Dropping columns and rows in Pandas

Introduction

Sometimes, data has extra information we don't need. Dropping columns or rows helps us keep only the useful parts.

You want to remove a column with irrelevant data from your table.

You need to delete rows that have missing or wrong information.

You want to simplify your data by keeping only certain columns.

You want to clean your data before analysis by removing unwanted rows or columns.

Syntax

Pandas

DataFrame.drop(labels=None, axis=0, inplace=False)

labels: Name or list of names of rows or columns to drop.

axis=0 means drop rows, axis=1 means drop columns.

Examples

Drop a single column named 'column_name'.

Pandas

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

Drop multiple rows named 'row1' and 'row2'.

Pandas

df.drop(['row1', 'row2'], axis=0)

Drop a column and change the original DataFrame directly.

Pandas

df.drop('column_name', axis=1, inplace=True)

Sample Program

This code creates a table with names, ages, and cities. Then it shows how to remove the 'City' column and how to remove the row for Bob (index 1).

Pandas

import pandas as pd

# Create a simple DataFrame
data = {'Name': ['Anna', 'Bob', 'Cara'],
        'Age': [25, 30, 22],
        'City': ['NY', 'LA', 'SF']}
df = pd.DataFrame(data)

print('Original DataFrame:')
print(df)

# Drop the 'City' column
df_dropped_col = df.drop('City', axis=1)
print('\nDataFrame after dropping column City:')
print(df_dropped_col)

# Drop the row with index 1 (Bob)
df_dropped_row = df.drop(1, axis=0)
print('\nDataFrame after dropping row with index 1:')
print(df_dropped_row)

Important Notes

Using inplace=True changes the original DataFrame without needing to assign it again.

If you try to drop a label that does not exist, pandas will raise an error.

Summary

Use drop() to remove rows or columns from your data.

Set axis=0 to drop rows, axis=1 to drop columns.

Use inplace=True to change the original data directly.

Practice

1. What does the drop() function do in pandas?

easy

A. Removes specified rows or columns from a DataFrame

B. Adds new rows or columns to a DataFrame

C. Sorts the DataFrame by index or column values

D. Calculates summary statistics of the DataFrame

Solution

1. Step 1: Understand the purpose of drop()

   The drop() function is used to remove data, either rows or columns, from a DataFrame.

2. Step 2: Identify what drop() removes

   It specifically removes rows or columns based on labels, not adding or sorting data.

3. Final Answer:

   Removes specified rows or columns from a DataFrame -> Option A

4. Quick Check:

   drop() removes rows/columns [OK]

Hint: Remember: drop means remove rows or columns [OK]

Common Mistakes:

• Thinking drop adds data instead of removing
• Confusing drop with sort or describe functions
• Assuming drop changes data without specifying inplace

2. Which of the following is the correct syntax to drop the column named 'Age' from a DataFrame df?

easy

A. df.drop('Age', axis=0)

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

C. df.drop(column='Age')

D. df.drop('Age')

Solution

1. Step 1: Identify axis for columns

   In pandas, axis=1 means columns, so to drop a column, use axis=1.

2. Step 2: Check syntax correctness

   The syntax df.drop('Age', axis=1) correctly drops the column named 'Age'. df.drop(column='Age') is close but incorrect syntax; it should be df.drop(columns=['Age']) with a list.

3. Final Answer:

   df.drop('Age', axis=1) -> Option B

4. Quick Check:

   Drop column uses axis=1 [OK]

Hint: Use axis=1 to drop columns, axis=0 for rows [OK]

Common Mistakes:

• Using axis=0 to drop columns
• Forgetting to specify axis parameter
• Using drop with wrong parameter names

3. Given the DataFrame df below:

   Name  Age  City
0  John   25  NY
1  Anna   30  LA
2  Mike   22  SF

What is the output of df.drop(1, axis=0)?

medium

A. Name Age City
0 John 25 NY
1 Anna 30 LA

B. Name Age City
1 Anna 30 LA

C. Name Age City
0 John 25 NY
2 Mike 22 SF

D. Name Age City
0 John 25 NY
1 Anna 30 LA
2 Mike 22 SF

Solution

1. Step 1: Understand dropping rows by index

   Using drop(1, axis=0) removes the row with index 1, which is the row for Anna.

2. Step 2: Identify remaining rows

   Rows with index 0 (John) and 2 (Mike) remain, so the output excludes Anna's row.

3. Final Answer:

   Name Age City 0 John 25 NY 2 Mike 22 SF -> Option C

4. Quick Check:

   Drop row index 1 removes Anna [OK]

Hint: Drop row by index with axis=0 [OK]

Common Mistakes:

• Confusing axis=0 and axis=1
• Expecting row to be removed by label instead of index
• Not realizing drop returns a new DataFrame by default

4. What is wrong with this code snippet to drop the column 'Salary' from DataFrame df?

df.drop('Salary')

medium

A. drop() cannot remove columns

B. Should use inplace=True to drop the column

C. Column name should be in a list like ['Salary']

D. Missing axis=1 to specify dropping a column

Solution

1. Step 1: Check default axis in drop()

   By default, drop() assumes axis=0, which means it tries to drop a row, not a column.

2. Step 2: Specify axis=1 to drop a column

   To drop a column, you must specify axis=1. Without it, pandas looks for a row named 'Salary' and fails if none exists.

3. Final Answer:

   Missing axis=1 to specify dropping a column -> Option D

4. Quick Check:

   Drop columns requires axis=1 [OK]

Hint: Always specify axis=1 to drop columns [OK]

Common Mistakes:

• Forgetting axis=1 when dropping columns
• Assuming drop changes DataFrame without inplace=True
• Using drop with a single string but wrong axis

5. You have a DataFrame df with columns ['Name', 'Age', 'City', 'Salary']. You want to remove the rows with index 0 and 2, and also drop the column 'Salary' in one line without changing the original DataFrame. Which code does this correctly?

hard

A. df.drop([0, 2], axis=0).drop('Salary', axis=1)

B. df.drop([0, 2, 'Salary'], axis=0)

C. df.drop(['Salary'], axis=1).drop([0, 2], axis=0, inplace=True)

D. df.drop([0, 2], axis=1).drop('Salary', axis=0)

Solution

1. Step 1: Drop rows with index 0 and 2

   Use df.drop([0, 2], axis=0) to remove rows 0 and 2 without changing original DataFrame.

2. Step 2: Drop column 'Salary' from the result

   Chain .drop('Salary', axis=1) to remove the column from the intermediate DataFrame.

3. Step 3: Verify no inplace=True used

   Since we want to keep original DataFrame unchanged, do not use inplace=True. The chained drops return a new DataFrame.

4. Final Answer:

   df.drop([0, 2], axis=0).drop('Salary', axis=1) -> Option A

5. Quick Check:

   Chain drops with correct axis, no inplace [OK]

Hint: Chain drop with axis=0 for rows, axis=1 for columns [OK]

Common Mistakes:

• Mixing axis values for rows and columns
• Using inplace=True which changes original DataFrame
• Trying to drop rows and columns in one drop call incorrectly