Select Rows & Columns by Name or Index in Pandas DataFrame using [ ], loc & iloc

Indexing in Pandas means selecting rows and columns of data from a Dataframe. It can be selecting all the rows and the particular number of columns, a particular number of rows, and all the columns or a particular number of rows and columns each. Indexing is also known as Subset selection.
Let’s create a simple dataframe with a list of tuples, say column names are: ‘Name’, ‘Age’, ‘City’ and ‘Salary’.

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# import pandas
import pandas as pd
  
# List of Tuples
employees = [('Stuti', 28, 'Varanasi', 20000),
            ('Saumya', 32, 'Delhi', 25000),
            ('Aaditya', 25, 'Mumbai', 40000),
            ('Saumya', 32, 'Delhi', 35000),
            ('Saumya', 32, 'Delhi', 30000),
            ('Saumya', 32, 'Mumbai', 20000),
            ('Aaditya', 40, 'Dehradun', 24000),
            ('Seema', 32, 'Delhi', 70000)
            ]
  
# Create a DataFrame object from list 
df = pd.DataFrame(employees, 
                columns =['Name', 'Age', 
                         'City', 'Salary'])
# Show the dataframe
df

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Output:

dataframe


Method 1: using Dataframe.[ ].

[ ] is used to select a column by mentioning the respective column name.


Example 1 :  to select single column.

Code:













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# import pandas 


import pandas as pd 


  


# List of Tuples 


employees = [('Stuti', 28, 'Varanasi', 20000), 


            ('Saumya', 32, 'Delhi', 25000), 


            ('Aaditya', 25, 'Mumbai', 40000), 


            ('Saumya', 32, 'Delhi', 35000), 


            ('Saumya', 32, 'Delhi', 30000), 


            ('Saumya', 32, 'Mumbai', 20000), 


            ('Aaditya', 40, 'Dehradun', 24000), 


            ('Seema', 32, 'Delhi', 70000) 


            ] 


  


# Create a DataFrame object from list  


df = pd.DataFrame(employees,  


                columns =['Name', 'Age',  


                         'City', 'Salary']) 


  


# Using the operator []  


# to select a column 


result = df["City"] 


  


# Show the dataframe 


result 



















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Output:

select single column from dataframe




Example 2:  to select multiple columns.

Code:













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                                    code
                                




















# import pandas 


import pandas as pd 


  


# List of Tuples 


employees = [('Stuti', 28, 'Varanasi', 20000), 


            ('Saumya', 32, 'Delhi', 25000), 


            ('Aaditya', 25, 'Mumbai', 40000), 


            ('Saumya', 32, 'Delhi', 35000), 


            ('Saumya', 32, 'Delhi', 30000), 


            ('Saumya', 32, 'Mumbai', 20000), 


            ('Aaditya', 40, 'Dehradun', 24000), 


            ('Seema', 32, 'Delhi', 70000) 


            ] 


  


# Create a DataFrame object from list  


df = pd.DataFrame(employees,  


                columns =['Name', 'Age', 


                        'City', 'Salary']) 


  


# Using the operator [] to  


# select multiple columns 


result = df[["Name", "Age", "Salary"]] 


  


# Show the dataframe 


result 



















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Output:

select multiple column from dataframe


Method 2: Using Dataframe.loc[ ].

.loc[] the function selects the data by labels of rows or columns. It can select a subset of rows and columns. There are many ways to use this function.

Example 1:  To select single row.

Code:













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# import pandas 


import pandas as pd 


  


# List of Tuples 


employees = [('Stuti', 28, 'Varanasi', 20000), 


            ('Saumya', 32, 'Delhi', 25000), 


            ('Aaditya', 25, 'Mumbai', 40000), 


            ('Saumya', 32, 'Delhi', 35000), 


            ('Saumya', 32, 'Delhi', 30000), 


            ('Saumya', 32, 'Mumbai', 20000), 


            ('Aaditya', 40, 'Dehradun', 24000), 


            ('Seema', 32, 'Delhi', 70000) 


            ] 


  


# Create a DataFrame object from list  


df = pd.DataFrame(employees, 


                 columns =['Name', 'Age', 


                  'City', 'Salary']) 


  


# Set 'Name' column as index  


# on a Dataframe 


df.set_index("Name", inplace = True) 


  


# Using the operator .loc[] 


# to select single row 


result = df.loc["Stuti"] 


  


# Show the dataframe 


result 



















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Output:

select single row


Example 2:  To select multiple rows.

Code:













                                    filter_none




                                    edit

                                    close




                                    play_arrow




                                    link

                                    brightness_4

                                    code
                                




















# import pandas 


import pandas as pd 


  


# List of Tuples 


employees = [('Stuti', 28, 'Varanasi', 20000), 


            ('Saumya', 32, 'Delhi', 25000), 


            ('Aaditya', 25, 'Mumbai', 40000), 


            ('Saumya', 32, 'Delhi', 35000), 


            ('Saumya', 32, 'Delhi', 30000), 


            ('Saumya', 32, 'Mumbai', 20000), 


            ('Aaditya', 40, 'Dehradun', 24000), 


            ('Seema', 32, 'Delhi', 70000) 


            ] 


  


# Create a DataFrame object from list  


df = pd.DataFrame(employees,  


                  columns =['Name', 'Age', 


                   'City', 'Salary']) 


  


# Set index on a Dataframe 


df.set_index("Name",  


              inplace = True) 


  


# Using the operator .loc[] 


# to select multiple rows 


result = df.loc[["Stuti", "Seema"]] 


  


# Show the dataframe 


result 



















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Output:

select multiple rows from dataframe


Example 3:  To select multiple rows and particular columns.


Syntax:  Dataframe.loc[["row1", "row2"...], ["column1", "column2", "column3"...]]


Code:















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                                    code
                                




















# import pandas 


import pandas as pd 


  


# List of Tuples 


employees = [('Stuti', 28, 'Varanasi', 20000), 


            ('Saumya', 32, 'Delhi', 25000), 


            ('Aaditya', 25, 'Mumbai', 40000), 


            ('Saumya', 32, 'Delhi', 35000), 


            ('Saumya', 32, 'Delhi', 30000), 


            ('Saumya', 32, 'Mumbai', 20000), 


            ('Aaditya', 40, 'Dehradun', 24000), 


            ('Seema', 32, 'Delhi', 70000) 


            ] 


  


# Create a DataFrame object from list  


df = pd.DataFrame(employees,  


                 columns =['Name', 'Age', 


                  'City', 'Salary']) 


  


# Set 'Name' column as index  


# on a Dataframe 


df.set_index("Name", inplace = True) 


  


# Using the operator .loc[] to  


# select multiple rows with some 


# particular columns 


result = df.loc[["Stuti", "Seema"], 


               ["City", "Salary"]] 


  


# Show the dataframe 


result 



















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Output:

select multiple rows and particular columns from dataframe


Example 4: To select all the rows with some particular columns. We use single colon [ : ] to select all rows and list of columns which we want to select as given below :


Syntax: Dataframe.loc[[:, ["column1", "column2", "column3"]]


Code:













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                                    code
                                




















# import pandas 


import pandas as pd 


  


# List of Tuples 


employees = [('Stuti', 28, 'Varanasi', 20000), 


            ('Saumya', 32, 'Delhi', 25000), 


            ('Aaditya', 25, 'Mumbai', 40000), 


            ('Saumya', 32, 'Delhi', 35000), 


            ('Saumya', 32, 'Delhi', 30000), 


            ('Saumya', 32, 'Mumbai', 20000), 


            ('Aaditya', 40, 'Dehradun', 24000), 


            ('Seema', 32, 'Delhi', 70000) 


            ] 


  


# Creating a DataFrame object from list  


df = pd.DataFrame(employees, 


                  columns =['Name', 'Age',  


                  'City', 'Salary']) 


  


# Set 'Name' column as index  


# on a Dataframe 


df.set_index("Name", inplace = True) 


  


# Using the operator .loc[] to 


# select all the rows with  


# some particular columns 


result = df.loc[:, ["City", "Salary"]] 


  


# Show the dataframe 


result 



















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Output:

select all the rows with some particular columns from dataframe


Method 3: Using Dataframe.iloc[ ].

iloc[ ] is used for selection based on position. It is similar to loc[] indexer but it takes only integer values to make selections.

Example 1 : to select a single row.

Code:













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                                    code
                                




















# import pandas 


import pandas as pd 


  


# List of Tuples 


employees = [('Stuti', 28, 'Varanasi', 20000), 


            ('Saumya', 32, 'Delhi', 25000), 


            ('Aaditya', 25, 'Mumbai', 40000), 


            ('Saumya', 32, 'Delhi', 35000), 


            ('Saumya', 32, 'Delhi', 30000), 


            ('Saumya', 32, 'Mumbai', 20000), 


            ('Aaditya', 40, 'Dehradun', 24000), 


            ('Seema', 32, 'Delhi', 70000) 


            ] 


  


# Create a DataFrame object from list  


df = pd.DataFrame(employees,  


                  columns =['Name', 'Age', 


                   'City', 'Salary']) 


  


# Using the operator .iloc[] 


# to select single row 


result = df.iloc[2] 


  


# Show the dataframe 


result 



















                        chevron_right







                        filter_none










Output:

select a single row from dataframe


Example 2: to select multiple rows.

Code:













                                    filter_none




                                    edit

                                    close




                                    play_arrow




                                    link

                                    brightness_4

                                    code
                                




















# import pandas 


import pandas as pd 


  


# List of Tuples 


employees = [('Stuti', 28, 'Varanasi', 20000), 


            ('Saumya', 32, 'Delhi', 25000), 


            ('Aaditya', 25, 'Mumbai', 40000), 


            ('Saumya', 32, 'Delhi', 35000), 


            ('Saumya', 32, 'Delhi', 30000), 


            ('Saumya', 32, 'Mumbai', 20000), 


            ('Aaditya', 40, 'Dehradun', 24000), 


            ('Seema', 32, 'Delhi', 70000) 


            ] 


  


# Create a DataFrame object from list  


df = pd.DataFrame(employees,  


                columns =['Name', 'Age', 


                'City', 'Salary']) 


  


# Using the operator .iloc[]  


# to select multiple rows 


result = df.iloc[[2, 3, 5]] 


  


# Show the dataframe 


result 



















                        chevron_right







                        filter_none










Output:

select multiple rows from dataframe


Example 3: to select multiple rows with some particular columns.

Code:













                                    filter_none




                                    edit

                                    close




                                    play_arrow




                                    link

                                    brightness_4

                                    code
                                




















# import pandas 


import pandas as pd 


  


# List of Tuples 


employees = [('Stuti', 28, 'Varanasi', 20000), 


            ('Saumya', 32, 'Delhi', 25000), 


            ('Aaditya', 25, 'Mumbai', 40000), 


            ('Saumya', 32, 'Delhi', 35000), 


            ('Saumya', 32, 'Delhi', 30000), 


            ('Saumya', 32, 'Mumbai', 20000), 


            ('Aaditya', 40, 'Dehradun', 24000), 


            ('Seema', 32, 'Delhi', 70000) 


            ] 


  


# Creating a DataFrame object from list  


df = pd.DataFrame(employees, 


                  columns =['Name', 'Age', 


                  'City', 'Salary']) 


  


# Using the operator .iloc[]  


# to select multiple rows with 


# some particular columns 


result = df.iloc[[2, 3, 5], 


                  [0, 1]] 


  


# Show the dataframe 


result 



















                        chevron_right







                        filter_none










Output:

select multiple rows with some particular columns from dataframe


Example 4: to select all the rows with some particular columns.

Code:













                                    filter_none




                                    edit

                                    close




                                    play_arrow




                                    link

                                    brightness_4

                                    code
                                




















# import pandas 


import pandas as pd 


  


# List of Tuples 


employees = [('Stuti', 28, 'Varanasi', 20000), 


            ('Saumya', 32, 'Delhi', 25000), 


            ('Aaditya', 25, 'Mumbai', 40000), 


            ('Saumya', 32, 'Delhi', 35000), 


            ('Saumya', 32, 'Delhi', 30000), 


            ('Saumya', 32, 'Mumbai', 20000), 


            ('Aaditya', 40, 'Dehradun', 24000), 


            ('Seema', 32, 'Delhi', 70000) 


            ] 


  


# Create a DataFrame object from list  


df = pd.DataFrame(employees,  


                columns =['Name', 'Age',  


               'City', 'Salary']) 


  


# Using the operator .iloc[] 


# to select all the rows with 


# some particular columns 


result = df.iloc[:, [0, 1]] 


  


# Show the dataframe 


result 



















                        chevron_right







                        filter_none










Output:

select all the rows with some particular columns





    

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