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Merged
merged 5 commits into from
Nov 28, 2023
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docs: add examples for dataframe.mean, dataframe.median, dataframe.va… #228

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docs: add examples for dataframe.mean, dataframe.median, dataframe.va…
Genesis929 Nov 23, 2023
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column to columns
Genesis929 Nov 23, 2023
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update var example
Genesis929 Nov 23, 2023
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Merge branch 'main' into huanc-stats-docstring-1
Genesis929 Nov 27, 2023
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Merge branch 'main' into huanc-stats-docstring-1
Genesis929 Nov 28, 2023
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105 changes: 103 additions & 2 deletions third_party/bigframes_vendored/pandas/core/frame.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2852,6 +2852,33 @@ def sum(self, axis=0, *, numeric_only: bool = False):
def mean(self, axis=0, *, numeric_only: bool = False):
"""Return the mean of the values over the requested axis.

**Examples:**

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

>>> df = bpd.DataFrame({"A": [1, 3], "B": [2, 4]})
>>> df
A B
0 1 2
1 3 4
<BLANKLINE>
[2 rows x 2 columns]

Calculating the mean of each column (the default behavior without an explicit axis parameter).

>>> df.mean()
A 2.0
B 3.0
dtype: Float64

Calculating the mean of each row.

>>> df.mean(axis=1)
0 1.5
1 3.5
dtype: Float64

Args:
axis ({index (0), columns (1)}):
Axis for the function to be applied on.
Expand All @@ -2865,7 +2892,27 @@ def mean(self, axis=0, *, numeric_only: bool = False):
raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)

def median(self, *, numeric_only: bool = False, exact: bool = False):
"""Return the median of the values over the requested axis.
"""Return the median of the values over colunms.

**Examples:**

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

>>> df = bpd.DataFrame({"A": [1, 3], "B": [2, 4]})
>>> df
A B
0 1 2
1 3 4
<BLANKLINE>
[2 rows x 2 columns]

Finding the median value of each column.

>>> df.median()
A 1.0
B 2.0
dtype: Float64

Args:
numeric_only (bool. default False):
Expand All @@ -2884,6 +2931,34 @@ def var(self, axis=0, *, numeric_only: bool = False):

Normalized by N-1 by default.

**Examples:**

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

>>> df = bpd.DataFrame({"A": [1, 3], "B": [2, 4]})
>>> df
A B
0 1 2
1 3 4
<BLANKLINE>
[2 rows x 2 columns]

Calculating the variance of each column (the default behavior without an explicit axis parameter).

>>> df.var()
A 2.0
B 2.0
dtype: Float64

Calculating the variance of each row.

>>> df.var(axis=1)
0 0.5
1 0.5
dtype: Float64


Args:
axis ({index (0), columns (1)}):
Axis for the function to be applied on.
Expand All @@ -2897,10 +2972,36 @@ def var(self, axis=0, *, numeric_only: bool = False):
raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)

def skew(self, *, numeric_only: bool = False):
"""Return unbiased skew over requested axis.
"""Return unbiased skew over columns.

Normalized by N-1.

**Examples:**

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

>>> df = bpd.DataFrame({'A': [1, 2, 3, 4, 5],
... 'B': [5, 4, 3, 2, 1],
... 'C': [2, 2, 3, 2, 2]})
>>> df
A B C
0 1 5 2
1 2 4 2
2 3 3 3
3 4 2 2
4 5 1 2
<BLANKLINE>
[5 rows x 3 columns]

Calculating the skewness of each column.

>>> df.skew()
A 0.0
B 0.0
C 2.236068
dtype: Float64

Args:
numeric_only (bool, default False):
Include only float, int, boolean columns.
Expand Down