-        mapper : callable
-            Function to be applied.
+        mapper : function, dict, or Series
+        na_action : {None, 'ignore'}
+            If 'ignore', propagate NA values, without passing them to the
+            mapping function
 
         Returns
         -------
@@ -2881,15 +2903,26 @@ def map(self, mapper):
             a MultiIndex will be returned.
 
         """
+
         from .multi import MultiIndex
-        mapped_values = self._arrmap(self.values, mapper)
+        new_values = super(Index, self)._map_values(
+            mapper, na_action=na_action)
         attributes = self._get_attributes_dict()
-        if mapped_values.size and isinstance(mapped_values[0], tuple):
-            return MultiIndex.from_tuples(mapped_values,
-                                          names=attributes.get('name'))
+        if new_values.size and isinstance(new_values[0], tuple):
+            if isinstance(self, MultiIndex):
+                names = self.names
+            elif attributes.get('name'):
+                names = [attributes.get('name')] * len(new_values[0])
+            else:
+                names = None
+            return MultiIndex.from_tuples(new_values,
+                                          names=names)
 
         attributes['copy'] = False
-        return Index(mapped_values, **attributes)
+
+        # we infer the result types based on the
+        # returned values
+        return Index(new_values, **attributes)
 
     def isin(self, values, level=None):
         """

pandas/core/indexes/datetimelike.py

@@ -136,7 +136,7 @@ def equals(self, other):
         elif not isinstance(other, type(self)):
             try:
                 other = type(self)(other)
-            except:
+            except Exception:
                 return False
 
         if not is_dtype_equal(self.dtype, other.dtype):
@@ -352,7 +352,7 @@ def map(self, f):
 
             # Try to use this result if we can
             if isinstance(result, np.ndarray):
-                self._shallow_copy(result)
+                result = Index(result)
 
             if not isinstance(result, Index):
                 raise TypeError('The map function must return an Index object')
@@ -698,6 +698,14 @@ def __rsub__(self, other):
     def _add_delta(self, other):
         return NotImplemented
 
+    @Appender(_index_shared_docs['_get_values_from_dict'])
+    def _get_values_from_dict(self, data):
+        if len(data):
+            return np.array([data.get(i, np.nan)
+                             for i in self.asobject.values])
+
+        return np.array([np.nan])
+
     def _add_delta_td(self, other):
         # add a delta of a timedeltalike
         # return the i8 result view

pandas/core/indexes/datetimes.py

@@ -1457,6 +1457,17 @@ def get_value_maybe_box(self, series, key):
                                         key, tz=self.tz)
         return _maybe_box(self, values, series, key)
 
+    @Appender(_index_shared_docs['_get_values_from_dict'])
+    def _get_values_from_dict(self, data):
+        if len(data):
+            # coerce back to datetime objects for lookup
+            data = com._dict_compat(data)
+            return lib.fast_multiget(data,
+                                     self.asobject.values,
+                                     default=np.nan)
+
+        return np.array([np.nan])
+
     def get_loc(self, key, method=None, tolerance=None):
         """
         Get integer location for requested label