Removing Duplicated Rows Based on Values in a Column

In this article, we will explore how to remove duplicated rows from a DataFrame based on values in a specific column. This is a common problem in data analysis and machine learning, where duplicate rows can cause issues with model training or result interpretation.

Understanding the Problem

The problem of removing duplicated rows from a DataFrame is a classic example of a data preprocessing task. In this case, we are given a DataFrame df that contains duplicate rows based on values in column Category. We want to remove these duplicates while keeping the information about category.

Solution Overview

To solve this problem, we can use the get_dummies function from pandas library to create new columns for each unique value in the Category column. Then, we can group the DataFrame by the original values and aggregate the new columns using either the max, last, or a custom aggregation function.

Solution 1: Using groupby and agg

One approach is to use the get_dummies function to create new columns for each unique value in the Category column. Then, we can group the DataFrame by the original values and aggregate the new columns using the max aggregation function.

# Step 1: Create new columns for each unique value in the 'Category' column
dummies = pd.get_dummies(df[['Category']], drop_first=True)

# Step 2: Group the DataFrame by the original values and aggregate the new columns using max
df = (pd.get_dummies(df, columns=['Category'])
       .groupby(['Name','Id'], as_index=False)
         .max())

This will create a new DataFrame df that contains only one row for each unique combination of values in Name and Id.

Solution 2: Using Custom Aggregation Function

Another approach is to use a custom aggregation function that takes into account both the numeric and non-numeric values. In this case, we will define a lambda function f that returns the maximum value if it’s a number, otherwise returns the last value.

# Step 1: Define a custom aggregation function
f = lambda x: x.max() if np.issubdtype(x.dtype, np.number) else x.iat[-1]

# Step 2: Group the DataFrame by 'Id' and aggregate using the custom function
df = (pd.get_dummies(df, columns=['Category'])
       .groupby('Id', as_index=False)
         .agg(f))

This will create a new DataFrame df that contains only one row for each unique value in Id.

Solution 3: Specifying Columns for Aggregation

In this approach, we specify the columns to be aggregated using a dictionary. We set 'Name' to ’last’, which means that for non-numeric values, the last value will be used.

# Step 1: Define the aggregation function for each column
d = dict.fromkeys(df.columns, 'max')
d['Name'] = 'last'
d['Id'] = 'max'

# Step 2: Group the DataFrame by 'Id' and aggregate using the specified columns
df = df.groupby('Id', as_index=False).agg(d)

This will create a new DataFrame df that contains only one row for each unique value in Id.

Conclusion

In this article, we explored how to remove duplicated rows from a DataFrame based on values in a specific column. We provided three solutions using different aggregation functions and techniques. The choice of solution depends on the specific requirements of the problem, such as handling non-numeric values or specifying columns for aggregation.

By applying these solutions, you can efficiently remove duplicated rows from your DataFrames and improve the quality of your data analysis results.

Code

# Import necessary libraries
import pandas as pd
import numpy as np

# Create a sample DataFrame with duplicates
data = {
    'Name': ['ABC', 'ABC', 'DEF', 'GHI', 'JKL'],
    'Id': [1, 2, 2, 3, 4],
    'Category': ['A', 'B', 'C', 'D', 'E']
}
df = pd.DataFrame(data)

# Print the original DataFrame
print("Original DataFrame:")
print(df)

# Solution 1: Using groupby and agg
dummies = pd.get_dummies(df[['Category']], drop_first=True)
df = (pd.get_dummies(df, columns=['Category'])
       .groupby(['Name','Id'], as_index=False)
         .max())
print("\nDataFrame after using groupby and agg:")
print(df)

# Solution 2: Using custom aggregation function
f = lambda x: x.max() if np.issubdtype(x.dtype, np.number) else x.iat[-1]
df = (pd.get_dummies(df, columns=['Category'])
       .groupby('Id', as_index=False)
         .agg(f))
print("\nDataFrame after using custom aggregation function:")
print(df)

# Solution 3: Specifying columns for aggregation
d = dict.fromkeys(df.columns, 'max')
d['Name'] = 'last'
d['Id'] = 'max'
df = df.groupby('Id', as_index=False).agg(d)
print("\nDataFrame after specifying columns for aggregation:")
print(df)

Last modified on 2023-05-28