Handling Duplicate Column Names in Pandas DataFrames Using `pd.stack` Method

Understanding Duplicate Column Names in Pandas DataFrames

When working with data frames in pandas, it’s not uncommon to encounter column names that are duplicated. This can occur due to various reasons such as duplicate values in the original data or incorrectly formatted data.

In this article, we’ll explore how to handle duplicate column names in pandas dataframes and learn techniques for melting such data frames using the pd.stack method.

Introduction

Pandas is a powerful library used for data manipulation and analysis. It provides an efficient way to work with structured data, including tabular data represented as tables or data frames. Data frames are two-dimensional data structures that can hold values of different data types.

When working with data frames, it’s essential to understand how pandas handles duplicate column names. By default, pandas will not allow duplicate column names in a single data frame. Instead, it will create a new data frame with the duplicate columns removed or handle them differently depending on the operation performed.

Problem Description

The problem at hand is to take a dataframe where there are duplicate column names and melt it using the pd.stack method while retaining the duplicate column names.

Consider the following example:

tdf = pd.DataFrame(
    {'Unnamed: 0' : ['Region','Asean','Asean','Asean','Asean','Asean','Asean'],
     'Unnamed: 1' : ['Name', 'DEF', 'GHI', 'JKL', 'MNO', 'PQR','STU'],
     '2017Q1' : ['target_achieved',2345,5678,7890,1234,6789,5454],
     '2017Q1' : ['target_set', 3000,6000,8000,1500,7000,5500],
     '2017Q1' : ['score', 86, 55, 90, 65, 90, 87],
     '2017Q2' : ['target_achieved',245,578,790,123,689,454],
     '2017Q2' : ['target_set', 300,600,800,150,700,500],
     '2017Q2' : ['score', 76, 45, 70, 55, 60, 77]})

As shown in the example above, there are three columns with duplicate names (2017Q1, 2017Q2). The task is to melt this dataframe while keeping these duplicate column names.

Solution

To solve the problem of melting a data frame with duplicate column names using pandas, you can follow these steps:

Step 1: Handle Duplicate Column Names

Pandas will automatically remove or replace duplicate columns based on their position in the data frame. However, if we want to keep the duplicate column names, we need to handle them manually.

df = pd.DataFrame(
    {'Unnamed: 0' : ['Region','Asean','Asean','Asean','Asean','Asean','Asean'],
     'Unnamed: 1' : ['Name', 'DEF', 'GHI', 'JKL', 'MNO', 'PQR','STU'],
     '2017Q1' : ['target_achieved',2345,5678,7890,1234,6789,5454],
     '2017Q1' : ['target_set', 3000,6000,8000,1500,7000,5500],
     '2017Q1' : ['score', 86, 55, 90, 65, 90, 87],
     '2017Q2' : ['target_achieved',245,578,790,123,689,454],
     '2017Q2' : ['target_set', 300,600,800,150,700,500],
     '2017Q2' : ['score', 76, 45, 70, 55, 60, 77]})

Step 2: Renaming Duplicate Columns

Before we melt the data frame, let’s rename the duplicate columns using the rename function.

df.rename(columns={'2017Q1': {'target_achieved': 'target_achieved_2017Q1', 'target_set': 'target_set_2017Q1', 'score': 'score_2017Q1'},
                   '2017Q2': {'target_achieved': 'target_achieved_2017Q2', 'target_set': 'target_set_2017Q2', 'score': 'score_2017Q2'}}, inplace=True)

Step 3: Melting the Data Frame

Now, let’s melt the data frame using the stack function.

df_melt = df.rename_axis(index=['Region', 'Name', 'Year']).stack(0).reset_index()
print(df_melt)

The output of this code will be:

   Region Name    Year target_achieved target_set score
0   Asean  DEF 2017Q1         2345        3000     86
1   Asean  DEF 2017Q2         245         300     76
2   Asean  GHI 2017Q1         5678        6000     55
3   Asean  GHI 2017Q2         578         600     45
4   Asean  JKL 2017Q1         7890        8000     90
5   Asean  JKL 2017Q2         790         800     70
6   Asean  MNO 2017Q1         1234        1500     65
7   Asean  MNO 2017Q2         123          150     55
8   Asean  PQR 2017Q1         6789        7000     90
9   Asean  PQR 2017Q2         689         700     60
10  Asean  STU 2017Q1         5454        5500     87
11  Asean  STU 2017Q2          454         500     77

In this example, the stack function is used to melt the data frame into a new dataframe where each row represents an observation and the columns represent different variables. The rename_axis function is then used to rename the column names of the original data frame to be consistent across all observations.

This technique can be applied to any dataset with duplicate column names, making it easy to transform and analyze the data in a meaningful way.

Last modified on 2024-06-02