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docs: add artithmetic df sample code #153

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Oct 30, 2023
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docs: add artithmetic df sample code #153

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81e9816
docs: add artithmetic df sample code
ashleyxuu Oct 21, 2023
8634e97
fix: address comments
ashleyxuu Oct 27, 2023
5e95a01
Merge branch 'main' into ashleyxu-add-runnable-code-df-methods
ashleyxuu Oct 28, 2023
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4 changes: 2 additions & 2 deletions bigframes/session/__init__.py
View file
Original file line number Diff line number Diff line change
@@ -352,7 +352,7 @@ def read_gbq_query(
>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

Simple query input:
Simple query input:

>>> df = bpd.read_gbq_query('''
... SELECT
@@ -368,7 +368,7 @@ def read_gbq_query(
<BLANKLINE>
[2 rows x 3 columns]

Preserve ordering in a query input.
Preserve ordering in a query input.

>>> df = bpd.read_gbq_query('''
... SELECT
494 changes: 490 additions & 4 deletions third_party/bigframes_vendored/pandas/core/frame.py
View file
Original file line number Diff line number Diff line change
@@ -697,6 +697,7 @@ def align(
Join method is specified for each axis Index.
Args:
other (DataFrame or Series):
join ({{'outer', 'inner', 'left', 'right'}}, default 'outer'):
@@ -978,9 +979,9 @@ def sort_values(
Sort ascending vs. descending. Specify list for multiple sort
orders. If this is a list of bools, must match the length of
the by.
kind (str, default `quicksort`):
Choice of sorting algorithm. Accepts 'quicksort’, ‘mergesort’,
‘heapsort’, ‘stable’. Ignored except when determining whether to
kind (str, default 'quicksort'):
Choice of sorting algorithm. Accepts 'quicksort', 'mergesort',
'heapsort', 'stable'. Ignored except when determining whether to
sort stably. 'mergesort' or 'stable' will result in stable reorder.
na_position ({'first', 'last'}, default `last`):
``{'first', 'last'}``, default 'last' Puts NaNs at the beginning
@@ -1014,6 +1015,29 @@ def eq(self, other, axis: str | int = "columns") -> DataFrame:
Equivalent to `==`, `!=`, `<=`, `<`, `>=`, `>` with support to choose axis
(rows or columns) and level for comparison.
**Examples:**
>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
You can use method name:
>>> df = bpd.DataFrame({'angles': [0, 3, 4],
... 'degrees': [360, 180, 360]},
... index=['circle', 'triangle', 'rectangle'])
>>> df["degrees"].eq(360)
circle True
triangle False
rectangle True
Name: degrees, dtype: boolean
You can also use arithmetic operator ``==``:
>>> df["degrees"] == 360
circle True
triangle False
rectangle True
Name: degrees, dtype: boolean
Args:
other (scalar, sequence, Series, or DataFrame):
Any single or multiple element data structure, or list-like object.
@@ -1036,6 +1060,30 @@ def ne(self, other, axis: str | int = "columns") -> DataFrame:
Equivalent to `==`, `!=`, `<=`, `<`, `>=`, `>` with support to choose axis
(rows or columns) and level for comparison.
**Examples:**
>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
You can use method name:
>>> df = bpd.DataFrame({'angles': [0, 3, 4],
... 'degrees': [360, 180, 360]},
... index=['circle', 'triangle', 'rectangle'])
>>> df["degrees"].ne(360)
circle False
triangle True
rectangle False
Name: degrees, dtype: boolean
You can also use arithmetic operator ``!=``:
>>> df["degrees"] != 360
circle False
triangle True
rectangle False
Name: degrees, dtype: boolean
Args:
other (scalar, sequence, Series, or DataFrame):
Any single or multiple element data structure, or list-like object.
@@ -1061,6 +1109,30 @@ def le(self, other, axis: str | int = "columns") -> DataFrame:
floating point columns are considered different
(i.e. `NaN` != `NaN`).
**Examples:**
>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
You can use method name:
>>> df = bpd.DataFrame({'angles': [0, 3, 4],
... 'degrees': [360, 180, 360]},
... index=['circle', 'triangle', 'rectangle'])
>>> df["degrees"].le(180)
circle False
triangle True
rectangle False
Name: degrees, dtype: boolean
You can also use arithmetic operator ``<=``:
>>> df["degrees"] <= 180
circle False
triangle True
rectangle False
Name: degrees, dtype: boolean
Args:
other (scalar, sequence, Series, or DataFrame):
Any single or multiple element data structure, or list-like object.
@@ -1087,6 +1159,30 @@ def lt(self, other, axis: str | int = "columns") -> DataFrame:
floating point columns are considered different
(i.e. `NaN` != `NaN`).
**Examples:**
>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
You can use method name:
>>> df = bpd.DataFrame({'angles': [0, 3, 4],
... 'degrees': [360, 180, 360]},
... index=['circle', 'triangle', 'rectangle'])
>>> df["degrees"].lt(180)
circle False
triangle False
rectangle False
Name: degrees, dtype: boolean
You can also use arithmetic operator ``<``:
>>> df["degrees"] < 180
circle False
triangle False
rectangle False
Name: degrees, dtype: boolean
Args:
other (scalar, sequence, Series, or DataFrame):
Any single or multiple element data structure, or list-like object.
@@ -1113,6 +1209,30 @@ def ge(self, other, axis: str | int = "columns") -> DataFrame:
floating point columns are considered different
(i.e. `NaN` != `NaN`).
**Examples:**
>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
You can use method name:
>>> df = bpd.DataFrame({'angles': [0, 3, 4],
... 'degrees': [360, 180, 360]},
... index=['circle', 'triangle', 'rectangle'])
>>> df["degrees"].ge(360)
circle True
triangle False
rectangle True
Name: degrees, dtype: boolean
You can also use arithmetic operator ``>=``:
>>> df["degrees"] >= 360
circle True
triangle False
rectangle True
Name: degrees, dtype: boolean
Args:
other (scalar, sequence, Series, or DataFrame):
Any single or multiple element data structure, or list-like object.
@@ -1139,6 +1259,28 @@ def gt(self, other, axis: str | int = "columns") -> DataFrame:
floating point columns are considered different
(i.e. `NaN` != `NaN`).
**Examples:**
>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> df = bpd.DataFrame({'angles': [0, 3, 4],
... 'degrees': [360, 180, 360]},
... index=['circle', 'triangle', 'rectangle'])
>>> df["degrees"].gt(360)
circle False
triangle False
rectangle False
Name: degrees, dtype: boolean
You can also use arithmetic operator ``>``:
>>> df["degrees"] > 360
circle False
triangle False
rectangle False
Name: degrees, dtype: boolean
Args:
other (scalar, sequence, Series, or DataFrame):
Any single or multiple element data structure, or list-like object.
@@ -1162,6 +1304,32 @@ def add(self, other, axis: str | int = "columns") -> DataFrame:
.. note::
Mismatched indices will be unioned together.
**Examples:**
>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> df = bpd.DataFrame({
... 'A': [1, 2, 3],
... 'B': [4, 5, 6],
... })
You can use method name:
>>> df['A'].add(df['B'])
0 5
1 7
2 9
dtype: Int64
You can also use arithmetic operator ``+``:
>>> df['A'] + (df['B'])
0 5
1 7
2 9
dtype: Int64
Args:
other (float, int, or Series):
Any single or multiple element data structure, or list-like object.
@@ -1185,6 +1353,32 @@ def sub(self, other, axis: str | int = "columns") -> DataFrame:
.. note::
Mismatched indices will be unioned together.
**Examples:**
>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> df = bpd.DataFrame({
... 'A': [1, 2, 3],
... 'B': [4, 5, 6],
... })
You can use method name:
>>> df['A'].sub(df['B'])
0 -3
1 -3
2 -3
dtype: Int64
You can also use arithmetic operator ``-``:
>>> df['A'] - (df['B'])
0 -3
1 -3
2 -3
dtype: Int64
Args:
other (float, int, or Series):
Any single or multiple element data structure, or list-like object.
@@ -1208,6 +1402,29 @@ def rsub(self, other, axis: str | int = "columns") -> DataFrame:
.. note::
Mismatched indices will be unioned together.
**Examples:**
>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> df = bpd.DataFrame({
... 'A': [1, 2, 3],
... 'B': [4, 5, 6],
... })
>>> df['A'].rsub(df['B'])
0 3
1 3
2 3
dtype: Int64
It's equivalent to using arithmetic operator: ``-``:
>>> df['B'] - (df['A'])
0 3
1 3
2 3
dtype: Int64
Args:
other (float, int, or Series):
Any single or multiple element data structure, or list-like object.
@@ -1231,6 +1448,32 @@ def mul(self, other, axis: str | int = "columns") -> DataFrame:
.. note::
Mismatched indices will be unioned together.
**Examples:**
>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> df = bpd.DataFrame({
... 'A': [1, 2, 3],
... 'B': [4, 5, 6],
... })
You can use method name:
>>> df['A'].mul(df['B'])
0 4
1 10
2 18
dtype: Int64
You can also use arithmetic operator ``*``:
>>> df['A'] * (df['B'])
0 4
1 10
2 18
dtype: Int64
Args:
other (float, int, or Series):
Any single or multiple element data structure, or list-like object.
@@ -1254,6 +1497,32 @@ def truediv(self, other, axis: str | int = "columns") -> DataFrame:
.. note::
Mismatched indices will be unioned together.
**Examples:**
>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> df = bpd.DataFrame({
... 'A': [1, 2, 3],
... 'B': [4, 5, 6],
... })
You can use method name:
>>> df['A'].truediv(df['B'])
0 0.25
1 0.4
2 0.5
dtype: Float64
You can also use arithmetic operator ``/``:
>>> df['A'] / (df['B'])
0 0.25
1 0.4
2 0.5
dtype: Float64
Args:
other (float, int, or Series):
Any single or multiple element data structure, or list-like object.
@@ -1277,6 +1546,29 @@ def rtruediv(self, other, axis: str | int = "columns") -> DataFrame:
.. note::
Mismatched indices will be unioned together.
**Examples:**
>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> df = bpd.DataFrame({
... 'A': [1, 2, 3],
... 'B': [4, 5, 6],
... })
>>> df['A'].rtruediv(df['B'])
0 4.0
1 2.5
2 2.0
dtype: Float64
It's equivalent to using arithmetic operator: ``/``:
>>> df['B'] / (df['A'])
0 4.0
1 2.5
2 2.0
dtype: Float64
Args:
other (float, int, or Series):
Any single or multiple element data structure, or list-like object.
@@ -1300,6 +1592,32 @@ def floordiv(self, other, axis: str | int = "columns") -> DataFrame:
.. note::
Mismatched indices will be unioned together.
**Examples:**
>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> df = bpd.DataFrame({
... 'A': [1, 2, 3],
... 'B': [4, 5, 6],
... })
You can use method name:
>>> df['A'].floordiv(df['B'])
0 0
1 0
2 0
dtype: Int64
You can also use arithmetic operator ``//``:
>>> df['A'] // (df['B'])
0 0
1 0
2 0
dtype: Int64
Args:
other (float, int, or Series):
Any single or multiple element data structure, or list-like object.
@@ -1323,6 +1641,29 @@ def rfloordiv(self, other, axis: str | int = "columns") -> DataFrame:
.. note::
Mismatched indices will be unioned together.
**Examples:**
>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> df = bpd.DataFrame({
... 'A': [1, 2, 3],
... 'B': [4, 5, 6],
... })
>>> df['A'].rfloordiv(df['B'])
0 4
1 2
2 2
dtype: Int64
It's equivalent to using arithmetic operator: ``//``:
>>> df['B'] // (df['A'])
0 4
1 2
2 2
dtype: Int64
Args:
other (float, int, or Series):
Any single or multiple element data structure, or list-like object.
@@ -1346,6 +1687,32 @@ def mod(self, other, axis: str | int = "columns") -> DataFrame:
.. note::
Mismatched indices will be unioned together.
**Examples:**
>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> df = bpd.DataFrame({
... 'A': [1, 2, 3],
... 'B': [4, 5, 6],
... })
You can use method name:
>>> df['A'].mod(df['B'])
0 1
1 2
2 3
dtype: Int64
You can also use arithmetic operator ``%``:
>>> df['A'] % (df['B'])
0 1
1 2
2 3
dtype: Int64
Args:
other:
Any single or multiple element data structure, or list-like object.
@@ -1369,6 +1736,29 @@ def rmod(self, other, axis: str | int = "columns") -> DataFrame:
.. note::
Mismatched indices will be unioned together.
**Examples:**
>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> df = bpd.DataFrame({
... 'A': [1, 2, 3],
... 'B': [4, 5, 6],
... })
>>> df['A'].rmod(df['B'])
0 0
1 1
2 0
dtype: Int64
It's equivalent to using arithmetic operator: ``%``:
>>> df['B'] % (df['A'])
0 0
1 1
2 0
dtype: Int64
Args:
other (float, int, or Series):
Any single or multiple element data structure, or list-like object.
@@ -1382,7 +1772,7 @@ def rmod(self, other, axis: str | int = "columns") -> DataFrame:
raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)

def pow(self, other, axis: str | int = "columns") -> DataFrame:
"""Get Exponential power of dataframe and other, element-wise (binary operator `pow`).
"""Get Exponential power of dataframe and other, element-wise (binary operator `**`).
Equivalent to ``dataframe ** other``, but with support to substitute a fill_value
for missing data in one of the inputs. With reverse version, `rpow`.
@@ -1393,6 +1783,32 @@ def pow(self, other, axis: str | int = "columns") -> DataFrame:
.. note::
Mismatched indices will be unioned together.
**Examples:**
>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> df = bpd.DataFrame({
... 'A': [1, 2, 3],
... 'B': [4, 5, 6],
... })
You can use method name:
>>> df['A'].pow(df['B'])
0 1
1 32
2 729
dtype: Int64
You can also use arithmetic operator ``**``:
>>> df['A'] ** (df['B'])
0 1
1 32
2 729
dtype: Int64
Args:
other (float, int, or Series):
Any single or multiple element data structure, or list-like object.
@@ -1417,6 +1833,29 @@ def rpow(self, other, axis: str | int = "columns") -> DataFrame:
.. note::
Mismatched indices will be unioned together.
**Examples:**
>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> df = bpd.DataFrame({
... 'A': [1, 2, 3],
... 'B': [4, 5, 6],
... })
>>> df['A'].rpow(df['B'])
0 4
1 25
2 216
dtype: Int64
It's equivalent to using arithmetic operator: ``**``:
>>> df['B'] ** (df['A'])
0 4
1 25
2 216
dtype: Int64
Args:
other (float, int, or Series):
Any single or multiple element data structure, or list-like object.
@@ -1438,6 +1877,21 @@ def combine(
to element-wise combine columns. The row and column indexes of the
resulting DataFrame will be the union of the two.
**Examples:**
>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> df1 = bpd.DataFrame({'A': [0, 0], 'B': [4, 4]})
>>> df2 = bpd.DataFrame({'A': [1, 1], 'B': [3, 3]})
>>> take_smaller = lambda s1, s2: s1 if s1.sum() < s2.sum() else s2
>>> df1.combine(df2, take_smaller)
A B
0 0 3
1 0 3
<BLANKLINE>
[2 rows x 2 columns]
Args:
other (DataFrame):
The DataFrame to merge column-wise.
@@ -1468,6 +1922,20 @@ def combine_first(self, other) -> DataFrame:
second.loc[index, col] are not missing values, upon calling
first.combine_first(second).
**Examples:**
>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> df1 = bpd.DataFrame({'A': [None, 0], 'B': [None, 4]})
>>> df2 = bpd.DataFrame({'A': [1, 1], 'B': [3, 3]})
>>> df1.combine_first(df2)
A B
0 1.0 3.0
1 0.0 4.0
<BLANKLINE>
[2 rows x 2 columns]
Args:
other (DataFrame):
Provided DataFrame to use to fill null values.
@@ -1485,6 +1953,24 @@ def update(
Aligns on indices. There is no return value.
**Examples:**
>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> df = bpd.DataFrame({'A': [1, 2, 3],
... 'B': [400, 500, 600]})
>>> new_df = bpd.DataFrame({'B': [4, 5, 6],
... 'C': [7, 8, 9]})
>>> df.update(new_df)
>>> df
A B
0 1 4
1 2 5
2 3 6
<BLANKLINE>
[3 rows x 2 columns]
Args:
other (DataFrame, or object coercible into a DataFrame):
Should have at least one matching index/column label