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. 

Creating a Dataframe to Select Rows & Columns in Pandas

A list of tuples, say column names are: ‘Name’, ‘Age’, ‘City’, and ‘Salary’. 

Output:

    Name    Age    City    Salary
0    Stuti    28    Varanasi    20000
1    Saumya    32    Delhi    25000
2    Aaditya    25    Mumbai    40000
3    Saumya    32    Delhi    35000
4    Saumya    32    Delhi    30000
5    Saumya    32    Mumbai    20000
6    Aaditya    40    Dehradun    24000
7    Seema    32    Delhi    70000

Select Columns by Name in Pandas DataFrame using [ ]

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

Example 1: 

Select a single column.

Output:

0    Varanasi
1       Delhi
2      Mumbai
3       Delhi
4       Delhi
5      Mumbai
6    Dehradun
7       Delhi
Name: City, dtype: object

Example 2: 

Select multiple columns. 

Output:

    Name    Age    Salary
0    Stuti    28    20000
1    Saumya    32    25000
2    Aaditya    25    40000
3    Saumya    32    35000
4    Saumya    32    30000
5    Saumya    32    20000
6    Aaditya    40    24000
7    Seema    32    70000

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. 

Example 1: 

Select a single row.

Output:

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

Example 2: 

Select multiple rows. 

Output:

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

Example 3: 

Select multiple rows and particular columns.

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

Output:

    City    Salary
Name        
Stuti    Varanasi    20000
Seema    Delhi    70000

Example 4:

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"]]

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

Select Rows by Index in Pandas DataFrame using iloc

The 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:

select a single row. 

Output:

Name      Aaditya
Age            25
City       Mumbai
Salary      40000
Name: 2, dtype: object

Example 2: 

Select multiple rows.

Output:

Name    Age    City    Salary
2    Aaditya    25    Mumbai    40000
3    Saumya    32    Delhi    35000
5    Saumya    32    Mumbai    20000

Example 3: 

Select multiple rows with some particular columns.

Output:

Name    Age
2    Aaditya    25
3    Saumya    32
5    Saumya    32

Example 4: 

Select all the rows with some particular columns. 

Output:

Name    Age
0    Stuti    28
1    Saumya    32
2    Aaditya    25
3    Saumya    32
4    Saumya    32
5    Saumya    32
6    Aaditya    40
7    Seema    32