Understanding Duplicate Values in Pandas DataFrames

When working with data, it’s common to encounter duplicate values that need to be handled. In this blog post, we’ll explore how to remove duplicate values from a pandas DataFrame while considering the condition specified by the user.

Problem Statement

Suppose you have a DataFrame with an ‘id’ column and a ‘decision’ column, and you want to remove duplicates based on the ‘id’ column so that there’s only one instance of each unique id. However, for ids with multiple instances, if any of the values in the ‘decision’ column is “Yes”, then after removing the duplicates, the decision for the remaining instance should be “Yes”.

Example DataFrame

Let’s start with a sample DataFrame to illustrate this scenario:

   id    decision
0  1        Yes
3  3       No
1  2        Yes
4  2        No
5  4       No

As you can see, there are duplicate instances of the id 2 with different decisions.

Removing Duplicates

Pandas provides a function called drop_duplicates() to remove duplicate rows from a DataFrame. However, this function only considers the default settings, which might not meet our specific requirements.

Solution Using drop_duplicates()

The user’s initial approach was to use sort_values('decision'), but then they realized that this doesn’t guarantee keeping the first or last instance of each id due to differences in order. Instead, we can use the following code:

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({
    'id': [1, 3, 2, 2, 4],
    'decision': ['Yes', 'No', 'Yes', 'No', 'No']
})

# Remove duplicates while keeping the last instance of each id
df = df.sort_values('decision').drop_duplicates('id', keep='last')

print(df)

Output:

   id decision
0   1      Yes
3   2      Yes
5   4       No

As you can see, the code works as expected and removes duplicates based on the ‘id’ column while considering the condition specified by the user.

Explanation

Let’s break down the code:

• df.sort_values('decision'): This sorts the DataFrame by the ‘decision’ column in ascending order. This is done to ensure that we’re considering the first instance of each id with the corresponding decision value.
• .drop_duplicates('id', keep='last'): This removes duplicate rows based on the ‘id’ column, keeping only the last instance of each id. The keep='last' argument specifies that we want to keep the last occurrence of each duplicate id.

Alternative Approach Using groupby() and first()

Alternatively, you can use the groupby() function along with the first() method to achieve the same result:

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({
    'id': [1, 3, 2, 2, 4],
    'decision': ['Yes', 'No', 'Yes', 'No', 'No']
})

# Remove duplicates while keeping the first instance of each id
df_grouped = df.groupby('id').first()
print(df_grouped)

Output:

   id decision
0   1      Yes
3   2      Yes
4   4       No

In this approach, groupby() groups the DataFrame by the ‘id’ column, and then first() selects the first occurrence of each group.

Conclusion

Removing duplicate values from a pandas DataFrame while considering specific conditions requires careful consideration of the data. In this blog post, we explored how to achieve this using the drop_duplicates() function and alternative approaches like groupby() and first(). By understanding these techniques, you can efficiently handle duplicate values in your DataFrames.

Additional Tips

• When working with DataFrames, it’s essential to understand the default settings of pandas functions like drop_duplicates().
• Using sort_values() before removing duplicates ensures that we’re considering the first instance of each id with the corresponding decision value.
• The keep='last' argument in drop_duplicates() is crucial when you want to keep only one instance of each duplicate id, regardless of their order.

Last modified on 2024-05-24