Iterating over rows and columns in Pandas DataFrame.

In this article we will learn about pandas, DataFrame and how we can iterate over rows and columns in the pandas DataFrame using various methods. Iteration is a basic operation we do when we have tabular data containing rows and columns.

To install the pandas in the system execute the command in cmd.

pip install pandas

Method 1. Using Iterrows() Method.

Example

import pandas as pd

df = pd.DataFrame({'Name': ['Kalyan', 'Gungun', 'Sona', 'Ram'],
   'Age': [21, 20, 23, 23],
   'Roll': [12, 13, 14, 15],
   'Game': ['Cricket', 'Lodu', 'Chess', 'Carrom']})

print("No", "Name", "Age", "Roll", "Game")
for index, row in df.iterrows():
   print(index, row['Name'], row['Age'], row['Roll'], row['Game'])

Output

No Name   Age Roll Game
0  Kalyan 21  12   Cricket
1  Gungun 20  13   Lodu
2  Sona   23  14   Chess
3  Ram    23  15   Carrom 

Explanation

In the above program we create a dataframe which contains columns as Name, Age,Roll and Game. We use the iterrows() method which allows us to iterate over the dataframe and returns rows as a tuple containing index and row data. Finally we print the data which we get after iteration.

Method 2. Using itertuples() method.

Example

import pandas as pd

df = pd.DataFrame({'Name': ['Kalyan', 'Gungun', 'Sona', 'Ram'],
   'Age': [21, 20, 23, 23],
   'Roll': [12, 13, 14, 15],
   'Game': ['Cricket', 'Lodu', 'Chess', 'Carrom']})

print("No", "Name", "Age", "Roll", "Game")
for row in df.itertuples():
   print(row.Index, row.Name, row.Age, row.Roll, row.Game)

Output

No Name   Age Roll Game
0  Kalyan 21  12   Cricket
1  Gungun 20  13   Lodu
2  Sona   23  14   Chess
3  Ram    23  15   Carrom 

Explanation

In the above program we create a dataframe which contains columns as Name, Age,Roll and Game. We use the itertuples() method which allows us to iterate over rows and returns named tuples for each row and column values. Finally we print the data which we get after iteration.

Method 3. Using Function Apply() With Axis=1 on row Iteration.

Example

import pandas as pd

df = pd.DataFrame({'Name': ['Kalyan', 'Gungun', 'Sona', 'Ram'],
   'Age': [21, 20, 23, 23],
   'Roll': [12, 13, 14, 15],
   'Game': ['Cricket', 'Lodu', 'Chess', 'Carrom']})

print("Name", "Age", "Roll", "Game")
def process_row(row):
   print(row['Name'], row['Age'], row['Roll'], row['Game'])

df.apply(process_row, axis=1)

Output

Name   Age Roll Game
Kalyan 21  12   Cricket
Gungun 20  13   Lodu
Sona   23  14   Chess
Ram    23  15   Carrom

Explanation

In the above program we create a dataframe which contains columns as Name, Age,Roll and Game. we use apply() function which allows us to apply custom function on each row of the dataframe. When we write axis=1 then we can iterate over the row and perform row wise operation.

Method 4. Using Function Apply() with Axis=0 on Column Iteration.

Example

import pandas as pd

df = pd.DataFrame({'Name': ['Kalyan', 'Gungun', 'Sona', 'Ram'],
   'Age': [21, 20, 23, 23],
   'Roll': [12, 13, 14, 15],
   'Game': ['Cricket', 'Lodu', 'Chess', 'Carrom']})


def process_column(column):
   print(column)

df.apply(process_column, axis=0)

Output

0    Kalyan
1    Gungun
2    Sona
3     Ram
Name: Name, dtype: object
0    21
1    20
2    23
3    23
Name: Age, dtype: int64
0    12
1    13
2    14
3    15
Name: Roll, dtype: int64
0    Cricket
1     Lodu
2     Chess
3     Carrom
Name: Game, dtype: object

Explanation

In the above program we created dataframe which contains columns as Name, Age, Roll and Game. We use apply() function which allows us to apply custom function on each column of the dataframe. When we write axis=0 then we can iterate over the row and perform row wise operation. It is similar to the row wise iteration (written in previous example).

Method 5. Using Lambda Function with Apply() Function.

Example

import pandas as pd

df = pd.DataFrame({'Name': ['Kalyan', 'Gungun', 'Sona', 'Ram'],
   'Age': [21, 20, 23, 23],
   'Roll': [12, 13, 14, 15],
   'Game': ['Cricket', 'Lodu', 'Chess', 'Carrom']})

print("Name", "Age", "Roll", "Game")
df.apply(lambda row: print(row['Name'], row['Age'], row['Roll'], row['Game']), axis=1)

Output

Name   Age Roll Game
Kalyan 21  12   Cricket
Gungun 20  13   Lodu
Sona   23  14   Chess
Ram    23  15   Carrom

Explanation

In the above program we create a dataframe which contains columns as Name, Age,Roll and Game. We use lambda function along with the apply() method for efficient iteration over the rows in the dataframe.

So, we get to know about various methods using which we can iterate over rows and columns in Pandas DataFrame. Though in the last method nditer() is not always suitable compared to other methods like iterrows(), itertuples(), or apply(). nditer() method is useful for large size arrays and when you need more control over iteration.