iloc() Function in Python
FREE
Overview
The iloc() function in python is one of the functions defined in the Pandas module that helps us to select a specific row or column from the data set. Using the iloc() function in python, we can easily retrieve any particular value from a row or column using index values.
Syntax of iloc() function in Python
The syntax of the iloc() function in python is very simple. We can invoke the iloc() function in python on the data set to retrieve rows and columns. As:
Parameters of iloc() function in Python
The iloc function in python takes two parameters. However, both parameters of the iloc() method is optional. Let us discuss both of these parameters:
- row: row parameter is an optional parameter that specifies the index position of the row in the form of an integer or list of integers.
- column: column parameter is also an optional parameter that specifies the index position of the column in the form of an integer or list of integers.
Note: We can only pass integer type value(s) as parameter(s) in the iLoc function in python.
Return Value of iloc() function in Python
The iloc() function in python returns a data set or series using the parameters provided. The return value changes according to the parameters provided in the iloc function in python:
- If we specify only one value, i.e., only row value, then the iloc() function in python returns a Pandas Series.
- If we specify two values, i.e., row value and column value, then the iloc function in python returns all the content of the specified cell. If we specify a list of values, then python's iloc function returns a Pandas DataFrame.
What is the iloc() function in Python?
The iloc() function in python comes under the library named pandas. So, before learning about the working, syntax, parameters, and return value of the iloc() function in python, let us first get a brief understanding of the pandas library.
Pandas is an open-source package (or library) that provides us with highly optimized data structures and data analysis tools. Pandas library is widely used in the field of data science, machine learning, and data analytics as it simplifies data importing and data analysis. The Pandas library is built on top of another library called NumPy library.
The Pandas module helps us to work with large data sets (or data frames) in terms of rows and columns. We generally use the Pandas module to deal with the CSV (Excel) files. So, the iloc() function in python is one of the functions defined in the Pandas module that helps us to select a specific row or column from the data set. Using the iloc() function in python, we can easily retrieve any specific value from a row or column using index values.
Working of Python's iloc() function
Using the iloc() function in python, we can easily retrieve any particular value from a row or column using index values. We can invoke the iloc function in python on the data set to retrieve rows and columns. The iloc function in python takes two optional parameters, i.e., row value(s) and column value(s). The iloc function in python uses the parameters provided and returns the data set or series present at the specified position.
To understand the working better, refer to the example section below.
Example of Python's iloc() Function
Let us take the example of the employee’s data set. We can use another pandas module method names read_csv(). As the name suggests, the read_csv() method allows us to read the data present in the form of .csv files (excel files).
The overview of the dataset present in the data.csv file is:
| e_id | age | salary | bonus |
|---|---|---|---|
| 1 | 26 | 45000 | 4000 |
| 2 | 25 | 46000 | 4000 |
| 3 | 46 | 72000 | 5600 |
| 4 | 41 | 70000 | 5778 |
| 5 | 31 | 58000 | 6200 |
The dataset is present in the data.csv file, and we want to retrieve all the records of the second index, i.e., all the records of the third employee (having e_id: 3).
Note: The index always starts from 0, so the e_id - 3 means the second indexed employee. So, we will specify index 2 as a parameter in the iloc() method.
Code:
Output:
We can also gather a range of datasets by specifying the starting row and ending row values.
Code:
Output:
| e_id | age | salary | bonus | |
|---|---|---|---|---|
| 1 | 1 | 26 | 45000 | 4000 |
| 2 | 2 | 25 | 46000 | 4000 |
Note: The iloc function in python excludes the last index. So, if we specify the row range as [1:5], then the output will include 1 up to 4 and does not include the index 5.
Conclusion
- The iloc() function in python is defined in the Pandas module that helps us to select a specific row or column from the data set.
- Using the iloc method in python, we can easily retrieve any particular value from a row or column by using index values.
- The syntax of the iloc function in python is very simple: pandas.dataset.iloc[row, column]
- The iloc function in python takes two optional parameters, i.e., row number(s) and column number(s). We can only pass integer type values as parameter(s) in the iloc function in python.
- Using the parameters provided, the iloc function in python returns a data set or series.
- If we specify only row value, then the iloc function returns a Pandas Series. If we specify the row value and column value, then the iloc function returns all the content of the specified cell. If we specify a list of values, the python iloc function returns a Pandas DataFrame.