Pandas iterrows() in python

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In Python, pandas.DataFrame.iterrows() is a method that allows you to loop over a DataFrame row by row. It generates an iterator, returning each index and row data as a pair.

Example

import pandas as pd

data = {'Name': ['Alice', 'Bob', 'Charlie'],
        'Age': [25, 30, 35]}

df = pd.DataFrame(data)

for index, row in df.iterrows():
    print(f"Index: {index}, Name: {row['Name']}, Age: {row['Age']}")

What it does

The iterrows() method loops through each row of the DataFrame. In each iteration, it returns:

  • The index of the row
  • A Series object containing the row’s data

Examples

Example 1: Basic usage with a small DataFrame

import pandas as pd

data = {'Fruit': ['Apple', 'Banana', 'Cherry'],
        'Quantity': [10, 20, 15]}

df = pd.DataFrame(data)

for index, row in df.iterrows():
    print(f"Fruit: {row['Fruit']}, Quantity: {row['Quantity']}")

This loops over each row, printing the fruit and its quantity. It’s simple and direct for accessing each row’s values individually.

Example 2: Using iterrows() to modify DataFrame content

import pandas as pd

data = {'Price': [5, 10, 7]}
df = pd.DataFrame(data)

for index, row in df.iterrows():
    df.at[index, 'Price'] = row['Price'] * 2

print(df)

In this example, iterrows() is used to update each row’s ‘Price’ by multiplying it by 2. Note: This method works, but using vectorized operations (like df['Price'] *= 2) is generally faster.

Example 3: Working with mixed data types

import pandas as pd

data = {'Item': ['Book', 'Pen', 'Notebook'],
        'Stock': [50, 200, 150],
        'InStock': [True, True, False]}

df = pd.DataFrame(data)

for index, row in df.iterrows():
    print(f"Item: {row['Item']}, Stock: {row['Stock']}, InStock: {row['InStock']}")

iterrows() can handle different data types, making it easy to access and print various kinds of information in the DataFrame, such as strings, integers, and booleans.

Example 4: Avoid modifying DataFrame while iterating

import pandas as pd

data = {'A': [1, 2, 3]}
df = pd.DataFrame(data)

for index, row in df.iterrows():
    row['A'] = row['A'] * 2  # This will not modify the DataFrame

print(df)

Here, modifying row within iterrows() does not change the DataFrame itself. To modify the DataFrame, use df.at[] or other methods. This shows a key limitation of iterrows().

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