Adventures in Machine Learning

Data Analysis Made Easy with Python iloc() Function

Python iloc() Function: A Comprehensive Guide

When it comes to working with datasets in Python, the iloc() function is a handy tool to have. Essentially, iloc() is used for retrieving specific data values from a dataset using index values.

In this article, we will delve into the workings of the Python iloc() function, including how it functions and some examples of how you can use it.

Working of the Python iloc() function

The iloc() function in Python is used to access elements from a dataset using index values. For instance, if we have a dataset consisting of rows and columns, we can use iloc() to retrieve a specific value from a particular row and column in the dataset.

The index values take in integers, and the function primarily fetches data based on numerical indexing. For instance, if we have a dataset with three rows and four columns, then we can use iloc() to retrieve the value in the fourth column and second row.

This is because, in iloc(), the column and row indices start at 0. Therefore, to retrieve a specific value, we input the corresponding row and column index number.

In our current example dataset, we will input 1 as the row index and 3 as the column index to retrieve the value from the second row and fourth column of the dataset. However, it is worth noting that iloc() only accepts integer-type index values.

If you input a non-integer index, iloc() will throw an error. Therefore, it is crucial that when using iloc(), all the inputs are integers to avoid errors.

Examples of the Python iloc() function

Now that we have understood the workings of the iloc() function, let us delve into some examples of how you can use this function.

Accessing all the data values of a specific index in every column of the dataset

Suppose we have a dataset consisting of seven rows and seven columns, and we want to extract all the values from the fifth row. We can use iloc() to achieve this.

The code snippet below illustrates how you can use iloc() to retrieve all the rows for the fifth index.

import pandas as pd

dataset = pd.read_csv(‘my_dataset.csv’)

specific_values = dataset.iloc[4, :]

print(specific_values)

This code fetches all the values from the fifth row of the dataset and outputs them.

Accessing data values of multiple rows equivalent to every column of the dataset

Suppose we want to extract data from multiple rows in every column of the dataset. We can use iloc() to fetch these values.

The code snippet below illustrates how we can retrieve data values from the third, fourth, and seventh row for all the columns.

import pandas as pd

dataset = pd.read_csv(‘my_dataset.csv’)

specific_values = dataset.iloc[[2,3,6], :]

print(specific_values)

Fetching records based on index values

Another example of using the iloc() function is to fetch records based on index values. For instance, let us assume that we have a dataset consisting of 1000 rows and 20 columns, and we want to retrieve the values of the 30th to the 50th index.

We can use the iloc() function to retrieve these values. The code snippet below illustrates how to retrieve records with such index values:

import pandas as pd

dataset = pd.read_csv(‘my_dataset.csv’)

specific_values = dataset.iloc[30:51, :]

print(specific_values)

Conclusion

In this article, we have seen how the Python iloc() function operates in retreating specific data points in a dataset. We also gave some examples of how to use iloc() to access data in various scenarios.

Understanding the iloc() function is essential in data analysis and using Python. You can use iloc() for data preprocessing and exploration and in the creation of models for machine learning.

In this article, we have learned about the Python iloc() function, which retrieves specific data values from a dataset using index values. We saw how the function works, accepting only integer-type index values, and provided examples of its usage to retrieve values, rows, and columns from datasets.

Understanding this function is important in data analysis, machine learning, and data preprocessing and exploration. By mastering this tool, you can better extract insights from your data and make informed decisions.

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