Python exploratory data analysis and why it's important
Exploratory data analysis is a key step to building the best models to gain insight from your data. Read on to see how Python can be a useful tool in this process.
Before implementing a new model, a data scientist must be sure they understand their data set. One method data scientists use to better understand their data and understand what model would work best for it is exploratory data analysis.
Though there are many tools to employ in this process, the Python programming language offers some of the most used tools in exploratory data analysis. Since Python is one of the most popular programming languages used in data analysis and data mining, most data scientists already find it accessible. Using Python exploratory data analysis tools allows you to easily summarize and visualize your data to choose the best algorithms and modeling to derive the most useful information from your data.
What is exploratory data analysis?
Exploratory data analysis focuses on what insights you can derive from the data. The process starts with defining the problem you need the data to solve. One of the first steps is to figure out what the business objective you need to address is. What is the specific end goal for your analysis?
Once you know the business objective, it's possible to prepare your data and develop different models to achieve that goal. It also allows you to summarize the available data, find correlations and create visualizations to present your findings to stakeholders.
Exploratory data analysis is an important part of defending your model to business stakeholders and proving that it can produce the desired results.
Exploratory data analysis essentials
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Commonly used tools
There are plenty of tools that can facilitate the exploratory data analysis process, from tools built into programming languages such as Python and R to tools supported by vendors such as SAS and KNIME.
Python exploratory data analysis is common among data scientists as Python is one of the most-used programming languages in the field. Available programming libraries such as pandas and Matplotlib can easily summarize and create visualizations for your data. Additionally, there are libraries in the language that can facilitate hypothesis testing and identify correlations between factors.
Learning to use Python for exploratory data analysis
By understanding your data set with exploratory data analysis, it is possible to determine the effectiveness of your model and better determine the correct machine learning model to reach your defined business goals. Learning Python exploratory data analysis can help you easily pull the most meaningful insights from your data using a tool you're already familiar with.
In their new book, Hands-On Exploratory Data Analysis with Python, published by Packt Publishing, authors Suresh Kumar Mukhiya and Usman Ahmed talked about useful techniques and Python tools for exploratory data analysis. The book includes links to sample code with step-by-step instruction on how to perform each process.
Here's a preview of the book. Click here to read Chapter 1: Exploratory Data Analysis Fundamentals.