Modifying Data in Pandas

Last Updated: 29th August 2025

What you'll learn

  • How to change values in a DataFrame or Series
  • How to replace values in a DataFrame or Series
  • replace() : Replace specific values with another value
  • where(): Replace values based on a condition.
  • mask(): Replace values based on a boolean mask.
  • How to add or remove rows or columns
  • Rename columns,index etc

Let Data is like this

import pandas as pd

df = pd.DataFrame({
  "Name": ["Amit", "Ravi", "Neha"],
  "Age": [25, 30, 22],
  "Marks": [85, 90, 95]
})

Modifying DataFrame

Change a Single Value

# Change one value using loc
df.loc[0, "Age"] = 26   # Amit's Age becomes 26

# Change one value using iloc
df.iloc[1, 2] = 92      # Ravi's Marks become 92

print(df)

Update Entire Column

# Increase all ages by 1
df["Age"] = df["Age"] + 1

print(df)

Change Multiple Values

# Change multiple rows/columns
df.loc[0:1, "Marks"] = [88, 93]   # Amit=88, Ravi=93
print(df)
df.loc[0:2,["Marks","Age"]]=[[97,21],[85,12]]
print(df)

Replace values

# Old Method
df["Age"].replace({25: 26, 30: 31}, inplace=True)  # Replace 25 with 26 and 30 with 31
print(df)

# New Method
df.replace({"Age": {25: 26, 30: 31}}, inplace=True)  # Replace 25 with 26 and 30 with 31
print(df)

# Change in  whole Data set
df.replace("Amit","amit",inplace=True)
print(df)

df.replace({"Name":"amit","Age":25},None,inplace=True)
print(df)

df.replace({None:"Unknown"})
print(df)

Update Column with condition

# Increase marks by 5 where Age > 25
df.loc[df["Age"] > 25, "Marks"] += 5
print(df)

# Replace values based on a condition
print(df.where(df["Age"] > 25, "Unknown"))

# Replace values based on a boolean mask
print(df.mask(df["Age"] > 25, "Unknown"))

Adding / Removing columns & rows

Adding a new column

# Add new column "Address"
df["Address"] = ["Kashi", "Delhi", "Lucknow"]
print(df)

Add column with same value

df["Country"] = "India"   # All rows get India
print(df)

Add column at specific position

# df.insert(index,newColName,Value)
df,insert(4,"ZipCode",[221010,213231,202020])

Adding Rows

# Add Single Row
new_row = {"Name": "Kiran", "Age": 28, "Marks": 88, "Country": "India","Address": "Noida"}
df = df._append(new_row, ignore_index=True)   # _append in latest Pandas

print(df)

# Add Multiple Rows
new_rows = [
    {"Name": "Sita", "Age": 24, "Marks": 91, "Country": "India","Address": "Delhi"},
    {"Name": "Sadhu", "Age": 2, "Marks": 0, "Country": "India","Address": "Kashi"}
]
df = df._append(new_rows, ignore_index=True)
print(df)

Removing Columns

# Remove one column
df = df.drop("Country", axis=1)

# Remove multiple columns
df = df.drop(["Age", "Marks"], axis=1)

print(df)

Removing Rows

# Remove row by index  (By default axis=0)
df = df.drop(1, axis=0)   # removes 2nd row (Ravi)

# Remove multiple rows
df = df.drop([0, 2], axis=0)

print(df)

Rename and Reindex

1. Rename columns

# Rename single column
df = df.rename(columns={"Marks": "Score"})

# Rename multiple columns
df = df.rename(columns={"Age": "Years", "Name": "Student_Name"})

2. Rename index

# Rename index
df = df.rename(index={0: "A", 1: "B", 2: "C"})

3. Indexing

# Set index
df = df.set_index("Name")
print(df)

# Reset index
df = df.reset_index()
print(df)
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