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@dmofot
Created February 11, 2017 02:37
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Working with time series data using pandas and matplotlib
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Working with Time Series Data\n",
"\n",
"Quick demonstration on using Pandas and matplotlib to plot time series data"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>AAPL_y</th>\n",
" </tr>\n",
" <tr>\n",
" <th>AAPL_x</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2014-01-02</th>\n",
" <td>77.445395</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-01-03</th>\n",
" <td>77.045575</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-01-06</th>\n",
" <td>74.896972</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-01-07</th>\n",
" <td>75.856461</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-01-08</th>\n",
" <td>75.091947</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" AAPL_y\n",
"AAPL_x \n",
"2014-01-02 77.445395\n",
"2014-01-03 77.045575\n",
"2014-01-06 74.896972\n",
"2014-01-07 75.856461\n",
"2014-01-08 75.091947"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# read in time series data\n",
"df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_apple_stock.csv', index_col=0, parse_dates=True)\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x105e08110>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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qJg/zpc7RSG5ZDSOiglxd/HaaTjZv3HYKo6KD+zy+u2iLXil1TNvSSwA4bXxMc5IHCA/0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\nysP9P2cmJjoNrlYFAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x105db4210>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# plot data\n",
"df.plot()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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