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docs: code samples for isna, isnull, dropna, isin (#289)
* docs: code samples for `isna`, `isnull`, `dropna`, `isin` * fix header alignment in rendering
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‎third_party/bigframes_vendored/pandas/core/frame.py‎

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@@ -1289,9 +1289,57 @@ def duplicated(self, subset=None, keep="first"):
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def dropna(
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self,
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*,
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axis: int | str = 0,
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how: str = "any",
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ignore_index=False,
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) -> DataFrame:
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"""Remove missing values.
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**Examples:**
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>>> import bigframes.pandas as bpd
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>>> bpd.options.display.progress_bar = None
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>>> df = bpd.DataFrame({"name": ['Alfred', 'Batman', 'Catwoman'],
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... "toy": [np.nan, 'Batmobile', 'Bullwhip'],
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... "born": [bpd.NA, "1940-04-25", bpd.NA]})
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>>> df
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name toy born
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0 Alfred <NA> <NA>
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1 Batman Batmobile 1940-04-25
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2 Catwoman Bullwhip <NA>
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<BLANKLINE>
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[3 rows x 3 columns]
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Drop the rows where at least one element is missing:
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>>> df.dropna()
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name toy born
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1 Batman Batmobile 1940-04-25
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<BLANKLINE>
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[1 rows x 3 columns]
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Drop the columns where at least one element is missing.
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>>> df.dropna(axis='columns')
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name
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0 Alfred
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1 Batman
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2 Catwoman
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<BLANKLINE>
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[3 rows x 1 columns]
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Drop the rows where all elements are missing:
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>>> df.dropna(how='all')
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name toy born
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0 Alfred <NA> <NA>
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1 Batman Batmobile 1940-04-25
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2 Catwoman Bullwhip <NA>
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<BLANKLINE>
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[3 rows x 3 columns]
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Args:
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axis ({0 or 'index', 1 or 'columns'}, default 'columns'):
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Determine if rows or columns which contain missing values are
@@ -1318,6 +1366,39 @@ def isin(self, values):
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"""
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Whether each element in the DataFrame is contained in values.
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**Examples:**
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>>> import bigframes.pandas as bpd
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>>> bpd.options.display.progress_bar = None
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>>> df = bpd.DataFrame({'num_legs': [2, 4], 'num_wings': [2, 0]},
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... index=['falcon', 'dog'])
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>>> df
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num_legs num_wings
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falcon 2 2
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dog 4 0
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<BLANKLINE>
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[2 rows x 2 columns]
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When ``values`` is a list check whether every value in the DataFrame is
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present in the list (which animals have 0 or 2 legs or wings).
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>>> df.isin([0, 2])
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num_legs num_wings
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falcon True True
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dog False True
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<BLANKLINE>
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[2 rows x 2 columns]
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When ``values`` is a dict, we can pass it to check for each column separately:
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>>> df.isin({'num_wings': [0, 3]})
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num_legs num_wings
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falcon False False
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dog False True
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<BLANKLINE>
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[2 rows x 2 columns]
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Args:
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values (iterable, or dict):
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The result will only be true at a location if all the

‎third_party/bigframes_vendored/pandas/core/generic.py‎

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@@ -499,6 +499,71 @@ def isna(self) -> NDFrame:
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False values. Characters such as empty strings ``''`` or
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:attr:`numpy.inf` are not considered NA values.
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**Examples:**
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>>> import bigframes.pandas as bpd
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>>> bpd.options.display.progress_bar = None
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>>> import numpy as np
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>>> df = bpd.DataFrame(dict(
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... age=[5, 6, np.nan],
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... born=[bpd.NA, "1940-04-25", "1940-04-25"],
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... name=['Alfred', 'Batman', ''],
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... toy=[None, 'Batmobile', 'Joker'],
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... ))
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>>> df
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age born name toy
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0 5.0 <NA> Alfred <NA>
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1 6.0 1940-04-25 Batman Batmobile
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2 <NA> 1940-04-25 Joker
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<BLANKLINE>
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[3 rows x 4 columns]
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Show which entries in a DataFrame are NA:
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>>> df.isna()
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age born name toy
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0 False True False True
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1 False False False False
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2 True False False False
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<BLANKLINE>
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[3 rows x 4 columns]
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>>> df.isnull()
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age born name toy
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0 False True False True
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1 False False False False
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2 True False False False
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<BLANKLINE>
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[3 rows x 4 columns]
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Show which entries in a Series are NA:
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>>> ser = bpd.Series([5, None, 6, np.nan, bpd.NA])
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>>> ser
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0 5.0
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1 <NA>
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2 6.0
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3 <NA>
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4 <NA>
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dtype: Float64
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>>> ser.isna()
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0 False
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1 True
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2 False
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3 True
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4 True
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dtype: boolean
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>>> ser.isnull()
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0 False
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1 True
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2 False
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3 True
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4 True
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dtype: boolean
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Returns:
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Mask of bool values for each element that indicates whether an
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element is an NA value.

‎third_party/bigframes_vendored/pandas/core/series.py‎

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@@ -1460,6 +1460,42 @@ def dropna(self, *, axis=0, inplace: bool = False, how=None) -> Series:
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"""
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Return a new Series with missing values removed.
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**Examples:**
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>>> import bigframes.pandas as bpd
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>>> bpd.options.display.progress_bar = None
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Drop NA values from a Series:
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>>> ser = bpd.Series([1., 2., np.nan])
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>>> ser
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0 1.0
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1 2.0
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2 <NA>
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dtype: Float64
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>>> ser.dropna()
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0 1.0
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1 2.0
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dtype: Float64
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Empty strings are not considered NA values. ``None`` is considered an NA value.
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>>> ser = bpd.Series(['2', bpd.NA, '', None, 'I stay'], dtype='object')
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>>> ser
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0 2
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1 <NA>
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2
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3 <NA>
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4 I stay
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dtype: string
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>>> ser.dropna()
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0 2
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2
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4 I stay
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dtype: string
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Args:
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axis (0 or 'index'):
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Unused. Parameter needed for compatibility with DataFrame.
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the same. That is, if any form of NaN is present in values, all forms
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of NaN in the series will be considered a match. (though pandas may not)
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**Examples:**
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>>> import bigframes.pandas as bpd
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>>> bpd.options.display.progress_bar = None
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>>> s = bpd.Series(['llama', 'cow', 'llama', 'beetle', 'llama',
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... 'hippo'], name='animal')
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>>> s
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0 llama
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1 cow
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2 llama
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3 beetle
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4 llama
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5 hippo
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Name: animal, dtype: string
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>>> s.isin(['cow', 'llama'])
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0 True
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1 True
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2 True
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3 False
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4 True
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5 False
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Name: animal, dtype: boolean
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Strings and integers are distinct and are therefore not comparable:
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>>> bpd.Series([1]).isin(['1'])
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0 False
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dtype: boolean
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>>> bpd.Series([1.1]).isin(['1.1'])
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0 False
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dtype: boolean
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Args:
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values (list-like):
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The sequence of values to test. Passing in a single string will raise a

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