8000 DOC: Changed import statements in examples in docstrings (#21774) · pandas-dev/pandas@3091755 · GitHub
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DOC: Changed import statements in examples in docstrings (#21774)
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15 files changed

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‎pandas/core/frame.py

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

‎pandas/core/generic.py

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

‎pandas/core/groupby/groupby.py

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

‎pandas/core/panel.py

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

‎pandas/core/reshape/melt.py

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

‎pandas/core/reshape/reshape.py

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@@ -58,7 +58,6 @@ class _Unstacker(object):
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Examples
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--------
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>>> import pandas as pd
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>>> index = pd.MultiIndex.from_tuples([('one', 'a'), ('one', 'b'),
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... ('two', 'a'), ('two', 'b')])
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>>> 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,
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Examples
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--------
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>>> import pandas as pd
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>>> s = pd.Series(list('abca'))
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>>> pd.get_dummies(s)

‎pandas/core/series.py

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

‎pandas/core/sparse/series.py

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

‎pandas/core/tools/numeric.py

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

‎pandas/io/pytables.py

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

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