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docs: add examples for dataframe.kurt, dataframe.std, dataframe.count #232

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Merged
merged 6 commits into from
Nov 28, 2023
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docs: add examples for dataframe.kurt, dataframe.std, dataframe.count #232

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docs: add examples for dataframe.kurt, dataframe.std, dataframe.count
Genesis929 Nov 27, 2023
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update count example
Genesis929 Nov 27, 2023
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update count example
Genesis929 Nov 27, 2023
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update examples
Genesis929 Nov 27, 2023
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update . to :
Genesis929 Nov 28, 2023
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Merge branch 'main' into huanc-stats-docstring-3
Genesis929 Nov 28, 2023
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96 changes: 87 additions & 9 deletions 96 third_party/bigframes_vendored/pandas/core/frame.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2597,14 +2597,14 @@ def any(self, *, axis=0, bool_only: bool = False):
<BLANKLINE>
[2 rows x 2 columns]

Checking if each column contains at least one True element (the default behavior without an explicit axis parameter).
Checking if each column contains at least one True element(the default behavior without an explicit axis parameter):

>>> df.any()
A True
B False
dtype: boolean

Checking if each row contains at least one True element.
Checking if each row contains at least one True element:

>>> df.any(axis=1)
0 True
Expand Down Expand Up @@ -2644,14 +2644,14 @@ def all(self, axis=0, *, bool_only: bool = False):
<BLANKLINE>
[2 rows x 2 columns]

Checking if all values in each column are True (the default behavior without an explicit axis parameter).
Checking if all values in each column are True(the default behavior without an explicit axis parameter):

>>> df.all()
A True
B False
dtype: boolean

Checking across rows to see if all values are True.
Checking across rows to see if all values are True:

>>> df.all(axis=1)
0 False
Expand Down Expand Up @@ -2688,14 +2688,14 @@ def prod(self, axis=0, *, numeric_only: bool = False):
<BLANKLINE>
[3 rows x 2 columns]

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

>>> df.prod()
A 6.0
B 160.875
dtype: Float64

Calculating the product of each row.
Calculating the product of each row:

>>> df.prod(axis=1)
0 4.5
Expand Down Expand Up @@ -2911,11 +2911,37 @@ def skew(self, *, numeric_only: bool = False):
raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)

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

Kurtosis obtained using Fisher's definition of
kurtosis (kurtosis of normal == 0.0). 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": [3, 4, 3, 2, 1],
... "C": [2, 2, 3, 2, 2]})
>>> df
A B C
0 1 3 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 kurtosis value of each column:

>>> df.kurt()
A -1.2
B -0.177515
C 5.0
dtype: Float64

Args:
numeric_only (bool, default False):
Include only float, int, boolean columns.
Expand All @@ -2926,10 +2952,36 @@ def kurt(self, *, numeric_only: bool = False):
raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)

def std(self, *, numeric_only: bool = False):
"""Return sample standard deviation over requested axis.
"""Return sample standard deviation over columns.

Normalized by N-1 by default.

**Examples:**

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

>>> df = bpd.DataFrame({"A": [1, 2, 3, 4, 5],
... "B": [3, 4, 3, 2, 1],
... "C": [2, 2, 3, 2, 2]})
>>> df
A B C
0 1 3 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 standard deviation of each column:

>>> df.std()
A 1.581139
B 1.140175
C 0.447214
dtype: Float64

Args:
numeric_only (bool. default False):
Default False. Include only float, int, boolean columns.
Expand All @@ -2941,11 +2993,37 @@ def std(self, *, numeric_only: bool = False):

def count(self, *, numeric_only: bool = False):
"""
Count non-NA cells for each column or row.
Count non-NA cells for each column.

The values `None`, `NaN`, `NaT`, and optionally `numpy.inf` (depending
on `pandas.options.mode.use_inf_as_na`) are considered NA.

**Examples:**

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

>>> df = bpd.DataFrame({"A": [1, None, 3, 4, 5],
... "B": [1, 2, 3, 4, 5],
... "C": [None, 3.5, None, 4.5, 5.0]})
>>> df
A B C
0 1.0 1 <NA>
1 <NA> 2 3.5
2 3.0 3 <NA>
3 4.0 4 4.5
4 5.0 5 5.0
<BLANKLINE>
[5 rows x 3 columns]

Counting non-NA values for each column:

>>> df.count()
A 4.0
B 5.0
C 3.0
dtype: Float64

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