Pandas.DataFrame.iterrows() function in Python

Pandas DataFrame.iterrows() is used to iterate 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 form of a series.   

Pandas.DataFrame.iterrows() 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

Pandas DataFrame iterrows() Method

Sometimes we need to iter over the data frame rows and columns without using any loops, in this situation Pandas DataFrame.iterrows() plays a crucial role.

Example 1: 

In the above example, we use Pandas DataFrame.iterrows() to iter over numeric data frame 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 iter over the data frame having no column names using Pandas DataFrame.iterrows()

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.