Transposing and Creating Flat Files Using Pandas

Introduction to the Problem

In this article, we will explore how to transpose a multi-level table into a flat structure using pandas. The original table has multiple levels of categorization (e.g., top-level 3, sub-levels 4,5,6, etc.) and some categories do not have any sub-levels. We need to create a new table with the same categories but only one level deep.

Understanding the Data

The data we are working with is a multi-indexed DataFrame, where each row represents an entry in our dataset. The columns are CODE, LEV, and NAME. The LEV column contains different levels of categorization (e.g., top-level 3), while the CODE column contains the actual code for each category, and the NAME column contains the name of the category.

Example Data

Let’s take a look at some example data to better understand our problem:

| CODE | LEV | NAME    |
|------|-----|---------|
| A00  | 3   | text    |
| A000 | 4   | text    |
| A001 | 4   | text    |
| A02  | 3   | text    |
| A022 | 4   | text    |
| A0220 | 5   | text    |
| A33  | 3   | text    |

Solution Overview

To solve this problem, we will use a combination of pandas data manipulation and grouping operations.

Step 1: Grouping by LEV and NAME

We need to group our original DataFrame by the LEV column (the top-level categorization) and the NAME column (the actual category names).

# Define our original DataFrame
import pandas as pd

data = {
    'CODE': ['A00', 'A000', 'A001', 'A02', 'A022', 'A0220', 'A33'],
    'LEV': [3, 4, 4, 3, 4, 5, 3],
    'NAME': ['text', 'text', 'text', 'text', 'text', 'text', 'text']
}

df = pd.DataFrame(data)

# Group by LEV and NAME
grouped_df = df.groupby(['LEV', 'NAME'])

Step 2: Aggregating CODE Values Using list

For each group, we need to aggregate the CODE values into a list. This will allow us to store multiple codes for each category.

# Aggregate CODE values using list
aggregated_df = grouped_df.agg(list).reset_index()

Step 3: Exploding CODE Values

Next, we need to explode our aggregated DataFrame back into separate rows, so that we can create a flat structure with only one level deep.

# Explode CODE values
exploded_df = aggregated_df.explode('CODE')

Step 4: Re-pivoting the Data

Now that we have exploded our data, we need to re-pivot it back into a flat structure. We will use the pivot_table function from pandas to do this.

# Re-pivot the data using pivot_table
re_pivoted_df = df.pivot_table(index=['index', 'NAME'], columns='LEV', values='CODE', aggfunc=list).reset_index()

Step 5: Renaming and Dropping Columns

Finally, we need to rename our column names to make them more meaningful. We also need to drop some columns that are no longer necessary.

# Rename column names
re_pivoted_df = re_pivoted_df.rename(columns={'index': 'L3', 'lev': 'L4', 'LEV': 'L5'}).rename_axis(None, axis=1)

# Drop unnecessary columns
final_df = re_pivoted_df.drop(['level_0'], axis=1).drop(['index'], axis=1)

Final Output

Our final output should be a flat DataFrame with the same categories but only one level deep.

| L3 | L4 | L5 | NAME  |
|----|----|----|-------|
| A00| -  | -  | text  |
| A02| -  | -  | text  |
| A33| -  | -  | text  |
| -  | A00 | -  | text  |
| -  | A001| -  | text  |
| -  | A022| -  | text  |
| -  | A0220| -  | text  |

Last modified on 2024-06-25