Create a Pipeline and remove a column from DataFrame - Python Pandas

Use the ColDrop() method of pdpipe library to remove a column from Pandas DataFrame. The pdpipe library provides a pipeline-based approach for data preprocessing operations.

Installing pdpipe

First, install the pdpipe library ?

pip install pdpipe

Importing Required Libraries

Import the required pdpipe and pandas libraries with their respective aliases ?

import pdpipe as pdp
import pandas as pd

Creating a DataFrame

Let us create a DataFrame with car data. Here, we have two columns ?

import pandas as pd

dataFrame = pd.DataFrame({
    "Car": ['BMW', 'Lexus', 'Audi', 'Mustang', 'Bentley', 'Jaguar'],
    "Units": [100, 150, 110, 80, 110, 90]
})

print("DataFrame...")
print(dataFrame)

DataFrame...
       Car  Units
0      BMW    100
1    Lexus    150
2     Audi    110
3  Mustang     80
4  Bentley    110
5   Jaguar     90

Removing a Column Using ColDrop()

To remove a column from the DataFrame, use the ColDrop() method. Here, we are removing the "Units" column ?

import pdpipe as pdp
import pandas as pd

dataFrame = pd.DataFrame({
    "Car": ['BMW', 'Lexus', 'Audi', 'Mustang', 'Bentley', 'Jaguar'],
    "Units": [100, 150, 110, 80, 110, 90]
})

# Remove column using pdpipe
resDF = pdp.ColDrop("Units").apply(dataFrame)
print("DataFrame after removing 'Units' column...")
print(resDF)

DataFrame after removing 'Units' column...
       Car
0      BMW
1    Lexus
2     Audi
3  Mustang
4  Bentley
5   Jaguar

Complete Pipeline Example

Here's a complete example showing both row and column removal operations ?

import pdpipe as pdp
import pandas as pd

# Create DataFrame
dataFrame = pd.DataFrame({
    "Car": ['BMW', 'Lexus', 'Audi', 'Mustang', 'Bentley', 'Jaguar'],
    "Units": [100, 150, 110, 80, 110, 90]
})

print("Original DataFrame...")
print(dataFrame)

# Remove a row with pdpipe
dataFrame = pdp.ValDrop(['Jaguar'], 'Car').apply(dataFrame)
print("\nDataFrame after removing 'Jaguar' row...")
print(dataFrame)

# Remove a column with pdpipe
resDF = pdp.ColDrop("Units").apply(dataFrame)
print("\nDataFrame after removing 'Units' column...")
print(resDF)

Original DataFrame...
       Car  Units
0      BMW    100
1    Lexus    150
2     Audi    110
3  Mustang     80
4  Bentley    110
5   Jaguar     90

DataFrame after removing 'Jaguar' row...
       Car  Units
0      BMW    100
1    Lexus    150
2     Audi    110
3  Mustang     80
4  Bentley    110

DataFrame after removing 'Units' column...
       Car
0      BMW
1    Lexus
2     Audi
3  Mustang
4  Bentley

Conclusion

The pdpipe library provides a clean pipeline approach for DataFrame operations. Use ColDrop() to remove columns and ValDrop() to remove rows based on specific values.