Select Rows by Name in Pandas DataFrame using loc

The .loc[] 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. 

1. Select a Single Row
2. Select Multiple Rows
3. Select Multiple Rows and Particular Columns
4. Select all the Rows With Some Particular Columns

Select a Single Row

Example : In this example code sets the “Name” column as the index for a DataFrame named ‘df’, then selects and displays the row with the index value “Stuti” using the .loc[] operator.

Output:

Age             28
City      Varanasi
Salary       20000
Name: Stuti, dtype: object

Select Multiple Rows

Example : In this example code sets the “Name” column as the index for a DataFrame named ‘df’, then selects and displays rows with index values “Stuti” and “Seema” using the .loc[] operator.

Output:

    Age    City    Salary
Name            
Stuti    28    Varanasi    20000
Seema    32    Delhi    70000

Select Multiple Rows and Particular Columns

Syntax: Dataframe.loc[[“row1”, “row2″…], [“column1”, “column2”, “column3″…]]

Example : In this example code sets the “Name” column as the index, then selects the “City” and “Salary” columns for the rows with names “Stuti” and “Seema” in the DataFrame, displaying the result.

Output:

    City    Salary
Name        
Stuti    Varanasi    20000
Seema    Delhi    70000

Select all the Rows With Some Particular Columns

We use a single colon [ : ] to select all rows and the list of columns that we want to select as given below :

Syntax: Dataframe.loc[[:, [“column1”, “column2”, “column3”]

Example : In this example code sets the “Name” column as the index and extracts the “City” and “Salary” columns into a new DataFrame named ‘result’.

Output:

    City    Salary
Name        
Stuti    Varanasi    20000
Saumya    Delhi    25000
Aaditya    Mumbai    40000
Saumya    Delhi    35000
Saumya    Delhi    30000
Saumya    Mumbai    20000
Aaditya    Dehradun    24000
Seema    Delhi    70000

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.