Skip to content

Commit 234b61c

Browse files
shobsishobsi
authored
docs: Fix bigframes.pandas.concat documentation (#382)
1 parent 93b02cd commit 234b61c

File tree

1 file changed

+93
-96
lines changed
  • third_party/bigframes_vendored/pandas/core/reshape

1 file changed

+93
-96
lines changed

‎third_party/bigframes_vendored/pandas/core/reshape/concat.py‎

Lines changed: 93 additions & 96 deletions
Original file line numberDiff line numberDiff line change
@@ -14,127 +14,124 @@ def concat(
1414
join: str = "outer",
1515
ignore_index: bool = False,
1616
):
17-
"""
18-
Concatenate BigQuery DataFrames objects along a particular axis.
17+
"""Concatenate BigQuery DataFrames objects along a particular axis.
1918
2019
Allows optional set logic along the other axes.
2120
2221
Can also add a layer of hierarchical indexing on the concatenation axis,
2322
which may be useful if the labels are the same (or overlapping) on
2423
the passed axis number.
2524
26-
Parameters
27-
----------
28-
objs:
29-
Objects to concatenate. Any None objects will be dropped silently unless
30-
they are all None in which case a ValueError will be raised.
31-
axis : {0/'index', 1/'columns'}, default 0
32-
The axis to concatenate along.
33-
join: {'inner', 'outer'}, default 'outer'
34-
How to handle indexes on other axis (or axes).
35-
ignore_index : bool, default False
36-
If True, do not use the index values along the concatenation axis. The
37-
resulting axis will be labeled 0, ..., n - 1. This is useful if you are
38-
concatenating objects where the concatenation axis does not have
39-
meaningful indexing information. Note the index values on the other
40-
axes are still respected in the join.
41-
42-
Returns
43-
-------
44-
object, type of objs
45-
When concatenating all ``Series`` along the index (axis=0), a
46-
``Series`` is returned. When ``objs`` contains at least one
47-
``DataFrame``, a ``DataFrame`` is returned.
48-
49-
Notes
50-
-----
51-
It is not recommended to build DataFrames by adding single rows in a
52-
for loop. Build a list of rows and make a DataFrame in a single concat.
53-
54-
Examples
55-
--------
25+
.. note::
26+
It is not recommended to build DataFrames by adding single rows in a
27+
for loop. Build a list of rows and make a DataFrame in a single concat.
28+
29+
**Examples:**
30+
31+
>>> import bigframes.pandas as pd
32+
>>> pd.options.display.progress_bar = None
33+
5634
Combine two ``Series``.
5735
58-
>>> import bigframes.pandas as pd
59-
>>> pd.options.display.progress_bar = None
60-
>>> s1 = pd.Series(['a', 'b'])
61-
>>> s2 = pd.Series(['c', 'd'])
62-
>>> pd.concat([s1, s2])
63-
0 a
64-
1 b
65-
0 c
66-
1 d
67-
dtype: string
36+
>>> s1 = pd.Series(['a', 'b'])
37+
>>> s2 = pd.Series(['c', 'd'])
38+
>>> pd.concat([s1, s2])
39+
0 a
40+
1 b
41+
0 c
42+
1 d
43+
dtype: string
6844
6945
Clear the existing index and reset it in the result
7046
by setting the ``ignore_index`` option to ``True``.
7147
72-
>>> pd.concat([s1, s2], ignore_index=True)
73-
0 a
74-
1 b
75-
2 c
76-
3 d
77-
dtype: string
48+
>>> pd.concat([s1, s2], ignore_index=True)
49+
0 a
50+
1 b
51+
2 c
52+
3 d
53+
dtype: string
7854
7955
Combine two ``DataFrame`` objects with identical columns.
8056
81-
>>> df1 = pd.DataFrame([['a', 1], ['b', 2]],
82-
... columns=['letter', 'number'])
83-
>>> df1
84-
letter number
85-
0 a 1
86-
1 b 2
87-
<BLANKLINE>
88-
[2 rows x 2 columns]
89-
>>> df2 = pd.DataFrame([['c', 3], ['d', 4]],
90-
... columns=['letter', 'number'])
91-
>>> df2
92-
letter number
93-
0 c 3
94-
1 d 4
95-
<BLANKLINE>
96-
[2 rows x 2 columns]
97-
>>> pd.concat([df1, df2])
98-
letter number
99-
0 a 1
100-
1 b 2
101-
0 c 3
102-
1 d 4
103-
<BLANKLINE>
104-
[4 rows x 2 columns]
57+
>>> df1 = pd.DataFrame([['a', 1], ['b', 2]],
58+
... columns=['letter', 'number'])
59+
>>> df1
60+
letter number
61+
0 a 1
62+
1 b 2
63+
<BLANKLINE>
64+
[2 rows x 2 columns]
65+
>>> df2 = pd.DataFrame([['c', 3], ['d', 4]],
66+
... columns=['letter', 'number'])
67+
>>> df2
68+
letter number
69+
0 c 3
70+
1 d 4
71+
<BLANKLINE>
72+
[2 rows x 2 columns]
73+
>>> pd.concat([df1, df2])
74+
letter number
75+
0 a 1
76+
1 b 2
77+
0 c 3
78+
1 d 4
79+
<BLANKLINE>
80+
[4 rows x 2 columns]
10581
10682
Combine ``DataFrame`` objects with overlapping columns
10783
and return everything. Columns outside the intersection will
10884
be filled with ``NaN`` values.
10985
110-
>>> df3 = pd.DataFrame([['c', 3, 'cat'], ['d', 4, 'dog']],
111-
... columns=['letter', 'number', 'animal'])
112-
>>> df3
113-
letter number animal
114-
0 c 3 cat
115-
1 d 4 dog
116-
<BLANKLINE>
117-
[2 rows x 3 columns]
118-
>>> pd.concat([df1, df3])
119-
letter number animal
120-
0 a 1 <NA>
121-
1 b 2 <NA>
122-
0 c 3 cat
123-
1 d 4 dog
124-
<BLANKLINE>
125-
[4 rows x 3 columns]
86+
>>> df3 = pd.DataFrame([['c', 3, 'cat'], ['d', 4, 'dog']],
87+
... columns=['letter', 'number', 'animal'])
88+
>>> df3
89+
letter number animal
90+
0 c 3 cat
91+
1 d 4 dog
92+
<BLANKLINE>
93+
[2 rows x 3 columns]
94+
>>> pd.concat([df1, df3])
95+
letter number animal
96+
0 a 1 <NA>
97+
1 b 2 <NA>
98+
0 c 3 cat
99+
1 d 4 dog
100+
<BLANKLINE>
101+
[4 rows x 3 columns]
126102
127103
Combine ``DataFrame`` objects with overlapping columns
128104
and return only those that are shared by passing ``inner`` to
129105
the ``join`` keyword argument.
130106
131-
>>> pd.concat([df1, df3], join="inner")
132-
letter number
133-
0 a 1
134-
1 b 2
135-
0 c 3
136-
1 d 4
137-
<BLANKLINE>
138-
[4 rows x 2 columns]
107+
>>> pd.concat([df1, df3], join="inner")
108+
letter number
109+
0 a 1
110+
1 b 2
111+
0 c 3
112+
1 d 4
113+
<BLANKLINE>
114+
[4 rows x 2 columns]
115+
116+
Args:
117+
objs (list of objects):
118+
Objects to concatenate. Any None objects will be dropped silently unless
119+
they are all None in which case a ValueError will be raised.
120+
axis ({0 or 'index', 1 or 'columns'}, default 0):
121+
The axis to concatenate along.
122+
join ({'inner', 'outer'}, default 'outer'):
123+
How to handle indexes on other axis (or axes).
124+
ignore_index (bool, default False):
125+
If True, do not use the index values along the concatenation axis. The
126+
resulting axis will be labeled 0, ..., n - 1. This is useful if you are
127+
concatenating objects where the concatenation axis does not have
128+
meaningful indexing information. Note the index values on the other
129+
axes are still respected in the join.
130+
131+
Returns:
132+
object, type of objs:
133+
When concatenating all ``Series`` along the index (axis=0), a
134+
``Series`` is returned. When ``objs`` contains at least one
135+
``DataFrame``, a ``DataFrame`` is returned.
139136
"""
140137
raise NotImplementedError(constants.ABSTRACT_METHOD_ERROR_MESSAGE)

0 commit comments

Comments
 (0)