Best Data Analysis Library in Python
<p>Imagine you have a bunch of data in Jupyter Notebook that you want to analyze and visualize. <strong>PyGWalker</strong> is like a magic tool that makes it super easy for you. It takes your data and turns it into a special kind of table that you can interact with, just like using Tableau. You can explore your data visually, play around with it, and see patterns and insights<strong> without getting lost in complicated code</strong>. <strong>PyGWalker</strong> simplifies everything, so you can analyze and understand your data quickly and effortlessly. PyGWalker, developed by Kanaries.</p>
<p>Official GitHub Repository — <a href="https://github.com/Kanaries/pygwalker" rel="noopener ugc nofollow" target="_blank">https://github.com/Kanaries/pygwalker</a></p>
<p>Code — <a href="https://colab.research.google.com/drive/171QUQeq-uTLgSj1u-P9DQig7Md1kpXQ2?usp=sharing" rel="noopener ugc nofollow" target="_blank">Google Colab</a> , <a href="https://www.kaggle.com/code/asmdef/pygwalker-test/notebook" rel="noopener ugc nofollow" target="_blank">Kaggle Notebook</a></p>
<p>Author Profile — <a href="https://github.com/ObservedObserver" rel="noopener ugc nofollow" target="_blank">https://github.com/ObservedObserver</a></p>
<p>Install PyGWalker library via pip</p>
<pre>
!pip install pygwalker -q</pre>
<p>To get started with <strong>PyGWalker</strong> in Jupyter Notebook, you need to import two essential libraries: <strong>pandas and pygwalker</strong>.</p>
<pre>
import pandas as pd
import pygwalker as pyg</pre>
<p>The “<strong>import pandas as pd</strong>” line allows you to work with data in a tabular format, while “<strong>import pygwalker as pyg</strong>” brings in the PyGWalker library.</p>
<p>Once imported, you can seamlessly integrate <strong>PyGWalker</strong> into your existing workflow.</p>
<p>For instance, you can load your data using pandas</p>
<p><a href="https://medium.com/@fareedkhandev/best-data-analysis-library-in-python-ad2572288017">Read More</a></p>