pandas.melt — pandas 0.24.0rc1 documentation (original) (raw)
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 usesframe.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. |
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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
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