Fast and Effective Exploratory Data Analysis (EDA) With Python and Pandas Profiling for Data Science

In this short Python EDA tutorial, we will cover the use of an excellent Python library called Pandas Profiling. This library helps us carry fast and automatic EDA on our dataset with minimal lines of code.

Exploratory Data Analysis (EDA) is an important and essential part of the data science and machine learning workflow. It allows us to become familiar with our data by exploring it, from multiple angles, through statistics, data visualisations, and data summaries. This helps discover patterns in the data, spot outliers, and gain a solid understanding of the data we are working with.

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[Funzelwicht]

Thank you! Without your series, I would have never known this!

[SouthwestStet]

This is amazing! I had no idea this tool existed. So much quicker than coding it all out with pandas+seaborn etc. Thanks for sharing

[omarabduljabar]

This video is Amazing Mr. Andy, very useful for me. Thank you sir!

[estherkerubo]

Hello Andy please how can I reach you? I have been watching your videos here and I'm learning a lot and getting very much inspired with your content to get into Machine Learning for Geoscience data analysis. I am doing my MSc. project in facies characteristics and prognosis of well lo data using the k-means clustering algorithm, please I need your help and guidance.

[alikoohi]

Quite useful introduction and tips..👏👏

[ronenTheBarbarian]

Hi Andy, thanks so much for this video. Would you be able to show the quickest way from EDA to using LR to predict something? Also, how do you select which features are most important?

[abdoulazizmahamadouhamidou]

HI Andy. Thanks for the always ussefull tutos. Unfortunatly i am getting a MArkupSafe error when trying to import ProfileReport, can u sugest a workaround solution please?
here is the error: ImportError: cannot import name 'soft_unicode' from 'markupsafe'
Thanks in advance