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@aaronmams
Created September 9, 2016 22:26
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mams notebook test
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Real Estate Values in Humboldt County, CA"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is a quick-and-dirty example of an IPython Notebook. They are great for documenting projects as they are developed. Each code chunk can be executed as stand-alone code and markdown syntax can be used to narrate code functionality. \n",
"\n",
"In this quick example I'm going to do something really basic: import and display some data on home values over time for Humboldt County, CA."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Applications/anaconda/lib/python2.7/site-packages/pandas/io/data.py:35: FutureWarning: \n",
"The pandas.io.data module is moved to a separate package (pandas-datareader) and will be removed from pandas in a future version.\n",
"After installing the pandas-datareader package (https://github.com/pydata/pandas-datareader), you can change the import ``from pandas.io import data, wb`` to ``from pandas_datareader import data, wb``.\n",
" FutureWarning)\n"
]
}
],
"source": [
"#first we need to import some libraries\n",
"import pylab\n",
"import pandas as pd\n",
"import pandas.io.data as web\n",
"import datetime\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib import style\n",
"import numpy as np\n",
"from Quandl import Quandl as quandl\n",
"style.use('ggplot')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, I'm going to bring data in from Quandl. The Quandl module for python makes this really easy. If you wanted to execute this on your own you would need to do 2 things:\n",
"\n",
"1. Install the Quandl module for Python with your Python distribution. I am using the Anaconda distribution so this was pretty easy for me...I was able to do \"conda install Quandl\" from the terminal and get it done. \n",
"\n",
"2. Sign up for an account at Quandl. When you do this they will give you an API key that you can use to pull data from thier databases\n",
"\n",
"The Quandl data series I'm going to use is ZILL/C00399_MSP. This is one of the ~1.4 million Zillow data sets available through Quandl. It has the Zillow estimate of median home value of all homes in Humboldt County, CA. Data are available monthly from 1996. "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Value\n",
"Date \n",
"1996-04-30 103825.0\n",
"1996-05-31 101125.0\n",
"1996-06-30 101000.0\n",
"1996-07-31 103200.0\n",
"1996-08-31 101050.0\n",
" Value\n",
"Date \n",
"2016-02-29 242550.0\n",
"2016-03-31 243450.0\n",
"2016-04-30 263500.0\n",
"2016-05-31 267750.0\n",
"2016-06-30 263875.0\n"
]
}
],
"source": [
"df = quandl.get(\"ZILL/CO00399_MSP\", authtoken=\"1i2uuiN7DQ-Ltizgjb_q\")\n",
"print(df.head())\n",
"print(df.tail())\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"Finally, I'm going to plot these data using a really basic plot call"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x114d43450>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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S1haD1phb36fitz9GHzt8afvW96HoDGrydJTThS6qI1m486FT59r3XUZFD8L4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\npFkIIYTwSJqFEEIIj6RZCCGE8Og/eyJ7yOKaVCkAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x103ebcc90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.plot()"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"Let's try one more...I'm going to change the series to 'ZILL/C00822_MSP' (Zillow estimated median market value of median sale price within Eureka, CA)."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Value\n",
"Date \n",
"1996-04-30 90175.0\n",
"1996-05-31 93600.0\n",
"1996-06-30 92450.0\n",
"1996-07-31 91350.0\n",
"1996-08-31 89450.0\n",
" Value\n",
"Date \n",
"2016-02-29 230950.0\n",
"2016-03-31 233600.0\n",
"2016-04-30 230700.0\n",
"2016-05-31 228150.0\n",
"2016-06-30 242450.0\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x11736d9d0>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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cPgpOhldIbqKtBrNDotOF3vJ6l64Nee+KMlRc43DU0eMZcuM34OyZoMtt6I//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\nIBBY5P8AEeYkOd24flIAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x117344ed0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df = quandl.get(\"ZILL/C00822_MSP\", authtoken=\"1i2uuiN7DQ-Ltizgjb_q\")\n",
"print(df.head())\n",
"print(df.tail())\n",
"df.plot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [Root]",
"language": "python",
"name": "Python [Root]"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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