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Fix PR01 errors for melt, option_context, read_fwf, reset_option #57806

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Mar 11, 2024
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Fix PR01 errors for melt, option_context, read_fwf, reset_option #57806

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483feac
Fix PR01 errors for melt, option_context, read_fwf, reset_option
jordan-d-murphy Mar 11, 2024
99b8cb6
removed shared docstring and fixed PR02 error in pandas.DataFrame.melt
jordan-d-murphy Mar 11, 2024
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6 changes: 1 addition & 5 deletions ci/code_checks.sh
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Original file line number Diff line number Diff line change
Expand Up @@ -490,11 +490,7 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
pandas.errors.AbstractMethodError\
pandas.errors.UndefinedVariableError\
pandas.get_option\
pandas.io.formats.style.Styler.to_excel\
pandas.melt\
pandas.option_context\
pandas.read_fwf\
pandas.reset_option # There should be no backslash in the final line, please keep this comment in the last ignored function
pandas.io.formats.style.Styler.to_excel # There should be no backslash in the final line, please keep this comment in the last ignored function
RET=$(($RET + $?)) ; echo $MSG "DONE"

MSG='Partially validate docstrings (PR07)' ; echo $MSG
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122 changes: 121 additions & 1 deletion pandas/core/frame.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9682,7 +9682,6 @@ def unstack(

return result.__finalize__(self, method="unstack")

@Appender(_shared_docs["melt"] % {"caller": "df.melt(", "other": "melt"})
def melt(
self,
id_vars=None,
Expand All @@ -9692,6 +9691,127 @@ def melt(
col_level: Level | None = None,
ignore_index: bool = True,
) -> DataFrame:
"""
Unpivot DataFrame from wide to long format, optionally leaving identifiers set.

This function is useful to massage a DataFrame into a format where one
or more columns are identifier variables (`id_vars`), while all other
columns, considered measured variables (`value_vars`), are "unpivoted" to
the row axis, leaving just two non-identifier columns, 'variable' and
'value'.

Parameters
----------
id_vars : scalar, tuple, list, or ndarray, optional
Column(s) to use as identifier variables.
value_vars : scalar, 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, default None
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, can't be an existing column label.
col_level : scalar, optional
If columns are a MultiIndex then use this level to melt.
ignore_index : bool, default True
If True, original index is ignored. If False, original index is retained.
Index labels will be repeated as necessary.

Returns
-------
DataFrame
Unpivoted DataFrame.

See Also
--------
melt : Identical method.
pivot_table : Create a spreadsheet-style pivot table as a DataFrame.
DataFrame.pivot : Return reshaped DataFrame organized
by given index / column values.
DataFrame.explode : Explode a DataFrame from list-like
columns to long format.

Notes
-----
Reference :ref:`the user guide <reshaping.melt>` for more examples.

Examples
--------
>>> df = pd.DataFrame(
... {
... "A": {0: "a", 1: "b", 2: "c"},
... "B": {0: 1, 1: 3, 2: 5},
... "C": {0: 2, 1: 4, 2: 6},
... }
... )
>>> df
A B C
0 a 1 2
1 b 3 4
2 c 5 6

>>> df.melt(id_vars=["A"], value_vars=["B"])
A variable value
0 a B 1
1 b B 3
2 c B 5

>>> df.melt(id_vars=["A"], value_vars=["B", "C"])
A variable value
0 a B 1
1 b B 3
2 c B 5
3 a C 2
4 b C 4
5 c C 6

The names of 'variable' and 'value' columns can be customized:

>>> df.melt(
... id_vars=["A"],
... value_vars=["B"],
... var_name="myVarname",
... value_name="myValname",
... )
A myVarname myValname
0 a B 1
1 b B 3
2 c B 5

Original index values can be kept around:

>>> df.melt(id_vars=["A"], value_vars=["B", "C"], ignore_index=False)
A variable value
0 a B 1
1 b B 3
2 c B 5
0 a C 2
1 b C 4
2 c C 6

If you have multi-index columns:

>>> df.columns = [list("ABC"), list("DEF")]
>>> df
A B C
D E F
0 a 1 2
1 b 3 4
2 c 5 6

>>> df.melt(col_level=0, id_vars=["A"], value_vars=["B"])
A variable value
0 a B 1
1 b B 3
2 c B 5

>>> df.melt(id_vars=[("A", "D")], value_vars=[("B", "E")])
(A, D) variable_0 variable_1 value
0 a B E 1
1 b B E 3
2 c B E 5
"""
return melt(
self,
id_vars=id_vars,
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128 changes: 124 additions & 4 deletions pandas/core/reshape/melt.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,8 +5,6 @@

import numpy as np

from pandas.util._decorators import Appender

from pandas.core.dtypes.common import is_list_like
from pandas.core.dtypes.concat import concat_compat
from pandas.core.dtypes.missing import notna
Expand All @@ -15,7 +13,6 @@
from pandas.core.indexes.api import MultiIndex
from pandas.core.reshape.concat import concat
from pandas.core.reshape.util import tile_compat
from pandas.core.shared_docs import _shared_docs
from pandas.core.tools.numeric import to_numeric

if TYPE_CHECKING:
Expand All @@ -40,7 +37,6 @@ def ensure_list_vars(arg_vars, variable: str, columns) -> list:
return []


@Appender(_shared_docs["melt"] % {"caller": "pd.melt(df, ", "other": "DataFrame.melt"})
def melt(
frame: DataFrame,
id_vars=None,
Expand All @@ -50,6 +46,130 @@ def melt(
col_level=None,
ignore_index: bool = True,
) -> DataFrame:
"""
Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.

This function is useful to massage a DataFrame into a format where one
or more columns are identifier variables (`id_vars`), while all other
columns, considered measured variables (`value_vars`), are "unpivoted" to
the row axis, leaving just two non-identifier columns, 'variable' and
'value'.

Parameters
----------
frame : DataFrame
The DataFrame to unpivot.
id_vars : scalar, tuple, list, or ndarray, optional
Column(s) to use as identifier variables.
value_vars : scalar, 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, default None
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, can't be an existing column label.
col_level : scalar, optional
If columns are a MultiIndex then use this level to melt.
ignore_index : bool, default True
If True, original index is ignored. If False, the original index is retained.
Index labels will be repeated as necessary.

Returns
-------
DataFrame
Unpivoted DataFrame.

See Also
--------
DataFrame.melt : Identical method.
pivot_table : Create a spreadsheet-style pivot table as a DataFrame.
DataFrame.pivot : Return reshaped DataFrame organized
by given index / column values.
DataFrame.explode : Explode a DataFrame from list-like
columns to long format.

Notes
-----
Reference :ref:`the user guide <reshaping.melt>` for more examples.

Examples
--------
>>> df = pd.DataFrame(
... {
... "A": {0: "a", 1: "b", 2: "c"},
... "B": {0: 1, 1: 3, 2: 5},
... "C": {0: 2, 1: 4, 2: 6},
... }
... )
>>> df
A B C
0 a 1 2
1 b 3 4
2 c 5 6

>>> pd.melt(df, id_vars=["A"], value_vars=["B"])
A variable value
0 a B 1
1 b B 3
2 c B 5

>>> pd.melt(df, id_vars=["A"], value_vars=["B", "C"])
A variable value
0 a B 1
1 b B 3
2 c B 5
3 a C 2
4 b C 4
5 c C 6

The names of 'variable' and 'value' columns can be customized:

>>> pd.melt(
... df,
... id_vars=["A"],
... value_vars=["B"],
... var_name="myVarname",
... value_name="myValname",
... )
A myVarname myValname
0 a B 1
1 b B 3
2 c B 5

Original index values can be kept around:

>>> pd.melt(df, id_vars=["A"], value_vars=["B", "C"], ignore_index=False)
A variable value
0 a B 1
1 b B 3
2 c B 5
0 a C 2
1 b C 4
2 c C 6

If you have multi-index columns:

>>> df.columns = [list("ABC"), list("DEF")]
>>> df
A B C
D E F
0 a 1 2
1 b 3 4
2 c 5 6

>>> pd.melt(df, col_level=0, id_vars=["A"], value_vars=["B"])
A variable value
0 a B 1
1 b B 3
2 c B 5

>>> pd.melt(df, id_vars=[("A", "D")], value_vars=[("B", "E")])
(A, D) variable_0 variable_1 value
0 a B E 1
1 b B E 3
2 c B E 5
"""
if value_name in frame.columns:
raise ValueError(
f"value_name ({value_name}) cannot match an element in "
Expand Down
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