Filtering One Pandas DataFrame with the Columns of Another DataFrame Efficiently Using GroupBy Approach

Filtering One Pandas DataFrame with the Columns of Another DataFrame

As a data analyst or scientist working with pandas DataFrames, you often need to perform various operations on your data. In this article, we will explore how to filter one pandas DataFrame using the columns of another DataFrame efficiently.

Problem Statement

Suppose you have two DataFrames: df1 and df2. You want to add a new column to df1 such that for each row in df1, it calculates the sum of values in df2 where the value is greater than or equal to the threshold defined in df1.

Non-Pythonic Approach

One way to achieve this using loops, as shown in your example:

import numpy as np
import pandas as pd

# Create sample DataFrames
df1 = pd.DataFrame({"id": ["s1", "s2", "s3"], "threshold": [1, 2, 7]})
df2 = pd.DataFrame({"id": ["s1", "s1", "s1", "s2", "s2", "s3", "s3", "s3", "s5", "s5"], "value": [2, -1, 1, -3, 3, 3, 4, 2, 1, 6]})

df1['my_stat_column'] = 0  # Initialize

for i in range(0, df1.shape[0]):
    s = df1.iloc[i]['id']
    t = df1.iloc[i]['threshold']

    for v in range(0, df2.shape[0]):
        non_pythonic_and_stupid_way = df2[(df2['id'] == s) & (df2['value'] >= t)]
        my_stat_value = non_pythonic_and_stupid_way['value'].sum()
        df1.iloc[i]['my_stat_column'] = my_stat

print(df1.head())

This approach is not recommended as it is slow and can lead to performance issues for large DataFrames.

Efficient Approach Using GroupBy

A more efficient way to achieve this using pandas’ groupby function:

import numpy as np
import pandas as pd

# Create sample DataFrames
df1 = pd.DataFrame({"id": ["s1", "s2", "s3"], "threshold": [1, 2, 7]})
df2 = pd.DataFrame({"id": ["s1", "s1", "s1", "s2", "s2", "s3", "s3", "s3", "s5", "s5"], "value": [2, -1, 1, -3, 3, 3, 4, 2, 1, 6]})

# Convert columns to numeric
df2['value'] = pd.to_numeric(df2['value'])
df1['threshold'] = pd.to_numeric(df1['threshold'])

# Set id as index for alignment
df1.set_index('id', inplace=True)
df2.set_index('id', inplace=True)

# Mark values greater than or equal to threshold
df2['valid'] = df2['value'].ge(df1['threshold'])
df2['valid'] = df2['valid'] * df2['value']

# Groupby and sum
df1['newcolumn'] = df2.groupby('id')['valid'].sum()

print(df1.head())

This approach is much faster and more efficient than the non-pythonic approach.

Alternative Approach Using Merge

Another way to achieve this using pandas’ merge function:

import numpy as np
import pandas as pd

# Create sample DataFrames
df1 = pd.DataFrame({"id": ["s1", "s2", "s3"], "threshold": [1, 2, 7]})
df2 = pd.DataFrame({"id": ["s1", "s1", "s1", "s2", "s2", "s3", "s3", "s3", "s5", "s5"], "value": [2, -1, 1, -3, 3, 3, 4, 2, 1, 6]})

# Convert columns to numeric
df2['value'] = pd.to_numeric(df2['value'])
df1['threshold'] = pd.to_numeric(df1['threshold'])

# Merge DataFrames on id
new_df = df2.merge(df1, on='id', how='outer')

# Mark values greater than or equal to threshold
new_df['valid'] = new_df['value'].ge(new_df['threshold']) * new_df['value']

# Groupby and sum
result = new_df.groupby('id')['valid'].sum()

print(result)

This approach is also efficient but may not be as readable as the groupby approach.

Conclusion

In this article, we explored how to filter one pandas DataFrame using the columns of another DataFrame efficiently. We discussed three approaches: a non-pythonic approach using loops, an efficient approach using groupby, and alternative approaches using merge. The groupby approach is recommended as it is both efficient and readable.

Last modified on 2024-05-24