DOC: remove imports and use pd in docstrings

pandas/core/algorithms.py

@@ -306,7 +306,7 @@ def unique(values):
     >>> pd.unique(pd.Series([2] + [1] * 5))
     array([2, 1])
 
-    >>> pd.unique(Series([pd.Timestamp('20160101'),
+    >>> pd.unique(pd.Series([pd.Timestamp('20160101'),
     ...                   pd.Timestamp('20160101')]))
     array(['2016-01-01T00:00:00.000000000'], dtype='datetime64[ns]')
 
@@ -326,18 +326,18 @@ def unique(values):
     An unordered Categorical will return categories in the
     order of appearance.
 
-    >>> pd.unique(Series(pd.Categorical(list('baabc'))))
+    >>> pd.unique(pd.Series(pd.Categorical(list('baabc'))))
     [b, a, c]
     Categories (3, object): [b, a, c]
 
-    >>> pd.unique(Series(pd.Categorical(list('baabc'),
+    >>> pd.unique(pd.Series(pd.Categorical(list('baabc'),
     ...                                 categories=list('abc'))))
     [b, a, c]
     Categories (3, object): [b, a, c]
 
     An ordered Categorical preserves the category ordering.
 
-    >>> pd.unique(Series(pd.Categorical(list('baabc'),
+    >>> pd.unique(pd.Series(pd.Categorical(list('baabc'),
     ...                                 categories=list('abc'),
     ...                                 ordered=True)))
     [b, a, c]

pandas/core/arrays/categorical.py

@@ -675,7 +675,7 @@ def _set_categories(self, categories, fastpath=False):
 
         Examples
         --------
-        >>> c = Categorical(['a', 'b'])
+        >>> c = pd.Categorical(['a', 'b'])
         >>> c
         [a, b]
         Categories (2, object): [a, b]
@@ -883,7 +883,7 @@ def rename_categories(self, new_categories, inplace=False):
 
         Examples
         --------
-        >>> c = Categorical(['a', 'a', 'b'])
+        >>> c = pd.Categorical(['a', 'a', 'b'])
         >>> c.rename_categories([0, 1])
         [0, 0, 1]
         Categories (2, int64): [0, 1]

pandas/core/dtypes/dtypes.py

@@ -171,7 +171,7 @@ class CategoricalDtype(PandasExtensionDtype, ExtensionDtype):
 
     Examples
     --------
-    >>> t = CategoricalDtype(categories=['b', 'a'], ordered=True)
+    >>> t = pd.CategoricalDtype(categories=['b', 'a'], ordered=True)
     >>> pd.Series(['a', 'b', 'a', 'c'], dtype=t)
     0      a
     1      b

pandas/core/frame.py

@@ -2853,8 +2853,6 @@ def query(self, expr, inplace=False, **kwargs):
 
         Examples
         --------
-        >>> import numpy as np
-        >>> import pandas as pd
         >>> df = pd.DataFrame(np.random.randn(10, 2), columns=list('ab'))
         >>> df.query('a > b')
         >>> df[df.a > df.b]  # same result as the previous expression
@@ -4914,8 +4912,8 @@ def combine(self, other, func, fill_value=None, overwrite=True):
 
         Examples
         --------
-        >>> df1 = DataFrame({'A': [0, 0], 'B': [4, 4]})
-        >>> df2 = DataFrame({'A': [1, 1], 'B': [3, 3]})
+        >>> df1 = pd.DataFrame({'A': [0, 0], 'B': [4, 4]})
+        >>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
         >>> df1.combine(df2, lambda s1, s2: s1 if s1.sum() < s2.sum() else s2)
            A  B
         0  0  3

pandas/core/groupby/groupby.py

@@ -1824,7 +1824,7 @@ def cumcount(self, ascending=True):
 
         Essentially this is equivalent to
 
-        >>> self.apply(lambda x: Series(np.arange(len(x)), x.index))
+        >>> self.apply(lambda x: pd.Series(np.arange(len(x)), x.index))
 
         Parameters
         ----------
@@ -2094,7 +2094,7 @@ def head(self, n=5):
         Examples
         --------
 
-        >>> df = DataFrame([[1, 2], [1, 4], [5, 6]],
+        >>> df = pd.DataFrame([[1, 2], [1, 4], [5, 6]],
                            columns=['A', 'B'])
         >>> df.groupby('A', as_index=False).head(1)
            A  B
@@ -2121,7 +2121,7 @@ def tail(self, n=5):
         Examples
         --------
 
-        >>> df = DataFrame([['a', 1], ['a', 2], ['b', 1], ['b', 2]],
+        >>> df = pd.DataFrame([['a', 1], ['a', 2], ['b', 1], ['b', 2]],
                            columns=['A', 'B'])
         >>> df.groupby('A').tail(1)
            A  B
@@ -3422,7 +3422,7 @@ def _selection_name(self):
     Examples
     --------
 
-    >>> s = Series([1, 2, 3, 4])
+    >>> s = pd.Series([1, 2, 3, 4])
 
     >>> s
     0    1

pandas/core/indexes/base.py

@@ -1272,11 +1272,11 @@ def set_names(self, names, level=None, inplace=False):
 
         Examples
         --------
-        >>> Index([1, 2, 3, 4]).set_names('foo')
+        >>> pd.Index([1, 2, 3, 4]).set_names('foo')
         Int64Index([1, 2, 3, 4], dtype='int64', name='foo')
-        >>> Index([1, 2, 3, 4]).set_names(['foo'])
+        >>> pd.Index([1, 2, 3, 4]).set_names(['foo'])
         Int64Index([1, 2, 3, 4], dtype='int64', name='foo')
-        >>> idx = MultiIndex.from_tuples([(1, u'one'), (1, u'two'),
+        >>> idx = pd.MultiIndex.from_tuples([(1, u'one'), (1, u'two'),
                                           (2, u'one'), (2, u'two')],
                                           names=['foo', 'bar'])
         >>> idx.set_names(['baz', 'quz'])
@@ -2844,8 +2844,8 @@ def symmetric_difference(self, other, result_name=None):
 
         Examples
         --------
-        >>> idx1 = Index([1, 2, 3, 4])
-        >>> idx2 = Index([2, 3, 4, 5])
+        >>> idx1 = pd.Index([1, 2, 3, 4])
+        >>> idx2 = pd.Index([2, 3, 4, 5])
         >>> idx1.symmetric_difference(idx2)
         Int64Index([1, 5], dtype='int64')
 

pandas/core/indexes/category.py

@@ -425,7 +425,7 @@ def get_loc(self, key, method=None):
         >>> monotonic_index.get_loc('b')
         slice(1, 3, None)
 
-        >>> non_monotonic_index = p.dCategoricalIndex(list('abcb'))
+        >>> non_monotonic_index = pd.CategoricalIndex(list('abcb'))
         >>> non_monotonic_index.get_loc('b')
         array([False,  True, False,  True], dtype=bool)
         """

pandas/core/indexes/multi.py

@@ -345,7 +345,7 @@ def set_levels(self, levels, level=None, inplace=False,
 
         Examples
         --------
-        >>> idx = MultiIndex.from_tuples([(1, u'one'), (1, u'two'),
+        >>> idx = pd.MultiIndex.from_tuples([(1, u'one'), (1, u'two'),
                                           (2, u'one'), (2, u'two')],
                                           names=['foo', 'bar'])
         >>> idx.set_levels([['a','b'], [1,2]])
@@ -441,7 +441,7 @@ def set_labels(self, labels, level=None, inplace=False,
 
         Examples
         --------
-        >>> idx = MultiIndex.from_tuples([(1, u'one'), (1, u'two'),
+        >>> idx = pd.MultiIndex.from_tuples([(1, u'one'), (1, u'two'),
                                           (2, u'one'), (2, u'two')],
                                           names=['foo', 'bar'])
         >>> idx.set_labels([[1,0,1,0], [0,0,1,1]])
@@ -1191,7 +1191,7 @@ def to_hierarchical(self, n_repeat, n_shuffle=1):
 
         Examples
         --------
-        >>> idx = MultiIndex.from_tuples([(1, u'one'), (1, u'two'),
+        >>> idx = pd.MultiIndex.from_tuples([(1, u'one'), (1, u'two'),
                                           (2, u'one'), (2, u'two')])
         >>> idx.to_hierarchical(3)
         MultiIndex(levels=[[1, 2], [u'one', u'two']],
@@ -1254,7 +1254,7 @@ def from_arrays(cls, arrays, sortorder=None, names=None):
         Examples
         --------
         >>> arrays = [[1, 1, 2, 2], ['red', 'blue', 'red', 'blue']]
-        >>> MultiIndex.from_arrays(arrays, names=('number', 'color'))
+        >>> pd.MultiIndex.from_arrays(arrays, names=('number', 'color'))
 
         See Also
         --------
@@ -1303,7 +1303,7 @@ def from_tuples(cls, tuples, sortorder=None, names=None):
         --------
         >>> tuples = [(1, u'red'), (1, u'blue'),
                       (2, u'red'), (2, u'blue')]
-        >>> MultiIndex.from_tuples(tuples, names=('number', 'color'))
+        >>> pd.MultiIndex.from_tuples(tuples, names=('number', 'color'))
 
         See Also
         --------
@@ -1356,7 +1356,7 @@ def from_product(cls, iterables, sortorder=None, names=None):
         --------
         >>> numbers = [0, 1, 2]
         >>> colors = [u'green', u'purple']
-        >>> MultiIndex.from_product([numbers, colors],
+        >>> pd.MultiIndex.from_product([numbers, colors],
                                      names=['number', 'color'])
         MultiIndex(levels=[[0, 1, 2], [u'green', u'purple']],
                    labels=[[0, 0, 1, 1, 2, 2], [0, 1, 0, 1, 0, 1]],

pandas/core/indexes/period.py

@@ -190,9 +190,9 @@ class PeriodIndex(PeriodArrayMixin, DatelikeOps, DatetimeIndexOpsMixin,
 
     Examples
     --------
-    >>> idx = PeriodIndex(year=year_arr, quarter=q_arr)
+    >>> idx = pd.PeriodIndex(year=year_arr, quarter=q_arr)
 
-    >>> idx2 = PeriodIndex(start='2000', end='2010', freq='A')
+    >>> idx2 = pd.PeriodIndex(start='2000', end='2010', freq='A')
 
     See Also
     ---------

pandas/core/resample.py

@@ -188,9 +188,8 @@ def pipe(self, func, *args, **kwargs):
 
     Examples
     --------
-    >>> s = Series([1,2,3,4,5],
-                    index=pd.date_range('20130101',
-                                        periods=5,freq='s'))
+    >>> s = pd.Series([1,2,3,4,5],
+                      index=pd.date_range('20130101', periods=5,freq='s'))
     2013-01-01 00:00:00    1
     2013-01-01 00:00:01    2
     2013-01-01 00:00:02    3

pandas/core/series.py

@@ -1868,7 +1868,7 @@ def quantile(self, q=0.5, interpolation='linear'):
 
         Examples
         --------
-        >>> s = Series([1, 2, 3, 4])
+        >>> s = pd.Series([1, 2, 3, 4])
         >>> s.quantile(.5)
         2.5
         >>> s.quantile([.25, .5, .75])
@@ -2229,8 +2229,8 @@ def combine(self, other, func, fill_value=None):
 
         Examples
         --------
-        >>> s1 = Series([1, 2])
-        >>> s2 = Series([0, 3])
+        >>> s1 = pd.Series([1, 2])
+        >>> s2 = pd.Series([0, 3])
         >>> s1.combine(s2, lambda x1, x2: x1 if x1 < x2 else x2)
         0    0
         1    2

pandas/core/sparse/series.py

@@ -795,7 +795,7 @@ def from_coo(cls, A, dense_index=False):
         matrix([[ 0.,  0.,  1.,  2.],
                 [ 3.,  0.,  0.,  0.],
                 [ 0.,  0.,  0.,  0.]])
-        >>> ss = SparseSeries.from_coo(A)
+        >>> ss = pd.SparseSeries.from_coo(A)
         >>> ss
         0  2    1
            3    2

pandas/core/strings.py

@@ -961,7 +961,7 @@ def str_extractall(arr, pat, flags=0):
     A pattern with one group will return a DataFrame with one column.
     Indices with no matches will not appear in the result.
 
-    >>> s = Series(["a1a2", "b1", "c1"], index=["A", "B", "C"])
+    >>> s = pd.Series(["a1a2", "b1", "c1"], index=["A", "B", "C"])
     >>> s.str.extractall(r"[ab](\d)")
              0
       match
@@ -1053,13 +1053,13 @@ def str_get_dummies(arr, sep='|'):
 
     Examples
     --------
-    >>> Series(['a|b', 'a', 'a|c']).str.get_dummies()
+    >>> pd.Series(['a|b', 'a', 'a|c']).str.get_dummies()
        a  b  c
     0  1  1  0
     1  1  0  0
     2  1  0  1
 
-    >>> Series(['a|b', np.nan, 'a|c']).str.get_dummies()
+    >>> pd.Series(['a|b', np.nan, 'a|c']).str.get_dummies()
        a  b  c
     0  1  1  0
     1  0  0  0
@@ -2364,7 +2364,7 @@ def rsplit(self, pat=None, n=-1, expand=False):
     Examples
     --------
 
-    >>> s = Series(['A_B_C', 'D_E_F', 'X'])
+    >>> s = pd.Series(['A_B_C', 'D_E_F', 'X'])
     0    A_B_C
     1    D_E_F
     2        X

pandas/core/window.py

@@ -1732,7 +1732,7 @@ class Expanding(_Rolling_and_Expanding):
     Examples
     --------
 
-    >>> df = DataFrame({'B': [0, 1, 2, np.nan, 4]})
+    >>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]})
          B
     0  0.0
     1  1.0
@@ -2017,7 +2017,7 @@ class EWM(_Rolling):
     Examples
     --------
 
-    >>> df = DataFrame({'B': [0, 1, 2, np.nan, 4]})
+    >>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]})
          B
     0  0.0
     1  1.0

pandas/io/pytables.py

@@ -455,7 +455,7 @@ class HDFStore(StringMixin):
     Examples
     --------
     >>> bar = pd.DataFrame(np.random.randn(10, 4))
-    >>> store = HDFStore('test.h5')
+    >>> store = pd.HDFStore('test.h5')
     >>> store['foo'] = bar   # write to HDF5
     >>> bar = store['foo']   # retrieve
     >>> store.close()

pandas/plotting/_misc.py

@@ -49,7 +49,7 @@ def scatter_matrix(frame, alpha=0.5, figsize=None, ax=None, grid=False,
 
     Examples
     --------
-    >>> df = DataFrame(np.random.randn(1000, 4), columns=['A','B','C','D'])
+    >>> df = pd.DataFrame(np.random.randn(1000, 4), columns=['A','B','C','D'])
     >>> scatter_matrix(df, alpha=0.2)
     """