{
"cells": [
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"cell_type": "markdown",
"metadata": {},
"source": [
"# Plots and Charts with Altair\n",
"\n",
"This is my notebook for messing around with Altair. But why learn Altair if you already know `matplotlib` and `seaborn` well? Here are a few reasons:\n",
"1. Altair makes supporting interactivity (such as selection, mouseovers, and more) extremely easy - don't worry about learning `plotly`! Just add `.interactive()` at the end of your call.\n",
"2. The 'grammar of graphics' for Altair is higher-level than the `seaborn` and `matplotlib` API; once you learn the idioms, creating the plot you want is a fairly straightforward process, requiring little to no fiddling with subplots and other annoyances. \n",
"3. Quit wasting time with `groupby` in pandas. (Or as Raymond Hettinger would say: \"don't do that, do this instead!\") Altair can do aggregation for you, within-call. Sums, averages, groupbys, and more are all supported.\n",
"4. Make visualizations for data you grab from a URL and don't even have. (Altair even provides the ability to rename variables within-call when you don't have access to the raw data.)\n",
"5. Intelligent interpretation of your variable types. And you can always hard-specify type in advance by adding :N, :O, :Q, or :T. (nominal, ordinal, quantitative, or temporal)\n",
"\n",
"To get up and running, I highly recommend [Jake VanderPlas' excellent PyCon 2018 tutorial.](https://www.youtube.com/watch?v=ms29ZPUKxbU&t=2548s) The tutorial will give you more of a bird's-eye view of what's possible (and what's unique) in Altair than you could get by poking aimlessly around the documentation.\n",
"\n",
"**Quick note on interactivity in this notebook:** if you're viewing this on GitHub, the interactive elements will not work for you. Plots are rendered to static images (and at a lower resolution than Altair actually makes them). To really experience the look and feel of these visualizations, feel free to fork the notebook and play around with it on your own machine."
]
},
{
"cell_type": "code",
"execution_count": 244,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"RendererRegistry.enable('notebook')"
]
},
"execution_count": 244,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import altair as alt\n",
"from vega_datasets import data\n",
"import pandas as pd\n",
"import numpy as np\n",
"import numpy.random as r\n",
"\n",
"# Suppress warnings\n",
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
"\n",
"# for the notebook only (not for JupyterLab) run this command once per session\n",
"alt.renderers.enable('notebook')"
]
},
{
"cell_type": "code",
"execution_count": 245,
"metadata": {},
"outputs": [
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"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" MSFT | \n",
" 2000-01-01 | \n",
" 39.81 | \n",
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\n",
" \n",
" | 1 | \n",
" MSFT | \n",
" 2000-02-01 | \n",
" 36.35 | \n",
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\n",
" \n",
" | 2 | \n",
" MSFT | \n",
" 2000-03-01 | \n",
" 43.22 | \n",
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\n",
" \n",
" | 3 | \n",
" MSFT | \n",
" 2000-04-01 | \n",
" 28.37 | \n",
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\n",
" \n",
" | 4 | \n",
" MSFT | \n",
" 2000-05-01 | \n",
" 25.45 | \n",
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" \n",
"
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\n",
"\n",
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" \n",
" \n",
" | \n",
" cluster | \n",
" country | \n",
" fertility | \n",
" life_expect | \n",
" pop | \n",
" year | \n",
" log_pop | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" 0 | \n",
" Afghanistan | \n",
" 7.7 | \n",
" 30.332 | \n",
" 8891209 | \n",
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" 16.000574 | \n",
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\n",
" \n",
" | 1 | \n",
" 0 | \n",
" Afghanistan | \n",
" 7.7 | \n",
" 31.997 | \n",
" 9829450 | \n",
" 1960 | \n",
" 16.100894 | \n",
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\n",
" \n",
" | 2 | \n",
" 0 | \n",
" Afghanistan | \n",
" 7.7 | \n",
" 34.020 | \n",
" 10997885 | \n",
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" 16.213214 | \n",
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\n",
" \n",
" | 3 | \n",
" 0 | \n",
" Afghanistan | \n",
" 7.7 | \n",
" 36.088 | \n",
" 12430623 | \n",
" 1970 | \n",
" 16.335674 | \n",
"
\n",
" \n",
" | 4 | \n",
" 0 | \n",
" Afghanistan | \n",
" 7.7 | \n",
" 38.438 | \n",
" 14132019 | \n",
" 1975 | \n",
" 16.463954 | \n",
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\n",
" \n",
" \n",
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" | 0 | \n",
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" | 2 | \n",
" 2012-01-03 | \n",
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\n",
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" \n",
" | 4 | \n",
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