Pandas iterrows() - Iterate over DataFrame Rows - Examples

Pandas - Iterate over Rows - iterrows()

To iterate over rows of a Pandas DataFrame, use DataFrame.iterrows() function which returns an iterator yielding index and row data for each row.

https://youtu.be/Z8myvjgSSJs

In this tutorial, we will go through examples demonstrating how to iterate over rows of a DataFrame using iterrows().

Syntax of iterrows()

The syntax of iterrows() is

DataFrame.iterrows(self)

iterrows yields

• index - index of the row in DataFrame. This could be a label for single index, or tuple of label for multi-index.
• data - data is the row data as Pandas Series.
• it - it is the generator that iterates over the rows of DataFrame.

Examples

1. Pandas iterrows() - Iterate over rows of DataFrame

In this example, we will initialize a DataFrame with four rows and iterate through them using Python For Loop and iterrows() function.

Python Program

import pandas as pd

#create dataframe
df_marks = pd.DataFrame({
    'name': ['apple', 'banana', 'orange', 'mango'],
	'calories': [68, 74, 77, 78]})

#iterate through each row of dataframe
for index, row in df_marks.iterrows():
    print(index, ': ', row['name'], 'has', row['calories'], 'calories.')

During each iteration, we are able to access the index of row, and the contents of row.

Output

0 :  apple has 68 calories.
1 :  banana has 74 calories.
2 :  orange has 77 calories.
3 :  mango has 78 calories.

Please note that the calories information is not factual. The example is for demonstrating the usage of iterrows().

2. iterrows() yeilds index, Series

In the previous example, we have seen that we can access index and row data.

In this example, we will investigate the type of row data that iterrows() returns during iteration.

Python Program

import pandas as pd

#create dataframe
df_marks = pd.DataFrame({
    'name': ['apple', 'banana', 'orange', 'mango'],
	'calories': [68, 74, 77, 78]})

#iterate through each row of dataframe
for index, row in df_marks.iterrows():
    print(type(index), type(row))

Output

<class 'int'> <class 'pandas.core.series.Series'>
<class 'int'> <class 'pandas.core.series.Series'>
<class 'int'> <class 'pandas.core.series.Series'>
<class 'int'> <class 'pandas.core.series.Series'>

We did not provide any index to the DataFrame, so the default index would be integers from zero and incrementing by one. So, iterrows() returned index as integer.

iterrows() returns the row data as Pandas Series.

Summary

In this Pandas Tutorial, we used DataFrame.iterrows() to iterate over the rows of Pandas DataFrame, with the help of detailed example programs.