{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#import dependencies\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"plt.style.use('fivethirtyeight')\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The code below is cited from Brandon Rhodes. His Pandas Tutorial is very helpful and teaches the core features of Pandas library. \n",
"\n",
"- Tutorial: https://www.youtube.com/watch?v=5JnMutdy6Fw\n",
"- Github: https://github.com/brandon-rhodes/pycon-pandas-tutorial\n",
"\n",
"I like using the css style into my projects because it looks nice in the eye to differ data, index and header. It comes very handy especially when you have multiple indexes with headers. "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.core.display import HTML\n",
"css = open('style-table.css').read() + open('style-notebook.css').read()\n",
"HTML(''.format(css))"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Name | \n",
" Year | \n",
" Gender | \n",
" Count | \n",
"
\n",
" \n",
" | Id | \n",
" | \n",
" | \n",
" | \n",
" | \n",
"
\n",
" \n",
" \n",
" \n",
" | 1 | \n",
" Mary | \n",
" 1880 | \n",
" F | \n",
" 7065 | \n",
"
\n",
" \n",
" | 2 | \n",
" Anna | \n",
" 1880 | \n",
" F | \n",
" 2604 | \n",
"
\n",
" \n",
" | 3 | \n",
" Emma | \n",
" 1880 | \n",
" F | \n",
" 2003 | \n",
"
\n",
" \n",
" | 4 | \n",
" Elizabeth | \n",
" 1880 | \n",
" F | \n",
" 1939 | \n",
"
\n",
" \n",
" | 5 | \n",
" Minnie | \n",
" 1880 | \n",
" F | \n",
" 1746 | \n",
"
\n",
" \n",
"
\n",
"