Adding & Modifying

DataFrames are size-mutable — you can grow them. There's one rule that runs through all of it: assign to a name that exists and you modify it; assign to a new name and you create it.

1Adding & changing a column

Assign to df['col']. If col doesn't exist, a new column appears; if it does, its values change. A single value fills the whole column; a list sets each row.

df_columns.py

import pandas as pd

df = pd.DataFrame({
    'Name':    ['Rinku', 'Ritu', 'Pankaj'],
    'English': [65, 77, 72],
    'IP':      [89, 83, 95],
}, index=['ID 1', 'ID 2', 'ID 3'])

print("--- BEFORE ---")
print(df)

# New column, single value -> every row gets 79
df['Hindi'] = 79

# New column, a list -> one value per row
df['French'] = [98, 77, 63]

# 'IP' already exists -> this MODIFIES it
df['IP'] = df['IP'] + 5   # bonus marks!

print("\n--- AFTER ---")
print(df)

import pandas as pd

df = pd.DataFrame({
    'Name':    ['Rinku', 'Ritu', 'Pankaj'],
    'English': [65, 77, 72],
    'IP':      [89, 83, 95],
}, index=['ID 1', 'ID 2', 'ID 3'])

print("--- BEFORE ---")
print(df)

# New column, single value -> every row gets 79
df['Hindi'] = 79

# New column, a list -> one value per row
df['French'] = [98, 77, 63]

# 'IP' already exists -> this MODIFIES it
df['IP'] = df['IP'] + 5   # bonus marks!

print("\n--- AFTER ---")
print(df)

Watch Out

If you give a list, its length must match the number of rows — otherwise Pandas raises a ValueError.

2Adding & changing a row

Use .loc (or .at) with a row label and : for all columns. A missing label creates a new row; an existing one is overwritten.

df_rows.py

import pandas as pd

df = pd.DataFrame({
    'Name':    ['Rinku', 'Ritu', 'Pankaj'],
    'English': [65, 77, 72],
    'IP':      [89, 83, 95],
}, index=['ID 1', 'ID 2', 'ID 3'])

print("--- BEFORE (3 rows) ---")
print(df)

# 'ID 4' doesn't exist -> a NEW row is created
df.loc['ID 4'] = ['Ajay', 81, 99]

# 'ID 2' exists -> this row is MODIFIED
df.loc['ID 2'] = ['Ritu K.', 80, 85]

print("\n--- AFTER (4 rows, and ID 2 has changed) ---")
print(df)

import pandas as pd

df = pd.DataFrame({
    'Name':    ['Rinku', 'Ritu', 'Pankaj'],
    'English': [65, 77, 72],
    'IP':      [89, 83, 95],
}, index=['ID 1', 'ID 2', 'ID 3'])

print("--- BEFORE (3 rows) ---")
print(df)

# 'ID 4' doesn't exist -> a NEW row is created
df.loc['ID 4'] = ['Ajay', 81, 99]

# 'ID 2' exists -> this row is MODIFIED
df.loc['ID 2'] = ['Ritu K.', 80, 85]

print("\n--- AFTER (4 rows, and ID 2 has changed) ---")
print(df)

3Modifying a single cell

To change just one value, target the exact cell with .at (labels) or .iat (positions). Row first, then column.

df_cell.py

import pandas as pd

df = pd.DataFrame({
    'Name':    ['Rinku', 'Ritu', 'Pankaj'],
    'English': [65, 77, 72],
    'IP':      [89, 83, 95],
}, index=['ID 1', 'ID 2', 'ID 3'])

print("--- BEFORE ---")
print(df)

df.at['ID 3', 'English'] = 88     # by labels
df.iat[0, 2] = 91                 # by positions (row 0, col 2 = IP)

print("\n--- AFTER (only two cells moved) ---")
print(df)

import pandas as pd

df = pd.DataFrame({
    'Name':    ['Rinku', 'Ritu', 'Pankaj'],
    'English': [65, 77, 72],
    'IP':      [89, 83, 95],
}, index=['ID 1', 'ID 2', 'ID 3'])

print("--- BEFORE ---")
print(df)

df.at['ID 3', 'English'] = 88     # by labels
df.iat[0, 2] = 91                 # by positions (row 0, col 2 = IP)

print("\n--- AFTER (only two cells moved) ---")
print(df)

Watch Out

Don't change a cell with the column-then-row shortcut df['English']['ID 2'] = 50. Older Pandas changes the value, with a warning. Pandas 3 does not change the table at all. Use df.at['ID 2', 'English'] = 50 instead. The shortcut is still fine for reading one value.

Key Takeaway

The golden rule: assigning to an existing column/row modifies it; assigning to a new label creates it. A single value broadcasts to all cells; a list sets them one by one.

Quick Check

df has no 'Science' column. What does df['Science'] = 50 do?

Quick Check

Which changes just ONE cell — Pankaj's IP mark?

🎬 Watch the video

DataFrame: Add, Change, Delete, Rename, in 6:45

One animated video of this topic, narrated. Watch it before the lessons for the big picture, or after them to revise.

Watch on YouTube ↗All the videos, in order ↗

🔊 Hindi mein sunna hai? Watch on YouTube with Hindi audio ↗(there, choose ⚙ Settings → Audio track → Hindi)

← Previous

🎯 Rows & Subsets: loc / iloc

Deleting & Renaming ✏️