1090 lines
29 KiB
Text
1090 lines
29 KiB
Text
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# 5 minute append to state column \n",
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"\n",
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"After taking some time to look around I found usaddress for parsing states. This notebook shows how easy it is to parse addresses with usaddress. I feel that the list of tuples is a bit clunky, but it works and I only spotted one error in the results... Indianna?? really??"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import seaborn as sns\n",
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"import usaddress\n",
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"% matplotlib inline"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Load Data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"df = pd.read_csv('https://query.data.world/s/78ou6jcu4jfseul53lu1w3nio')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>location</th>\n",
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" <th>crowd-low-estimate</th>\n",
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" <th>crowd-high-estimate</th>\n",
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" <th>mean-high-low</th>\n",
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" <th>source</th>\n",
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" <th>Latitude</th>\n",
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" <th>Longitude</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>Abilene, TX</td>\n",
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" <td>200</td>\n",
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" <td>200</td>\n",
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" <td>200.0</td>\n",
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" <td>http://www.reporternews.com/story/news/local/2...</td>\n",
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" <td>32.576489</td>\n",
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" <td>-99.665323</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>Accident, MD</td>\n",
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" <td>54</td>\n",
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" <td>54</td>\n",
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" <td>54.0</td>\n",
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" <td>Twitter; on-site witness</td>\n",
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" <td>39.628700</td>\n",
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" <td>-79.319760</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>Adak, AK</td>\n",
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" <td>10</td>\n",
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" <td>10</td>\n",
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" <td>10.0</td>\n",
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" <td>adn.com</td>\n",
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" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>Adrian, MI</td>\n",
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" <td>130</td>\n",
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" <td>150</td>\n",
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" <td>140.0</td>\n",
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" <td>https://www.facebook.com/events/847360115406578/</td>\n",
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" <td>41.889943</td>\n",
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" <td>-84.065892</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>Ajo, AZ</td>\n",
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" <td>250</td>\n",
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" <td>250</td>\n",
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" <td>250.0</td>\n",
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" <td>https://www.facebook.com/plugins/post.php?href...</td>\n",
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" <td>32.384890</td>\n",
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" <td>-112.890110</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" location crowd-low-estimate crowd-high-estimate mean-high-low \\\n",
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"0 Abilene, TX 200 200 200.0 \n",
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"1 Accident, MD 54 54 54.0 \n",
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"2 Adak, AK 10 10 10.0 \n",
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"3 Adrian, MI 130 150 140.0 \n",
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"4 Ajo, AZ 250 250 250.0 \n",
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"\n",
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" source Latitude Longitude \n",
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"0 http://www.reporternews.com/story/news/local/2... 32.576489 -99.665323 \n",
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"1 Twitter; on-site witness 39.628700 -79.319760 \n",
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"2 adn.com 0.000000 0.000000 \n",
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"3 https://www.facebook.com/events/847360115406578/ 41.889943 -84.065892 \n",
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"4 https://www.facebook.com/plugins/post.php?href... 32.384890 -112.890110 "
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df.head()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## original method\n",
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"using last 2 characters of location"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"df['State'] = df['location'].str[-2:]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>location</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>State</th>\n",
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" <th></th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>CA</th>\n",
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" <td>69</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>WA</th>\n",
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" <td>27</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>NY</th>\n",
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" <td>25</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>OR</th>\n",
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" <td>24</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>AK</th>\n",
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" <td>22</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" location\n",
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"State \n",
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"CA 69\n",
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"WA 27\n",
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"NY 25\n",
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"OR 24\n",
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"AK 22"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df.groupby('State').count().sort_values('location', ascending=False)[['location']].head()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Looks Like there are a few errors in State that would need cleansed (at least 12)\n",
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"\n",
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"15 minutes is not enough to fix this by hand, I know there is a package that would do this faster that has been mentioned on talk python to me. Ill have to look into this package another day"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"62"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"len(df.groupby('State').count())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>location</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>State</th>\n",
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" <th></th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>DE</th>\n",
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" <td>2</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>OK</th>\n",
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" <td>2</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>LA</th>\n",
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" <td>2</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>DC</th>\n",
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" <td>1</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>RI</th>\n",
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" <td>1</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>ah</th>\n",
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" <td>1</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>er</th>\n",
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" <td>1</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>es</th>\n",
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" <td>1</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>ge</th>\n",
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" <td>1</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>le</th>\n",
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" <td>1</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>na</th>\n",
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" <td>1</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>nd</th>\n",
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" <td>1</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>on</th>\n",
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" <td>1</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>t)</th>\n",
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" <td>1</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>te</th>\n",
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" <td>1</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" location\n",
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"State \n",
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"DE 2\n",
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"OK 2\n",
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"LA 2\n",
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"DC 1\n",
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"RI 1\n",
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"ah 1\n",
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"er 1\n",
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"es 1\n",
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"ge 1\n",
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"le 1\n",
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"na 1\n",
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"nd 1\n",
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"on 1\n",
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"t) 1\n",
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"te 1"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df.groupby('State').count().sort_values('location', ascending=False)[['location']].tail(15)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Usaddress Method"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"df['Address'] = df['location'].apply(usaddress.parse)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"10 [(Albuquerque,, PlaceName), (NM, StateName)]\n",
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"11 [(Almanor, PlaceName), (West,, PlaceName), (CA...\n",
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"12 [(Alpine,, PlaceName), (TX, StateName)]\n",
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"13 [(Amarillo,, PlaceName), (TX, StateName)]\n",
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"14 [(Amelia, StreetName), (Island,, StreetNamePos...\n",
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"15 [(Anacortes,, PlaceName), (WA, StateName)]\n",
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"16 [(Anchorage,, PlaceName), (AK, StateName)]\n",
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"17 [(Ann, PlaceName), (Arbor,, PlaceName), (MI, S...\n",
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"18 [(Annapolis,, PlaceName), (MD, StateName)]\n",
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"19 [(Arlington,, PlaceName), (VA, StateName)]\n",
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"Name: Address, dtype: object"
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]
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
|
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"df['Address'].iloc[10:20]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
|
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>0</th>\n",
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" <th>1</th>\n",
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" <th>2</th>\n",
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" <th>3</th>\n",
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" <th>4</th>\n",
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" <th>5</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>10</th>\n",
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" <td>(Albuquerque,, PlaceName)</td>\n",
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" <td>(NM, StateName)</td>\n",
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" <td>None</td>\n",
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" <td>None</td>\n",
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" <td>None</td>\n",
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" <td>None</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>11</th>\n",
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" <td>(Almanor, PlaceName)</td>\n",
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" <td>(West,, PlaceName)</td>\n",
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" <td>(CA, StateName)</td>\n",
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" <td>None</td>\n",
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" <td>None</td>\n",
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" <td>None</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>12</th>\n",
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" <td>(Alpine,, PlaceName)</td>\n",
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" <td>(TX, StateName)</td>\n",
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" <td>None</td>\n",
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" <td>None</td>\n",
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" <td>None</td>\n",
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" <td>None</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>13</th>\n",
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" <td>(Amarillo,, PlaceName)</td>\n",
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" <td>(TX, StateName)</td>\n",
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" <td>None</td>\n",
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" <td>None</td>\n",
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" <td>None</td>\n",
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" <td>None</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>14</th>\n",
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" <td>(Amelia, StreetName)</td>\n",
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" <td>(Island,, StreetNamePostType)</td>\n",
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" <td>(FL, OccupancyType)</td>\n",
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" <td>None</td>\n",
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" <td>None</td>\n",
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" <td>None</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>15</th>\n",
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" <td>(Anacortes,, PlaceName)</td>\n",
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" <td>(WA, StateName)</td>\n",
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" <td>None</td>\n",
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" <td>None</td>\n",
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" <td>None</td>\n",
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" <td>None</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>16</th>\n",
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" <td>(Anchorage,, PlaceName)</td>\n",
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" <td>(AK, StateName)</td>\n",
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" <td>None</td>\n",
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" <td>None</td>\n",
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" <td>None</td>\n",
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" <td>None</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>17</th>\n",
|
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" <td>(Ann, PlaceName)</td>\n",
|
|
" <td>(Arbor,, PlaceName)</td>\n",
|
|
" <td>(MI, StateName)</td>\n",
|
|
" <td>None</td>\n",
|
|
" <td>None</td>\n",
|
|
" <td>None</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>18</th>\n",
|
|
" <td>(Annapolis,, PlaceName)</td>\n",
|
|
" <td>(MD, StateName)</td>\n",
|
|
" <td>None</td>\n",
|
|
" <td>None</td>\n",
|
|
" <td>None</td>\n",
|
|
" <td>None</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>19</th>\n",
|
|
" <td>(Arlington,, PlaceName)</td>\n",
|
|
" <td>(VA, StateName)</td>\n",
|
|
" <td>None</td>\n",
|
|
" <td>None</td>\n",
|
|
" <td>None</td>\n",
|
|
" <td>None</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" 0 1 \\\n",
|
|
"10 (Albuquerque,, PlaceName) (NM, StateName) \n",
|
|
"11 (Almanor, PlaceName) (West,, PlaceName) \n",
|
|
"12 (Alpine,, PlaceName) (TX, StateName) \n",
|
|
"13 (Amarillo,, PlaceName) (TX, StateName) \n",
|
|
"14 (Amelia, StreetName) (Island,, StreetNamePostType) \n",
|
|
"15 (Anacortes,, PlaceName) (WA, StateName) \n",
|
|
"16 (Anchorage,, PlaceName) (AK, StateName) \n",
|
|
"17 (Ann, PlaceName) (Arbor,, PlaceName) \n",
|
|
"18 (Annapolis,, PlaceName) (MD, StateName) \n",
|
|
"19 (Arlington,, PlaceName) (VA, StateName) \n",
|
|
"\n",
|
|
" 2 3 4 5 \n",
|
|
"10 None None None None \n",
|
|
"11 (CA, StateName) None None None \n",
|
|
"12 None None None None \n",
|
|
"13 None None None None \n",
|
|
"14 (FL, OccupancyType) None None None \n",
|
|
"15 None None None None \n",
|
|
"16 None None None None \n",
|
|
"17 (MI, StateName) None None None \n",
|
|
"18 None None None None \n",
|
|
"19 None None None None "
|
|
]
|
|
},
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"pd.DataFrame.from_records(df['Address'].values.tolist()).iloc[10:20]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 12,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"def get_state(lst):\n",
|
|
" for tpl in lst:\n",
|
|
" if tpl[1] == 'StateName':\n",
|
|
" return tpl[0]\n",
|
|
" return None"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [],
|
|
"source": [
|
|
"df['State'] = df['Address'].apply(get_state)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 14,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"53"
|
|
]
|
|
},
|
|
"execution_count": 14,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"len(df.groupby('State').count())"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Errors? 53?\n",
|
|
"Looks like there were 53 states it counted PR (Puerto Rico), and DC which is good. But it also included Indianna?? not sure why, but I am impressed with the reults."
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 15,
|
|
"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>location</th>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>State</th>\n",
|
|
" <th></th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>CA</th>\n",
|
|
" <td>70</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>WA</th>\n",
|
|
" <td>27</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>NY</th>\n",
|
|
" <td>25</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>OR</th>\n",
|
|
" <td>24</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>AK</th>\n",
|
|
" <td>22</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>MI</th>\n",
|
|
" <td>20</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>TX</th>\n",
|
|
" <td>20</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>PA</th>\n",
|
|
" <td>19</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>CO</th>\n",
|
|
" <td>19</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>WI</th>\n",
|
|
" <td>16</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>NC</th>\n",
|
|
" <td>15</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>FL</th>\n",
|
|
" <td>14</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>VA</th>\n",
|
|
" <td>13</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>NM</th>\n",
|
|
" <td>11</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>OH</th>\n",
|
|
" <td>11</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>AZ</th>\n",
|
|
" <td>11</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>ME</th>\n",
|
|
" <td>11</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>NJ</th>\n",
|
|
" <td>10</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>MN</th>\n",
|
|
" <td>10</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>MA</th>\n",
|
|
" <td>10</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>IL</th>\n",
|
|
" <td>9</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>UT</th>\n",
|
|
" <td>8</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>IN</th>\n",
|
|
" <td>8</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>ID</th>\n",
|
|
" <td>8</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>MD</th>\n",
|
|
" <td>7</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>TN</th>\n",
|
|
" <td>7</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>NH</th>\n",
|
|
" <td>7</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>IA</th>\n",
|
|
" <td>6</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>GA</th>\n",
|
|
" <td>6</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>HI</th>\n",
|
|
" <td>5</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>CT</th>\n",
|
|
" <td>5</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>SD</th>\n",
|
|
" <td>4</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>SC</th>\n",
|
|
" <td>4</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>PR</th>\n",
|
|
" <td>4</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>MS</th>\n",
|
|
" <td>4</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>MO</th>\n",
|
|
" <td>4</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>AR</th>\n",
|
|
" <td>4</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>NV</th>\n",
|
|
" <td>3</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>VT</th>\n",
|
|
" <td>3</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>NE</th>\n",
|
|
" <td>3</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>KY</th>\n",
|
|
" <td>3</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>MT</th>\n",
|
|
" <td>3</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>ND</th>\n",
|
|
" <td>3</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>WY</th>\n",
|
|
" <td>3</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>AL</th>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>LA</th>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>KS</th>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>DE</th>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>OK</th>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>WV</th>\n",
|
|
" <td>2</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>RI</th>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>Indiana</th>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>DC</th>\n",
|
|
" <td>1</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" location\n",
|
|
"State \n",
|
|
"CA 70\n",
|
|
"WA 27\n",
|
|
"NY 25\n",
|
|
"OR 24\n",
|
|
"AK 22\n",
|
|
"MI 20\n",
|
|
"TX 20\n",
|
|
"PA 19\n",
|
|
"CO 19\n",
|
|
"WI 16\n",
|
|
"NC 15\n",
|
|
"FL 14\n",
|
|
"VA 13\n",
|
|
"NM 11\n",
|
|
"OH 11\n",
|
|
"AZ 11\n",
|
|
"ME 11\n",
|
|
"NJ 10\n",
|
|
"MN 10\n",
|
|
"MA 10\n",
|
|
"IL 9\n",
|
|
"UT 8\n",
|
|
"IN 8\n",
|
|
"ID 8\n",
|
|
"MD 7\n",
|
|
"TN 7\n",
|
|
"NH 7\n",
|
|
"IA 6\n",
|
|
"GA 6\n",
|
|
"HI 5\n",
|
|
"CT 5\n",
|
|
"SD 4\n",
|
|
"SC 4\n",
|
|
"PR 4\n",
|
|
"MS 4\n",
|
|
"MO 4\n",
|
|
"AR 4\n",
|
|
"NV 3\n",
|
|
"VT 3\n",
|
|
"NE 3\n",
|
|
"KY 3\n",
|
|
"MT 3\n",
|
|
"ND 3\n",
|
|
"WY 3\n",
|
|
"AL 2\n",
|
|
"LA 2\n",
|
|
"KS 2\n",
|
|
"DE 2\n",
|
|
"OK 2\n",
|
|
"WV 2\n",
|
|
"RI 1\n",
|
|
"Indiana 1\n",
|
|
"DC 1"
|
|
]
|
|
},
|
|
"execution_count": 15,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"df.groupby('State').count().sort_values('location', ascending=False)[['location']]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 22,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"[('Amarillo,', 'PlaceName'), ('TX', 'StateName')]"
|
|
]
|
|
},
|
|
"execution_count": 22,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"df['Address'].values.tolist()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 24,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"warning: LF will be replaced by CRLF in .ipynb_checkpoints/5min append State-checkpoint.ipynb.\n",
|
|
"The file will have its original line endings in your working directory.\n",
|
|
"warning: LF will be replaced by CRLF in 5min append State.ipynb.\n",
|
|
"The file will have its original line endings in your working directory.\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"[master c370888] added 5min append State exporing the usaddress package\n",
|
|
" Committer: Rhiannon Walker <Rhiannon Walker>\n",
|
|
"Your name and email address were configured automatically based\n",
|
|
"on your username and hostname. Please check that they are accurate.\n",
|
|
"You can suppress this message by setting them explicitly. Run the\n",
|
|
"following command and follow the instructions in your editor to edit\n",
|
|
"your configuration file:\n",
|
|
"\n",
|
|
" git config --global --edit\n",
|
|
"\n",
|
|
"After doing this, you may fix the identity used for this commit with:\n",
|
|
"\n",
|
|
" git commit --amend --reset-author\n",
|
|
"\n",
|
|
" 2 files changed, 1998 insertions(+)\n",
|
|
" create mode 100644 .ipynb_checkpoints/5min append State-checkpoint.ipynb\n",
|
|
" create mode 100644 5min append State.ipynb\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"To https://github.com/WaylonWalker/15min_WomensMarch.git\n",
|
|
" ! [rejected] master -> master (fetch first)\n",
|
|
"error: failed to push some refs to 'https://github.com/WaylonWalker/15min_WomensMarch.git'\n",
|
|
"hint: Updates were rejected because the remote contains work that you do\n",
|
|
"hint: not have locally. This is usually caused by another repository pushing\n",
|
|
"hint: to the same ref. You may want to first integrate the remote changes\n",
|
|
"hint: (e.g., 'git pull ...') before pushing again.\n",
|
|
"hint: See the 'Note about fast-forwards' in 'git push --help' for details.\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"!git add .\n",
|
|
"!git commit -m \"added 5min append State exporing the usaddress package\"\n",
|
|
"!git push"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 25,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Merge made by the 'recursive' strategy.\n",
|
|
" README.md | 8 ++++++++\n",
|
|
" 1 file changed, 8 insertions(+)\n",
|
|
" create mode 100644 README.md\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"From https://github.com/WaylonWalker/15min_WomensMarch\n",
|
|
" 680e352..291d5f8 master -> origin/master\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"!git pull"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"anaconda-cloud": {},
|
|
"kernelspec": {
|
|
"display_name": "Python [conda root]",
|
|
"language": "python",
|
|
"name": "conda-root-py"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.4.5"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 1
|
|
}
|