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 namedf = 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 framedf = 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.
