{"cells": [{"cell_type": "markdown", "metadata": {}, "source": ["# Exercise 6.2: Automating scatter plots\n", "\n", "<hr>"]}, {"cell_type": "markdown", "metadata": {}, "source": ["\n", "We will soon use HoloViews to quickly make scatter plots. Nonetheless, I think coding up your own function to make scatter plots with coloring based on a column of a tidy data frame will help you understand how high level plotting works (and also allow you to practice manipulating data frames).\n", "\n", "Write a function that takes as input a tidy data frame and generates a scatter plot based on two columns of the data frame and colors the glyphs according to a third column that contains categorical variables. The minimal (you can add other kwargs if you want) call signature should be\n", "\n", "```python\n", "scatter(data, cat, x, y)\n", "```\n", "\n", "You will of course test out your function while writing it, and the next exercises give you lots of opportunities to use it."]}, {"cell_type": "markdown", "metadata": {}, "source": ["<br />"]}], "metadata": {"anaconda-cloud": {}, "kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.7"}}, "nbformat": 4, "nbformat_minor": 4}