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

  • Last Updated : 10 Jul, 2020

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’.




# 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

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:




# 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

Output:
select single column from dataframe



Example 2: to select multiple columns.
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

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:




# 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

Output:
select single row

Example 2: To select multiple rows.
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

Output:
select multiple rows from dataframe

Example 3: To select multiple rows and particular columns.

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

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

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:




# 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

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:




# 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

Output:
select a single row from dataframe

Example 2: to select multiple rows.
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

Output:
select multiple rows from dataframe

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

Output:
select multiple rows with some particular columns from dataframe

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

Output:
select all the rows with some particular columns

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