fix issue pandas-dev#24071

Author: giantpanpan

pandas/core/base.py

@@ -291,6 +291,12 @@ def _gotitem(self, key, ndim, subset=None):
         raise AbstractMethodError(self)
 
     def aggregate(self, func, *args, **kwargs):
+        """
+        :param func:
+        :param args:
+        :param kwargs:
+        :return:
+        """
         raise AbstractMethodError(self)
 
     agg = aggregate
@@ -1254,33 +1260,29 @@ def is_unique(self):
 
     @property
     def is_monotonic(self):
-        """
-        Return boolean if values in the object are
+        """Return boolean if values in the object are
         monotonic_increasing
 
         .. versionadded:: 0.19.0
 
         Returns
         -------
-        is_monotonic : boolean
-        """
+        is_monotonic : boolean"""
         from pandas import Index
         return Index(self).is_monotonic
 
     is_monotonic_increasing = is_monotonic
 
     @property
     def is_monotonic_decreasing(self):
-        """
-        Return boolean if values in the object are
+        """Return boolean if values in the object are
         monotonic_decreasing
 
         .. versionadded:: 0.19.0
 
         Returns
         -------
-        is_monotonic_decreasing : boolean
-        """
+        is_monotonic_decreasing : boolean"""
         from pandas import Index
         return Index(self).is_monotonic_decreasing
 

pandas/core/frame.py

@@ -6030,8 +6030,7 @@ def melt(self, id_vars=None, value_vars=None, var_name=None,
     # Time series-related
 
     def diff(self, periods=1, axis=0):
-        """
-        First discrete difference of element.
+        """First discrete difference of element.
 
         Calculates the difference of a DataFrame element compared with another
         element in the DataFrame (default is the element in the same column
@@ -6114,8 +6113,7 @@ def diff(self, periods=1, axis=0):
         2 -1.0 -1.0  -7.0
         3 -1.0 -2.0  -9.0
         4 -1.0 -3.0 -11.0
-        5  NaN  NaN   NaN
-        """
+        5  NaN  NaN   NaN"""
         bm_axis = self._get_block_manager_axis(axis)
         new_data = self._data.diff(n=periods, axis=bm_axis)
         return self._constructor(new_data)
@@ -6880,8 +6878,7 @@ def _series_round(s, decimals):
     # Statistical methods, etc.
 
     def corr(self, method='pearson', min_periods=1):
-        """
-        Compute pairwise correlation of columns, excluding NA/null values.
+        """Compute pairwise correlation of columns, excluding NA/null values.
 
         Parameters
         ----------
@@ -6911,8 +6908,7 @@ def corr(self, method='pearson', min_periods=1):
         >>> df.corr(method=histogram_intersection)
               dogs cats
         dogs   1.0  0.3
-        cats   0.3  1.0
-        """
+        cats   0.3  1.0"""
         numeric_df = self._get_numeric_data()
         cols = numeric_df.columns
         idx = cols.copy()
@@ -6955,8 +6951,7 @@ def corr(self, method='pearson', min_periods=1):
         return self._constructor(correl, index=idx, columns=cols)
 
     def cov(self, min_periods=None):
-        """
-        Compute pairwise covariance of columns, excluding NA/null values.
+        """Compute pairwise covariance of columns, excluding NA/null values.
 
         Compute the pairwise covariance among the series of a DataFrame.
         The returned data frame is the `covariance matrix
@@ -7045,8 +7040,7 @@ def cov(self, min_periods=None):
                   a         b         c
         a  0.316741       NaN -0.150812
         b       NaN  1.248003  0.191417
-        c -0.150812  0.191417  0.895202
-        """
+        c -0.150812  0.191417  0.895202"""
         numeric_df = self._get_numeric_data()
         cols = numeric_df.columns
         idx = cols.copy()
@@ -7066,8 +7060,7 @@ def cov(self, min_periods=None):
         return self._constructor(baseCov, index=idx, columns=cols)
 
     def corrwith(self, other, axis=0, drop=False):
-        """
-        Compute pairwise correlation between rows or columns of two DataFrame
+        """Compute pairwise correlation between rows or columns of two DataFrame
         objects.
 
         Parameters
@@ -7080,8 +7073,7 @@ def corrwith(self, other, axis=0, drop=False):
 
         Returns
         -------
-        correls : Series
-        """
+        correls : Series"""
         axis = self._get_axis_number(axis)
         this = self._get_numeric_data()
 
@@ -7404,8 +7396,7 @@ def nunique(self, axis=0, dropna=True):
         return self.apply(Series.nunique, axis=axis, dropna=dropna)
 
     def idxmin(self, axis=0, skipna=True):
-        """
-        Return index of first occurrence of minimum over requested axis.
+        """Return index of first occurrence of minimum over requested axis.
         NA/null values are excluded.
 
         Parameters
@@ -7431,17 +7422,15 @@ def idxmin(self, axis=0, skipna=True):
 
         See Also
         --------
-        Series.idxmin
-        """
+        Series.idxmin"""
         axis = self._get_axis_number(axis)
         indices = nanops.nanargmin(self.values, axis=axis, skipna=skipna)
         index = self._get_axis(axis)
         result = [index[i] if i >= 0 else np.nan for i in indices]
         return Series(result, index=self._get_agg_axis(axis))
 
     def idxmax(self, axis=0, skipna=True):
-        """
-        Return index of first occurrence of maximum over requested axis.
+        """Return index of first occurrence of maximum over requested axis.
         NA/null values are excluded.
 
         Parameters
@@ -7467,8 +7456,7 @@ def idxmax(self, axis=0, skipna=True):
 
         See Also
         --------
-        Series.idxmax
-        """
+        Series.idxmax"""
         axis = self._get_axis_number(axis)
         indices = nanops.nanargmax(self.values, axis=axis, skipna=skipna)
         index = self._get_axis(axis)
@@ -7574,8 +7562,7 @@ def f(s):
 
     def quantile(self, q=0.5, axis=0, numeric_only=True,
                  interpolation='linear'):
-        """
-        Return values at the given quantile over requested axis.
+        """Return values at the given quantile over requested axis.
 
         Parameters
         ----------
@@ -7640,8 +7627,7 @@ def quantile(self, q=0.5, axis=0, numeric_only=True,
         See Also
         --------
         pandas.core.window.Rolling.quantile
-        numpy.percentile
-        """
+        numpy.percentile"""
         self._check_percentile(q)
 
         data = self._get_numeric_data() if numeric_only else self

pandas/core/generic.py

@@ -3338,8 +3338,7 @@ def _take(self, indices, axis=0, is_copy=True):
         return result
 
     def take(self, indices, axis=0, convert=None, is_copy=True, **kwargs):
-        """
-        Return the elements in the given *positional* indices along an axis.
+        """Return the elements in the given *positional* indices along an axis.
 
         This means that we are not indexing according to actual values in
         the index attribute of the object. We are indexing according to the
@@ -3419,8 +3418,7 @@ class  max_speed
         >>> df.take([-1, -2])
              name   class  max_speed
         1  monkey  mammal        NaN
-        3    lion  mammal       80.5
-        """
+        3    lion  mammal       80.5"""
         if convert is not None:
             msg = ("The 'convert' parameter is deprecated "
                    "and will be removed in a future version.")
@@ -5903,8 +5901,7 @@ def infer_objects(self):
 
     def fillna(self, value=None, method=None, axis=None, inplace=False,
                limit=None, downcast=None):
-        """
-        Fill NA/NaN values using the specified method
+        """Fill NA/NaN values using the specified method
 
         Parameters
         ----------
@@ -5994,8 +5991,7 @@ def fillna(self, value=None, method=None, axis=None, inplace=False,
         0   0.0 2.0 2.0 0
         1   3.0 4.0 NaN 1
         2   NaN 1.0 NaN 5
-        3   NaN 3.0 NaN 4
-        """
+        3   NaN 3.0 NaN 4"""
         inplace = validate_bool_kwarg(inplace, 'inplace')
         value, method = validate_fillna_kwargs(value, method)
 
@@ -8913,8 +8909,7 @@ def slice_shift(self, periods=1, axis=0):
         return new_obj.__finalize__(self)
 
     def tshift(self, periods=1, freq=None, axis=0):
-        """
-        Shift the time index, using the index's frequency if available.
+        """Shift the time index, using the index's frequency if available.
 
         Parameters
         ----------
@@ -8933,8 +8928,7 @@ def tshift(self, periods=1, freq=None, axis=0):
 
         Returns
         -------
-        shifted : NDFrame
-        """
+        shifted : NDFrame"""
 
         index = self._get_axis(axis)
         if freq is None:

pandas/core/groupby/generic.py

@@ -1020,7 +1020,9 @@ def true_and_notna(x, *args, **kwargs):
         return filtered
 
     def nunique(self, dropna=True):
-        """ Returns number of unique elements in the group """
+        """
+        Returns number of unique elements in the group
+        """
         ids, _, _ = self.grouper.group_info
 
         val = self.obj.get_values()
@@ -1083,7 +1085,14 @@ def describe(self, **kwargs):
 
     def value_counts(self, normalize=False, sort=True, ascending=False,
                      bins=None, dropna=True):
-
+        """
+        :param normalize:
+        :param sort:
+        :param ascending:
+        :param bins:
+        :param dropna:
+        :return:
+        """
         from pandas.core.reshape.tile import cut
         from pandas.core.reshape.merge import _get_join_indexers
 
@@ -1490,7 +1499,9 @@ def _fill(self, direction, limit=None):
         return concat((self._wrap_transformed_output(output), res), axis=1)
 
     def count(self):
-        """ Compute count of group, excluding missing values """
+        """
+        Compute count of group, excluding missing values
+        """
         from pandas.core.dtypes.missing import _isna_ndarraylike as _isna
 
         data, _ = self._get_data_to_aggregate()

pandas/core/groupby/groupby.py

@@ -167,7 +167,7 @@ class providing the base-class of operations.
     dtype: int64
     """)
 
-_pipe_template = """\
+_pipe_template = """
 Apply a function `func` with arguments to this %(klass)s object and return
 the function's result.
 
@@ -716,6 +716,12 @@ def _iterate_slices(self):
         yield self._selection_name, self._selected_obj
 
     def transform(self, func, *args, **kwargs):
+        """
+        :param func:
+        :param args:
+        :param kwargs:
+        :return:
+        """
         raise AbstractMethodError(self)
 
     def _cumcount_array(self, ascending=True):
@@ -1306,6 +1312,25 @@ def last(x):
                                      numeric_only=False)
         cls.last = groupby_function('last', 'last', last_compat,
                                     numeric_only=False)
+        cls.sum.__doc__ = """
+        sum 
+        """
+        cls.prod.__doc__ = """
+        prod
+        """
+        cls.min.__doc__ = """
+        min
+        """
+        cls.max.__doc__ = """
+        max
+        """
+        cls.first.__doc__= """
+        first
+        """
+        cls.last.__doc__="""
+        last
+        """
+
 
     @Substitution(name='groupby')
     @Appender(_doc_template)

pandas/core/series.py

@@ -1503,8 +1503,7 @@ def mode(self, dropna=True):
         return algorithms.mode(self, dropna=dropna)
 
     def unique(self):
-        """
-        Return unique values of Series object.
+        """Return unique values of Series object.
 
         Uniques are returned in order of appearance. Hash table-based unique,
         therefore does NOT sort.
@@ -1545,8 +1544,7 @@ def unique(self):
         >>> pd.Series(pd.Categorical(list('baabc'), categories=list('abc'),
         ...                          ordered=True)).unique()
         [b, a, c]
-        Categories (3, object): [a < b < c]
-        """
+        Categories (3, object): [a < b < c]"""
         result = super(Series, self).unique()
 
         if is_datetime64tz_dtype(self.dtype):
@@ -2903,8 +2901,7 @@ def argsort(self, axis=0, kind='quicksort', order=None):
                 dtype='int64').__finalize__(self)
 
     def nlargest(self, n=5, keep='first'):
-        """
-        Return the largest `n` elements.
+        """Return the largest `n` elements.
 
         Parameters
         ----------
@@ -2994,13 +2991,11 @@ def nlargest(self, n=5, keep='first'):
         Malta         434000
         Maldives      434000
         Brunei        434000
-        dtype: int64
-        """
+        dtype: int64"""
         return algorithms.SelectNSeries(self, n=n, keep=keep).nlargest()
 
     def nsmallest(self, n=5, keep='first'):
-        """
-        Return the smallest `n` elements.
+        """Return the smallest `n` elements.
 
         Parameters
         ----------
@@ -3089,8 +3084,7 @@ def nsmallest(self, n=5, keep='first'):
         Nauru       11300
         Tuvalu      11300
         Anguilla    11300
-        dtype: int64
-        """
+        dtype: int64"""
         return algorithms.SelectNSeries(self, n=n, keep=keep).nsmallest()
 
     def swaplevel(self, i=-2, j=-1, copy=True):