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Plotting with Pandas

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**Talking Points**: This lesson introduces the Pandas library and the beginnings of Exploratory Data Analysis. The majority of the lesson should be spent going through code -- whether that is via Jupyter Slides or a Jupyter Notebook demonstration. To present this content, begin with `04-plotting-with-pandas.ipynb` to introduce Pandas as a library and data integrity. Transition to the Jupyter Notebook to introduce reading in data, column manipulation, filtering and sorting; conclude with exercises. **Teaching Tips**: - There are **Class Questions** littered throughout the notebook. Use as much/little time on these as you see fit relative to how your class is pacing - There is no **Independent Exercise** at the end of this lesson. It is aspirational to have time to let students work entirely independently on this time-wise, so consider doing a guided code-along or paired programming. Use this time to have students set their own challenges. - Pause after learning objectives and level-set for what students will get out of the lesson

--- ## A Note on Delivery - This unit's lessons will occur in [jupyter notebooks](http://jupyter.org/) - Slides will be an introduction to the lesson (no code, just overview) - Then, we'll open a notebook and start coding! --- ## Plotting with Pandas - Pandas `.plot()` functionality is effectively a wrapper for [matplotlib](https://matplotlib.org/) - Matplotlib is a charting library for python and scientific computing - It's considered the de-facto standard for charting locally - It's best for scientific papers, EDA, and general introspection of data - It's not so great for production level charts that are embedded in applications (check out [d3.js](https://d3js.org/) --- ## So, Pandas and Matplotlib Whats a wrapper? - A program that _abstracts_ another program to modify its interface ??? - Pandas `.plot()` functionality references matplotlib behind the scenes - Matplotlib has a reputation for being fairly complex - Even for fairly simple charts, you will frequently write loops - A fairly plain chart can be 20-30 lines of code - Pandas helps us here and most charts can be produced with 1-2 lines of code - Some functionality is reduced, but _effort is minimized in most cases_ --- ## Talk Data to Me We'll be using three data sets for this lesson: - Football Records: International football results from 1872 to 2018 - Avocado Prices: Historical data on avocado prices and sales volume in multiple US markets - Chocolate Bar Ratings: Expert ratings of over 1,700 chocolate bars All datasets have been graciously downloaded from Kaggle.com, and we'll discover that the right visualization can often replace a bit of fancy machine learning, if done properly. --- ## Chart Types We'll be covering the following chart types during this lesson: - Time series line charts - Categorical bar charts - Histograms of single columns - Histograms of entire data frames - Scatter plots (continuous vs continuous) - Scatter matricies (multiple scatter plots in a grid) - Scatter plots with class colors for data points --- ## Let's Go! - Open up your dataset! ---