{
"cells": [
{
"cell_type": "markdown",
"id": "357a45b3",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Other useful Python packages"
]
},
{
"cell_type": "markdown",
"id": "9fdaec8e",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Announcements\n",
"\n",
"- CAPES and survey available!\n",
" - Survey: https://forms.gle/xox1KgV6FwCYoESX6\n",
" - CAPES: https://cape.ucsd.edu/student/instructions.html\n"
]
},
{
"cell_type": "markdown",
"id": "08a31438",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Goals of this lecture\n",
"\n",
"- Review of course: what we've learned this quarter.\n",
"- Overview of other useful Python **packages**:\n",
" - `seaborn`: easily and quickly make **data visualizations**. \n",
" - `scipy`: tools for **statistical analyses**. \n",
" - `nltk`: tools for **Natural Language Processing**, like *sentiment analysis*."
]
},
{
"cell_type": "markdown",
"id": "979d6a04",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## What we've learned\n",
"\n",
"- Reflect on the very first day of class: most of you had never programmed before!\n",
"- Now you know:\n",
" - How to use **Jupyter notebooks**. \n",
" - How to write `if` statements and `for` loops.\n",
" - How to create **custom functions**. \n",
" - How to **read in files** of various types. \n",
" - How to work with **tabular data** using `pandas`.\n",
"\n",
"That's a lot for ten weeks!"
]
},
{
"cell_type": "markdown",
"id": "c58b6107",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Data visualization with `seaborn`"
]
},
{
"cell_type": "markdown",
"id": "372cb677",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### What is data visualization?\n",
"\n",
"[Data visualization](https://en.wikipedia.org/wiki/Data_visualization) refers to the process (and result) of representing data graphically.\n",
"\n",
"- **CSS 2** will dedicate much more time to this. \n",
"- Today: introduction to `seaborn`."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "09c4961f",
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt # conventionalized abbreviation\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"%matplotlib inline \n",
"%config InlineBackend.figure_format = 'retina'"
]
},
{
"cell_type": "markdown",
"id": "996c7dfc",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Example dataset"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "b82526bf",
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"outputs": [
{
"data": {
"text/plain": [
"6433"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Load taxis dataset\n",
"df_taxis = sns.load_dataset(\"taxis\")\n",
"len(df_taxis)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "73ca5014",
"metadata": {
"slideshow": {
"slide_type": "-"
}
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"outputs": [
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