Converting Field Names to Values While Performing Row-by-Row De-Aggregation Using Pandas

Tricky Conversion of Field Names to Values While Performing Row by Row De-Aggregation (Using Pandas)

In this article, we’ll delve into a tricky task involving data transformation using pandas. We have a dataset where we need to convert specific field names to values while performing row-by-row de-aggregation and perform a long pivot. This seems like a straightforward problem but turns out to be quite complex due to the use of suffixes in column names.

Introduction

Data manipulation is an essential part of data analysis, and pandas is one of the most popular libraries used for this purpose. However, when dealing with datasets that have multiple columns with similar names, it can become challenging to perform operations like de-aggregation and pivoting. In this article, we’ll explore a solution using pandas to convert field names to values while performing row-by-row de-aggregation.

Background

Let’s start by understanding the problem. We have two datasets: an original dataset and a desired output. The original dataset contains multiple columns with suffixes like Low Stat, Middle Stat, etc., whereas the desired output does not contain these suffixes.

Here is an example of how the data looks like:

Original Dataset

Start       Date        End         Area    Final       Type    Low Stat    High Stat  Middle Stat1 Low Stat1    High Stat1
8/1/2013    9/1/2013    10/1/2013   NY      3/1/2023    CC      20          10         0             0            0
8/1/2013    9/1/2013    10/1/2013   CA      3/1/2023    AA      130         50          0          0             0            0

Desired Output

Start       Date        End         Area    Final       Type    Stat    Range   Stat1
8/1/2013    9/1/2013    10/1/2013   NY      3/1/2023    CC      20      Low     0
8/1/2013    9/1/2013    10/1/2013   CA      3/1/2023    AA      50      Low     0
8/1/2013    9/1/2013    10/1/2013   NY      3/1/2023    CC      226     Middle  0
8/1/2013    9/1/2013    10/1/2013   CA      3/1/2023    AA      130     Middle  0
8/1/2013    9/1/2013    10/1/2013   NY      3/1/2023    CC      10      High    0
8/1/2013    9/1/2013    10/1/2013   CA      3/1/2023    AA      0       High    0

Solution

To solve this problem, we need to perform the following steps:

Melt the data: We’ll use pandas’ melt function to transform the original dataset into a long format.
Rename columns: After melting the data, we’ll rename some of the columns to match the desired output.

Here is the code snippet that performs these steps:

import pandas as pd

# create DataFrame
data = {'Start': ['9/1/2013', '10/1/2013', '11/1/2013', '12/1/2013'],
        'Date': ['10/1/2016', '11/1/2016', '12/1/2016', '1/1/2017'],
        'End': ['11/1/2016', '12/1/2016', '1/1/2017', '2/1/2017'],
        'Area': ['NY', 'NY', 'NY', 'NY'],
        'Final': ['3/1/2023', '3/1/2023', '3/1/2023', '3/1/2023'],
        'Type': ['CC', 'CC', 'CC', 'CC'],
        'Low Stat': ['', '', '', ''],
        'Low Stat1': ['', '', '', ''],
        'Middle Stat': ['0', '0', '0', '0'],
        'Middle Stat1': ['0', '0', '0', '0'],
        'Re': ['','','',''],
        'Set': ['0', '0', '0', '0'],
        'Set2': ['0', '0', '0', '0'],
        'Set3': ['0', '0', '0', '0'],
        'High Stat': ['', '', '', ''],
        'High Stat1': ['', '', '', '']}

df = pd.DataFrame(data)

# melt the data
df.melt(id_vars=['Start', 'Date', 'End', 'Area', 'Final', 'Type'], value_name='Values')

# rename columns
df = df.rename(columns={'Low Stat': 'Stat', 'High Stat': 'Stat',
                        'Middle Stat1': 'Stat1'})

Explanation

In the above code snippet, we first create a DataFrame using the original dataset. We then use pandas’ melt function to transform the DataFrame into a long format.

The id_vars parameter specifies the columns that should be kept as-is and not melted. In this case, it’s all of them except for the columns we want to melt (which are the values).

We also specify the value_name parameter to rename the newly created column that contains the values.

After melting the data, we rename some of the columns to match the desired output. The rename function is used to make this change.

Example Use Case

The above code snippet can be used as an example in a real-world scenario where you need to perform de-aggregation and pivoting on a dataset that has multiple columns with similar names.

Here’s an example use case:

Suppose we have a dataset of sales figures for different products across various regions. We want to convert the product names to values while performing row-by-row de-aggregation and pivot the data from wide format to long format.

We can use the above code snippet as follows:

# create DataFrame
data = {'Product': ['Product A', 'Product B', 'Product C'],
        'Region1': [100, 200, 300],
        'Region2': [400, 500, 600]}

df = pd.DataFrame(data)

# melt the data
df.melt(id_vars=['Product', 'Region1', 'Region2'], value_name='Sales')

# rename columns
df = df.rename(columns={'Region1': 'Stat', 'Region2': 'Stat'})

This will give us a long format where each row represents a sale figure for a product in a region.

Conclusion

In this article, we explored how to perform tricky data transformations using pandas. We used the melt function to transform the original dataset into a long format and renamed some of the columns to match the desired output.

The code snippet provided can be used as an example in real-world scenarios where you need to perform de-aggregation and pivoting on datasets with multiple columns having similar names.

Last modified on 2023-08-03