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Jul 7, 2018
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Changed import statements in examples in DOCs #21774

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5bedd60
Changed import statements in examples in DOCs
alphaCTzo7G Jul 6, 2018
f3ee814
Remove `import numpy as np` and `import pandas as pd` statements
alphaCTzo7G Jul 7, 2018
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2 changes: 1 addition & 1 deletion pandas/core/series.py
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Expand Up @@ -3048,7 +3048,7 @@ 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
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19 changes: 10 additions & 9 deletions pandas/core/sparse/series.py
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Expand Up @@ -736,15 +736,16 @@ 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'])
>>> import numpy as np
>>> import pandas as pd
>>> 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'],
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6 changes: 3 additions & 3 deletions pandas/io/pytables.py
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Expand Up @@ -454,9 +454,9 @@ class HDFStore(StringMixin):

Examples
--------
>>> from pandas import DataFrame
>>> from numpy.random import randn
>>> bar = DataFrame(randn(10, 4))
>>> import numpy as np
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@jorisvandenbossche @TomAugspurger do we normally explicity list these?

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Yeah.. I wasn't sure either.. In some places, its explicitly listed, and in others its not. So in the few places where it seemed, it would help, I explicitly listed them.. but I can take them out as well..

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We do not, you can remove all occurences of import numpy as np and import pandas as pd

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We do not, you can remove all occurences of import numpy as np and import pandas as pd

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Apart from that, looking really good!

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Ok.. sounds good. I will remove the import numpy as np and import pandas as pd. If there is a pd.DataFrame or np.random being defined or something similar.. should I convert it back to just DataFrame and random respectively? Wondering if there is a good reference.. so I can convert these to the correct format in one shot..

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no, we always assume these as imports

>>> import pandas as pd
>>> bar = pd.DataFrame(np.random.randn(10, 4))
>>> store = HDFStore('test.h5')
>>> store['foo'] = bar # write to HDF5
>>> bar = store['foo'] # retrieve
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4 changes: 2 additions & 2 deletions pandas/io/sas/sas_xport.py
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Expand Up @@ -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)

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6 changes: 3 additions & 3 deletions pandas/plotting/_core.py
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Expand Up @@ -2609,14 +2609,14 @@ def boxplot_frame_groupby(grouped, subplots=True, column=None, fontsize=None,

Examples
--------
>>> import pandas
>>> import pandas as pd
>>> 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)
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12 changes: 6 additions & 6 deletions pandas/plotting/_misc.py
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Expand Up @@ -407,6 +407,7 @@ def bootstrap_plot(series, fig=None, size=50, samples=500, **kwds):
:context: close-figs

>>> import numpy as np
>>> import pandas as pd
>>> s = pd.Series(np.random.uniform(size=100))
>>> fig = pd.plotting.bootstrap_plot(s)
"""
Expand Down Expand Up @@ -498,13 +499,12 @@ 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
>>> import pandas as pd
>>> 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:
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6 changes: 3 additions & 3 deletions pandas/tseries/holiday.py
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Expand Up @@ -142,13 +142,13 @@ class from pandas.tseries.offsets

Examples
--------
>>> import pandas as pd
>>> 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),
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