Python | Pandas.melt() (original) (raw)

To make the analysis of data in a table easier, we can reshape the data into a more computer-friendly form using Pandas in Python. Pandas.melt() is one of the functions to do so.. Pandas.melt() unpivots a DataFrame from wide format to long format.

**Pandas melt() function is useful to massage a DataFrame into a format where one or more columns are identifier variables, while all other columns, considered measured variables, are unpivoted to the row axis, leaving just two non-identifier columns, variable and value.

Python Pandas.melt() Function Syntax

**Syntax: pandas.melt(frame, id_vars=None, value_vars=None,

var_name=None, value_name='value', col_level=None)

**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.

**Creating a Sample DataFrame

Here, we have created a sample DataFrame that we will use in this article.

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importing pandas as pd

import pandas as pd

creating a dataframe

df = pd.DataFrame({'Name': {0: 'John', 1: 'Bob', 2: 'Shiela'}, 'Course': {0: 'Masters', 1: 'Graduate', 2: 'Graduate'}, 'Age': {0: 27, 1: 23, 2: 21}}) df

melt () do in Pandas Example

Below are the example of how we can use Pandas melt() Function in different ways in Pandas:

Example 1: Pandas melt() Example

In this example, the pd.melt function is used to unpivot the 'Course' column while keeping 'Name' as the identifier variable. The resulting DataFrame has three columns: 'Name', 'variable' (containing the column name 'Course'), and 'value' (containing corresponding values from the 'Course' column).

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Name is id_vars and Course is value_vars

pd.melt(df, id_vars=['Name'], value_vars=['Course'])

**Output:

Name variable   value

0 John Course Masters
1 Bob Course Graduate
2 Shiela Course Graduate

Example 2: Using id_vars and value_vars to melt() of a Pandas DataFrame

In this example, the pd.melt function is employed to unpivot the 'Course' and 'Age' columns while using 'Name' as the identifier variable.

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multiple unpivot columns

pd.melt(df, id_vars=['Name'], value_vars=['Course', 'Age'])

**Output:

Name variable     value

0 John Course Masters
1 Bob Course Graduate
2 Shiela Course Graduate
3 John Age 27
4 Bob Age 23
5 Shiela Age 21

Example 3: Using var_name and value_name to melt() of a Pandas DataFrame

In this example, the pd.melt function is utilized with customized column names. The 'Course' column is unpivoted while preserving 'Name' as the identifier. The resulting DataFrame has columns 'Name', 'ChangedVarname' (for the melted column name, set to 'Course'), and 'ChangedValname' (containing corresponding values from the 'Course' column).

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Names of ‘variable’ and ‘value’ columns can be customized

pd.melt(df, id_vars=['Name'], value_vars=['Course'], var_name='ChangedVarname', value_name='ChangedValname')

**Output:

Name ChangedVarname ChangedValname

0 John Course Masters
1 Bob Course Graduate
2 Shiela Course Graduate

Example 4: Using ignore_index with Pandas.melt() Function

In this example, the pd.melt function is applied to unpivot the 'Course' and 'Age' columns while using 'Name' as the identifier variable. The original index is ignored due to the ignore_index=True parameter.

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multiple unpivot columns with ignore_index

result = pd.melt(df, id_vars=['Name'], value_vars=['Course', 'Age'], ignore_index=True) print(result)

**Output:

Name variable     value

0 John Course Masters
1 Bob Course Graduate
2 Shiela Course Graduate
3 John Age 27
4 Bob Age 23
5 Shiela Age 21