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clarkfitzg/iidata.ipynb

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iidata.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Python for R Users\n",
"\n",
"## Clark Fitzgerald \n",
"\n",
"`@clarkfitzg`\n",
"\n",
"## UC Davis iidata\n",
"\n",
"May 21, 2016\n",
"\n",
"To follow along: http://bit.ly/1XoUzbZ"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# The Standard Library\n",
"\n",
"Python's philosophy is 'batteries included'\n",
"\n",
"This functionality is available with all distributions of Python."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The Zen of Python, by Tim Peters\n",
"\n",
"Beautiful is better than ugly.\n",
"Explicit is better than implicit.\n",
"Simple is better than complex.\n",
"Complex is better than complicated.\n",
"Flat is better than nested.\n",
"Sparse is better than dense.\n",
"Readability counts.\n",
"Special cases aren't special enough to break the rules.\n",
"Although practicality beats purity.\n",
"Errors should never pass silently.\n",
"Unless explicitly silenced.\n",
"In the face of ambiguity, refuse the temptation to guess.\n",
"There should be one-- and preferably only one --obvious way to do it.\n",
"Although that way may not be obvious at first unless you're Dutch.\n",
"Now is better than never.\n",
"Although never is often better than *right* now.\n",
"If the implementation is hard to explain, it's a bad idea.\n",
"If the implementation is easy to explain, it may be a good idea.\n",
"Namespaces are one honking great idea -- let's do more of those!\n"
]
}
],
"source": [
"# Come for the language, stay for the community\n",
"import this"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Project Euler\n",
"\n",
"Algorithm / math problems that you can help you learn to program\n",
"\n",
"### Largest palindrome product\n",
"### Problem 4\n",
"\n",
"A palindromic number reads the same both ways. The largest palindrome made from the product of two 2-digit numbers is 9009 = 91 × 99.\n",
"\n",
"Find the largest palindrome made from the product of two 3-digit numbers."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Trying:\n",
" palindrome(9009)\n",
"Expecting:\n",
" True\n",
"ok\n",
"Trying:\n",
" palindrome(123)\n",
"Expecting:\n",
" False\n",
"ok\n",
"1 items had no tests:\n",
" __main__\n",
"1 items passed all tests:\n",
" 2 tests in __main__.palindrome\n",
"2 tests in 2 items.\n",
"2 passed and 0 failed.\n",
"Test passed.\n"
]
},
{
"data": {
"text/plain": [
"TestResults(failed=0, attempted=2)"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def palindrome(number):\n",
" \"\"\" Check if a number is a palindrome\n",
" \n",
" >>> palindrome(9009)\n",
" True\n",
" >>> palindrome(123)\n",
" False\n",
" \n",
" \"\"\"\n",
" # Convert it to a string\n",
" numstr = str(number)\n",
" # Check if it's the same forwards and backwards\n",
" return numstr == numstr[::-1]\n",
"\n",
"\n",
"import doctest\n",
"doctest.testmod(verbose=True)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"palindrome(9009)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"palindrome(123)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"906609"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Now let's actually solve that problem\n",
"current = 0\n",
"for i in range(999):\n",
" for j in range(999):\n",
" prod = i * j\n",
" if palindrome(prod):\n",
" current = max(current, prod)\n",
" \n",
"current"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"906609"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# But there's a more elegant way :)\n",
"import itertools\n",
"\n",
"pairs = itertools.product(range(999), range(999))\n",
"products = (a * b for a, b in pairs)\n",
"max(p for p in products if palindrome(p))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Slicing\n",
"\n",
"numerical indexing to slice an iterable"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'abcdef'"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"letters = 'abcdef'\n",
"letters"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'abc'"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"letters[:3]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'ace'"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"letters[::2]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['a', 'b', 'c', 'd', 'e', 'f']"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Python generally avoids copies! This is different than R\n",
"a = list(letters)\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['A', 'b', 'c', 'd', 'e', 'f']"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Python also has 0 based indexing- R is 1 based\n",
"\n",
"b = a\n",
"b[0] = 'A'\n",
"b"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['A', 'b', 'c', 'd', 'e', 'f']"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'fedcba'"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Reverse a string\n",
"letters[::-1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data Analysis\n",
"\n",
"Here is where we need more than the standard library"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# A few preliminaries\n",
"\n",
"# Import the data analysis libraries we'll use\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"# Lets us plot\n",
"%matplotlib inline\n",
"plt.style.use('ggplot')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Pandas\n",
"\n",
"Brings R's `DataFrame` to Python\n",
"\n",
"Today we'll download a dataset from STS98, data studies. (Thanks Nick!)\n",
"\n",
"http://anson.ucdavis.edu/~nulle/cl_apartments.zip"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"apt = pd.read_csv('cl_apartments.csv')"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(18084, 22)"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"apt.shape"
]
},
{
"cell_type": "code",
"execution_count": 18,
"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>title</th>\n",
" <th>text</th>\n",
" <th>date_posted</th>\n",
" <th>date_updated</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>$995 / 3br - 1350ft2 - Lancaster Apartment Uni...</td>\n",
" <td>Beautiful Remodeled 3 Bed, 2 Bath House in Wes...</td>\n",
" <td>2016-04-13 15:34:06</td>\n",
" <td>2016-05-01 15:46:36</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>$1935 / 2br - 1154ft2 - No place like The Colo...</td>\n",
" <td>View Our Captivating Pools, Spa &amp; Picnic Area\\...</td>\n",
" <td>2016-04-16 17:55:45</td>\n",
" <td>2016-05-01 19:40:19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>$1825 / 2br - 1056ft2 - No place like The Colo...</td>\n",
" <td>*******Centrally located near Saugus, The Ante...</td>\n",
" <td>2016-04-16 17:58:01</td>\n",
" <td>2016-05-01 19:39:54</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>$650 / 1br - FURNISHED 1 BED GUEST QUARTERS, D...</td>\n",
" <td>FURNISHED 1 BEDROOM &amp; DEN W/ KITCHENETTE GUEST...</td>\n",
" <td>2016-04-17 21:26:15</td>\n",
" <td>2016-05-01 12:49:13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>$1599 / 2br - 951ft2 - Big Savings!</td>\n",
" <td>Accepting applications now.</td>\n",
" <td>2016-04-28 11:58:28</td>\n",
" <td>2016-05-01 10:44:00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" title \\\n",
"0 $995 / 3br - 1350ft2 - Lancaster Apartment Uni... \n",
"1 $1935 / 2br - 1154ft2 - No place like The Colo... \n",
"2 $1825 / 2br - 1056ft2 - No place like The Colo... \n",
"3 $650 / 1br - FURNISHED 1 BED GUEST QUARTERS, D... \n",
"4 $1599 / 2br - 951ft2 - Big Savings! \n",
"\n",
" text date_posted \\\n",
"0 Beautiful Remodeled 3 Bed, 2 Bath House in Wes... 2016-04-13 15:34:06 \n",
"1 View Our Captivating Pools, Spa & Picnic Area\\... 2016-04-16 17:55:45 \n",
"2 *******Centrally located near Saugus, The Ante... 2016-04-16 17:58:01 \n",
"3 FURNISHED 1 BEDROOM & DEN W/ KITCHENETTE GUEST... 2016-04-17 21:26:15 \n",
"4 Accepting applications now. 2016-04-28 11:58:28 \n",
"\n",
" date_updated \n",
"0 2016-05-01 15:46:36 \n",
"1 2016-05-01 19:40:19 \n",
"2 2016-05-01 19:39:54 \n",
"3 2016-05-01 12:49:13 \n",
"4 2016-05-01 10:44:00 "
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"apt.iloc[:5, :4]"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"title object\n",
"text object\n",
"date_posted object\n",
"date_updated object\n",
"deleted bool\n",
"craigslist object\n",
"latitude float64\n",
"longitude float64\n",
"city object\n",
"price float64\n",
"date object\n",
"sqft float64\n",
"bedrooms float64\n",
"bathrooms float64\n",
"pets object\n",
"laundry object\n",
"parking object\n",
"shp_place object\n",
"shp_city object\n",
"shp_urban object\n",
"shp_county object\n",
"shp_state object\n",
"dtype: object"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"apt.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Plotting\n",
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x11b0b17b8>"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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dvV409B3cfaKxE+fag+/iynAkDRzcne2Aw8qQjVcM3QTnUwV63AMrK/oZZAlG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\nOmLoEhHp6P8D3RaxX95jfpsAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11b0b17f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.lmplot(x='sqft', y='price', data=apt.iloc[200:300, ])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Statistical Modeling"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import statsmodels.formula.api as smf"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"smf.ols?"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>OLS Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>price</td> <th> R-squared: </th> <td> 0.001</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.001</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 18.05</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Sat, 21 May 2016</td> <th> Prob (F-statistic):</th> <td>2.16e-05</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>08:43:56</td> <th> Log-Likelihood: </th> <td>-1.1169e+05</td>\n",
"</tr>\n",
"<tr>\n",
" <th>No. Observations:</th> <td> 13159</td> <th> AIC: </th> <td>2.234e+05</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Df Residuals:</th> <td> 13157</td> <th> BIC: </th> <td>2.234e+05</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Df Model:</th> <td> 1</td> <th> </th> <td> </td> \n",
"</tr>\n",
"<tr>\n",
" <th>Covariance Type:</th> <td>nonrobust</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>Intercept</th> <td> 2330.1164</td> <td> 10.514</td> <td> 221.625</td> <td> 0.000</td> <td> 2309.508 2350.725</td>\n",
"</tr>\n",
"<tr>\n",
" <th>sqft</th> <td> 0.0108</td> <td> 0.003</td> <td> 4.249</td> <td> 0.000</td> <td> 0.006 0.016</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <th>Omnibus:</th> <td>9908.362</td> <th> Durbin-Watson: </th> <td> 1.077</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Prob(Omnibus):</th> <td> 0.000</td> <th> Jarque-Bera (JB): </th> <td>536353.862</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Skew:</th> <td> 3.111</td> <th> Prob(JB): </th> <td> 0.00</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Kurtosis:</th> <td>33.651</td> <th> Cond. No. </th> <td>4.26e+03</td> \n",
"</tr>\n",
"</table>"
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: price R-squared: 0.001\n",
"Model: OLS Adj. R-squared: 0.001\n",
"Method: Least Squares F-statistic: 18.05\n",
"Date: Sat, 21 May 2016 Prob (F-statistic): 2.16e-05\n",
"Time: 08:43:56 Log-Likelihood: -1.1169e+05\n",
"No. Observations: 13159 AIC: 2.234e+05\n",
"Df Residuals: 13157 BIC: 2.234e+05\n",
"Df Model: 1 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [95.0% Conf. Int.]\n",
"------------------------------------------------------------------------------\n",
"Intercept 2330.1164 10.514 221.625 0.000 2309.508 2350.725\n",
"sqft 0.0108 0.003 4.249 0.000 0.006 0.016\n",
"==============================================================================\n",
"Omnibus: 9908.362 Durbin-Watson: 1.077\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 536353.862\n",
"Skew: 3.111 Prob(JB): 0.00\n",
"Kurtosis: 33.651 Cond. No. 4.26e+03\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"[2] The condition number is large, 4.26e+03. This might indicate that there are\n",
"strong multicollinearity or other numerical problems.\n",
"\"\"\""
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = smf.ols('price ~ sqft', data=apt).fit()\n",
"model.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Do highly-populated areas tend to have smaller apartments? If you're unfamiliar with California's cities, it may help to consult this page: https://en.wikipedia.org/wiki/List_of_cities_and_towns_in_California"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"On average, what are the most expensive cities?"
]
},
{
"cell_type": "code",
"execution_count": 26,
"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>price</th>\n",
" </tr>\n",
" <tr>\n",
" <th>shp_city</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Piedmont</th>\n",
" <td>7300.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>San Marino</th>\n",
" <td>6900.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Belvedere</th>\n",
" <td>6500.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Beverly Hills</th>\n",
" <td>4325.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Los Altos</th>\n",
" <td>3617.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Woodside</th>\n",
" <td>3600.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Manhattan Beach</th>\n",
" <td>3500.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Culver City</th>\n",
" <td>3493.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Tiburon</th>\n",
" <td>3479.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>San Francisco</th>\n",
" <td>3400.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>El Segundo</th>\n",
" <td>3200.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Foster City</th>\n",
" <td>3168.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Millbrae</th>\n",
" <td>3122.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Larkspur</th>\n",
" <td>3100.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Santa Monica</th>\n",
" <td>3097.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" price\n",
"shp_city \n",
"Piedmont 7300.0\n",
"San Marino 6900.0\n",
"Belvedere 6500.0\n",
"Beverly Hills 4325.0\n",
"Los Altos 3617.5\n",
"Woodside 3600.0\n",
"Manhattan Beach 3500.0\n",
"Culver City 3493.5\n",
"Tiburon 3479.0\n",
"San Francisco 3400.0\n",
"El Segundo 3200.0\n",
"Foster City 3168.0\n",
"Millbrae 3122.0\n",
"Larkspur 3100.0\n",
"Santa Monica 3097.0"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Method chaining code can be easier to read.\n",
"# Similar to pipes\n",
"\n",
"(apt.loc[:, ['shp_city', 'price']]\n",
" .groupby('shp_city')\n",
" .median()\n",
" .sort_values('price', ascending=False)\n",
" .head(15)\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{nan,\n",
" 'Calabasas',\n",
" 'San Marino',\n",
" 'Citrus Heights',\n",
" 'Folsom',\n",
" 'Santee',\n",
" 'Calistoga',\n",
" 'West Covina',\n",
" 'Culver City',\n",
" 'Claremont',\n",
" 'El Monte',\n",
" 'Fairfax',\n",
" 'Del Mar',\n",
" 'Coronado',\n",
" 'Solana Beach',\n",
" 'Escondido',\n",
" 'Lake Elsinore',\n",
" 'Alameda',\n",
" 'Lafayette',\n",
" 'Burbank',\n",
" 'Cerritos',\n",
" 'Lancaster',\n",
" 'Union City',\n",
" 'Benicia',\n",
" 'Ross',\n",
" 'Brea',\n",
" 'El Cajon',\n",
" 'Concord',\n",
" 'Santa Monica',\n",
" 'Vacaville',\n",
" 'Manhattan Beach',\n",
" 'Pomona',\n",
" 'Palmdale',\n",
" 'Fullerton',\n",
" 'Piedmont',\n",
" 'Isleton',\n",
" 'Arcadia',\n",
" 'Torrance',\n",
" 'Berkeley',\n",
" 'Los Altos Hills',\n",
" 'Newark',\n",
" 'Anaheim',\n",
" 'Windsor',\n",
" 'Pleasant Hill',\n",
" 'Sierra Madre',\n",
" 'Davis',\n",
" 'Pleasanton',\n",
" 'Covina',\n",
" 'Norwalk',\n",
" 'Healdsburg',\n",
" 'San Diego',\n",
" 'Santa Rosa',\n",
" 'Sonoma',\n",
" 'Antioch',\n",
" 'Atherton',\n",
" 'Sebastopol',\n",
" 'Hesperia',\n",
" 'Yreka',\n",
" 'Palm Desert',\n",
" 'Dublin',\n",
" 'San Bruno',\n",
" 'Morgan Hill',\n",
" 'Long Beach',\n",
" 'Milpitas',\n",
" 'Santa Clara',\n",
" 'Woodland',\n",
" 'Lemon Grove',\n",
" 'Martinez',\n",
" 'Redwood City',\n",
" 'Sunnyvale',\n",
" 'Daly City',\n",
" 'Ukiah',\n",
" 'Emeryville',\n",
" 'Brisbane',\n",
" 'Lincoln',\n",
" 'Pinole',\n",
" 'Oakley',\n",
" 'Santa Clarita',\n",
" 'Chula Vista',\n",
" 'Pittsburg',\n",
" 'Walnut Creek',\n",
" 'Oceanside',\n",
" 'Monterey Park',\n",
" 'Lakewood',\n",
" 'Imperial Beach',\n",
" 'Elk Grove',\n",
" 'Beverly Hills',\n",
" 'San Mateo',\n",
" 'Irvine',\n",
" 'Rohnert Park',\n",
" 'Stockton',\n",
" 'Cupertino',\n",
" 'Hollister',\n",
" 'Menlo Park',\n",
" 'San Carlos',\n",
" 'San Jose',\n",
" 'Agoura Hills',\n",
" 'Fremont',\n",
" 'Carson',\n",
" 'Vista',\n",
" 'Garden Grove',\n",
" 'Poway',\n",
" 'Vallejo',\n",
" 'Half Moon Bay',\n",
" 'American Canyon',\n",
" 'Baldwin Park',\n",
" 'Petaluma',\n",
" 'Grass Valley',\n",
" 'Cotati',\n",
" 'San Anselmo',\n",
" 'Woodside',\n",
" 'Auburn',\n",
" 'Hayward',\n",
" 'Hermosa Beach',\n",
" 'West Sacramento',\n",
" 'Los Gatos',\n",
" 'Los Altos',\n",
" 'South Pasadena',\n",
" 'Hercules',\n",
" 'Azusa',\n",
" 'San Rafael',\n",
" 'Palo Alto',\n",
" 'Yuba City',\n",
" 'San Ramon',\n",
" 'Danville',\n",
" 'Tiburon',\n",
" 'Los Angeles',\n",
" 'Pasadena',\n",
" 'Moraga',\n",
" 'Manteca',\n",
" 'Bellflower',\n",
" 'Rancho Cucamonga',\n",
" 'Rocklin',\n",
" 'Glendora',\n",
" 'Clearlake',\n",
" 'San Leandro',\n",
" 'West Hollywood',\n",
" 'Cloverdale',\n",
" 'South San Francisco',\n",
" 'San Clemente',\n",
" 'Mountain View',\n",
" 'La Verne',\n",
" 'Pacifica',\n",
" 'Temecula',\n",
" 'Bell Gardens',\n",
" 'Marysville',\n",
" 'Tracy',\n",
" 'National City',\n",
" 'Mill Valley',\n",
" 'Monrovia',\n",
" 'Seal Beach',\n",
" 'Corte Madera',\n",
" 'Burlingame',\n",
" 'Downey',\n",
" 'Campbell',\n",
" 'Roseville',\n",
" 'Placerville',\n",
" 'Montebello',\n",
" 'Glendale',\n",
" 'San Pablo',\n",
" 'Carlsbad',\n",
" 'Millbrae',\n",
" 'Riverside',\n",
" 'Orinda',\n",
" 'St. Helena',\n",
" 'Encinitas',\n",
" 'Fairfield',\n",
" 'Rancho Cordova',\n",
" 'Alhambra',\n",
" 'Whittier',\n",
" 'Saratoga',\n",
" 'Gardena',\n",
" 'Inglewood',\n",
" 'Suisun City',\n",
" 'San Francisco',\n",
" 'Santa Ana',\n",
" 'Upland',\n",
" 'Murrieta',\n",
" 'El Cerrito',\n",
" 'Belmont',\n",
" 'San Dimas',\n",
" 'Novato',\n",
" 'San Marcos',\n",
" 'Livermore',\n",
" 'Foster City',\n",
" 'Colma',\n",
" 'Larkspur',\n",
" 'Belvedere',\n",
" 'Richmond',\n",
" 'Redondo Beach',\n",
" 'Lomita',\n",
" 'Gilroy',\n",
" 'Sausalito',\n",
" 'Sacramento',\n",
" 'La Mesa',\n",
" 'El Segundo',\n",
" 'Industry',\n",
" 'Hawthorne',\n",
" 'Dixon',\n",
" 'Albany',\n",
" 'Truckee',\n",
" 'Yountville',\n",
" 'East Palo Alto',\n",
" 'Napa',\n",
" 'Oakland',\n",
" 'Hillsborough'}"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"set(apt.shp_city)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Let's scrape the web!\n",
"cali = pd.read_html('https://en.wikipedia.org/wiki/List_of_cities_and_towns_in_California')"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"list"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Always know what your data types are\n",
"type(cali)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"7"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(cali)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"[(1, 2), (485, 7), (1, 2), (8, 11), (7, 2), (14, 19), (13, 2)]"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[x.shape for x in cali]"
]
},
{
"cell_type": "code",
"execution_count": 32,
"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>0</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>4</th>\n",
" <th>5</th>\n",
" <th>6</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Name</td>\n",
" <td>Type</td>\n",
" <td>County</td>\n",
" <td>Population (2010)[1][7][8]</td>\n",
" <td>Land area[1]</td>\n",
" <td>Incorporated[6]</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>sq mi</td>\n",
" <td>km2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Adelanto</td>\n",
" <td>City</td>\n",
" <td>San Bernardino</td>\n",
" <td>31765</td>\n",
" <td>56.01</td>\n",
" <td>145.1</td>\n",
" <td>000000001970-12-22-0000December 22, 1970</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Agoura Hills</td>\n",
" <td>City</td>\n",
" <td>Los Angeles</td>\n",
" <td>20330</td>\n",
" <td>7.79</td>\n",
" <td>20.2</td>\n",
" <td>000000001982-12-08-0000December 8, 1982</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Alameda</td>\n",
" <td>City</td>\n",
" <td>Alameda</td>\n",
" <td>73812</td>\n",
" <td>10.61</td>\n",
" <td>27.5</td>\n",
" <td>000000001854-04-19-0000April 19, 1854</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1 2 3 \\\n",
"0 Name Type County Population (2010)[1][7][8] \n",
"1 sq mi km2 NaN NaN \n",
"2 Adelanto City San Bernardino 31765 \n",
"3 Agoura Hills City Los Angeles 20330 \n",
"4 Alameda City Alameda 73812 \n",
"\n",
" 4 5 6 \n",
"0 Land area[1] Incorporated[6] NaN \n",
"1 NaN NaN NaN \n",
"2 56.01 145.1 000000001970-12-22-0000December 22, 1970 \n",
"3 7.79 20.2 000000001982-12-08-0000December 8, 1982 \n",
"4 10.61 27.5 000000001854-04-19-0000April 19, 1854 "
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cities = cali[1]\n",
"cities.head()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"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>shp_city</th>\n",
" <th>population</th>\n",
" <th>land</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Adelanto</td>\n",
" <td>31765</td>\n",
" <td>56.01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Agoura Hills</td>\n",
" <td>20330</td>\n",
" <td>7.79</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Alameda</td>\n",
" <td>73812</td>\n",
" <td>10.61</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Albany</td>\n",
" <td>18539</td>\n",
" <td>1.79</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Alhambra</td>\n",
" <td>83089</td>\n",
" <td>7.63</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" shp_city population land\n",
"2 Adelanto 31765 56.01\n",
"3 Agoura Hills 20330 7.79\n",
"4 Alameda 73812 10.61\n",
"5 Albany 18539 1.79\n",
"6 Alhambra 83089 7.63"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"c2 = cities.iloc[2:, [0, 3, 4]]\n",
"c2.columns = ['shp_city', 'population', 'land']\n",
"\n",
"c2.head()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'123400'"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Footnotes and commas in HTML! Dang!\n",
"import re\n",
"x = '123,400[8]'\n",
"pattern = re.compile(r',|\\[[0-9]*\\]')\n",
"re.sub(pattern, '', x, count=5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`lambda` just means make a function in one line."
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"c2[['population', 'land']] = (c2[['population', 'land']]\n",
" .applymap(lambda x: re.sub(pattern, '', x))\n",
" .astype('float64')\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"c2['density'] = c2['population'] / c2['land']"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"shp_city object\n",
"population float64\n",
"land float64\n",
"density float64\n",
"dtype: object"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"c2.dtypes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we're ready to join it into the main table"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(15245, 25)"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"apt2 = pd.merge(apt, c2)\n",
"apt2.shape"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Better check how well this worked\n",
"sum(apt2.population.isnull())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Recall the question: Do highly-populated areas tend to have smaller apartments?"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.0037722587722730811"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"apt2.sqft.corr(apt2.population)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.027354362941064529"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"apt2.sqft.corr(apt2.density)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Common sense check- what are the most densely populated cities in California?"
]
},
{
"cell_type": "code",
"execution_count": 42,
"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>shp_city</th>\n",
" <th>population</th>\n",
" <th>land</th>\n",
" <th>density</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>373</th>\n",
" <td>San Francisco</td>\n",
" <td>805235.0</td>\n",
" <td>46.87</td>\n",
" <td>17180.179219</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105</th>\n",
" <td>Daly City</td>\n",
" <td>101123.0</td>\n",
" <td>7.66</td>\n",
" <td>13201.436031</td>\n",
" </tr>\n",
" <tr>\n",
" <th>193</th>\n",
" <td>Inglewood</td>\n",
" <td>109673.0</td>\n",
" <td>9.07</td>\n",
" <td>12091.841235</td>\n",
" </tr>\n",
" <tr>\n",
" <th>390</th>\n",
" <td>Santa Ana</td>\n",
" <td>324528.0</td>\n",
" <td>27.27</td>\n",
" <td>11900.550055</td>\n",
" </tr>\n",
" <tr>\n",
" <th>127</th>\n",
" <td>El Monte</td>\n",
" <td>113475.0</td>\n",
" <td>9.56</td>\n",
" <td>11869.769874</td>\n",
" </tr>\n",
" <tr>\n",
" <th>290</th>\n",
" <td>Norwalk</td>\n",
" <td>105549.0</td>\n",
" <td>9.71</td>\n",
" <td>10870.133883</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>Berkeley</td>\n",
" <td>112580.0</td>\n",
" <td>10.47</td>\n",
" <td>10752.626552</td>\n",
" </tr>\n",
" <tr>\n",
" <th>155</th>\n",
" <td>Garden Grove</td>\n",
" <td>170883.0</td>\n",
" <td>17.94</td>\n",
" <td>9525.250836</td>\n",
" </tr>\n",
" <tr>\n",
" <th>237</th>\n",
" <td>Long Beach</td>\n",
" <td>462257.0</td>\n",
" <td>50.29</td>\n",
" <td>9191.827401</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118</th>\n",
" <td>Downey</td>\n",
" <td>111772.0</td>\n",
" <td>12.41</td>\n",
" <td>9006.607575</td>\n",
" </tr>\n",
" <tr>\n",
" <th>242</th>\n",
" <td>Los Angeles</td>\n",
" <td>3792621.0</td>\n",
" <td>468.67</td>\n",
" <td>8092.305887</td>\n",
" </tr>\n",
" <tr>\n",
" <th>303</th>\n",
" <td>Oxnard</td>\n",
" <td>197899.0</td>\n",
" <td>26.89</td>\n",
" <td>7359.576051</td>\n",
" </tr>\n",
" <tr>\n",
" <th>436</th>\n",
" <td>Torrance</td>\n",
" <td>145538.0</td>\n",
" <td>20.48</td>\n",
" <td>7106.347656</td>\n",
" </tr>\n",
" <tr>\n",
" <th>185</th>\n",
" <td>Huntington Beach</td>\n",
" <td>189992.0</td>\n",
" <td>26.75</td>\n",
" <td>7102.504673</td>\n",
" </tr>\n",
" <tr>\n",
" <th>97</th>\n",
" <td>Costa Mesa</td>\n",
" <td>109960.0</td>\n",
" <td>15.65</td>\n",
" <td>7026.198083</td>\n",
" </tr>\n",
" <tr>\n",
" <th>293</th>\n",
" <td>Oakland</td>\n",
" <td>390724.0</td>\n",
" <td>55.79</td>\n",
" <td>7003.477326</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Anaheim</td>\n",
" <td>336265.0</td>\n",
" <td>49.84</td>\n",
" <td>6746.890048</td>\n",
" </tr>\n",
" <tr>\n",
" <th>462</th>\n",
" <td>West Covina</td>\n",
" <td>106098.0</td>\n",
" <td>16.04</td>\n",
" <td>6614.588529</td>\n",
" </tr>\n",
" <tr>\n",
" <th>330</th>\n",
" <td>Pomona</td>\n",
" <td>149058.0</td>\n",
" <td>22.95</td>\n",
" <td>6494.901961</td>\n",
" </tr>\n",
" <tr>\n",
" <th>364</th>\n",
" <td>Salinas</td>\n",
" <td>150441.0</td>\n",
" <td>23.18</td>\n",
" <td>6490.120794</td>\n",
" </tr>\n",
" <tr>\n",
" <th>426</th>\n",
" <td>Sunnyvale</td>\n",
" <td>140081.0</td>\n",
" <td>21.99</td>\n",
" <td>6370.213734</td>\n",
" </tr>\n",
" <tr>\n",
" <th>392</th>\n",
" <td>Santa Clara</td>\n",
" <td>116468.0</td>\n",
" <td>18.41</td>\n",
" <td>6326.344378</td>\n",
" </tr>\n",
" <tr>\n",
" <th>158</th>\n",
" <td>Glendale</td>\n",
" <td>191719.0</td>\n",
" <td>30.45</td>\n",
" <td>6296.190476</td>\n",
" </tr>\n",
" <tr>\n",
" <th>153</th>\n",
" <td>Fullerton</td>\n",
" <td>135161.0</td>\n",
" <td>22.35</td>\n",
" <td>6047.472036</td>\n",
" </tr>\n",
" <tr>\n",
" <th>314</th>\n",
" <td>Pasadena</td>\n",
" <td>137122.0</td>\n",
" <td>22.97</td>\n",
" <td>5969.612538</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td>Burbank</td>\n",
" <td>103340.0</td>\n",
" <td>17.34</td>\n",
" <td>5959.630911</td>\n",
" </tr>\n",
" <tr>\n",
" <th>266</th>\n",
" <td>Modesto</td>\n",
" <td>201165.0</td>\n",
" <td>36.87</td>\n",
" <td>5456.061839</td>\n",
" </tr>\n",
" <tr>\n",
" <th>298</th>\n",
" <td>Orange</td>\n",
" <td>134616.0</td>\n",
" <td>24.80</td>\n",
" <td>5428.064516</td>\n",
" </tr>\n",
" <tr>\n",
" <th>377</th>\n",
" <td>San Jose</td>\n",
" <td>945942.0</td>\n",
" <td>176.53</td>\n",
" <td>5358.533960</td>\n",
" </tr>\n",
" <tr>\n",
" <th>450</th>\n",
" <td>Ventura</td>\n",
" <td>106433.0</td>\n",
" <td>21.65</td>\n",
" <td>4916.073903</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>399</th>\n",
" <td>Santa Rosa</td>\n",
" <td>167815.0</td>\n",
" <td>41.29</td>\n",
" <td>4064.301284</td>\n",
" </tr>\n",
" <tr>\n",
" <th>295</th>\n",
" <td>Oceanside</td>\n",
" <td>167086.0</td>\n",
" <td>41.23</td>\n",
" <td>4052.534562</td>\n",
" </tr>\n",
" <tr>\n",
" <th>370</th>\n",
" <td>San Diego</td>\n",
" <td>1301617.0</td>\n",
" <td>325.19</td>\n",
" <td>4002.635382</td>\n",
" </tr>\n",
" <tr>\n",
" <th>91</th>\n",
" <td>Concord</td>\n",
" <td>122067.0</td>\n",
" <td>30.55</td>\n",
" <td>3995.646481</td>\n",
" </tr>\n",
" <tr>\n",
" <th>94</th>\n",
" <td>Corona</td>\n",
" <td>152374.0</td>\n",
" <td>38.83</td>\n",
" <td>3924.130827</td>\n",
" </tr>\n",
" <tr>\n",
" <th>133</th>\n",
" <td>Escondido</td>\n",
" <td>143911.0</td>\n",
" <td>36.81</td>\n",
" <td>3909.562619</td>\n",
" </tr>\n",
" <tr>\n",
" <th>449</th>\n",
" <td>Vallejo</td>\n",
" <td>115942.0</td>\n",
" <td>30.67</td>\n",
" <td>3780.306488</td>\n",
" </tr>\n",
" <tr>\n",
" <th>276</th>\n",
" <td>Moreno Valley</td>\n",
" <td>193365.0</td>\n",
" <td>51.27</td>\n",
" <td>3771.503803</td>\n",
" </tr>\n",
" <tr>\n",
" <th>354</th>\n",
" <td>Riverside</td>\n",
" <td>303871.0</td>\n",
" <td>81.14</td>\n",
" <td>3745.020951</td>\n",
" </tr>\n",
" <tr>\n",
" <th>129</th>\n",
" <td>Elk Grove</td>\n",
" <td>153015.0</td>\n",
" <td>42.19</td>\n",
" <td>3626.807300</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>Antioch</td>\n",
" <td>102372.0</td>\n",
" <td>28.35</td>\n",
" <td>3611.005291</td>\n",
" </tr>\n",
" <tr>\n",
" <th>366</th>\n",
" <td>San Bernardino</td>\n",
" <td>209924.0</td>\n",
" <td>59.20</td>\n",
" <td>3546.013514</td>\n",
" </tr>\n",
" <tr>\n",
" <th>348</th>\n",
" <td>Richmond</td>\n",
" <td>103701.0</td>\n",
" <td>30.07</td>\n",
" <td>3448.653143</td>\n",
" </tr>\n",
" <tr>\n",
" <th>454</th>\n",
" <td>Visalia</td>\n",
" <td>124442.0</td>\n",
" <td>36.25</td>\n",
" <td>3432.882759</td>\n",
" </tr>\n",
" <tr>\n",
" <th>393</th>\n",
" <td>Santa Clarita</td>\n",
" <td>176320.0</td>\n",
" <td>52.72</td>\n",
" <td>3344.461305</td>\n",
" </tr>\n",
" <tr>\n",
" <th>432</th>\n",
" <td>Temecula</td>\n",
" <td>100097.0</td>\n",
" <td>30.15</td>\n",
" <td>3319.966833</td>\n",
" </tr>\n",
" <tr>\n",
" <th>297</th>\n",
" <td>Ontario</td>\n",
" <td>163924.0</td>\n",
" <td>49.94</td>\n",
" <td>3282.418903</td>\n",
" </tr>\n",
" <tr>\n",
" <th>360</th>\n",
" <td>Roseville</td>\n",
" <td>118788.0</td>\n",
" <td>36.22</td>\n",
" <td>3279.624517</td>\n",
" </tr>\n",
" <tr>\n",
" <th>196</th>\n",
" <td>Irvine</td>\n",
" <td>212375.0</td>\n",
" <td>66.11</td>\n",
" <td>3212.448949</td>\n",
" </tr>\n",
" <tr>\n",
" <th>173</th>\n",
" <td>Hayward</td>\n",
" <td>144186.0</td>\n",
" <td>45.32</td>\n",
" <td>3181.509267</td>\n",
" </tr>\n",
" <tr>\n",
" <th>281</th>\n",
" <td>Murrieta</td>\n",
" <td>103466.0</td>\n",
" <td>33.58</td>\n",
" <td>3081.179273</td>\n",
" </tr>\n",
" <tr>\n",
" <th>412</th>\n",
" <td>Simi Valley</td>\n",
" <td>124237.0</td>\n",
" <td>41.48</td>\n",
" <td>2995.106075</td>\n",
" </tr>\n",
" <tr>\n",
" <th>138</th>\n",
" <td>Fairfield</td>\n",
" <td>105321.0</td>\n",
" <td>37.39</td>\n",
" <td>2816.822680</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65</th>\n",
" <td>Carlsbad</td>\n",
" <td>105328.0</td>\n",
" <td>37.72</td>\n",
" <td>2792.364793</td>\n",
" </tr>\n",
" <tr>\n",
" <th>151</th>\n",
" <td>Fremont</td>\n",
" <td>214089.0</td>\n",
" <td>77.46</td>\n",
" <td>2763.865221</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>Bakersfield</td>\n",
" <td>347483.0</td>\n",
" <td>142.16</td>\n",
" <td>2444.309229</td>\n",
" </tr>\n",
" <tr>\n",
" <th>434</th>\n",
" <td>Thousand Oaks</td>\n",
" <td>126683.0</td>\n",
" <td>55.03</td>\n",
" <td>2302.071597</td>\n",
" </tr>\n",
" <tr>\n",
" <th>222</th>\n",
" <td>Lancaster</td>\n",
" <td>156633.0</td>\n",
" <td>94.28</td>\n",
" <td>1661.359779</td>\n",
" </tr>\n",
" <tr>\n",
" <th>452</th>\n",
" <td>Victorville</td>\n",
" <td>115903.0</td>\n",
" <td>73.18</td>\n",
" <td>1583.807051</td>\n",
" </tr>\n",
" <tr>\n",
" <th>308</th>\n",
" <td>Palmdale</td>\n",
" <td>152750.0</td>\n",
" <td>105.96</td>\n",
" <td>1441.581729</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>66 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" shp_city population land density\n",
"373 San Francisco 805235.0 46.87 17180.179219\n",
"105 Daly City 101123.0 7.66 13201.436031\n",
"193 Inglewood 109673.0 9.07 12091.841235\n",
"390 Santa Ana 324528.0 27.27 11900.550055\n",
"127 El Monte 113475.0 9.56 11869.769874\n",
"290 Norwalk 105549.0 9.71 10870.133883\n",
"39 Berkeley 112580.0 10.47 10752.626552\n",
"155 Garden Grove 170883.0 17.94 9525.250836\n",
"237 Long Beach 462257.0 50.29 9191.827401\n",
"118 Downey 111772.0 12.41 9006.607575\n",
"242 Los Angeles 3792621.0 468.67 8092.305887\n",
"303 Oxnard 197899.0 26.89 7359.576051\n",
"436 Torrance 145538.0 20.48 7106.347656\n",
"185 Huntington Beach 189992.0 26.75 7102.504673\n",
"97 Costa Mesa 109960.0 15.65 7026.198083\n",
"293 Oakland 390724.0 55.79 7003.477326\n",
"11 Anaheim 336265.0 49.84 6746.890048\n",
"462 West Covina 106098.0 16.04 6614.588529\n",
"330 Pomona 149058.0 22.95 6494.901961\n",
"364 Salinas 150441.0 23.18 6490.120794\n",
"426 Sunnyvale 140081.0 21.99 6370.213734\n",
"392 Santa Clara 116468.0 18.41 6326.344378\n",
"158 Glendale 191719.0 30.45 6296.190476\n",
"153 Fullerton 135161.0 22.35 6047.472036\n",
"314 Pasadena 137122.0 22.97 5969.612538\n",
"53 Burbank 103340.0 17.34 5959.630911\n",
"266 Modesto 201165.0 36.87 5456.061839\n",
"298 Orange 134616.0 24.80 5428.064516\n",
"377 San Jose 945942.0 176.53 5358.533960\n",
"450 Ventura 106433.0 21.65 4916.073903\n",
".. ... ... ... ...\n",
"399 Santa Rosa 167815.0 41.29 4064.301284\n",
"295 Oceanside 167086.0 41.23 4052.534562\n",
"370 San Diego 1301617.0 325.19 4002.635382\n",
"91 Concord 122067.0 30.55 3995.646481\n",
"94 Corona 152374.0 38.83 3924.130827\n",
"133 Escondido 143911.0 36.81 3909.562619\n",
"449 Vallejo 115942.0 30.67 3780.306488\n",
"276 Moreno Valley 193365.0 51.27 3771.503803\n",
"354 Riverside 303871.0 81.14 3745.020951\n",
"129 Elk Grove 153015.0 42.19 3626.807300\n",
"14 Antioch 102372.0 28.35 3611.005291\n",
"366 San Bernardino 209924.0 59.20 3546.013514\n",
"348 Richmond 103701.0 30.07 3448.653143\n",
"454 Visalia 124442.0 36.25 3432.882759\n",
"393 Santa Clarita 176320.0 52.72 3344.461305\n",
"432 Temecula 100097.0 30.15 3319.966833\n",
"297 Ontario 163924.0 49.94 3282.418903\n",
"360 Roseville 118788.0 36.22 3279.624517\n",
"196 Irvine 212375.0 66.11 3212.448949\n",
"173 Hayward 144186.0 45.32 3181.509267\n",
"281 Murrieta 103466.0 33.58 3081.179273\n",
"412 Simi Valley 124237.0 41.48 2995.106075\n",
"138 Fairfield 105321.0 37.39 2816.822680\n",
"65 Carlsbad 105328.0 37.72 2792.364793\n",
"151 Fremont 214089.0 77.46 2763.865221\n",
"28 Bakersfield 347483.0 142.16 2444.309229\n",
"434 Thousand Oaks 126683.0 55.03 2302.071597\n",
"222 Lancaster 156633.0 94.28 1661.359779\n",
"452 Victorville 115903.0 73.18 1583.807051\n",
"308 Palmdale 152750.0 105.96 1441.581729\n",
"\n",
"[66 rows x 4 columns]"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dense = c2.loc[c2.population > 1e5, :].sort_values('density', ascending=False)\n",
"dense"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x11b3caf28>"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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5wkhJHNytQw+BeE8REbEHLZEtIiIililIiIiIiGUKEiIiImKZgoSIiIhYpiAhIiIililI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\nFCRERETEMgUJERERsez/A5/nCqsluxrtAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11b36fa58>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"dense.plot(x='population', y='density', kind='scatter')"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x120918828>"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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1mkMB3dkS/OjJ86Z/FgD8FfAeiZdfflnnz5/Xxo0btWHDBtlsNi1ZskQrV66U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\nJAAAgGkECQAAYBpBAgAAmEaQAAAApv1/urqSrQvmf38AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x12093feb8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"dense.loc[dense.population < 1e6, :].plot(x='land', y='population', kind='scatter')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Machine Learning\n",
"\n",
"We went to all this trouble to get the data, might as well use it!"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"title object\n",
"text object\n",
"date_posted object\n",
"date_updated object\n",
"deleted bool\n",
"craigslist object\n",
"latitude float64\n",
"longitude float64\n",
"city object\n",
"price float64\n",
"date object\n",
"sqft float64\n",
"bedrooms float64\n",
"bathrooms float64\n",
"pets object\n",
"laundry object\n",
"parking object\n",
"shp_place object\n",
"shp_city object\n",
"shp_urban object\n",
"shp_county object\n",
"shp_state object\n",
"population float64\n",
"land float64\n",
"density float64\n",
"dtype: object"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"apt2.dtypes"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To put it into a machine learning algorithm we need to first take care of all the missing values"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(10832, 9)"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"apt3 = apt2.loc[:, ['price', 'latitude', 'longitude', 'sqft', 'bedrooms', 'bathrooms', 'population', 'land', 'density']]\n",
"nullrows = apt3.isnull().any(axis=1)\n",
"apt4 = apt3.loc[~nullrows, :]\n",
"apt4.shape"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"X = apt4.iloc[:, 1:].as_matrix()\n",
"y = apt4.iloc[:, 0].as_matrix()"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from sklearn.ensemble import RandomForestRegressor"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"rf = RandomForestRegressor().fit(X, y)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.95718784983256378"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rf.score(X, y)"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>OLS Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>y</td> <th> R-squared: </th> <td> 0.895</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.895</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td>1.150e+04</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Sat, 21 May 2016</td> <th> Prob (F-statistic):</th> <td> 0.00</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>08:44:33</td> <th> Log-Likelihood: </th> <td> -88815.</td> \n",
"</tr>\n",
"<tr>\n",
" <th>No. Observations:</th> <td> 10832</td> <th> AIC: </th> <td>1.776e+05</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Residuals:</th> <td> 10824</td> <th> BIC: </th> <td>1.777e+05</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Df Model:</th> <td> 8</td> <th> </th> <td> </td> \n",
"</tr>\n",
"<tr>\n",
" <th>Covariance Type:</th> <td>nonrobust</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>x1</th> <td> -10.4376</td> <td> 7.983</td> <td> -1.307</td> <td> 0.191</td> <td> -26.086 5.211</td>\n",
"</tr>\n",
"<tr>\n",
" <th>x2</th> <td> -7.3445</td> <td> 2.517</td> <td> -2.918</td> <td> 0.004</td> <td> -12.279 -2.410</td>\n",
"</tr>\n",
"<tr>\n",
" <th>x3</th> <td> 0.0019</td> <td> 0.002</td> <td> 0.962</td> <td> 0.336</td> <td> -0.002 0.006</td>\n",
"</tr>\n",
"<tr>\n",
" <th>x4</th> <td> 303.4792</td> <td> 15.268</td> <td> 19.877</td> <td> 0.000</td> <td> 273.551 333.408</td>\n",
"</tr>\n",
"<tr>\n",
" <th>x5</th> <td> 503.1857</td> <td> 22.420</td> <td> 22.444</td> <td> 0.000</td> <td> 459.239 547.133</td>\n",
"</tr>\n",
"<tr>\n",
" <th>x6</th> <td> 0.0001</td> <td> 2.92e-05</td> <td> 4.146</td> <td> 0.000</td> <td> 6.37e-05 0.000</td>\n",
"</tr>\n",
"<tr>\n",
" <th>x7</th> <td> -1.6553</td> <td> 0.226</td> <td> -7.325</td> <td> 0.000</td> <td> -2.098 -1.212</td>\n",
"</tr>\n",
"<tr>\n",
" <th>x8</th> <td> 0.1364</td> <td> 0.003</td> <td> 50.568</td> <td> 0.000</td> <td> 0.131 0.142</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <th>Omnibus:</th> <td>8159.177</td> <th> Durbin-Watson: </th> <td> 1.178</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Prob(Omnibus):</th> <td> 0.000</td> <th> Jarque-Bera (JB): </th> <td>654728.978</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Skew:</th> <td> 2.972</td> <th> Prob(JB): </th> <td> 0.00</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Kurtosis:</th> <td>40.621</td> <th> Cond. No. </th> <td>3.89e+06</td> \n",
"</tr>\n",
"</table>"
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: y R-squared: 0.895\n",
"Model: OLS Adj. R-squared: 0.895\n",
"Method: Least Squares F-statistic: 1.150e+04\n",
"Date: Sat, 21 May 2016 Prob (F-statistic): 0.00\n",
"Time: 08:44:33 Log-Likelihood: -88815.\n",
"No. Observations: 10832 AIC: 1.776e+05\n",
"Df Residuals: 10824 BIC: 1.777e+05\n",
"Df Model: 8 \n",
"Covariance Type: nonrobust \n",
"==============================================================================\n",
" coef std err t P>|t| [95.0% Conf. Int.]\n",
"------------------------------------------------------------------------------\n",
"x1 -10.4376 7.983 -1.307 0.191 -26.086 5.211\n",
"x2 -7.3445 2.517 -2.918 0.004 -12.279 -2.410\n",
"x3 0.0019 0.002 0.962 0.336 -0.002 0.006\n",
"x4 303.4792 15.268 19.877 0.000 273.551 333.408\n",
"x5 503.1857 22.420 22.444 0.000 459.239 547.133\n",
"x6 0.0001 2.92e-05 4.146 0.000 6.37e-05 0.000\n",
"x7 -1.6553 0.226 -7.325 0.000 -2.098 -1.212\n",
"x8 0.1364 0.003 50.568 0.000 0.131 0.142\n",
"==============================================================================\n",
"Omnibus: 8159.177 Durbin-Watson: 1.178\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 654728.978\n",
"Skew: 2.972 Prob(JB): 0.00\n",
"Kurtosis: 40.621 Cond. No. 3.89e+06\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"[2] The condition number is large, 3.89e+06. This might indicate that there are\n",
"strong multicollinearity or other numerical problems.\n",
"\"\"\""
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"linmod = smf.OLS(y, X).fit()\n",
"linmod.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Database\n",
"\n",
"Write the results into a relational database"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sqlalchemy import create_engine\n",
"\n",
"engine = create_engine('sqlite:///apartment.db')\n",
"apt2.to_sql('apartment', engine)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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