-
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: 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
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
pandas.melt()
2017-01-12 04:50:27
Please login to continue.