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docs: add example for dataframe.melt, dataframe.pivot, dataframe.stac… (#252)
* docs: add example for dataframe.melt, dataframe.pivot, dataframe.stack, dataframe.unstack * remove empty line * docstring fix * spacing update * docs: correct the params rendering for `ml.remote` and `ml.ensemble` modules (#248) Thank you for opening a Pull Request! Before submitting your PR, there are a few things you can do to make sure it goes smoothly: - [ ] Make sure to open an issue as a [bug/issue](https://togithub.com/googleapis/python-bigquery-dataframes/issues/new/choose) before writing your code! That way we can discuss the change, evaluate designs, and agree on the general idea - [ ] Ensure the tests and linter pass - [ ] Code coverage does not decrease (if any source code was changed) - [x] Appropriate docs were updated (if necessary) - `ensemble.RandomForestClassifier`: https://screenshot.googleplex.com/4Q88xgdm5hkaYXu - `ensemble.RandomForestRegressor`: https://screenshot.googleplex.com/3CU6pJBjYHQvnDo - `remote.VertexAIModel`: https://screenshot.googleplex.com/8SL2max6GfPMwFe Fixes internal issue 314150462 🦕 * docs: add examples for dataframe.nunique, dataframe.diff, dataframe.a… (#251) * docs: add examples for dataframe.nunique, dataframe.diff, dataframe.agg, dataframe.describe * update spacing * update ordering * docs: Fix return annotation in API docstrings (#253) Thank you for opening a Pull Request! Before submitting your PR, there are a few things you can do to make sure it goes smoothly: - [ ] Make sure to open an issue as a [bug/issue](https://togithub.com/googleapis/python-bigquery-dataframes/issues/new/choose) before writing your code! That way we can discuss the change, evaluate designs, and agree on the general idea - [ ] Ensure the tests and linter pass - [ ] Code coverage does not decrease (if any source code was changed) - [ ] Appropriate docs were updated (if necessary) Fixes internal issue 314367409 🦕 * feat: add nunique method to Series/DataFrameGroupby (#256) Thank you for opening a Pull Request! Before submitting your PR, there are a few things you can do to make sure it goes smoothly: - [ ] Make sure to open an issue as a [bug/issue](https://togithub.com/googleapis/python-bigquery-dataframes/issues/new/choose) before writing your code! That way we can discuss the change, evaluate designs, and agree on the general idea - [ ] Ensure the tests and linter pass - [ ] Code coverage does not decrease (if any source code was changed) - [ ] Appropriate docs were updated (if necessary) Fixes #<issue_number_goes_here> 🦕 * docs: add example for dataframe.melt, dataframe.pivot, dataframe.stack, dataframe.unstack * docstring fix --------- Co-authored-by: Ashley Xu <[email protected]> Co-authored-by: Shobhit Singh <[email protected]> Co-authored-by: TrevorBergeron <[email protected]>
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‎third_party/bigframes_vendored/pandas/core/frame.py

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Original file line numberDiff line numberDiff line change
@@ -3414,18 +3414,75 @@ def melt(self, id_vars, value_vars, var_name, value_name):
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the row axis, leaving just two non-identifier columns, 'variable' and
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'value'.
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Parameters
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----------
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id_vars (tuple, list, or ndarray, optional):
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Column(s) to use as identifier variables.
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value_vars (tuple, list, or ndarray, optional):
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Column(s) to unpivot. If not specified, uses all columns that
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are not set as `id_vars`.
3424-
var_name (scalar):
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Name to use for the 'variable' column. If None it uses
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``frame.columns.name`` or 'variable'.
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value_name (scalar, default 'value'):
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Name to use for the 'value' column.
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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({"A": [1, None, 3, 4, 5],
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... "B": [1, 2, 3, 4, 5],
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... "C": [None, 3.5, None, 4.5, 5.0]})
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>>> df
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A B C
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0 1.0 1 <NA>
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1 <NA> 2 3.5
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2 3.0 3 <NA>
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3 4.0 4 4.5
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4 5.0 5 5.0
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<BLANKLINE>
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[5 rows x 3 columns]
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Using `melt` without optional arguments:
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>>> df.melt()
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variable value
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0 A 1.0
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1 A <NA>
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2 A 3.0
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3 A 4.0
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4 A 5.0
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5 B 1.0
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6 B 2.0
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7 B 3.0
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8 B 4.0
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9 B 5.0
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10 C <NA>
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11 C 3.5
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12 C <NA>
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13 C 4.5
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14 C 5.0
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<BLANKLINE>
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[15 rows x 2 columns]
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Using `melt` with `id_vars` and `value_vars`:
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>>> df.melt(id_vars='A', value_vars=['B', 'C'])
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A variable value
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0 1.0 B 1
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1 <NA> B 2
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2 3.0 B 3
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3 4.0 B 4
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4 5.0 B 5
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5 1.0 C <NA>
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6 <NA> C 3
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7 3.0 C <NA>
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8 4.0 C 4
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9 5.0 C 5
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<BLANKLINE>
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[10 rows x 3 columns]
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Args:
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id_vars (tuple, list, or ndarray, optional):
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Column(s) to use as identifier variables.
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value_vars (tuple, list, or ndarray, optional):
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Column(s) to unpivot. If not specified, uses all columns that
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are not set as `id_vars`.
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var_name (scalar):
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Name to use for the 'variable' column. If None it uses
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``frame.columns.name`` or 'variable'.
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value_name (scalar, default 'value'):
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Name to use for the 'value' column.
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Returns:
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DataFrame: Unpivoted DataFrame.
@@ -3757,6 +3814,52 @@ def pivot(self, *, columns, index=None, values=None):
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do not together uniquely identify input rows, the output will be
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silently non-deterministic.
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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({
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... "foo": ["one", "one", "one", "two", "two"],
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... "bar": ["A", "B", "C", "A", "B"],
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... "baz": [1, 2, 3, 4, 5],
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... "zoo": ['x', 'y', 'z', 'q', 'w']
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... })
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>>> df
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foo bar baz zoo
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0 one A 1 x
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1 one B 2 y
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2 one C 3 z
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3 two A 4 q
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4 two B 5 w
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<BLANKLINE>
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[5 rows x 4 columns]
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Using `pivot` without optional arguments:
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3841+
>>> df.pivot(columns='foo')
3842+
bar baz zoo
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foo one two one two one two
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0 A <NA> 1 <NA> x <NA>
3845+
1 B <NA> 2 <NA> y <NA>
3846+
2 C <NA> 3 <NA> z <NA>
3847+
3 <NA> A <NA> 4 <NA> q
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4 <NA> B <NA> 5 <NA> w
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<BLANKLINE>
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[5 rows x 6 columns]
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Using `pivot` with `index` and `values`:
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3854+
>>> df.pivot(columns='foo', index='bar', values='baz')
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foo one two
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bar
3857+
A 1 4
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B 2 5
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C 3 <NA>
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<BLANKLINE>
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[3 rows x 2 columns]
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37603863
Args:
37613864
columns (str or object or a list of str):
37623865
Column to use to make new frame's columns.
@@ -3774,7 +3877,7 @@ def pivot(self, *, columns, index=None, values=None):
37743877
"""
37753878
raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
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3777-
def stack(self):
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def stack(self, level=-1):
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"""
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Stack the prescribed level(s) from columns to index.
37803883
@@ -3792,12 +3895,36 @@ def stack(self):
37923895
BigQuery DataFrames does not support stack operations that would
37933896
combine columns of different dtypes.
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**Example:**
3899+
3900+
>>> import bigframes.pandas as bpd
3901+
>>> bpd.options.display.progress_bar = None
3902+
3903+
>>> df = bpd.DataFrame({'A': [1, 3], 'B': [2, 4]}, index=['foo', 'bar'])
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>>> df
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A B
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foo 1 2
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bar 3 4
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<BLANKLINE>
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[2 rows x 2 columns]
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>>> df.stack()
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foo A 1
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B 2
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bar A 3
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B 4
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dtype: Int64
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3918+
Args:
3919+
level (int, str, or list of these, default -1 (last level)):
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Level(s) to stack from the column axis onto the index axis.
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Returns:
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DataFrame or Series: Stacked dataframe or series.
37973924
"""
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raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)
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3800-
def unstack(self):
3927+
def unstack(self, level=-1):
38013928
"""
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Pivot a level of the (necessarily hierarchical) index labels.
38033930
@@ -3807,6 +3934,30 @@ def unstack(self):
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If the index is not a MultiIndex, the output will be a Series
38083935
(the analogue of stack when the columns are not a MultiIndex).
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3937+
**Example:**
3938+
3939+
>>> import bigframes.pandas as bpd
3940+
>>> bpd.options.display.progress_bar = None
3941+
3942+
>>> df = bpd.DataFrame({'A': [1, 3], 'B': [2, 4]}, index=['foo', 'bar'])
3943+
>>> df
3944+
A B
3945+
foo 1 2
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bar 3 4
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<BLANKLINE>
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[2 rows x 2 columns]
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3950+
>>> df.unstack()
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A foo 1
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bar 3
3953+
B foo 2
3954+
bar 4
3955+
dtype: Int64
3956+
3957+
Args:
3958+
level (int, str, or list of these, default -1 (last level)):
3959+
Level(s) of index to unstack, can pass level name.
3960+
38103961
Returns:
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DataFrame or Series: DataFrame or Series.
38123963
"""

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