Pandas DataFrame Iterrows Method

Introduction

In this lab, we will explore the Python Pandas DataFrame.iterrows() method. This method allows us to iterate over the rows of a Pandas DataFrame, returning the index and data for each row.

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Create a DataFrame

First, we need to create a DataFrame to work with. Let's create a simple DataFrame using the Pandas library.

#importing pandas as pd
import pandas as pd

#creating DataFrame
df=pd.DataFrame({"Name":["Navya","Vindya"],"Age":[25,24],"Education":["M.Tech","Ph.d"]},index=['id001', 'id002'])

Iterate over rows using the iterrows() method

To iterate over the rows of the DataFrame, we can use the iterrows() method. This method returns a generator object that contains a tuple of the index and data for each row.

#print the DataFrame
print("The DataFrame is:")
print(df)

#print the generator object
print("Iterate over rows:")
print(df.iterrows())

Use a for loop to access the rows

To access the index and data for each row, we can use a for loop. The row data can be accessed using the row_data variable, and the index can be accessed using the row_index variable.

#for loop to iterate over rows
print("Iterate over rows:")
for row_index, row_data in df.iterrows():
    print("Index:", row_index)
    print("Data:", row_data)

Accessing specific data from a row

We can also access a specific data from a row by specifying the index number. Let's print the value of the "Name" column for each row.

#for loop to access the "Name" column for each row
print("Accessing specific data:")
for row_index, row_data in df.iterrows():
    print("Name:", row_data['Name'])

Summary

In this lab, we learned how to use the iterrows() method in Pandas to iterate over the rows of a DataFrame. We explored how to access the index and data for each row using a for loop, and how to access specific data from a row. The iterrows() method is a useful tool for analyzing and manipulating data in a DataFrame.