5 Effective Ways to Delete a Column from a DataFrame Using the pop Function in Python

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πŸ’‘ Problem Formulation: You’re working with a DataFrame in Python using the pandas library and you need to remove a specific column. For instance, starting with a DataFrame that includes columns [‘A’, ‘B’, ‘C’], you want to delete the column ‘B’ to have a DataFrame with just columns [‘A’, ‘C’]. This article provides several methods to accomplish this task efficiently, focusing particularly on the pop() function.

Method 1: Using the pop() Function

The pop() function is a versatile method to remove a column from a DataFrame. It not only deletes the column but also returns the series for further use if needed, which can be highly convenient in many scenarios.

Here’s an example:

import pandas as pd

df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6],
    'C': [7, 8, 9]
})
popped_col = df.pop('B')

Output:

   A  C
0  1  7
1  2  8
2  3  9

This snippet shows the removal of column ‘B’ from the DataFrame ‘df’. The pop() function not only removes the column but also stores it in the variable ‘popped_col’. This can be useful if you need to use the popped column later in your code.

Method 2: Dropping a Column Inline

This method involves the use of the drop() function, which is an alternative to the pop() function. Unlike pop(), the drop() function requires specifying the axis parameter as 1 or ‘columns’ to target columns specifically.

Here’s an example:

df = df.drop('B', axis=1)

Output:

   A  C
0  1  7
1  2  8
2  3  9

In this approach, we use drop() to remove the column ‘B’. We need to specify axis=1 to indicate that we want to drop a column rather than a row.

Method 3: Using the del Keyword

The Python del keyword is a straightforward way to remove objects. When handling DataFrames, you can easily delete a column using this method.

Here’s an example:

del df['B']

Output:

   A  C
0  1  7
1  2  8
2  3  9

By using the del keyword, we instruct Python to delete the column ‘B’ from the DataFrame ‘df’. This operation is performed in place and the column is removed immediately, without the option to be returned or stored.

Method 4: Reassign DataFrame Columns

Another way to remove a column is by reassigning the DataFrame to include only the columns we want to keep. This method provides flexibility when working with multiple columns.

Here’s an example:

df = df[['A', 'C']]

Output:

   A  C
0  1  7
1  2  8
2  3  9

This code demonstrates the removal of column ‘B’ by selecting only the columns ‘A’ and ‘C’ and reassigning them back to ‘df’. This method is particularly useful when you want to keep a specific subset of columns and discard the rest.

Bonus One-Liner Method 5: Select Columns with loc[]

Using the loc[] attribute, you can specify the columns to keep, effectively removing the ones you do not include in the selection.

Here’s an example:

df = df.loc[:, df.columns != 'B']

Output:

   A  C
0  1  7
1  2  8
2  3  9

Here, the loc[] attribute is used with boolean indexing to select all columns except ‘B’, effectively deleting it from the DataFrame.

Summary/Discussion

  • Method 1: Using pop() function. Strengths: returns the removed series. Weaknesses: can only remove one column at a time.
  • Method 2: Dropping a column inline using drop(). Strengths: Can drop multiple columns at once. Weaknesses: a bit more verbose and requires the axis parameter.
  • Method 3: Using the del keyword. Strengths: Very Pythonic and quick. Weaknesses: Permanent operation, doesn’t allow return of the column.
  • Method 4: Reassign DataFrame Columns. Strengths: Intuitive and flexible for selecting multiple columns. Weaknesses: Can be inefficient with large DataFrames.
  • Method 5: Select Columns with loc[]. Strengths: Powerful selection capabilities. Weaknesses: Slightly more complex syntax.