Understanding the Issue with Mapping Two Values in a Column

When working with dataframes in Python, it’s not uncommon to encounter issues when mapping values from one column to another. In this article, we’ll delve into the problem of having duplicate values in a column after concatenating two dataframes and explore ways to resolve this issue.

Introduction to Dataframe Concatenation

Dataframe concatenation is a common operation in data science when working with pandas dataframes. It allows us to combine multiple dataframes into a single dataframe, which can be useful for various tasks such as creating aggregated data or merging data from different sources.

However, when we concatenate two dataframes using the concat() function, we may encounter issues related to duplicate values in certain columns. This is because the concatenation operation preserves the original data types and structure of each dataframe, including any existing duplicates.

The Problem with Mapping Two Values

In our example, we concatenated two dataframes and then tried to map some values in the new dataframe. However, we encountered an issue when trying to count the occurrences of certain values in the “Survey” column using value_counts(). Specifically, we had duplicate values like “CUSTOMER CARE” which appeared twice in the output.

Understanding the Cause

To understand why this happened, let’s take a closer look at the data. The original dataframe had two columns: “BOUTIQUE” and “OUTLET”, which were mapped to each other using map(). This worked fine for the values without leading or trailing spaces.

However, when we concatenated the second dataframe, it introduced new values with leading or trailing spaces, including “CUSTOMER CARE ‘”. When we tried to count these occurrences using value_counts(), pandas treated them as separate values due to the extra spaces.

Resolving the Issue

To resolve this issue, we need to remove any leading or trailing spaces from the “Survey” column. We can do this using the str.strip() method provided by pandas dataframes. This method removes any leading or trailing whitespace characters from each string value in the specified column.

Here’s an example of how we can apply str.strip() to our dataframe:

Final_DF['Survey'] = Final_DF['Survey'].str.strip()

By applying this step, we ensure that pandas treats the duplicate values as a single entity, rather than separate values due to leading or trailing spaces.

Verifying the Solution

After removing the extra spaces from the “Survey” column, we can re-run value_counts() to verify that our solution worked correctly. This time, we should see a single value for each unique survey response without any duplicates.

Let’s take a look at the corrected code:

import pandas as pd

# Create sample dataframes
df1 = pd.DataFrame({'BOUTIQUE': [100, 200], 'OUTLET': [300, 400]})
df2 = pd.DataFrame({'Survey': ['CUSTOMER CARE ', 'E-COMMERCE', False]})

# Concatenate the dataframes
Final_DF = pd.concat([df1, df2])

# Map values in the new dataframe
Final_DF['BOUTIQUE'] = Final_DF['BOUTIQUE'].map({'BOUTIQUE': 100, 'OUTLET': 300})
Final_DF['OUTLET'] = Final_DF['OUTLET'].map({'OUTLET': 400})

# Remove leading/trailing spaces from the Survey column
Final_DF['Survey'] = Final_DF['Survey'].str.strip()

# Count occurrences of unique survey responses
survey_counts = Final_DF['Survey'].value_counts()
print(survey_counts)

When we run this code, we should see a single value for each unique survey response without any duplicates.

Conclusion

In conclusion, when working with dataframes in Python, it’s essential to understand how concatenation operations can lead to duplicate values in certain columns. By removing leading or trailing spaces from the affected column using str.strip(), we can resolve this issue and get accurate counts of unique survey responses.

This article demonstrated a common problem that arises when mapping two values of the same column after concatenating dataframes, provided a step-by-step solution to resolve it, and covered the underlying concepts and technical details involved.

Last modified on 2025-01-13