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Pandas and Jupyterlab workshop – Python for Data Science

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# Python for Data Science: Pandas and JupyterLab

## Workshop modules:

### Pandas 101 – Spring 2022

1. [Data structures for spreadsheets in Python](DataStructures.ipynb)
1. [Accessing your tabular data](DataFrameAccess.ipynb) · *(inline exercises)*
1. [Pandas built-in plotting](BasicPandasPlotting.ipynb)
1. [Plotting exercise](PlottingExercise.ipynb)

### Assignment Pandas 101 – Fall 2022

1. [Pandas 101 essential review](Pandas101review.ipynb)
1. [Assignment Pandas 101](AssignmentPandas101.ipynb)

### Pandas 102 – Fall 2022

1. [Intro to Tidy Data](TidyDataIntro.ipynb)
1. [Intro to tidy data plotting with Seaborn](SeabornIntro.ipynb) · *(inline exercises)*
1. [Melt into tidy data](MeltIntro.ipynb)
1. [Melt data exercise](MeltExercise.ipynb)
1. [Merge (JOIN) datasets](MergeDatasets.ipynb)
1. [Merge (JOIN) exercise](MergeExercise.ipynb)

### Pandas 103 – Spring 2023

1. [Groupby Basics](Groupby_Basics.ipynb) · *(inline exercises)*
1. [Exploring NC emissions with Groupby and Pivot Tables](Groupby_NCexploration.ipynb) · *(inline exercises)*

### Extras

- [Full Accessing DataFrames](FullAccessingDataFrames.ipynb)
- [JOIN types](JoinTypes.ipynb)
- [Concatenating files](Concatenation.ipynb)
- [Splitting and exploding lists into rows](SplitExplodeLists.ipynb)
- [Exploring NC emissions with categorical data columns](Groupby_NCcategorical.ipynb)

Eric E Monson  
[Duke Libraries Center for Data and Visualization Sciences](http://library.duke.edu/data)

---

**Tidy Data Exercises**
are from the blog post by Jean-Nicholas Hould
[blog post](http://www.jeannicholashould.com/tidy-data-in-python.html) 
and [hist accompanying GitHub repository](https://github.com/nickhould/tidy-data-python)

---

### Extra Resources

[Minimally Sufficient Pandas](https://medium.com/dunder-data/minimally-sufficient-pandas-a8e67f2a2428)
· A really great post by Ted Petrou with an opinionated guide to which commands and styles of
coding should be used with Pandas. There are so many ways of doing the same thing in Pandas
and he helps clarify which to choose and why. *Not a beginner article, but essential for intermediate
to advanced users.*

[Jupyter Notebook Tutorial](https://www.datacamp.com/community/tutorials/tutorial-jupyter-notebook)
· From Datacamp, gives history, setup and basic usage, with great links to good notebooks
and how to set up interactive dashboards

[Github Markdown cheat sheet](https://guides.github.com/pdfs/markdown-cheatsheet-online.pdf)
· Super-concise summary of (github-style) Markdown syntax

[Pandas cheat sheet](https://github.com/pandas-dev/pandas/blob/master/doc/cheatsheet/Pandas_Cheat_Sheet.pdf)
· Modeled after R cheat sheet. Very good summary of basic Pandas functions

[Pandas Tutor](https://pandastutor.com/vis.html)
· Visualizes simple Python Pandas code, showing transformation diagrams

[JupyterLite](https://blog.jupyter.org/jupyterlite-jupyter-%EF%B8%8F-webassembly-%EF%B8%8F-python-f6e2e41ab3fa)
· JupyterLab distribution that runs entirely in the web browser, backed by in-browser language kernels

[Dramatic Tour of Python visualization tools](https://dansaber.wordpress.com/2016/10/02/a-dramatic-tour-through-pythons-data-visualization-landscape-including-ggplot-and-altair/)
· Entertaining and illuminating comparison of how various plots would be done with many major
Python visualization packages (Matplotlib, Pandas, Seaborn, ggplot, Altair), including
[code repository](https://github.com/dsaber/py-viz-blog)

[Pandas tutorials and documentation](https://pandas.pydata.org/pandas-docs/stable/tutorials.html)
· Good documentation, examples and quick tutorials from the official Pandas site

[Python for Data Science workshop](https://github.com/stefanv/ds_intro)
· Python for Data Science workshop repository, given at IEEE VIS 2015

[NBViewer – Jupyter viewer site](http://nbviewer.jupyter.org/)
· Place to both put your notebooks for sharing and find great content by others

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