Difference Between loc() and iloc() in Pandas

Use Cases of Loc Function

To understand it more in-depth, let's look at an example. Let's create a sample Dataframe using Pandas: 

Code Implementation

//import python library 
Import pandas as pd

df = pd.DataFrame({
‘Name’ : [‘Ninja_1’,‘Ninja_2’,‘Ninja_3’,‘Ninja_4’,
‘Department’ : [‘Marketing’,’Sales’,’Marketing,’Engineering’],
‘Salary’ : [50000,60000,55000,7000]
//create a sample DataFrame with attributes name, department and salary.
})                                                    

 

Output

Name	Department	Salary
Ninja_1	Marketing	50000
Ninja_2	Sales	60000
Ninja_3	Marketing	55000
Ninja_4	Engineering	70000

We have created a sample DataFrame which consists of 3 columns named 'name,' 'Department,' and' Salary.' 

There are different use cases like:

Selecting A Single Row

Code Implementation

Row = df.loc[2]

 

Output

Name	Department	Salary
Ninja_3	Marketing	55000

Explanation 

As you can see, the code selected the row that is labeled with “2”. This is indicating the third row in the DataFrame since Python uses 0-based indexing technique. 

Selecting Multiple Rows

Code Implementation

Row = df.loc[[1, 2, 3]]

 

Output

Name	Department	Salary
Ninja_2	Sales	60000
Ninja_3	Marketing	55000
Ninja_4	Engineering	70000

Explanation 

As you can see, the rows with labels '1','2','3' are selected. Here it is indicating towards the second, third and fourth rows in DataFrame since Python uses 0-based indexing technique.

Selecting Multiple Columns

Code Implementation

Cols = df.loc[:, [‘Name’,’Salary’]]

 

Output

Name	Salary
Ninja_1	50000
Ninja_2	60000
Ninja_3	55000
Ninja_4	70000

Explanation 

As you can see, it is pointing towards the all rows and “name” and “Salary” column since Python uses 0-based indexing technique.

Selecting A Subset Of Rows And Columns

Code Implementation

Subset = df.loc[df [‘Department’] == ‘Marketing’, [‘Name’,’Salary’]]

 

Output

Name	Department	Salary
Ninja_1	Marketing	50000
Ninja_3	Marketing	55000

Explanation 

It is indicating to all rows where the value in the “Department” is Marketing. And also include the “Name” and “Salary” column since Python uses 0-based indexing technique.

Selecting A Value

Code Implementation

value = df.loc[1, ‘Salary’] = 65000

 

Output

Name	Department	Salary
Ninja_1	Marketing	50000
Ninja_2	Sales	65000
Ninja_3	Marketing	55000
Ninja_4	Engineering	70000

Explanation 

In this the value in 'Salary' column is getting updated to 65000 for row label with “1” since Python uses 0-based indexing technique.

Selecting Multiple Values

Code Implementation

value_1 = df.loc[[0,3],’Salary’] = 60000

 

Output

Name	Department	Salary
Ninja_1	Marketing	60000
Ninja_2	Sales	65000
Ninja_3	Marketing	55000
Ninja_4	Engineering	60000

Explanation 

In this the value in 'Salary' column is getting updated to 60000 for row labels with “0” and “3” since Python uses 0-based indexing technique.

We can see that many operations can be held using the "loc" method.

Advantages of loc

Here are a few advantages of loc.

• Allowed in cases like labeled-based indexing. It is easy to read and understand.
   
• It can be used with Boolean arrays to solve problems.
   
• Can be used on both single and multiple indexes.

Disadvantages of loc

Some of the disadvantages of loc are as follows:

• It can be low with large DataFrames.
   
• It uses unique indexes, but it may sometimes be different.

What is the iloc function in Python?

The iloc function in Python is an index-based function. In this function, we select an integer position instead of selecting rows or columns. It can also work across multiple DataFrame Objects.

"iloc" method is a valuable tool for selecting rows and columns by an integer. It can also access specific values in a DataFrame.It does not accept the boolean data. We have to follow the syntax below:

Syntax

df.iloc[row_index_value, column_index_value]

Use Cases Of iloc() Function

To understand it more in-depth, let's look at an example. Let's create a sample Dataframe using Pandas:

There are different use cases like:

Code Implementation

//import python library
Import pandas as pd

#create a sample DataFrame
df = pd.DataFrame({
‘Name’ : [‘Ninja_1’,‘Ninja_2’,‘Ninja_3’,‘Ninja_4’,
‘Department’ : [‘Marketing’,’Sales’,’Marketing,’Engineering’],
‘Salary’ : [50000,60000,55000,7000]
//create a sample DataFrame with attributes name, department and salary
})

Selecting Specific Rows And Columns

Code Implementation

row = df.iloc[[1,3], [0,2]]

 

Output

Name	Salary
Ninja_2	60000
Ninja_4	70000

Updating Values

Code Implementation

updated_value = df.iloc[1,2] =100

 

Output

Name	Department	Salary
Ninja_1	Marketing	50000
Ninja_2	Sales	100
Ninja_3	Marketing	55000
Ninja_4	Engineering	70000

Slicing

Code Implementation

s = df.iloc[0:3, 0:2]

 

Output

Name	Department
Ninja_1	Marketing
Ninja_2	Sales
Ninja_3	Marketing

‘iloc’ method in Pandas is a valuable tool for selecting rows and column values using integer values. Many operations can be held using the 'iloc' method. In the article, We will also discuss the difference between 'loc' and 'iloc' and the key advantages and disadvantages of 'loc' and 'iloc.'

In the article further, we will discuss the advantages and disadvantages of loc and iloc functions. We will also discuss the difference between 'loc' and 'iloc.'

Advantages of iloc

Some of the advantages of iloc are as follows:

• Allowed in cases like integer-based indexing.
   
• More effective for large DataFrames.
   
• It uses integer arrays to solve complex problems.
   
• Can be used on both single and multiple indexes.

Disadvantages of iloc

Some of the disadvantages of iloc are as follows:

• It's hard to read and understand for new users.
   
• It can create problems if not updated regularly and lead to data misinterpretation.

So we can say that it depends on the uses' needs. But in general, 'loc' is used for label-based indexing, and 'iloc' is used for integer-based indexing.

Difference Between loc and iloc

loc function	Iloc function
Select rows and columns by labels	Select rows and columns by integer positions
Slicing with labels	Slicing with integer positions
Use Boolean arrays	Does not uses Boolean arrays
Label-based indexing	Position based indexing
Syntax : Dataframe.loc[specific rows, specific columns]	Syntax : Dataframe.iloc[row_index_value, column_index_value]
Example: df.loc[2, 'Salary'] df.loc[df['Department'] == 'Marketing', ['Name', 'Salary']]	Example: df.iloc[2, 2] df.iloc[1:3, :2]

Frequently Asked Questions

When should we use loc instead of iloc from a Pandas DataFrame?

To select data based on label-based indexing, it is recommended to use loc. And iloc should be used when there is a need to choose data that is classified via integer-based indexing.

What happens if you use a non-unique label when using loc to select data?

Using loc with a non-unique label will throw a KeyError exception. This is because loc assumes that the labels used for indexing are unique and will return a single row or column. We should use Boolean indexing to select more than one row or column where duplicates exist.

What happens if you pick rows or columns in a Pandas DataFrame using a negative integer with iloc?

If there is a negative integer used with 'iloc', it will start counting rows and columns from the end of the DataFrame. For example if you want to select the last row in the DataFrame then you can use ‘-1’.

How are missing values handled when the developer is using loc and iloc functions?

They are simply removed from the selection. If you want to remove missing values you can use fillna() or dropna() function which will either remove or fill missing values with null.

Where do you use loc and iloc functions?

Both of the functions are used in data analysis and data manipulation tasks. They are used in data filtering, data transformation or in data aggregation.

Conclusion

In conclusion, the 'loc' function is mainly used when we want to select rows and columns based on their labels. Whereas 'iloc' is used when choosing rows and columns based on specific positions.

Recommended Reading:

Difference Between IOT and M2M

The main difference we saw is that in 'loc,' we include start and end positions, but it is different with 'iloc'. We have analyzed the difference between loc and iloc for effective indexing and selecting data in DataFrame.
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