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Explore Your Dataset With pandas (Summary)

In this course, you’ve learned how to start exploring a dataset with the pandas Python library. You saw how you could access specific rows and columns to manage even the largest of datasets. You’ve also seen multiple techniques to prepare and clean your data, by specifying the data type of columns, dealing with missing values, and more. You’ve even created queries, aggregations, and plots based on those.

Now you can:

  • Work with Series and DataFrame objects
  • Subset your data with .loc, .iloc, and the indexing operator
  • Answer questions with queries, grouping, and aggregation
  • Handle missing, invalid, and inconsistent data
  • Visualize your dataset in a Jupyter Notebook

You can further develop these skills with Fast, Flexible, Easy and Intuitive: How to Speed Up Your pandas Projects and Python pandas: Tricks & Features You May Not Know. With enough practice, you will be able to tackle any datasets you find interesting and share your insights and observations with your friends and colleagues!


Sample Code (.zip)

64.8 KB

Course Slides (.pdf)

723.7 KB

Vijaya Kumar Marneni on June 5, 2021

I am beginner for pandas, it is quite useful to get flavour of it. I deep dive into library with this knowledge. Thank you Douglas Starnes

aniketbarphe on Nov. 25, 2021

Thank You!

pnmcdos on April 7, 2022

Very informative. I was able to follow along with your video. This was extremely helpful.

wrucknroll on Feb. 18, 2023

Hi thanks for the videos. Great content if a little fast with the typing speed ups. My brain needs some time to process what is happening!

The recommended reading tutorial that is linked to this course exactly the same and I found it to be great way to digest what I just watched.

Emmanuel Ozemhoya on June 1, 2023

This is the reason I subscribed for a second year; I can comprehend and understand Python at this level. Now, I can get similar datasets and test my knowledge henceforth.

JulianV on Sept. 5, 2023

It will be helpfull to read the Fedex csv files I get from Fedex portal.

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