![]() ![]() ![]() The idea behind the system is quickly visualizing target values and comparing datasets. The output is a self-contained HTML application. It is an open-source Python library that generates beautiful, high-density visualizations to kickstart the EDA (Exploratory Data Analysis) process with just two lines of code. ![]() Sweetviz is one of my, if not my favorite, Exploratory data Analysis library. Give them a try in your spare time, and let me know your favorite. Below are few libraries that may make EDA faster and a bit more intuitive, especially if you are not a code-savvy person. As technology advances, a few libraries were created to ease the process and save a lot of time writing repetitive code. What do Data Scientist use as Tools for Exploratory Data Analysis (EDA)Ī trained data scientist often does EDA through standard programming tools such as Python and Pandas. It can also help determine if the statistical techniques considered for data analysis are suitable. It gives a better understanding of the data variables and features, along with the relationships between them. EDA is essentially used to understand what data can show beyond the conventional hypothesis testing task. Additionally, the whole process makes it easier to spot anomalies, test a hypothesis, discover patterns, or check assumptions. The results of an EDA help a Data Scientist learn the best way to handle data sources to get the insights you need. Indeed, it happens through summary information presented as insights and accompanied by various data visualization methods. Practically, data scientists use this methodology to analyze, examine and summarize the main characteristics of their dataset. The goal is to understand what the data is going to tell you about the studied topic. If like me, you were confused by the original definition, you can think of EDA as a process in which the data analyst analyses/examines/go through a dataset without having any preconceived idea as to what he/she is going to discover. He defined it as “detective work – numerical detective work – or counting detective work – or graphical detective work”. What is Exploratory Data Analysis aka EDA? For those of you who do not know what exploratory data analysis (EDA) is, it is a term that appeared first in 1977 from a statistician name John W. In this article, I am going to share with you the top 10 Exploratory Data Analysis (EDA) Tools you can try to make this process easier and faster for you. All data scientists have to do this step to get a better understanding of the data they are working on. Exploratory Data Analysis (EDA) is an essential step in the data science project lifecycle. Helping companies make sense of their data. Best Libraries That Will Assist You In EDA: 2021 Edition ![]()
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