Select a row of Pandas DataFrame by integer index

In this article, we will discuss multiple ways to select a row (or multiple rows) of pandas DataFrame by integer index.

Table of Contents

Preparing dataset for solution

To quickly get started, let’s create a sample dataframe to experiment. We’ll use the pandas library with some random data.

import pandas as pd
import numpy as np

# List of Tuples
employees= [('Shubham', 'Data Scientist', 'Tech',   5),
            ('Riti', 'Data Engineer', 'Tech' ,   7),
            ('Shanky', 'Program Manager', 'PMO' ,   2),
            ('Shreya', 'Graphic Designer', 'Design' ,   2),
            ('Aadi', 'Backend Developer', 'Tech', 11),
            ('Sim', 'Data Engineer', 'Tech', 4)]

# Create a DataFrame object from list of tuples
df = pd.DataFrame(employees,
                  columns=['Name', 'Designation', 'Team', 'Experience'],
                  index=["r1","r2","r3","r4","r5","r6"])
print(df)

Contents of the created dataframe are,

       Name        Designation    Team  Experience
r1  Shubham     Data Scientist    Tech           5
r2     Riti      Data Engineer    Tech           7
r3   Shanky    Program Manager     PMO           2
r4   Shreya   Graphic Designer  Design           2
r5     Aadi  Backend Developer    Tech          11
r6      Sim      Data Engineer    Tech           4

Let’s see how to select a row from this DataFrame by index.

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Select a row of Pandas DataFrame by integer index using iloc[]

The iloc[] is the method to select rows based on the row position (or number). For example, let’s say, we need to filter the second row from the above DataFrame.

# filter second row (index wise 1)
print (df.iloc[1])

Output

Name                    Riti
Designation    Data Engineer
Team                    Tech
Experience                 7
Name: r2, dtype: object

We have just passed the index in the iloc function and it returns the column wise values of the second row. In case, we wanted to select multiple rows, we can simply pass it in the iloc function again.

# filter consecutive rows
print (df.iloc[2:4])

Output

      Name       Designation    Team  Experience
r3  Shanky   Program Manager     PMO           2
r4  Shreya  Graphic Designer  Design           2

In case the rows are not consecutive, we can also pass the list of row positions to be filtered as below.

# filter defined rows
print (df.iloc[[1,4,5]])

Output

    Name        Designation  Team  Experience
r2  Riti      Data Engineer  Tech           7
r5  Aadi  Backend Developer  Tech          11
r6   Sim      Data Engineer  Tech           4

Here, the output contains all three rows that we have selected.

The complete example is as follows,

import pandas as pd
import numpy as np

# List of Tuples
employees= [('Shubham', 'Data Scientist', 'Tech',   5),
            ('Riti', 'Data Engineer', 'Tech' ,   7),
            ('Shanky', 'Program Manager', 'PMO' ,   2),
            ('Shreya', 'Graphic Designer', 'Design' ,   2),
            ('Aadi', 'Backend Developer', 'Tech', 11),
            ('Sim', 'Data Engineer', 'Tech', 4)]

# Create a DataFrame object from list of tuples
df = pd.DataFrame(employees,
                  columns=['Name', 'Designation', 'Team', 'Experience'],
                  index=["r1","r2","r3","r4","r5","r6"])
print(df)

# filter second row (index wise 1)
print (df.iloc[1])

# filter consecutive rows
print (df.iloc[2:4])

# filter defined rows
print (df.iloc[[1,4,5]])

Output:

       Name        Designation    Team  Experience
r1  Shubham     Data Scientist    Tech           5
r2     Riti      Data Engineer    Tech           7
r3   Shanky    Program Manager     PMO           2
r4   Shreya   Graphic Designer  Design           2
r5     Aadi  Backend Developer    Tech          11
r6      Sim      Data Engineer    Tech           4

Name                    Riti
Designation    Data Engineer
Team                    Tech
Experience                 7
Name: r2, dtype: object

      Name       Designation    Team  Experience
r3  Shanky   Program Manager     PMO           2
r4  Shreya  Graphic Designer  Design           2

    Name        Designation  Team  Experience
r2  Riti      Data Engineer  Tech           7
r5  Aadi  Backend Developer  Tech          11
r6   Sim      Data Engineer  Tech           4

Select a row of Pandas DataFrame by integer index using loc[]

The loc[] function also works similarly to the iloc[] function, the only difference here is we need to pass the row names instead of position in the function call. Let’s quickly try to get the same output as above using the loc function.

# filter second row "r2"
print (df.loc["r2"])

Output

Name                    Riti
Designation    Data Engineer
Team                    Tech
Experience                 7
Name: r2, dtype: object

Here you go, we have similar output, i.e., all the column wise details of the second row. Also, to select multiple rows using loc function, again we need to pass the index names separated by a colon.

# filter consecutive rows
print (df.loc["r3":"r4"])

Output

      Name       Designation    Team  Experience
r3  Shanky   Program Manager     PMO           2
r4  Shreya  Graphic Designer  Design           2

We have filtered the third and fourth successfully. Note that, here both start (“r3”) and end (“r4”) are included while filtering from the DataFrame.

The complete example is as follows,

import pandas as pd
import numpy as np

# List of Tuples
employees= [('Shubham', 'Data Scientist', 'Tech',   5),
            ('Riti', 'Data Engineer', 'Tech' ,   7),
            ('Shanky', 'Program Manager', 'PMO' ,   2),
            ('Shreya', 'Graphic Designer', 'Design' ,   2),
            ('Aadi', 'Backend Developer', 'Tech', 11),
            ('Sim', 'Data Engineer', 'Tech', 4)]

# Create a DataFrame object from list of tuples
df = pd.DataFrame(employees,
                  columns=['Name', 'Designation', 'Team', 'Experience'],
                  index=["r1","r2","r3","r4","r5","r6"])
print(df)

# filter second row "r2"
print (df.loc["r2"])

# filter consecutive rows
print (df.loc["r3":"r4"])

Output:

       Name        Designation    Team  Experience
r1  Shubham     Data Scientist    Tech           5
r2     Riti      Data Engineer    Tech           7
r3   Shanky    Program Manager     PMO           2
r4   Shreya   Graphic Designer  Design           2
r5     Aadi  Backend Developer    Tech          11
r6      Sim      Data Engineer    Tech           4

Name                    Riti
Designation    Data Engineer
Team                    Tech
Experience                 7
Name: r2, dtype: object

      Name       Designation    Team  Experience
r3  Shanky   Program Manager     PMO           2
r4  Shreya  Graphic Designer  Design           2

Summary

In this article, we have discussed multiple ways to select a row (or multiple rows) of pandas DataFrame by integer index. Thanks.

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