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https://github.com/guipsamora/pandas_exercises
Practice your pandas skills!
https://github.com/guipsamora/pandas_exercises
data-analysis exercise pandas practice tutorial
Last synced: 10 days ago
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Practice your pandas skills!
- Host: GitHub
- URL: https://github.com/guipsamora/pandas_exercises
- Owner: guipsamora
- License: bsd-3-clause
- Created: 2016-07-12T22:46:43.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2024-08-16T12:24:00.000Z (3 months ago)
- Last Synced: 2024-10-22T09:28:20.918Z (17 days ago)
- Topics: data-analysis, exercise, pandas, practice, tutorial
- Language: Jupyter Notebook
- Homepage:
- Size: 17.5 MB
- Stars: 10,810
- Watchers: 312
- Forks: 8,242
- Open Issues: 39
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
- StarryDivineSky - guipsamora/pandas_exercises
README
# Pandas Exercises
Fed up with a ton of tutorials but no easy way to find exercises I decided to create a repo just with exercises to practice pandas.
Don't get me wrong, tutorials are great resources, but to learn is to do. So unless you practice you won't learn.
There will be three different types of files:
1. Exercise instructions
2. Solutions without code
3. Solutions with code and comments
My suggestion is that you learn a topic in a tutorial, video or documentation and then do the first exercises.
Learn one more topic and do more exercises. If you are stuck, don't go directly to the solution with code files. Check the solutions only and try to get the correct answer.
Suggestions and collaborations are more than welcome.🙂 Please open an issue or make a PR indicating the exercise and your problem/solution.
# Lessons
| | | |
|:-----------------------------------------------:|:----------------------------------------------:|:-----------------:|
|[Getting and knowing](#getting-and-knowing) | [Merge](#merge) |[Time Series](#time-series)|
|[Filtering and Sorting](#filtering-and-sorting) | [Stats](#stats) |[Deleting](#deleting) |
|[Grouping](#grouping) | [Visualization](#visualization) |Indexing |
|[Apply](#apply) | [Creating Series and DataFrames](#creating-series-and-dataframes) |Exporting|
### [Getting and knowing](https://github.com/guipsamora/pandas_exercises/tree/master/01_Getting_%26_Knowing_Your_Data)
[Chipotle](https://github.com/guipsamora/pandas_exercises/tree/master/01_Getting_%26_Knowing_Your_Data/Chipotle)
[Occupation](https://github.com/guipsamora/pandas_exercises/tree/master/01_Getting_%26_Knowing_Your_Data/Occupation)
[World Food Facts](https://github.com/guipsamora/pandas_exercises/tree/master/01_Getting_%26_Knowing_Your_Data/World%20Food%20Facts)
### [Filtering and Sorting](https://github.com/guipsamora/pandas_exercises/tree/master/02_Filtering_%26_Sorting)
[Chipotle](https://github.com/guipsamora/pandas_exercises/tree/master/02_Filtering_%26_Sorting/Chipotle)
[Euro12](https://github.com/guipsamora/pandas_exercises/tree/master/02_Filtering_%26_Sorting/Euro12)
[Fictional Army](https://github.com/guipsamora/pandas_exercises/tree/master/02_Filtering_%26_Sorting/Fictional%20Army)
### [Grouping](https://github.com/guipsamora/pandas_exercises/tree/master/03_Grouping)
[Alcohol Consumption](https://github.com/guipsamora/pandas_exercises/tree/master/03_Grouping/Alcohol_Consumption)
[Occupation](https://github.com/guipsamora/pandas_exercises/tree/master/03_Grouping/Occupation)
[Regiment](https://github.com/guipsamora/pandas_exercises/tree/master/03_Grouping/Regiment)
### [Apply](https://github.com/guipsamora/pandas_exercises/tree/master/04_Apply)
[Students Alcohol Consumption](https://github.com/guipsamora/pandas_exercises/tree/master/04_Apply/Students_Alcohol_Consumption)
[US_Crime_Rates](https://github.com/guipsamora/pandas_exercises/tree/master/04_Apply/US_Crime_Rates)
### [Merge](https://github.com/guipsamora/pandas_exercises/tree/master/05_Merge)
[Auto_MPG](https://github.com/guipsamora/pandas_exercises/tree/master/05_Merge/Auto_MPG)
[Fictitious Names](https://github.com/guipsamora/pandas_exercises/tree/master/05_Merge/Fictitous%20Names)
[House Market](https://github.com/guipsamora/pandas_exercises/tree/master/05_Merge/Housing%20Market)
### [Stats](https://github.com/guipsamora/pandas_exercises/tree/master/06_Stats)
[US_Baby_Names](https://github.com/guipsamora/pandas_exercises/tree/master/06_Stats/US_Baby_Names)
[Wind_Stats](https://github.com/guipsamora/pandas_exercises/tree/master/06_Stats/Wind_Stats)
### [Visualization](https://github.com/guipsamora/pandas_exercises/tree/master/07_Visualization)
[Chipotle](https://github.com/guipsamora/pandas_exercises/tree/master/07_Visualization/Chipotle)
[Titanic Disaster](https://github.com/guipsamora/pandas_exercises/tree/master/07_Visualization/Titanic_Desaster)
[Scores](https://github.com/guipsamora/pandas_exercises/tree/master/07_Visualization/Scores)
[Online Retail](https://github.com/guipsamora/pandas_exercises/tree/master/07_Visualization/Online_Retail)
[Tips](https://github.com/guipsamora/pandas_exercises/tree/master/07_Visualization/Tips)
### [Creating Series and DataFrames](https://github.com/guipsamora/pandas_exercises/tree/master/08_Creating_Series_and_DataFrames)
[Pokemon](https://github.com/guipsamora/pandas_exercises/tree/master/08_Creating_Series_and_DataFrames/Pokemon)
### [Time Series](https://github.com/guipsamora/pandas_exercises/tree/master/09_Time_Series)
[Apple_Stock](https://github.com/guipsamora/pandas_exercises/tree/master/09_Time_Series/Apple_Stock)
[Getting_Financial_Data](https://github.com/guipsamora/pandas_exercises/tree/master/09_Time_Series/Getting_Financial_Data)
[Investor_Flow_of_Funds_US](https://github.com/guipsamora/pandas_exercises/tree/master/09_Time_Series/Getting_Financial_Data)
### [Deleting](https://github.com/guipsamora/pandas_exercises/tree/master/10_Deleting)
[Iris](https://github.com/guipsamora/pandas_exercises/tree/master/10_Deleting/Iris)
[Wine](https://github.com/guipsamora/pandas_exercises/tree/master/10_Deleting/Wine)
# Video Solutions
Video tutorials of data scientists working through the above exercises:
[Data Talks - Pandas Learning By Doing](https://www.youtube.com/watch?v=pu3IpU937xs&list=PLgJhDSE2ZLxaY_DigHeiIDC1cD09rXgJv)