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:
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:
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:
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:
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:
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:
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:
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:
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:
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:
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:
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