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Pandas.DataFrame.iterrows() function in Python

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  • Difficulty Level : Basic
  • Last Updated : 23 May, 2022

Pandas DataFrame.iterrows() is used to iterate over a pandas Data frame 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 series.   

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

Example 1: 

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.

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:

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

Example 2:

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 :

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

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


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