melt

In pandas, the melt function is used to ==transform (Data Transformation) a DataFrame from a wide format to a long format==. This is especially useful for data analysis and visualization tasks where long-format data is preferred or required. The wide format typically has multiple columns for different variables, whereas the long format has a single column for variable names and a single column for values.

Related:

• Database Techniques
• Turning a flat file into a database

In DE_Tools see:

• https://github.com/rhyslwells/DE_Tools/blob/main/Explorations/Transformation/reshaping.ipynb

Key Reasons to Use melt:

1. Normalisation

   • Wide to Long Transformation: melt helps in converting data with many columns (wide format) into a more normalized form with fewer columns (long format). This is useful for many statistical and visualization libraries that prefer long-format data.

2. Easier Analysis and Data Visualisation:

   • Compatibility with Plotting Libraries: Many plotting libraries like seaborn and ggplot require data in a long format for creating certain types of plots, such as Grouped plots.

3. Simplifying Complex Data Structures:

   • Handling Multi-index Columns: If a DataFrame has multiple levels of columns, melt can help flatten this structure, making it easier to work with.

4. Preparation for Aggregation:

   • Facilitating Groupby Operations: Long-format data is often more suitable for these.

Parameters of melt:

• id_vars: Columns to use as identifier variables. These columns are kept as-is in the output.
• value_vars: Columns to unpivot. These columns are transformed into a single column.
• var_name: Name to use for the variable column in the output.
• value_name: Name to use for the value column in the output.

Example Usage:

Consider a DataFrame in wide format:

import pandas as pd
 
# Sample wide format data
data = {
    'id': [1, 2, 3],
    'math_score': [88, 92, 95],
    'science_score': [85, 90, 89],
    'english_score': [78, 85, 88]
}
df_wide = pd.DataFrame(data)
print(df_wide)

Output:

   id  math_score  science_score  english_score
0   1          88             85             78
1   2          92             90             85
2   3          95             89             88

To convert this wide-format DataFrame into a long-format DataFrame using melt:

# Melt the DataFrame
df_long = pd.melt(df_wide, id_vars=['id'], 
                  value_vars=['math_score', 'science_score', 'english_score'], 
                  var_name='subject', value_name='score')
print(df_long)

Output:

   id       subject  score
0   1    math_score     88
1   2    math_score     92
2   3    math_score     95
3   1  science_score     85
4   2  science_score     90
5   3  science_score     89
6   1  english_score     78
7   2  english_score     85
8   3  english_score     88