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Fix SS06 formatting errors #29304

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Fix SS06 formatting errors #29304

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47e6ac9
Fix SS06 formatting error #29254
Oct 31, 2019
e737e7f
Fix SS06 formatting error #29254 with Appender Functions
Oct 31, 2019
3251991
fixed formatting issues
Oct 31, 2019
8f9464a
Correcting spelling error
Oct 31, 2019
fd8037c
fixing pep8 issues
Oct 31, 2019
f95cfeb
Added punctuation
Oct 31, 2019
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29 changes: 17 additions & 12 deletions pandas/core/frame.py
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Original file line number Diff line number Diff line change
Expand Up @@ -310,10 +310,12 @@

class DataFrame(NDFrame):
"""
Two-dimensional size-mutable, potentially heterogeneous tabular data
structure with labeled axes (rows and columns). Arithmetic operations
align on both row and column labels. Can be thought of as a dict-like
container for Series objects. The primary pandas data structure.
Two-dimensional size-mutable, potentially heterogeneous tabular data structure.

Data Structure has labeled axes (rows and columns).
Arithmetic operations align on both row and column labels.
Can be thought of as a dict-like container for Series objects.
This is the primary pandas data structure.

Parameters
----------
Expand Down Expand Up @@ -4798,9 +4800,10 @@ def dropna(self, axis=0, how="any", thresh=None, subset=None, inplace=False):

def drop_duplicates(self, subset=None, keep="first", inplace=False):
"""
Return DataFrame with duplicate rows removed, optionally only
considering certain columns. Indexes, including time indexes
are ignored.
Return DataFrame with duplicate rows removed.

Optionally only considers certain columns.
Indexes, including time indexes, are ignored.

Parameters
----------
Expand Down Expand Up @@ -4834,8 +4837,9 @@ def drop_duplicates(self, subset=None, keep="first", inplace=False):

def duplicated(self, subset=None, keep="first"):
"""
Return boolean Series denoting duplicate rows, optionally only
considering certain columns.
Return boolean Series denoting duplicate rows.

Optionally only considers certain columns.

Parameters
----------
Expand Down Expand Up @@ -7536,9 +7540,10 @@ def cov(self, min_periods=None):

def corrwith(self, other, axis=0, drop=False, method="pearson"):
"""
Compute pairwise correlation between rows or columns of DataFrame
with rows or columns of Series or DataFrame. DataFrames are first
aligned along both axes before computing the correlations.
Compute pairwise correlation between rows or columns of DataFrame.

Correlation is computed with rows or columns of Series or DataFrame.
DataFrames are first aligned along both axes before computing the correlations.

Parameters
----------
Expand Down
30 changes: 15 additions & 15 deletions pandas/core/generic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5156,8 +5156,9 @@ def pipe(self, func, *args, **kwargs):
_shared_docs[
"transform"
] = """
Call ``func`` on self producing a %(klass)s with transformed values
and that has the same axis length as self.
Call ``func`` on self producing a %(klass)s with transformed values.

%(klass)s has the same axis length as self.

Parameters
----------
Expand Down Expand Up @@ -8741,8 +8742,7 @@ def ranker(data):
_shared_docs[
"align"
] = """
Align two objects on their axes with the
specified join method for each axis Index.
Align two objects on their axes with the specified join for each axis Index.

Parameters
----------
Expand Down Expand Up @@ -9965,14 +9965,14 @@ def abs(self):

def describe(self, percentiles=None, include=None, exclude=None):
"""
Generate descriptive statistics that summarize the central tendency,
dispersion and shape of a dataset's distribution, excluding
``NaN`` values.
Generate descriptive statistics that summarizes a dataset.
Statistics summarizes the central tendency, dispersion and
shape of a dataset's distribution, excluding ``NaN`` values.

Analyzes both numeric and object series, as well
as ``DataFrame`` column sets of mixed data types. The output
will vary depending on what is provided. Refer to the notes
below for more detail.
Analyzes both numeric and object series.
In addition, it also analyses ``DataFrame`` column sets of mixed data types.
The output will vary depending on what is provided.
Refer to the notes below for more detail.

Parameters
----------
Expand Down Expand Up @@ -10649,7 +10649,7 @@ def compound(self, axis=None, skipna=None, level=None):
name,
name2,
axis_descr,
"Return unbiased skew over requested axis\nNormalized by N-1.",
"Return unbiased skew over requested axis normalized by N-1.",
nanops.nanskew,
)
cls.kurt = _make_stat_function(
Expand All @@ -10658,9 +10658,9 @@ def compound(self, axis=None, skipna=None, level=None):
name,
name2,
axis_descr,
"Return unbiased kurtosis over requested axis using Fisher's "
"definition of\nkurtosis (kurtosis of normal == 0.0). Normalized "
"by N-1.",
"Return unbiased kurtosis over requested axis."
"\n\nFisher's definition of kurtosis is used(kurtosis of normal == 0.0)."
"\nNormalized by N-1.",
nanops.nankurt,
)
cls.kurtosis = cls.kurt
Expand Down