+pd.DataFrame.melt.

Author: ResidentMario

pandas/core/frame.py

@@ -4051,6 +4051,105 @@ def unstack(self, level=-1, fill_value=None):
  from pandas.core.reshape import unstack
  return unstack(self, level, fill_value)
 
+ _shared_docs['melt'] = """
+ "Unpivots" a DataFrame from wide format to long format, optionally leaving
+ identifier variables 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
+ 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.
+ col_level : int or string, optional
+ If columns are a MultiIndex then use this level to melt.
+
+ See also
+ --------
+ pivot_table
+ DataFrame.pivot
+
+ Examples
+ --------
+ >>> import pandas as pd
+ >>> 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
+
+ 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
+
+ """
+
+ @Appender(_shared_docs['melt'], indents=2)
+ def melt(self, id_vars=None, value_vars=None, var_name=None,
+ value_name='value', col_level=None):
+ from pandas.core.reshape import melt
+ return melt(self, id_vars=id_vars, value_vars=value_vars,
+ var_name=var_name, value_name=value_name,
+ col_level=col_level)
+
  # ----------------------------------------------------------------------
  # Time series-related
 

pandas/core/reshape.py

@@ -28,6 +28,9 @@
 import pandas.core.algorithms as algos
 from pandas._libs import algos as _algos, reshape as _reshape
 
+from pandas.core.frame import _shared_docs
+from pandas.util.decorators import Appender
+
 from pandas.core.index import MultiIndex, _get_na_value
 
 
@@ -701,98 +704,9 @@ def _convert_level_number(level_num, columns):
  return result
 
 
+@Appender(_shared_docs['melt'], indents=2)
 def melt(frame, id_vars=None, value_vars=None, var_name=None,
  value_name='value', col_level=None):
- """
- "Unpivots" a DataFrame from wide format to long format, optionally leaving
- identifier variables 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
- 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.
- col_level : int or string, optional
- If columns are a MultiIndex then use this level to melt.
-
- See also
- --------
- pivot_table
- DataFrame.pivot
-
- Examples
- --------
- >>> import pandas as pd
- >>> 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
-
- 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
-
- """
  # TODO: what about the existing index?
  if id_vars is not None:
  if not is_list_like(id_vars):