Pandas DataFrame iterrows() Method | Pandas Method

Pandas DataFrame iterrows() iterates over a Pandas DataFrame rows in the form of (index, series) pair. This function iterates over the data frame column, it will return a tuple with the column name and content in the form of a series.

Example:

Python

import pandas as pd
df = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, 32, 37]
})
for index, row in df.iterrows():
    print(f"Row {index} data:")
    print(f"Name: {row['Name']}, Age: {row['Age']}")

Output:

Row 0 data:
Name: Alice, Age: 25
Row 1 data:
Name: Bob, Age: 32
Row 2 data:
Name: Charlie, Age: 37

Syntax

Syntax: DataFrame.iterrows()

Yields:

• index- The index of the row. A tuple for a MultiIndex 
• data- The data of the row as a Series 

Returns: it: A generator that iterates over the rows of the frame

Examples

Let’s understand how to iterate over the rows of DataFrame using iterrows method of Pandas library:

Example 1: 

In the below example, we use Pandas DataFrame.iterrows() to iter over numeric DataFrame rows.

Python3

import pandas as pd

# Creating a data frame along with column name
df = pd.DataFrame([[2, 2.5, 100, 4.5, 8.8, 95]], columns=[
                  'int', 'float', 'int', 'float', 'float', 'int'])

# Iter over the data frame rows
# # using df.iterrows()
itr = next(df.iterrows())[1]
itr

Output:

Example 2:

In the example, we iterate over the DataFrame having no column names using Pandas DataFrame.iterrows() function

Python3

import pandas as pd

# Creating a data frame
df = pd.DataFrame([['Animal', 'Baby', 'Cat', 'Dog',
                    'Elephant', 'Frog', 'Gragor']])

# Iterating over the data frame rows
# using df.iterrows()
itr = next(df.iterrows())[1]
itr

Output :

Note: As iterrows returns a Series for each row, it does not preserve dtypes across the rows.