Python for Data Analysis: Exploring and Cleaning Data

This video examines a variety of data exploration and preparation tasks you should consider after loading a data a set to prepare it for analysis, an examples of how to perform those tasks in Python.

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This is lesson 14 of a 30-part introduction to the Python programming language for data analysis and predictive modeling. Link to the code notebook below:

Python for Data Analysis: Exploring and Cleaning Data

This guide does not assume any prior exposure to Python, programming or data science. It is intended for beginners with an interest in data science and those who might know other programming languages and would like to learn Python.

I will create the videos for this guide such that you should be able to learn a lot just watching on YouTube, but to get the most out of the guide, it is recommended that you create a Kaggle account so that you can copy and edit each lesson so that you can follow along and run code yourself.

Introduction to Python Playlist:

Link to the Python for Data Analysis written guide index page:

[Pythagoras]

I think your instruction is going to take my Python skills to another level. The pace was too fast initially but this is exactly what I need to watch after getting a basic understanding of object oriented programming.

[atombarako]

Best and most complete Python for statistics course I found in YouTube! Thank you!

[khalidrabayah]

what I like the most is the provision of the code at your account in Kaggle, and the provision of the data sets. thank you

[muhammadnaqiuddinkamilmohd]

Thank you Sir for all these videos on Python. Just started using Python after learning R. Your videos are very helpful. Keep on posting more videos on Python Data Analysis.

[adventurouskidsnetwork]

It took me about 72 hours to find this useful chennel talking about python. Can't just thank you enough!!!!

[adamshenk]

boss thank you so much. Going through this whole series in order. Thank you for making these.

[rain]

Loving this! I'll have to rewatch this one to get everything. I'm practicing with mta turnstile data on my end

[osoriomatucurane]

Awesome tutorial on data exploration, thank you, Sir. I specifically found valuable the trick to isolate the categorical data type objects and get the overview/summary.

[willhallahan]

Another great video, I sometimes paused it to try out bits of code in my own notebook. Thank you.

[bigmao]

This kinda video is really helpful for newbies to the data analytics. Learning is by doing. And this is a very detailed example of doing it.
Thanks for the great video!

[EasyEnglishMovie]

Thank you sir, your videos are very helpful, simple and easy to understand.. thank you

[khalidrabayah]

Thank you for adding these very useful videos. They really add alot sepcially to beginners.

[sepehrtarani]

thank you for your really really perfect and step by step training.
your video was very helpful for me,

[judycheng]

Thanks so much for your video. This must be an extra tutorial out of my Data analytics paper. Really appreciate.

[truongvuolap]

it is better when you have homeworks, but i love this series.

[TripleMasterA]

Thank you for this video. Really insightful, and I can tell how experienced you are since you ask a lot of the right questions when starting a data analysis project!

[CreativeheadX]

Thank you for your knowledge sharing. Very nice explanation with samples for each steps.

[adiityadwivedi]

sir what you taught in this lecture, my teacher took 12 hours and 2 Thank you

[osoriomatucurane]

Replacing the nan values for the age variable with the artimethic mean. I am thinking loud, there is an alternative. Grouping the age into categories, then find the weighted mean (with the frequencies as weights) and fill the nan with the weighted mean.

[adiityadwivedi]

sir, i am doing this on google colab, my college told me to and some of your code from kaggle, like np.array, are not working on google colab, i removed it then it works fine and my teacher uses ' ' and you use " " and it doesnt effect the code yet what should i prefer, so as to master many diifrent coding languages