Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier.
Pandas dataframe.melt() function unpivots a DataFrame from wide format to long format, optionally leaving identifier variables set. This function is useful to message 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’.
Syntax:DataFrame.melt(id_vars=None, value_vars=None, var_name=None, value_name=’value’, col_level=None)
Parameters :
frame : DataFrame
id_vars : Column(s) to use as identifier variables
value_vars : Column(s) to unpivot. If not specified, uses all columns that are not set as id_vars.
var_name : Name to use for the ‘variable’ column. If None it uses frame.columns.name or ‘variable’.
value_name : Name to use for the ‘value’ column
col_level : If columns are a MultiIndex then use this level to melt.Returns: DataFrame into a format where one or more columns are identifier variables
Example #1: Use melt() function to set column “A” as the identifier variable and column “B” as value variable.
# importing pandas as pd import pandas as pd
# Creating the dataframe df = pd.DataFrame({"A":[12, 4, 5, 44, 1],
"B":[5, 2, 54, 3, 2],
"C":[20, 16, 7, 3, 8],
"D":[14, 3, 17, 2, 6]})
# Print the dataframe df |
Lets use the dataframe.melt() function to set column “A” as identifier variable and column “B” as the value variable.
# function to unpivot the dataframe df.melt(id_vars =['A'], value_vars =['B'])
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Output :
Example #2: Use melt() function to set column “A” as the identifier variable and column “B” and “C” as value variable. Also customize the names of both the value and variable column.
# importing pandas as pd import pandas as pd
# Creating the dataframe df = pd.DataFrame({"A":[12, 4, 5, 44, 1],
"B":[5, 2, 54, 3, 2],
"C":[20, 16, 7, 3, 8],
"D":[14, 3, 17, 2, 6]})
# Print the dataframe df |
Lets use the dataframe.melt() function to set column “A” as identifier variable and column “B” and “C” as the value variable.
# function to unpivot the dataframe # We will also provide a customized name to the value and variable column df.melt(id_vars =['A'], value_vars =['B', 'C'],
var_name ='Variable_column', value_name ='Value_column')
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Output :