Looping Through DataFrame in Python

This tutorial will discuss how to loop through rows in a Pandas DataFrame.

How to Use Pandas to Cycle Through Rows in a Pandas DataFrame?

Python has a great environment of data-centric Python modules, which makes it a great tool for performing data analysis. One such tool, Pandas, greatly simplifies collecting and analyzing data.

Using the Index Attribute of the DataFrame

Code

# Python program to loop over the rows of a DataFrame using index attribute

# Importing the pandas package
import pandas as pd
  
# Defining a dictionary containing our data for the DataFrame
dictt = {'1': ['a', 'b', 'c', 'd', 'e'],
         '2': ['a1', 'b1', 'c1', 'd1', 'e1'],
         '3': ['a2', 'b2', 'c2', 'd2', 'e2']}
  
# Converting our dictionary into a Pandas DataFrame
df_pd = pd.DataFrame(dictt)
  
print("Given DataFrame:\n", df_pd)
  
print("\nIterating over the rows using the index attribute:\n")

# iterating through each row and selecting 1 and 2 columns
for ind in df_pd.index:
    print(df_pd['1'][ind], df_pd['2'][ind])

Output:

Given DataFrame:
    1   2   3
0  a  a1  a2
1  b  b1  b2
2  c  c1  c2
3  d  d1  d2
4  e  e1  e2

Iterating over the rows using the index attribute:

a a1
b b1
c c1
d d1
e e1

Using loc[] Function of the DataFrame.

Code

# Python program to loop over the rows of a DataFrame using the loc function

# Importing the pandas package
import pandas as pd
  
# Defining a dictionary containing our data for the DataFrame
dictt = {'1': ['a', 'b', 'c', 'd', 'e'],
         '2': ['a1', 'b1', 'c1', 'd1', 'e1'],
         '3': ['a2', 'b2', 'c2', 'd2', 'e2']}
  
# Converting our dictionary into a Pandas DataFrame
df_pd = pd.DataFrame(dictt)
  
print("Given DataFrame:\n", df_pd)
  
print("\nIterating over the rows using the index attribute:\n")

# iterating through each row and selecting 1 and 2 columns
for ind in range(len(df_pd)):
    print(df_pd.loc[ind, "1"], df_pd.loc[ind, "2"])

Output:

Given DataFrame:
    1   2   3
0  a  a1  a2
1  b  b1  b2
2  c  c1  c2
3  d  d1  d2
4  e  e1  e2

Iterating over the rows using the index attribute:

a a1
b b1
c c1
d d1
e e1

Using iloc[] Method of the Pandas DataFrame

Code

# Python program to loop over the rows of a DataFrame using the iloc function

# Importing the pandas package
import pandas as pd
  
# Defining a dictionary containing our data for the DataFrame
dictt = {'1': ['a', 'b', 'c', 'd', 'e'],
         '2': ['a1', 'b1', 'c1', 'd1', 'e1'],
         '3': ['a2', 'b2', 'c2', 'd2', 'e2']}
  
# Converting our dictionary into a Pandas DataFrame
df_pd = pd.DataFrame(dictt)
  
print("Given DataFrame:\n", df_pd)
  
print("\nIterating over the rows using the index attribute:\n")

# iterating through each row and selecting 1 and 2 columns
for ind in range(len(df_pd)):
    print(df_pd.iloc[ind, 0], df_pd.iloc[ind, 2])

Output:

Given DataFrame:
    1   2   3
0  a  a1  a2
1  b  b1  b2
2  c  c1  c2
3  d  d1  d2
4  e  e1  e2

Iterating over the rows using the index attribute:

a a2
b b2
c c2
d d2
e e2

Using iterrows() Method of the DataFrame

Code

# Python program to loop over the rows of a DataFrame using the iterrows method

# Importing the pandas package
import pandas as pd
  
# Defining a dictionary containing our data for the DataFrame
dictt = {'1': ['a', 'b', 'c', 'd', 'e'],
         '2': ['a1', 'b1', 'c1', 'd1', 'e1'],
         '3': ['a2', 'b2', 'c2', 'd2', 'e2']}
  
# Converting our dictionary into a Pandas DataFrame
df_pd = pd.DataFrame(dictt)
  
print("Given DataFrame:\n", df_pd)
  
print("\nIterating over the rows using the iterrows() method:\n")
  
# iterating through each row and selecting 1 and 2 columns
for ind, row in df_pd.iterrows():
    print(row["1"], row["2"])

Output:

Given DataFrame:
    1   2   3
0  a  a1  a2
1  b  b1  b2
2  c  c1  c2
3  d  d1  d2
4  e  e1  e2

Iterating over the rows using the iterrows() method:

a a1
b b1
c c1
d d1
e e1

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