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docs: add example for dataframe.melt, dataframe.pivot, dataframe.stac… #252

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docs: add example for dataframe.melt, dataframe.pivot, dataframe.stac…
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179 changes: 165 additions & 14 deletions third_party/bigframes_vendored/pandas/core/frame.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3414,18 +3414,75 @@ def melt(self, id_vars, value_vars, var_name, value_name):
the row axis, leaving just two non-identifier columns, 'variable' and
'value'.

Parameters
----------
id_vars (tuple, list, or ndarray, optional):
Column(s) to use as identifier variables.
value_vars (tuple, list, or ndarray, optional):
Column(s) to unpivot. If not specified, uses all columns that
are not set as `id_vars`.
var_name (scalar):
Name to use for the 'variable' column. If None it uses
``frame.columns.name`` or 'variable'.
value_name (scalar, default 'value'):
Name to use for the 'value' column.
**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]

Using `melt` without optional arguments:

>>> df.melt()
variable value
0 A 1.0
1 A <NA>
2 A 3.0
3 A 4.0
4 A 5.0
5 B 1.0
6 B 2.0
7 B 3.0
8 B 4.0
9 B 5.0
10 C <NA>
11 C 3.5
12 C <NA>
13 C 4.5
14 C 5.0
<BLANKLINE>
[15 rows x 2 columns]

Using `melt` with `id_vars` and `value_vars`:

>>> df.melt(id_vars='A', value_vars=['B', 'C'])
A variable value
0 1.0 B 1
1 <NA> B 2
2 3.0 B 3
3 4.0 B 4
4 5.0 B 5
5 1.0 C <NA>
6 <NA> C 3
7 3.0 C <NA>
8 4.0 C 4
9 5.0 C 5
<BLANKLINE>
[10 rows x 3 columns]


Args:
id_vars (tuple, list, or ndarray, optional):
Column(s) to use as identifier variables.
value_vars (tuple, list, or ndarray, optional):
Column(s) to unpivot. If not specified, uses all columns that
are not set as `id_vars`.
var_name (scalar):
Name to use for the 'variable' column. If None it uses
``frame.columns.name`` or 'variable'.
value_name (scalar, default 'value'):
Name to use for the 'value' column.

Returns:
DataFrame: Unpivoted DataFrame.
Expand Down Expand Up @@ -3757,6 +3814,52 @@ def pivot(self, *, columns, index=None, values=None):
do not together uniquely identify input rows, the output will be
silently non-deterministic.

**Examples:**

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

>>> df = bpd.DataFrame({
... "foo": ["one", "one", "one", "two", "two"],
... "bar": ["A", "B", "C", "A", "B"],
... "baz": [1, 2, 3, 4, 5],
... "zoo": ['x', 'y', 'z', 'q', 'w']
... })

>>> df
foo bar baz zoo
0 one A 1 x
1 one B 2 y
2 one C 3 z
3 two A 4 q
4 two B 5 w
<BLANKLINE>
[5 rows x 4 columns]

Using `pivot` without optional arguments:

>>> df.pivot(columns='foo')
bar baz zoo
foo one two one two one two
0 A <NA> 1 <NA> x <NA>
1 B <NA> 2 <NA> y <NA>
2 C <NA> 3 <NA> z <NA>
3 <NA> A <NA> 4 <NA> q
4 <NA> B <NA> 5 <NA> w
<BLANKLINE>
[5 rows x 6 columns]

Using `pivot` with `index` and `values`:

>>> df.pivot(columns='foo', index='bar', values='baz')
foo one two
bar
A 1 4
B 2 5
C 3 <NA>
<BLANKLINE>
[3 rows x 2 columns]

Args:
columns (str or object or a list of str):
Column to use to make new frame's columns.
Expand All @@ -3774,7 +3877,7 @@ def pivot(self, *, columns, index=None, values=None):
"""
raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)

def stack(self):
def stack(self, level=-1):
"""
Stack the prescribed level(s) from columns to index.

Expand All @@ -3792,12 +3895,36 @@ def stack(self):
BigQuery DataFrames does not support stack operations that would
combine columns of different dtypes.

**Example:**

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

>>> df = bpd.DataFrame({'A': [1, 3], 'B': [2, 4]}, index=['foo', 'bar'])
>>> df
A B
foo 1 2
bar 3 4
<BLANKLINE>
[2 rows x 2 columns]

>>> df.stack()
foo A 1
B 2
bar A 3
B 4
dtype: Int64

Args:
level (int, str, or list of these, default -1 (last level)):
Level(s) to stack from the column axis onto the index axis.

Returns:
DataFrame or Series: Stacked dataframe or series.
"""
raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)

def unstack(self):
def unstack(self, level=-1):
"""
Pivot a level of the (necessarily hierarchical) index labels.

Expand All @@ -3807,6 +3934,30 @@ def unstack(self):
If the index is not a MultiIndex, the output will be a Series
(the analogue of stack when the columns are not a MultiIndex).

**Example:**

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

>>> df = bpd.DataFrame({'A': [1, 3], 'B': [2, 4]}, index=['foo', 'bar'])
>>> df
A B
foo 1 2
bar 3 4
<BLANKLINE>
[2 rows x 2 columns]

>>> df.unstack()
A foo 1
bar 3
B foo 2
bar 4
dtype: Int64

Args:
level (int, str, or list of these, default -1 (last level)):
Level(s) of index to unstack, can pass level name.

Returns:
DataFrame or Series: DataFrame or Series.
"""
Expand Down