Deleting & Renaming

Real data is messy — you'll often need to throw out a column or row, or give a label a better name. drop() and rename() do exactly that, and both hinge on two arguments: axis and inplace.

1Deleting with drop()

drop() removes rows or columns. The axis argument decides which: axis=0 for a row (the default), axis=1 for a column.

df_drop.py

import pandas as pd

df = pd.DataFrame({
    'Name':    ['Rinku', 'Ritu', 'Pankaj', 'Ajay'],
    'English': [65, 77, 72, 81],
    'IP':      [89, 83, 95, 99],
    'Hindi':   [79, 63, 59, 93],
}, index=['ID 1', 'ID 2', 'ID 3', 'ID 4'])

# Drop a COLUMN -> axis=1
print(df.drop('Hindi', axis=1))

# Drop a ROW -> axis=0 (the default)
print("\n--- drop row ID 2 ---")
print(df.drop('ID 2'))

# Drop several at once with a list
print("\n--- drop two columns ---")
print(df.drop(['English', 'Hindi'], axis=1))

import pandas as pd

df = pd.DataFrame({
    'Name':    ['Rinku', 'Ritu', 'Pankaj', 'Ajay'],
    'English': [65, 77, 72, 81],
    'IP':      [89, 83, 95, 99],
    'Hindi':   [79, 63, 59, 93],
}, index=['ID 1', 'ID 2', 'ID 3', 'ID 4'])

# Drop a COLUMN -> axis=1
print(df.drop('Hindi', axis=1))

# Drop a ROW -> axis=0 (the default)
print("\n--- drop row ID 2 ---")
print(df.drop('ID 2'))

# Drop several at once with a list
print("\n--- drop two columns ---")
print(df.drop(['English', 'Hindi'], axis=1))

Note

By default drop() returns a new DataFrame and leaves the original alone. Add inplace=True to change the original itself: df.drop('Hindi', axis=1, inplace=True).

Tip

There's also the del statement — del df['Hindi'] — but it can only delete a column, and it always changes the original.

2Renaming with rename()

rename() changes labels using a dictionary of {old: new}. Use the index= argument for rows and columns= for columns — you can do both at once.

df_rename.py

import pandas as pd

df = pd.DataFrame({
    'Name':    ['Rinku', 'Ritu', 'Pankaj', 'Ajay'],
    'English': [65, 77, 72, 81],
    'IP':      [89, 83, 95, 99],
    'Hindi':   [79, 63, 59, 93],
}, index=['ID 1', 'ID 2', 'ID 3', 'ID 4'])

# Rename rows
print(df.rename(index={'ID 1': 'Roll 1', 'ID 2': 'Roll 2'}))

# Rename columns
print("\n--- rename columns ---")
print(df.rename(columns={'English': 'English Score', 'IP': 'IP Score'}))

# Both at once
print("\n--- both ---")
print(df.rename(index={'ID 4': 'Roll 4'}, columns={'IP': 'IP Score'}))

import pandas as pd

df = pd.DataFrame({
    'Name':    ['Rinku', 'Ritu', 'Pankaj', 'Ajay'],
    'English': [65, 77, 72, 81],
    'IP':      [89, 83, 95, 99],
    'Hindi':   [79, 63, 59, 93],
}, index=['ID 1', 'ID 2', 'ID 3', 'ID 4'])

# Rename rows
print(df.rename(index={'ID 1': 'Roll 1', 'ID 2': 'Roll 2'}))

# Rename columns
print("\n--- rename columns ---")
print(df.rename(columns={'English': 'English Score', 'IP': 'IP Score'}))

# Both at once
print("\n--- both ---")
print(df.rename(index={'ID 4': 'Roll 4'}, columns={'IP': 'IP Score'}))

Key Takeaway

drop() removes — axis=0 a row, axis=1 a column. rename() relabels — index= rows, columns= columns. Both need inplace=True to change the original DataFrame.

Quick Check

How do you drop the column 'Hindi'?

Quick Check

After df.drop('ID 2', axis=0), why is the original df unchanged?

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