10 Automated EDA Tools That Will Save You Hours Of Work
<p>Exploratory Data Analysis (EDA) is the process of analyzing and summarizing the main characteristics of a data set, through visual and statistical methods. It’s an important step in the data science process that helps to understand the data, identify patterns and trends, detect outliers and anomalies, and formulate hypotheses for further investigation. EDA is typically done before building a model or making predictions, and it can be done using various tools and techniques such as data visualization, summary statistics, and statistical tests.</p>
<p><img alt="" src="https://miro.medium.com/v2/resize:fit:630/1*eP7D1tLIEpQ0b6YYugyvAA.jpeg" style="height:467px; width:700px" /></p>
<p>Implementation of Exploratory Data Analysis libraries with a few lines of Python code</p>
<p><strong>Table of Contents</strong></p>
<p>1. Pandas-Profiling</p>
<p>2. SweetViz</p>
<p>3. AutoViz</p>
<p>4. DataPrep</p>
<p>5. D-Tale</p>
<p>6. dabl</p>
<p>7. QuickDA</p>
<p>8. Datatile</p>
<p>9. Lux</p>
<p>10. ExploriPy</p>
<p>Automated EDA (Exploratory Data Analysis) packages can perform EDA in a few lines of Python code. In this article, we will discuss 10 Automated EDA Tools that can perform EDA and generate insights about the data<strong>.</strong></p>
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