DataFrame.melt() - ( Pandas DataFrame Basics )

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Syntax

DataFrame.melt(id_vars=None, value_vars=None, var_name=None, value_name='value')

Example

import pandas as pd

df = pd.DataFrame({
        'Country':['USA','Canada','Mexico'],
        '2010':[1.2, 3.4, 2.1],
        '2011':[2.4, 4.5, 2.8],
        '2012':[3.5, 2.3, 4.5]
    })

print(df.melt(id_vars=['Country'], value_vars=['2010', '2011', '2012'], var_name='year', value_name='population'))

Output

  Country  year  population
0     USA  2010         1.2
1  Canada  2010         3.4
2  Mexico  2010         2.1
3     USA  2011         2.4
4  Canada  2011         4.5
5  Mexico  2011         2.8
6     USA  2012         3.5
7  Canada  2012         2.3
8  Mexico  2012         4.5

Explanation

Pandas melt function is used to transform or reshape data frame. It is used to transform wide DataFrames into tall DataFrames and the opposite.

The id_vars parameter is used to specify the columns that should remain in the resulting DataFrame. The value_vars parameter is used to specify the columns that should be "unpivoted" or "melted down". The var_name parameter specifies that the column name of the melted column, and the value_name parameter specifies the column name of the value column.

Use

The melt function is a powerful tool for data manipulation and wrangling. It is commonly used to reshape data frames by converting columns to rows, so that data can be easily further processed, analyzed, or visualized.

Important Points

• The melt function is used to transform or reshape data frames from wide to tall, and vice versa.
• The id_vars parameter specifies the columns that should remain unchanged in the resulting DataFrame.
• The value_vars parameter specifies the columns that should be "unpivoted" or "melted down".
• The var_name parameter specifies the column name of the resulting dataframe's column and the value_name specifies the column name of the resulting data frame's cells.

Summary

In conclusion, the melt function in pandas is a useful tool to transform or reshape wide data frames to tall data frames and vice versa. It is used to transform, reshape, or pivot data frame, to make the data suitable for further processing, analyzing, or visualizing. The melt function is easy to use and highly flexible, enabling you to specify the columns that should remain unchanged, those that should be "melted" down, and the resulting data frame's resulting columns name along with cell values name.