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Select a row of series or dataframe by given integer index

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  • Last Updated : 13 Sep, 2022
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The Dataframe.iloc[] is used to select a row of Series/Dataframe by a given integer index in Python

Creating DataFrame to Select a Row by Index

Here, we are creating a Pandas Dataframe with ID, Product, Price, Color, and Discount with there some values in it.

Python3




# import pandas library
import pandas as pd
 
# Create the dataframe
df = pd.DataFrame({'ID': ['114', '345',
                         '157788', '5626'],
                'Product': ['shirt', 'trousers',
                           'tie', 'belt'],
                'Price': [1200, 1500,
                         600, 352],
                'Color': ['White','Black',
                         'Red', 'Brown'],
                'Discount': [10, 10,
                            10, 10]})
 
# Show the dataframe
df

Output:

 

Select the first row only

Here, we are selecting the first rows using iloc, Dataframe.iloc[] method is used when the index label of a data frame is something other than numeric series.

Python3




# select first row
# from the dataframe
df.iloc[0]

Output: 

 

Selecting  0, 1, 2  rows.

Here, we are selecting the first second, and third rows using iloc[0:3], starting with 0 indexes and ending with (3-1=2) index.

Python3




# select 0, 1, 2 rows
#from the dataframe
df.iloc[0 : 3]

Output:

 

Select rows from 0 to 2 and columns from 0 to 1

Here, we are selecting the first second third rows, and the first and second columns using iloc[0:3].

Python3




# selecting rows from 0 to
# 2 and columns 0 to 1
df.iloc[0 : 3, 0 : 2]

Output: 

 

Select all rows and 2nd column

Selecting all the rows and only the second columns from the Dataframe.

Python3




# selecting all rows and
# 3rd column
df.iloc[ : , 2]

Output:

 

Select all rows and columns from 0 to 3. 

Selecting all rows and columns.

Python3




# selecting all rows and
# columns from 0 to 3
df.iloc[ : , 0 : 4]

Output:

 


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