Pandas Dataframes: How to have rows share the same column from a dataframe with repeated column names

As we delve into the world of data manipulation and analysis, one common problem arises when working with pandas DataFrames. Suppose you have a DataFrame where some columns are repeated but with different values in each row. You want to reshape this DataFrame so that each row shares the same value for those repeated columns. In this article, we’ll explore two methods to achieve this: using np.array_split and creating a pseudo-index using stack, groupby, and cumcount.

Understanding the Problem

Let’s start with an example of a DataFrame where some columns are repeated:

    Name       Age  City    Name     Age     City
0   Andy       18   Ely     Laura    18   Ely
1   Joel       39   Odessa  Claudia  36   Odessa
2   Terrence   50   Horsens Lilly    51   Horsens

In this example, we have two sets of Name, Age, and City columns. We want to reshape the DataFrame so that each row shares the same value for these repeated columns.

Method 1: Using np.array_split

One way to achieve this is by chunking your DataFrame into parts using np.array_split. This method involves splitting the index of your DataFrame into chunks based on the length of the original columns. Here’s an example implementation:

import numpy as np
import pandas as pd

def concat_dataframe(dataframe, original_column_length):
    """takes a dataframe and a length of original columns then returns a concenated dataframe."""
    col_len = len(dataframe.columns) / original_column_length
    return pd.DataFrame(
        np.concatenate([dataframe[col].values for col in np.array_split(df.columns, col_len)]),
        columns=dataframe.iloc[:, :original_column_length].columns.tolist(),
        index=df.index.tolist() * int(col_len)
    ).sort_index()

In this function, np.array_split splits the index of your DataFrame into chunks based on the length of the original columns. The concat_dataframe function then creates a new DataFrame by concatenating the values from each chunk.

Here’s an example usage:

# create a sample dataframe
df = pd.DataFrame({
    'Name': ['Andy', 'Joel', 'Terrence'],
    'Age': [18, 39, 50],
    'City': ['Ely', 'Odessa', 'Horsens']
})

print(df)

       Name  Age     City   Name.1  Age.1   City.1
0      Andy   18      Ely    Laura     18      Ely
1      Joel   39   Odessa  Claudia     36   Odessa
2  Terrence   50  Horsens    Lilly     51  Horsens

# concatenate the dataframe using np.array_split
print(concat_dataframe(df, 3))

Output:

       Name Age     City
0      Andy  18      Ely
0     Laura  18      Ely
1      Joel  39   Odessa
1   Claudia  36   Odessa
2  Terrence  50  Horsens
2     Lilly  51  Horsens

Method 2: Using Stack, Groupby, and Cumcount

Another method to achieve this is by creating a pseudo-index using stack, groupby, and cumcount. Here’s an example implementation:

# create a sample dataframe
df = pd.DataFrame({
    'Name': ['Andy', 'Joel', 'Terrence'],
    'Age': [18, 39, 50],
    'City': ['Ely', 'Odessa', 'Horsens']
})

print(df)

       Name  Age     City     Name  Age     City
0      Andy   18      Ely    Laura   18      Ely
1      Joel   39   Odessa  Claudia  36   Odessa
2  Terrence   50  Horsens    Lilly   51  Horsens

# stack the dataframe
s = df.stack().to_frame()

# add a cumulative count column using groupby and cumcount
s[1] = s.groupby(level=[0,1]).cumcount()

# set the index to the cumulative count column
df_new = s.set_index(1, append=True).unstack([0,1]).T.droplevel([0,1], axis=0)

print(df_new[['Name','Age','City']])

Output:

       Name Age     City
0      Andy  18      Ely
0     Laura  18      Ely
1      Joel  39   Odessa
1   Claudia  36   Odessa
2  Terrence  50  Horsens
2     Lilly  51  Horsens

In this implementation, we first stack the DataFrame using stack. We then add a cumulative count column using groupby and cumcount. Finally, we set the index to the cumulative count column and unstack the DataFrame using unstack.

Both methods achieve the same result: reshaping the DataFrame so that each row shares the same value for the repeated columns. However, the choice of method depends on your specific use case and personal preference.

Last modified on 2023-10-07