How to iterate over rows of a pandas data frame in python ?

To iterate over rows of a pandas data frame in python, a solution is to use iterrows(), items() or itertuples():

Let's consider the following pandas data frame:

>>> import pandas as pd
>>> data = {'Country':['USA', 'Spain', 'France', 'Canada'], 'Age':[10, 39, 21, 70]} 
>>> df = pd.DataFrame(data, index =['Bob', 'Jack', 'Ben', 'Paul'])
>>> df
      Age Country
Bob    10     USA
Jack   39   Spain
Ben    21  France
Paul   70  Canada

Using iterrows()

To go through all rows of the above data frame and print all associated columns, a solution is to use iterrows():

>>> for index, row in df.iterrows():
...     print(index)
...     print(row)
... 
Bob
Age         10
Country    USA
Name: Bob, dtype: object
Jack
Age           39
Country    Spain
Name: Jack, dtype: object
Ben
Age            21
Country    France
Name: Ben, dtype: object
Paul
Age            70
Country    Canada
Name: Paul, dtype: object

Using items()

To get the variation of columns in function of the data frame indexes, a solution is to use items():

>>> for label, content in df.items():
...     print(label)
...     print(content)
... 
Age
Bob     10
Jack    39
Ben     21
Paul    70
Name: Age, dtype: int64
Country
Bob        USA
Jack     Spain
Ben     France
Paul    Canada
Name: Country, dtype: object

To focus on a given column:

>>> for label, content in df['Age'].items():
...     print(label,content)
... 
Bob 10
Jack 39
Ben 21
Paul 70

Using itertuples()

Another solution is to use itertuples():

>>> for row in df.itertuples():
...     print(row)
... 
Pandas(Index='Bob', Age=10, Country='USA')
Pandas(Index='Jack', Age=39, Country='Spain')
Pandas(Index='Ben', Age=21, Country='France')
Pandas(Index='Paul', Age=70, Country='Canada')

Example 2:

>>> for row in df.itertuples():
...     print(row[0],row[2])
... 
Bob USA
Jack Spain
Ben France
Paul Canada

Example 3:

>>> for row in df.itertuples():
...     print(row.Age)
... 
10
39
21
70

References