DOC: Changed import statements in examples in docstrings (pandas-dev#21774)

Author: alphaCTzo7G

pandas/core/frame.py

@@ -5655,7 +5655,6 @@ def unstack(self, level=-1, fill_value=None):
 
     Examples
     --------
-    >>> import pandas as pd
     >>> df = pd.DataFrame({'A': {0: 'a', 1: 'b', 2: 'c'},
     ...                    'B': {0: 1, 1: 3, 2: 5},
     ...                    'C': {0: 2, 1: 4, 2: 6}})

pandas/core/generic.py

@@ -4914,7 +4914,6 @@ def ftypes(self):
 
         Examples
         --------
-        >>> import numpy as np
         >>> arr = np.random.RandomState(0).randn(100, 4)
         >>> arr[arr < .8] = np.nan
         >>> pd.DataFrame(arr).ftypes

pandas/core/groupby/groupby.py

@@ -3694,7 +3694,6 @@ def filter(self, func, dropna=True, *args, **kwargs):  # noqa
 
         Examples
         --------
-        >>> import pandas as pd
         >>> df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
         ...                           'foo', 'bar'],
         ...                    'B' : [1, 2, 3, 4, 5, 6],
@@ -4528,7 +4527,6 @@ def filter(self, func, dropna=True, *args, **kwargs):  # noqa
 
         Examples
         --------
-        >>> import pandas as pd
         >>> df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
         ...                           'foo', 'bar'],
         ...                    'B' : [1, 2, 3, 4, 5, 6],

pandas/core/panel.py

@@ -89,7 +89,6 @@ def panel_index(time, panels, names=None):
 
     or
 
-    >>> import numpy as np
     >>> years = np.repeat(range(1960,1963), 3)
     >>> panels = np.tile(['A', 'B', 'C'], 3)
     >>> panel_idx = panel_index(years, panels)

pandas/core/reshape/melt.py

@@ -103,7 +103,6 @@ def lreshape(data, groups, dropna=True, label=None):
 
     Examples
     --------
-    >>> import pandas as pd
     >>> data = pd.DataFrame({'hr1': [514, 573], 'hr2': [545, 526],
     ...                      'team': ['Red Sox', 'Yankees'],
     ...                      'year1': [2007, 2007], 'year2': [2008, 2008]})
@@ -217,8 +216,6 @@ def wide_to_long(df, stubnames, i, j, sep="", suffix=r'\d+'):
 
     Examples
     --------
-    >>> import pandas as pd
-    >>> import numpy as np
     >>> np.random.seed(123)
     >>> df = pd.DataFrame({"A1970" : {0 : "a", 1 : "b", 2 : "c"},
     ...                    "A1980" : {0 : "d", 1 : "e", 2 : "f"},

pandas/core/reshape/reshape.py

@@ -58,7 +58,6 @@ class _Unstacker(object):
 
     Examples
     --------
-    >>> import pandas as pd
     >>> index = pd.MultiIndex.from_tuples([('one', 'a'), ('one', 'b'),
     ...                                    ('two', 'a'), ('two', 'b')])
     >>> s = pd.Series(np.arange(1, 5, dtype=np.int64), index=index)
@@ -771,7 +770,6 @@ def get_dummies(data, prefix=None, prefix_sep='_', dummy_na=False,
 
     Examples
     --------
-    >>> import pandas as pd
     >>> s = pd.Series(list('abca'))
 
     >>> pd.get_dummies(s)

pandas/core/series.py

@@ -2749,8 +2749,6 @@ def nlargest(self, n=5, keep='first'):
 
         Examples
         --------
-        >>> import pandas as pd
-        >>> import numpy as np
         >>> s = pd.Series(np.random.randn(10**6))
         >>> s.nlargest(10)  # only sorts up to the N requested
         219921    4.644710
@@ -2796,8 +2794,6 @@ def nsmallest(self, n=5, keep='first'):
 
         Examples
         --------
-        >>> import pandas as pd
-        >>> import numpy as np
         >>> s = pd.Series(np.random.randn(10**6))
         >>> s.nsmallest(10)  # only sorts up to the N requested
         288532   -4.954580
@@ -3048,7 +3044,7 @@ def _gotitem(self, key, ndim, subset=None):
     Examples
     --------
 
-    >>> s = Series(np.random.randn(10))
+    >>> s = pd.Series(np.random.randn(10))
 
     >>> s.agg('min')
     -1.3018049988556679
@@ -3127,8 +3123,6 @@ def apply(self, func, convert_dtype=True, args=(), **kwds):
 
         Create a series with typical summer temperatures for each city.
 
-        >>> import pandas as pd
-        >>> import numpy as np
         >>> series = pd.Series([20, 21, 12], index=['London',
         ... 'New York','Helsinki'])
         >>> series

pandas/core/sparse/series.py

@@ -736,15 +736,14 @@ def to_coo(self, row_levels=(0, ), column_levels=(1, ), sort_labels=False):
 
         Examples
         --------
-        >>> from numpy import nan
-        >>> s = Series([3.0, nan, 1.0, 3.0, nan, nan])
-        >>> s.index = MultiIndex.from_tuples([(1, 2, 'a', 0),
-                                              (1, 2, 'a', 1),
-                                              (1, 1, 'b', 0),
-                                              (1, 1, 'b', 1),
-                                              (2, 1, 'b', 0),
-                                              (2, 1, 'b', 1)],
-                                              names=['A', 'B', 'C', 'D'])
+        >>> s = pd.Series([3.0, np.nan, 1.0, 3.0, np.nan, np.nan])
+        >>> s.index = pd.MultiIndex.from_tuples([(1, 2, 'a', 0),
+                                                (1, 2, 'a', 1),
+                                                (1, 1, 'b', 0),
+                                                (1, 1, 'b', 1),
+                                                (2, 1, 'b', 0),
+                                                (2, 1, 'b', 1)],
+                                                names=['A', 'B', 'C', 'D'])
         >>> ss = s.to_sparse()
         >>> A, rows, columns = ss.to_coo(row_levels=['A', 'B'],
                                          column_levels=['C', 'D'],

pandas/core/tools/numeric.py

@@ -58,7 +58,6 @@ def to_numeric(arg, errors='raise', downcast=None):
     --------
     Take separate series and convert to numeric, coercing when told to
 
-    >>> import pandas as pd
     >>> s = pd.Series(['1.0', '2', -3])
     >>> pd.to_numeric(s)
     0    1.0

pandas/io/pytables.py

@@ -454,9 +454,7 @@ class HDFStore(StringMixin):
 
     Examples
     --------
-    >>> from pandas import DataFrame
-    >>> from numpy.random import randn
-    >>> bar = DataFrame(randn(10, 4))
+    >>> bar = pd.DataFrame(np.random.randn(10, 4))
     >>> store = HDFStore('test.h5')
     >>> store['foo'] = bar   # write to HDF5
     >>> bar = store['foo']   # retrieve

pandas/io/sas/sas_xport.py

@@ -68,11 +68,11 @@
 --------
 Read a SAS Xport file:
 
->>> df = pandas.read_sas('filename.XPT')
+>>> df = pd.read_sas('filename.XPT')
 
 Read a Xport file in 10,000 line chunks:
 
->>> itr = pandas.read_sas('filename.XPT', chunksize=10000)
+>>> itr = pd.read_sas('filename.XPT', chunksize=10000)
 >>> for chunk in itr:
 >>>     do_something(chunk)
 

pandas/io/stata.py

@@ -102,7 +102,6 @@
 --------
 Read a Stata dta file:
 
->>> import pandas as pd
 >>> df = pd.read_stata('filename.dta')
 
 Read a Stata dta file in 10,000 line chunks:
@@ -216,7 +215,6 @@ def _stata_elapsed_date_to_datetime_vec(dates, fmt):
 
     Examples
     --------
-    >>> import pandas as pd
     >>> dates = pd.Series([52])
     >>> _stata_elapsed_date_to_datetime_vec(dates , "%tw")
     0   1961-01-01
@@ -1946,7 +1944,6 @@ class StataWriter(StataParser):
 
     Examples
     --------
-    >>> import pandas as pd
     >>> data = pd.DataFrame([[1.0, 1]], columns=['a', 'b'])
     >>> writer = StataWriter('./data_file.dta', data)
     >>> writer.write_file()
@@ -2709,7 +2706,6 @@ class StataWriter117(StataWriter):
 
     Examples
     --------
-    >>> import pandas as pd
     >>> from pandas.io.stata import StataWriter117
     >>> data = pd.DataFrame([[1.0, 1, 'a']], columns=['a', 'b', 'c'])
     >>> writer = StataWriter117('./data_file.dta', data)

pandas/plotting/_core.py

@@ -2609,14 +2609,11 @@ def boxplot_frame_groupby(grouped, subplots=True, column=None, fontsize=None,
 
     Examples
     --------
-    >>> import pandas
-    >>> import numpy as np
     >>> import itertools
-    >>>
     >>> tuples = [t for t in itertools.product(range(1000), range(4))]
-    >>> index = pandas.MultiIndex.from_tuples(tuples, names=['lvl0', 'lvl1'])
+    >>> index = pd.MultiIndex.from_tuples(tuples, names=['lvl0', 'lvl1'])
     >>> data = np.random.randn(len(index),4)
-    >>> df = pandas.DataFrame(data, columns=list('ABCD'), index=index)
+    >>> df = pd.DataFrame(data, columns=list('ABCD'), index=index)
     >>>
     >>> grouped = df.groupby(level='lvl1')
     >>> boxplot_frame_groupby(grouped)

pandas/plotting/_misc.py

@@ -406,7 +406,6 @@ def bootstrap_plot(series, fig=None, size=50, samples=500, **kwds):
     .. plot::
             :context: close-figs
 
-            >>> import numpy as np
             >>> s = pd.Series(np.random.uniform(size=100))
             >>> fig = pd.plotting.bootstrap_plot(s)
     """
@@ -498,13 +497,11 @@ def parallel_coordinates(frame, class_column, cols=None, ax=None, color=None,
 
     Examples
     --------
-    >>> from pandas import read_csv
-    >>> from pandas.tools.plotting import parallel_coordinates
     >>> from matplotlib import pyplot as plt
-    >>> df = read_csv('https://raw.github.com/pandas-dev/pandas/master'
-                      '/pandas/tests/data/iris.csv')
-    >>> parallel_coordinates(df, 'Name', color=('#556270',
-                             '#4ECDC4', '#C7F464'))
+    >>> df = pd.read_csv('https://raw.github.com/pandas-dev/pandas/master'
+                        '/pandas/tests/data/iris.csv')
+    >>> pd.plotting.parallel_coordinates(df, 'Name',
+                                        color=('#556270', '#4ECDC4', '#C7F464'))
     >>> plt.show()
     """
     if axvlines_kwds is None:

pandas/tseries/holiday.py

@@ -143,12 +143,11 @@ class from pandas.tseries.offsets
         Examples
         --------
         >>> from pandas.tseries.holiday import Holiday, nearest_workday
-        >>> from pandas import DateOffset
         >>> from dateutil.relativedelta import MO
         >>> USMemorialDay = Holiday('MemorialDay', month=5, day=24,
-                                    offset=DateOffset(weekday=MO(1)))
+                                    offset=pd.DateOffset(weekday=MO(1)))
         >>> USLaborDay = Holiday('Labor Day', month=9, day=1,
-                            offset=DateOffset(weekday=MO(1)))
+                                offset=pd.DateOffset(weekday=MO(1)))
         >>> July3rd = Holiday('July 3rd', month=7, day=3,)
         >>> NewYears = Holiday('New Years Day', month=1,  day=1,
                                observance=nearest_workday),