Python for Data Analysis: Chi-Squared Tests

This video covers the basics of how to perform chi-squared tests in Python.

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This is lesson 25 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: Chi-Squared Tests

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:

[pavloseimskyi]

Man, your videos are just absolutely amazing. Simple, clean, great voice and quality of audio. Also, you explain what you're about to do in both more complex scientific notation and simple human words. This way, I definitely feel much less intimidated by all those definitions. I work since recently as a data analyst and need to fill tons of gaps in my stats knowledge and your channel is the number one so far in terms of sources where I learn from. I really trust that what you say is actually true (not always the case for other content on the web!).

I really hope that one day your channel becomes freakin' famous, you really deserve it! Keep it up!!!

[wenanyaugustine]

OMG ! You are so good at explaining this . I just don't know how you have 30K views. That explanation was excellent 👌

[panmichal]

I need to improve my knowledge of statistics for Data Science and this channel is a really helpful one! Everything is short and clear. Thank you for your work!

[dilaraesmer]

Thank you so much ! :) I used these codes and their details for my study

[shubhi]

Great video! Concise, with information articulated in a simplifed manner. Thanks for sharing your knowledge. :)

[DoYouHaveAName]

Thank you so much!!!! Great explanations

[urd]

Thank you for the videos. articulating it through out the example is very helpful indeed. cheers!

[bhavinmoriya]

To reject H0, do we need : pval < .05 and chi3 stat > crit value ?

[alebecker]

Hi! Thanks for the video! Very good explanation!

But I still have a doubt, what if I melt the table and put the columns as row values, for example, [asian-democrat, asian-independent, asian-republican, black-democrat, black-independent, black-republican] and [23, 11, 25, 61, 28, 64] (like, instead of a data frame with four columns, a df with just one column "quantity" and the specific demographics in the row? Would that be wrong?

[qya.]

Great video. Thanks
I am just wondering do you need to calculate the observed and expected values manually? If Yes, wouldn't using R for statistical analysis be much simpler?

[kristianovari]

Hi. I tried it with gender and with marital status on population and sample dataframe. But my results are always [0.]. If i use the .pdf instead of .cdf than the results is [1.]. With the round function is the same. Any idea what went wrong? 🙏

[bhavinmoriya]

OR either of these two would reject the H0?