pandas.melt()

pandas.melt(frame, id_vars=None, value_vars=None, var_name=None, value_name='value', col_level=None) [source]

?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:

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.

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.

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

Links:

http://pandas.pydata.org/pandas-docs/version/0.19.2/generated/pandas.melt.html

doc_Pandas

2025-01-10 15:47:30

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