ENH: Allow .where to accept callable as condition

Author: sinhrks

doc/source/indexing.rst

@@ -79,6 +79,10 @@ of multi-axis indexing.
   - A slice object with labels ``'a':'f'``, (note that contrary to usual python
     slices, **both** the start and the stop are included!)
   - A boolean array
+  - A ``callable`` function with one argument (the calling Series, DataFrame or Panel) and
+    that returns valid output for indexing (one of the above)
+
+      .. versionadded:: 0.18.1
 
   See more at :ref:`Selection by Label <indexing.label>`
 
@@ -93,6 +97,10 @@ of multi-axis indexing.
   - A list or array of integers ``[4, 3, 0]``
   - A slice object with ints ``1:7``
   - A boolean array
+  - A ``callable`` function with one argument (the calling Series, DataFrame or Panel) and
+    that returns valid output for indexing (one of the above)
+
+      .. versionadded:: 0.18.1
 
   See more at :ref:`Selection by Position <indexing.integer>`
 
@@ -110,6 +118,8 @@ of multi-axis indexing.
   See more at :ref:`Advanced Indexing <advanced>` and :ref:`Advanced
   Hierarchical <advanced.advanced_hierarchical>`.
 
+- ``.loc``, ``.iloc``, ``.ix`` and also ``[]`` indexing can accept a ``callable`` as indexer. See more at :ref:`Selection By Callable <indexing.callable>`.
+
 Getting values from an object with multi-axes selection uses the following
 notation (using ``.loc`` as an example, but applies to ``.iloc`` and ``.ix`` as
 well). Any of the axes accessors may be the null slice ``:``. Axes left out of
@@ -317,6 +327,7 @@ The ``.loc`` attribute is the primary access method. The following are valid inp
 - A list or array of labels ``['a', 'b', 'c']``
 - A slice object with labels ``'a':'f'`` (note that contrary to usual python slices, **both** the start and the stop are included!)
 - A boolean array
+- A ``callable``, see :ref:`Selection By Callable <indexing.callable>`
 
 .. ipython:: python
 
@@ -340,13 +351,13 @@ With a DataFrame
                       index=list('abcdef'),
                       columns=list('ABCD'))
    df1
-   df1.loc[['a','b','d'],:]
+   df1.loc[['a', 'b', 'd'], :]
 
 Accessing via label slices
 
 .. ipython:: python
 
-   df1.loc['d':,'A':'C']
+   df1.loc['d':, 'A':'C']
 
 For getting a cross section using a label (equiv to ``df.xs('a')``)
 
@@ -358,15 +369,15 @@ For getting values with a boolean array
 
 .. ipython:: python
 
-   df1.loc['a']>0
-   df1.loc[:,df1.loc['a']>0]
+   df1.loc['a'] > 0
+   df1.loc[:, df1.loc['a'] > 0]
 
 For getting a value explicitly (equiv to deprecated ``df.get_value('a','A')``)
 
 .. ipython:: python
 
    # this is also equivalent to ``df1.at['a','A']``
-   df1.loc['a','A']
+   df1.loc['a', 'A']
 
 .. _indexing.integer:
 
@@ -387,6 +398,7 @@ The ``.iloc`` attribute is the primary access method. The following are valid in
 - A list or array of integers ``[4, 3, 0]``
 - A slice object with ints ``1:7``
 - A boolean array
+- A ``callable``, see :ref:`Selection By Callable <indexing.callable>`
 
 .. ipython:: python
 
@@ -416,26 +428,26 @@ Select via integer slicing
 .. ipython:: python
 
    df1.iloc[:3]
-   df1.iloc[1:5,2:4]
+   df1.iloc[1:5, 2:4]
 
 Select via integer list
 
 .. ipython:: python
 
-   df1.iloc[[1,3,5],[1,3]]
+   df1.iloc[[1, 3, 5], [1, 3]]
 
 .. ipython:: python
 
-   df1.iloc[1:3,:]
+   df1.iloc[1:3, :]
 
 .. ipython:: python
 
-   df1.iloc[:,1:3]
+   df1.iloc[:, 1:3]
 
 .. ipython:: python
 
    # this is also equivalent to ``df1.iat[1,1]``
-   df1.iloc[1,1]
+   df1.iloc[1, 1]
 
 For getting a cross section using an integer position (equiv to ``df.xs(1)``)
 
@@ -471,8 +483,8 @@ returned)
 
    dfl = pd.DataFrame(np.random.randn(5,2), columns=list('AB'))
    dfl
-   dfl.iloc[:,2:3]
-   dfl.iloc[:,1:3]
+   dfl.iloc[:, 2:3]
+   dfl.iloc[:, 1:3]
    dfl.iloc[4:6]
 
 A single indexer that is out of bounds will raise an ``IndexError``.
@@ -481,12 +493,52 @@ A list of indexers where any element is out of bounds will raise an
 
 .. code-block:: python
 
-   dfl.iloc[[4,5,6]]
+   dfl.iloc[[4, 5, 6]]
    IndexError: positional indexers are out-of-bounds
 
-   dfl.iloc[:,4]
+   dfl.iloc[:, 4]
    IndexError: single positional indexer is out-of-bounds
 
+.. _indexing.callable:
+
+Selection By Callable
+---------------------
+
+.. versionadded:: 0.18.1
+
+``.loc``, ``.iloc``, ``.ix`` and also ``[]`` indexing can accept a ``callable`` as indexer.
+The ``callable`` must be a function with one argument (the calling Series, DataFrame or Panel) and that returns valid output for indexing.
+
+.. ipython:: python
+
+   df1 = pd.DataFrame(np.random.randn(6, 4),
+                      index=list('abcdef'),
+                      columns=list('ABCD'))
+   df1
+
+   df1.loc[lambda df: df.A > 0, :]
+   df1.loc[:, lambda df: ['A', 'B']]
+
+   df1.iloc[:, lambda df: [0, 1]]
+
+   df1[lambda df: df.columns[0]]
+
+
+You can use callable indexing in ``Series``.
+
+.. ipython:: python
+
+   df1.A.loc[lambda s: s > 0]
+
+Using these methods / indexers, you can chain data selection operations
+without using temporary variable.
+
+.. ipython:: python
+
+   bb = pd.read_csv('data/baseball.csv', index_col='id')
+   (bb.groupby(['year', 'team']).sum()
+      .loc[lambda df: df.r > 100])
+
 .. _indexing.basics.partial_setting:
 
 Selecting Random Samples
@@ -848,6 +900,19 @@ This is equivalent (but faster than) the following.
    df2 = df.copy()
    df.apply(lambda x, y: x.where(x>0,y), y=df['A'])
 
+.. versionadded:: 0.18.1
+
+Where can accept a callable as condition and ``other`` arguments. The function must
+be with one argument (the calling Series or DataFrame) and that returns valid output
+as condition and ``other`` argument.
+
+.. ipython:: python
+
+   df3 = pd.DataFrame({'A': [1, 2, 3],
+                       'B': [4, 5, 6],
+                       'C': [7, 8, 9]})
+   df3.where(lambda x: x > 4, lambda x: x + 10)
+
 **mask**
 
 ``mask`` is the inverse boolean operation of ``where``.

doc/source/whatsnew/v0.18.1.txt

@@ -13,6 +13,8 @@ Highlights include:
 - ``pd.to_datetime()`` has gained the ability to assemble dates from a ``DataFrame``, see :ref:`here <whatsnew_0181.enhancements.assembling>`
 - Custom business hour offset, see :ref:`here <whatsnew_0181.enhancements.custombusinesshour>`.
 - Many bug fixes in the handling of ``sparse``, see :ref:`here <whatsnew_0181.sparse>`
+- Method chaining improvements, see :ref:`here <whatsnew_0181.enhancements.method_chain>`.
+
 
 .. contents:: What's new in v0.18.1
     :local:
@@ -94,6 +96,66 @@ Now you can do:
 
    df.groupby('group').resample('1D').ffill()
 
+.. _whatsnew_0181.enhancements.method_chain:
+
+Method chaininng improvements
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The following methods / indexers now accept ``callable``. It is intended to make
+these more useful in method chains, see :ref:`Selection By Callable <indexing.callable>`.
+(:issue:`11485`, :issue:`12533`)
+
+- ``.where()`` and ``.mask()``
+- ``.loc[]``, ``iloc[]`` and ``.ix[]``
+- ``[]`` indexing
+
+``.where()`` and ``.mask()``
+""""""""""""""""""""""""""""
+
+These can accept a callable as condition and ``other``
+arguments.
+
+.. ipython:: python
+
+   df = pd.DataFrame({'A': [1, 2, 3],
+                      'B': [4, 5, 6],
+                      'C': [7, 8, 9]})
+   df.where(lambda x: x > 4, lambda x: x + 10)
+
+``.loc[]``, ``.iloc[]``, ``.ix[]``
+""""""""""""""""""""""""""""""""""
+
+These can accept a callable, and tuple of callable as a slicer. The callable
+can return valid ``bool`` indexer or anything which is valid for these indexer's input.
+
+.. ipython:: python
+
+   # callable returns bool indexer
+   df.loc[lambda x: x.A >= 2, lambda x: x.sum() > 10]
+
+   # callable returns list of labels
+   df.loc[lambda x: [1, 2], lambda x: ['A', 'B']]
+
+``[]`` indexing
+"""""""""""""""
+
+Finally, you can use a callable in ``[]`` indexing of Series, DataFrame and Panel.
+The callable must return valid input for ``[]`` indexing depending on its
+class and index type.
+
+.. ipython:: python
+
+   df[lambda x: 'A']
+
+Using these methods / indexers, you can chain data selection operations
+without using temporary variable.
+
+.. ipython:: python
+
+   bb = pd.read_csv('data/baseball.csv', index_col='id')
+   (bb.groupby(['year', 'team']).sum()
+      .loc[lambda df: df.r > 100])
+
 .. _whatsnew_0181.partial_string_indexing:
 
 Partial string indexing on ``DateTimeIndex`` when part of a ``MultiIndex``

pandas/core/common.py

@@ -1843,6 +1843,16 @@ def _get_callable_name(obj):
     return None
 
 
+def _apply_if_callable(maybe_callable, obj, **kwargs):
+    """
+    Evaluate possibly callable input using obj and kwargs if it is callable,
+    otherwise return as it is
+    """
+    if callable(maybe_callable):
+        return maybe_callable(obj, **kwargs)
+    return maybe_callable
+
+
 _string_dtypes = frozenset(map(_get_dtype_from_object, (compat.binary_type,
                                                         compat.text_type)))
 

pandas/core/frame.py

@@ -1970,6 +1970,7 @@ def iget_value(self, i, j):
         return self.iat[i, j]
 
     def __getitem__(self, key):
+        key = com._apply_if_callable(key, self)
 
         # shortcut if we are an actual column
         is_mi_columns = isinstance(self.columns, MultiIndex)
@@ -2138,6 +2139,9 @@ def query(self, expr, inplace=False, **kwargs):
         >>> df.query('a > b')
         >>> df[df.a > df.b]  # same result as the previous expression
         """
+        if not isinstance(expr, compat.string_types):
+            msg = "expr must be a string to be evaluated, {0} given"
+            raise ValueError(msg.format(type(expr)))
         kwargs['level'] = kwargs.pop('level', 0) + 1
         kwargs['target'] = None
         res = self.eval(expr, **kwargs)
@@ -2336,6 +2340,7 @@ def _box_col_values(self, values, items):
                                                    name=items, fastpath=True)
 
     def __setitem__(self, key, value):
+        key = com._apply_if_callable(key, self)
 
         # see if we can slice the rows
         indexer = convert_to_index_sliceable(self, key)
@@ -2454,8 +2459,9 @@ def assign(self, **kwargs):
         kwargs : keyword, value pairs
             keywords are the column names. If the values are
             callable, they are computed on the DataFrame and
-            assigned to the new columns. If the values are
-            not callable, (e.g. a Series, scalar, or array),
+            assigned to the new columns. The callable must not
+            change input DataFrame (though pandas doesn't check it).
+            If the values are not callable, (e.g. a Series, scalar, or array),
             they are simply assigned.
 
         Returns
@@ -2513,11 +2519,7 @@ def assign(self, **kwargs):
         # do all calculations first...
         results = {}
         for k, v in kwargs.items():
-
-            if callable(v):
-                results[k] = v(data)
-            else:
-                results[k] = v
+            results[k] = com._apply_if_callable(v, data)
 
         # ... and then assign
         for k, v in sorted(results.items()):

pandas/core/generic.py

@@ -4283,8 +4283,26 @@ def _align_series(self, other, join='outer', axis=None, level=None,
 
         Parameters
         ----------
-        cond : boolean %(klass)s or array
-        other : scalar or %(klass)s
+        cond : boolean %(klass)s, array or callable
+            If cond is callable, it is computed on the %(klass)s and
+            should return boolean %(klass)s or array.
+            The callable must not change input %(klass)s
+            (though pandas doesn't check it).
+
+            .. versionadded:: 0.18.1
+
+            A callable can be used as cond.
+
+        other : scalar, %(klass)s, or callable
+            If other is callable, it is computed on the %(klass)s and
+            should return scalar or %(klass)s.
+            The callable must not change input %(klass)s
+            (though pandas doesn't check it).
+
+            .. versionadded:: 0.18.1
+
+            A callable can be used as other.
+
         inplace : boolean, default False
             Whether to perform the operation in place on the data
         axis : alignment axis if needed, default None
@@ -4304,6 +4322,9 @@ def _align_series(self, other, join='outer', axis=None, level=None,
     def where(self, cond, other=np.nan, inplace=False, axis=None, level=None,
               try_cast=False, raise_on_error=True):
 
+        cond = com._apply_if_callable(cond, self)
+        other = com._apply_if_callable(other, self)
+
         if isinstance(cond, NDFrame):
             cond, _ = cond.align(self, join='right', broadcast_axis=1)
         else:
@@ -4461,6 +4482,9 @@ def where(self, cond, other=np.nan, inplace=False, axis=None, level=None,
     @Appender(_shared_docs['where'] % dict(_shared_doc_kwargs, cond="False"))
     def mask(self, cond, other=np.nan, inplace=False, axis=None, level=None,
              try_cast=False, raise_on_error=True):
+
+        cond = com._apply_if_callable(cond, self)
+
         return self.where(~cond, other=other, inplace=inplace, axis=axis,
                           level=level, try_cast=try_cast,
                           raise_on_error=raise_on_error)