In this article we will different ways to iterate over all or certain columns of a Dataframe.

Let’s first create a Dataframe i.e.

# List of Tuples
empoyees = [('jack', 34, 'Sydney') ,
           ('Riti', 31, 'Delhi') ,
           ('Aadi', 16, 'New York') ,
           ('Mohit', 32,'Delhi') ,
            ]

# Create a DataFrame object
empDfObj = pd.DataFrame(empoyees, columns=['Name', 'Age', 'City'], index=['a', 'b', 'c', 'd'])

Contents of created dataframe empDfObj  are,
    Name  Age      City
a   jack   34    Sydney
b   Riti   31     Delhi
c   Aadi   16  New York
d  Mohit   32     Delhi

Iterate over columns of a DataFrame using DataFrame.iteritems()

Dataframe class provides a member function iteritems() i.e.

DataFrame.iteritems()

It yields an iterator which can can be used to iterate over all the columns of a dataframe. For each column in the Dataframe it returns an iterator to the tuple containing the column name and column contents as series.

Let’s user iteritems() to iterate over the columns of above created Dataframe,

# Yields a tuple of column name and series for each column in the dataframe
for (columnName, columnData) in empDfObj.iteritems():
   print('Colunm Name : ', columnName)
   print('Column Contents : ', columnData.values)

Output:
Colunm Name :  Name
Column Contents :  ['jack' 'Riti' 'Aadi' 'Mohit']
Colunm Name :  Age
Column Contents :  [34 31 16 32]
Colunm Name :  City
Column Contents :  ['Sydney' 'Delhi' 'New York' 'Delhi']

As there were 3 columns so 3 tuples were returned during iteration.

Iterate over columns in dataframe using Column Names

Dataframe.columns returns a sequence of column names. We can iterate over these column names and for each column name we can select the column contents by column name i.e.

# Iterate over the sequence of column names
for column in empDfObj:
   # Select column contents by column name using [] operator
   columnSeriesObj = empDfObj[column]
   print('Colunm Name : ', column)
   print('Column Contents : ', columnSeriesObj.values)

Output:
Colunm Name :  Name
Column Contents :  ['jack' 'Riti' 'Aadi' 'Mohit']
Colunm Name :  Age
Column Contents :  [34 31 16 32]
Colunm Name :  City
Column Contents :  ['Sydney' 'Delhi' 'New York' 'Delhi']

Iterate over certain columns in dataframe

Suppose we want to iterate over two columns i.e. Name & Age in the above created dataframe. To do the we can select those columns only from dataframe and then iterate over them i.e.

# Iterate over two given columns only from the dataframe
for column in empDfObj[['Name', 'City']]:
   # Select column contents by column name using [] operator
   columnSeriesObj = empDfObj[column]
   print('Colunm Name : ', column)
   print('Column Contents : ', columnSeriesObj.values)

Output:
Colunm Name :  Name
Column Contents :  ['jack' 'Riti' 'Aadi' 'Mohit']
Colunm Name :  City
Column Contents :  ['Sydney' 'Delhi' 'New York' 'Delhi']

Iterate Over columns in dataframe in reverse order

As Dataframe.columns returns a sequence of column names. We can reverse iterate over these column names and for each column name we can select the column contents by column name i.e.

# Iterate over the sequence of column names in reverse order
for column in reversed(empDfObj.columns):
   # Select column contents by column name using [] operator
   columnSeriesObj = empDfObj[column]
   print('Colunm Name : ', column)
   print('Column Contents : ', columnSeriesObj.values)

Output:
Colunm Name :  City
Column Contents :  ['Sydney' 'Delhi' 'New York' 'Delhi']
Colunm Name :  Age
Column Contents :  [34 31 16 32]
Colunm Name :  Name
Column Contents :  ['jack' 'Riti' 'Aadi' 'Mohit']

It basically printed the all the columns of Dataframe in reverse order.

Iterate Over columns in dataframe by index using iloc[]

To iterate over the columns of a Dataframe by index we can iterate over a range i.e. 0 to Max number of columns then for each index we can select the columns contents using iloc[]. Let’s see how to iterate over all columns of dataframe from 0th index to last index i.e.

# Iterate over the index range from o to max number of columns in dataframe
for index in range(empDfObj.shape[1]):
   print('Column Number : ', index)
   # Select column by index position using iloc[]
   columnSeriesObj = empDfObj.iloc[: , index]
   print('Column Contents : ', columnSeriesObj.values)

Output:
Column Number :  0
Column Contents :  ['jack' 'Riti' 'Aadi' 'Mohit']
Column Number :  1
Column Contents :  [34 31 16 32]
Column Number :  2
Column Contents :  ['Sydney' 'Delhi' 'New York' 'Delhi']

Complete example is as follows,
import pandas as pd

def main():

    # List of Tuples
    empoyees = [('jack', 34, 'Sydney') ,
               ('Riti', 31, 'Delhi') ,
               ('Aadi', 16, 'New York') ,
               ('Mohit', 32,'Delhi') ,
                ]

    # Create a DataFrame object
    empDfObj = pd.DataFrame(empoyees, columns=['Name', 'Age', 'City'], index=['a', 'b', 'c', 'd'])
    print("Contents of the Dataframe : ")
    print(empDfObj)

    print('**** Iterate Over columns in Dataframe using Dataframe.iteritems() ')

    # Yields a tuple of column name and series for each column in the dataframe
    for (columnName, columnData) in empDfObj.iteritems():
       print('Colunm Name : ', columnName)
       print('Column Contents : ', columnData.values)


    print('*** Iterate over columns in dataframe using Column Names ***"')

    # Iterate over the sequence of column names
    for column in empDfObj:
       # Select column contents by column name using [] operator
       columnSeriesObj = empDfObj[column]
       print('Colunm Name : ', column)
       print('Column Contents : ', columnSeriesObj.values)

    print('*** Iterate over certain columns in dataframe ***"')

    # Iterate over two given columns only from the dataframe
    for column in empDfObj[['Name', 'City']]:
       # Select column contents by column name using [] operator
       columnSeriesObj = empDfObj[column]
       print('Colunm Name : ', column)
       print('Column Contents : ', columnSeriesObj.values)


    print('**** Iterate Over columns in dataframe in reverse order ****')

    # Iterate over the sequence of column names in reverse order
    for column in reversed(empDfObj.columns):
       # Select column contents by column name using [] operator
       columnSeriesObj = empDfObj[column]
       print('Colunm Name : ', column)
       print('Column Contents : ', columnSeriesObj.values)

    print('**** Iterate Over columns in dataframe by index using iloc[] ****')

    # Iterate over the index range from o to max number of columns in dataframe
    for index in range(empDfObj.shape[1]):
       print('Column Number : ', index)
       # Select column by index position using iloc[]
       columnSeriesObj = empDfObj.iloc[: , index]
       print('Column Contents : ', columnSeriesObj.values)



if __name__ == '__main__':
  main()


Output:
Contents of the Dataframe : 
    Name  Age      City
a   jack   34    Sydney
b   Riti   31     Delhi
c   Aadi   16  New York
d  Mohit   32     Delhi
**** Iterate Over columns in Dataframe using Dataframe.iteritems() 
Colunm Name :  Name
Column Contents :  ['jack' 'Riti' 'Aadi' 'Mohit']
Colunm Name :  Age
Column Contents :  [34 31 16 32]
Colunm Name :  City
Column Contents :  ['Sydney' 'Delhi' 'New York' 'Delhi']
*** Iterate over columns in dataframe using Column Names ***"
Colunm Name :  Name
Column Contents :  ['jack' 'Riti' 'Aadi' 'Mohit']
Colunm Name :  Age
Column Contents :  [34 31 16 32]
Colunm Name :  City
Column Contents :  ['Sydney' 'Delhi' 'New York' 'Delhi']
*** Iterate over certain columns in dataframe ***"
Colunm Name :  Name
Column Contents :  ['jack' 'Riti' 'Aadi' 'Mohit']
Colunm Name :  City
Column Contents :  ['Sydney' 'Delhi' 'New York' 'Delhi']
**** Iterate Over columns in dataframe in reverse order ****
Colunm Name :  City
Column Contents :  ['Sydney' 'Delhi' 'New York' 'Delhi']
Colunm Name :  Age
Column Contents :  [34 31 16 32]
Colunm Name :  Name
Column Contents :  ['jack' 'Riti' 'Aadi' 'Mohit']
**** Iterate Over columns in dataframe by index using iloc[] ****
Column Number :  0
Column Contents :  ['jack' 'Riti' 'Aadi' 'Mohit']
Column Number :  1
Column Contents :  [34 31 16 32]
Column Number :  2
Column Contents :  ['Sydney' 'Delhi' 'New York' 'Delhi']