{
"cells": [
{
"cell_type": "markdown",
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"source": [
"# Data visualization, pt. 2 (`seaborn`)"
]
},
{
"cell_type": "markdown",
"id": "118f7491",
"metadata": {
"slideshow": {
"slide_type": "slide"
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"source": [
"## Goals of this lecture\n",
"\n",
"- Introducting `seaborn`. \n",
"- Putting `seaborn` into practice:\n",
" - **Univariate** plots (histograms). \n",
" - **Bivariate** continuous plots (scatterplots and line plots).\n",
" - **Bivariate** categorical plots (bar plots, box plots, and strip plots)."
]
},
{
"cell_type": "markdown",
"id": "5fa26f5e",
"metadata": {
"slideshow": {
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"source": [
"## Introducing `seaborn`"
]
},
{
"cell_type": "markdown",
"id": "6a4ffbb5",
"metadata": {
"slideshow": {
"slide_type": "slide"
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"source": [
"### What is `seaborn`?\n",
"\n",
"> [`seaborn`](https://seaborn.pydata.org/) is a data visualization library based on `matplotlib`.\n",
"\n",
"- In general, it's easier to make nice-looking graphs with `seaborn`.\n",
"- The trade-off is that `matplotlib` offers more flexibility."
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "6a3c41f6",
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"outputs": [],
"source": [
"import seaborn as sns ### importing seaborn\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt ## just in case we need it\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "1c0815db",
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"outputs": [],
"source": [
"%matplotlib inline \n",
"%config InlineBackend.figure_format = 'retina'"
]
},
{
"cell_type": "markdown",
"id": "b926c887",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### The `seaborn` hierarchy of plot types\n",
"\n",
"We'll learn more about exactly what this hierarchy means today (and in next lecture).\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"id": "914ef46e",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Example dataset\n",
"\n",
"Today we'll work with a new dataset, from [Gapminder](https://www.gapminder.org/data/documentation/). \n",
"\n",
"- **Gapminder** is an independent Swedish foundation dedicated to publishing and analyzing data to correct misconceptions about the world.\n",
"- Between 1952-2007, has data about `life_exp`, `gdp_cap`, and `population`."
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "6b8d6ac8",
"metadata": {},
"outputs": [],
"source": [
"df_gapminder = pd.read_csv(\"data/viz/gapminder_full.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "3fcb4d6e",
"metadata": {},
"outputs": [
{
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\n",
" \n",
" \n",
" | \n",
" country | \n",
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" continent | \n",
" life_exp | \n",
" gdp_cap | \n",
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\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" Afghanistan | \n",
" 1952 | \n",
" 8425333 | \n",
" Asia | \n",
" 28.801 | \n",
" 779.445314 | \n",
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\n",
" \n",
" | 1 | \n",
" Afghanistan | \n",
" 1957 | \n",
" 9240934 | \n",
" Asia | \n",
" 30.332 | \n",
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