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Nov 17, 2023
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docs: code samples for Series.where and Series.mask #217

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6600cda
docs: code samples for `Series.where` and `Series.mask`
shobsi Nov 17, 2023
6f14a8d
add examples with other as Series for `where` and `mask`
shobsi Nov 17, 2023
e468634
fix typo "the the" -> "the"
shobsi Nov 17, 2023
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114 changes: 114 additions & 0 deletions third_party/bigframes_vendored/pandas/core/series.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1696,6 +1696,49 @@ def kurt(self):
def where(self, cond, other):
"""Replace values where the condition is False.

**Examples:**

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

>>> s = bpd.Series([10, 11, 12, 13, 14])
>>> s
0 10
1 11
2 12
3 13
4 14
dtype: Int64

You can filter the values in the Series based on a condition. The values
matching the condition would be kept, and not matching would be replaced.
The default replacement value is ``NA``.

>>> s.where(s % 2 == 0)
0 10
1 <NA>
2 12
3 <NA>
4 14
dtype: Int64

You can specify a custom replacement value for non-matching values.

>>> s.where(s % 2 == 0, -1)
0 10
1 -1
2 12
3 -1
4 14
dtype: Int64
>>> s.where(s % 2 == 0, 100*s)
0 10
1 1100
2 12
3 1300
4 14
dtype: Int64

Args:
cond (bool Series/DataFrame, array-like, or callable):
Where cond is True, keep the original value. Where False, replace
Expand All @@ -1720,6 +1763,77 @@ def where(self, cond, other):
def mask(self, cond, other):
"""Replace values where the condition is True.

**Examples:**

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

>>> s = bpd.Series([10, 11, 12, 13, 14])
>>> s
0 10
1 11
2 12
3 13
4 14
dtype: Int64

You can mask the values in the Series based on a condition. The values
matching the condition would be masked.

>>> s.mask(s % 2 == 0)
0 <NA>
1 11
2 <NA>
3 13
4 <NA>
dtype: Int64

You can specify a custom mask value.

>>> s.mask(s % 2 == 0, -1)
0 -1
1 11
2 -1
3 13
4 -1
dtype: Int64
>>> s.mask(s % 2 == 0, 100*s)
0 1000
1 11
2 1200
3 13
4 1400
dtype: Int64

You can also use a remote function to evaluate the mask condition. This
is useful in situation such as the following, where the mask
condition is evaluated based on a complicated business logic which cannot
be expressed in form of a Series.

>>> @bpd.remote_function([str], bool, reuse=False)
... def should_mask(name):
... hash = 0
... for char_ in name:
... hash += ord(char_)
... return hash % 2 == 0

>>> s = bpd.Series(["Alice", "Bob", "Caroline"])
>>> s
0 Alice
1 Bob
2 Caroline
dtype: string
>>> s.mask(should_mask)
0 <NA>
1 Bob
2 Caroline
dtype: string
>>> s.mask(should_mask, "REDACTED")
0 REDACTED
1 Bob
2 Caroline
dtype: string

Args:
cond (bool Series/DataFrame, array-like, or callable):
Where cond is False, keep the original value. Where True, replace
Expand Down