{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Stacked bar charts"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We've only been looking at the largest cities by population, but we have a whole bunch of other data we can use."
]
},
{
"cell_type": "code",
"execution_count": 68,
"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>id</th>\n",
" <th>Country</th>\n",
" <th>City</th>\n",
" <th>Region</th>\n",
" <th>Population</th>\n",
" <th>Latitude</th>\n",
" <th>Longitude</th>\n",
" </tr>\n",
" <tr>\n",
" <th>AccentCity</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Alabaster</th>\n",
" <td>2907755</td>\n",
" <td>us</td>\n",
" <td>alabaster</td>\n",
" <td>AL</td>\n",
" <td>26738.0</td>\n",
" <td>33.244167</td>\n",
" <td>-86.816389</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Albertville</th>\n",
" <td>2907759</td>\n",
" <td>us</td>\n",
" <td>albertville</td>\n",
" <td>AL</td>\n",
" <td>18368.0</td>\n",
" <td>34.267500</td>\n",
" <td>-86.208889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Alexander City</th>\n",
" <td>2907765</td>\n",
" <td>us</td>\n",
" <td>alexander city</td>\n",
" <td>AL</td>\n",
" <td>14993.0</td>\n",
" <td>32.943889</td>\n",
" <td>-85.953889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Anniston</th>\n",
" <td>2907804</td>\n",
" <td>us</td>\n",
" <td>anniston</td>\n",
" <td>AL</td>\n",
" <td>23423.0</td>\n",
" <td>33.659722</td>\n",
" <td>-85.831667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Athens</th>\n",
" <td>2907848</td>\n",
" <td>us</td>\n",
" <td>athens</td>\n",
" <td>AL</td>\n",
" <td>20470.0</td>\n",
" <td>34.802778</td>\n",
" <td>-86.971667</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id Country City Region Population Latitude \\\n",
"AccentCity \n",
"Alabaster 2907755 us alabaster AL 26738.0 33.244167 \n",
"Albertville 2907759 us albertville AL 18368.0 34.267500 \n",
"Alexander City 2907765 us alexander city AL 14993.0 32.943889 \n",
"Anniston 2907804 us anniston AL 23423.0 33.659722 \n",
"Athens 2907848 us athens AL 20470.0 34.802778 \n",
"\n",
" Longitude \n",
"AccentCity \n",
"Alabaster -86.816389 \n",
"Albertville -86.208889 \n",
"Alexander City -85.953889 \n",
"Anniston -85.831667 \n",
"Athens -86.971667 "
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas\n",
"\n",
"df = pandas.read_csv('US_cities.csv', index_col=\"AccentCity\")\n",
"df[:5]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We have over 4000 cities and towns in the database, definitely too many for a bar chart. But we could look at the population data at the state level. First, let's try just a simple groupby in pandas. "
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
" Region\n",
"Population AK 425708.0\n",
" AL 1995225.0\n",
" AR 1212898.0\n",
" AZ 4838335.0\n",
" CA 30890865.0\n",
"dtype: float64"
]
},
"execution_count": 86,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"states_pop = df[[\"Population\", \"Region\"]].groupby(by=[\"Region\"]).sum()\n",
"states_pop[:5]"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEYCAYAAACnYrZxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x22b1ccf5748>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"states_pop.plot.bar()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's make a stacked bar chart that shows us how much the three largest cities contribute to population compared to the rest of the state. First, we'll need to get a separate list of the three top cities. To do that, we'll be using the pandas groupby method."
]
},
{
"cell_type": "code",
"execution_count": 71,
"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>id</th>\n",
" <th>Country</th>\n",
" <th>City</th>\n",
" <th>Region</th>\n",
" <th>Population</th>\n",
" <th>Latitude</th>\n",
" <th>Longitude</th>\n",
" </tr>\n",
" <tr>\n",
" <th>AccentCity</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Birmingham</th>\n",
" <td>2908046</td>\n",
" <td>us</td>\n",
" <td>birmingham</td>\n",
" <td>AL</td>\n",
" <td>231621.0</td>\n",
" <td>33.520556</td>\n",
" <td>-86.802500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Anchorage</th>\n",
" <td>2912001</td>\n",
" <td>us</td>\n",
" <td>anchorage</td>\n",
" <td>AK</td>\n",
" <td>276263.0</td>\n",
" <td>61.218056</td>\n",
" <td>-149.900278</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Phoenix</th>\n",
" <td>2913986</td>\n",
" <td>us</td>\n",
" <td>phoenix</td>\n",
" <td>AZ</td>\n",
" <td>1428509.0</td>\n",
" <td>33.448333</td>\n",
" <td>-112.073333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Little Rock</th>\n",
" <td>2916351</td>\n",
" <td>us</td>\n",
" <td>little rock</td>\n",
" <td>AR</td>\n",
" <td>184217.0</td>\n",
" <td>34.746389</td>\n",
" <td>-92.289444</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Los Angeles</th>\n",
" <td>2920652</td>\n",
" <td>us</td>\n",
" <td>los angeles</td>\n",
" <td>CA</td>\n",
" <td>3877129.0</td>\n",
" <td>34.052222</td>\n",
" <td>-118.242778</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id Country City Region Population Latitude \\\n",
"AccentCity \n",
"Birmingham 2908046 us birmingham AL 231621.0 33.520556 \n",
"Anchorage 2912001 us anchorage AK 276263.0 61.218056 \n",
"Phoenix 2913986 us phoenix AZ 1428509.0 33.448333 \n",
"Little Rock 2916351 us little rock AR 184217.0 34.746389 \n",
"Los Angeles 2920652 us los angeles CA 3877129.0 34.052222 \n",
"\n",
" Longitude \n",
"AccentCity \n",
"Birmingham -86.802500 \n",
"Anchorage -149.900278 \n",
"Phoenix -112.073333 \n",
"Little Rock -92.289444 \n",
"Los Angeles -118.242778 "
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"largest_cities_idx = df.groupby(by=[\"Region\"], sort=False)[\"Population\"].transform(max) == df[\"Population\"]\n",
"df[largest_cities_idx][:5]"
]
},
{
"cell_type": "code",
"execution_count": 72,
"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>id</th>\n",
" <th>Country</th>\n",
" <th>City</th>\n",
" <th>Region</th>\n",
" <th>Population</th>\n",
" <th>Latitude</th>\n",
" <th>Longitude</th>\n",
" </tr>\n",
" <tr>\n",
" <th>AccentCity</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Montgomery</th>\n",
" <td>2910236</td>\n",
" <td>us</td>\n",
" <td>montgomery</td>\n",
" <td>AL</td>\n",
" <td>198325.0</td>\n",
" <td>32.366667</td>\n",
" <td>-86.300000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Juneau</th>\n",
" <td>2912251</td>\n",
" <td>us</td>\n",
" <td>juneau</td>\n",
" <td>AK</td>\n",
" <td>31796.0</td>\n",
" <td>58.301944</td>\n",
" <td>-134.419722</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Tucson</th>\n",
" <td>2914501</td>\n",
" <td>us</td>\n",
" <td>tucson</td>\n",
" <td>AZ</td>\n",
" <td>518907.0</td>\n",
" <td>32.221667</td>\n",
" <td>-110.925833</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Fort Smith</th>\n",
" <td>2915717</td>\n",
" <td>us</td>\n",
" <td>fort smith</td>\n",
" <td>AR</td>\n",
" <td>81985.0</td>\n",
" <td>35.385833</td>\n",
" <td>-94.398333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>San Diego</th>\n",
" <td>2922103</td>\n",
" <td>us</td>\n",
" <td>san diego</td>\n",
" <td>CA</td>\n",
" <td>1287050.0</td>\n",
" <td>32.715278</td>\n",
" <td>-117.156389</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id Country City Region Population Latitude \\\n",
"AccentCity \n",
"Montgomery 2910236 us montgomery AL 198325.0 32.366667 \n",
"Juneau 2912251 us juneau AK 31796.0 58.301944 \n",
"Tucson 2914501 us tucson AZ 518907.0 32.221667 \n",
"Fort Smith 2915717 us fort smith AR 81985.0 35.385833 \n",
"San Diego 2922103 us san diego CA 1287050.0 32.715278 \n",
"\n",
" Longitude \n",
"AccentCity \n",
"Montgomery -86.300000 \n",
"Juneau -134.419722 \n",
"Tucson -110.925833 \n",
"Fort Smith -94.398333 \n",
"San Diego -117.156389 "
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def max2(cities):\n",
" return nth_largest(cities, 2)\n",
"def max3(cities):\n",
" return nth_largest(cities, 3)\n",
"def nth_largest(cities, n):\n",
" nlargest = cities.nlargest(n)\n",
" if len(nlargest) < n:\n",
" return None\n",
" return nlargest[n-1]\n",
"second_largest_idx = df.groupby(by=[\"Region\"], sort=False)[\"Population\"].transform(max2) == df[\"Population\"]\n",
"third_largest_idx = df.groupby(by=[\"Region\"], sort=False)[\"Population\"].transform(max3) == df[\"Population\"]\n",
"df[second_largest_idx][:5]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we just need to sum the rest of the cities that we haven't counted yet."
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEYCAYAAACnYrZxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkIK7iEgB/X+0gbfGkBpoiQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x22b26fcb9e8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAZQAAAFbCAYAAADye3jBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n8H/ubUCb8reVeQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x22b282219e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"smaller_cities = df[~largest_cities_idx & ~second_largest_idx & ~third_largest_idx].copy()\n",
"\n",
"smaller_cities[[\"Population\", \"Region\"]].groupby(by=[\"Region\"]).sum().plot.bar(stacked=True)\n",
"df[largest_cities_idx][[\"Population\", \"Region\"]].groupby(by=[\"Region\"]).sum().plot.bar(stacked=True)\n",
"# df[second_largest_idx].plot.bar()\n",
"# df[third_largest_idx].plot.bar()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 89,
"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>id</th>\n",
" <th>Country</th>\n",
" <th>City</th>\n",
" <th>Region</th>\n",
" <th>Population</th>\n",
" <th>Latitude</th>\n",
" <th>Longitude</th>\n",
" </tr>\n",
" <tr>\n",
" <th>AccentCity</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Alabaster</th>\n",
" <td>2907755</td>\n",
" <td>us</td>\n",
" <td>alabaster</td>\n",
" <td>AL</td>\n",
" <td>26738.0</td>\n",
" <td>33.244167</td>\n",
" <td>-86.816389</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Albertville</th>\n",
" <td>2907759</td>\n",
" <td>us</td>\n",
" <td>albertville</td>\n",
" <td>AL</td>\n",
" <td>18368.0</td>\n",
" <td>34.267500</td>\n",
" <td>-86.208889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Alexander City</th>\n",
" <td>2907765</td>\n",
" <td>us</td>\n",
" <td>alexander city</td>\n",
" <td>AL</td>\n",
" <td>14993.0</td>\n",
" <td>32.943889</td>\n",
" <td>-85.953889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Anniston</th>\n",
" <td>2907804</td>\n",
" <td>us</td>\n",
" <td>anniston</td>\n",
" <td>AL</td>\n",
" <td>23423.0</td>\n",
" <td>33.659722</td>\n",
" <td>-85.831667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Athens</th>\n",
" <td>2907848</td>\n",
" <td>us</td>\n",
" <td>athens</td>\n",
" <td>AL</td>\n",
" <td>20470.0</td>\n",
" <td>34.802778</td>\n",
" <td>-86.971667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Auburn</th>\n",
" <td>2907855</td>\n",
" <td>us</td>\n",
" <td>auburn</td>\n",
" <td>AL</td>\n",
" <td>49833.0</td>\n",
" <td>32.609722</td>\n",
" <td>-85.480833</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bessemer</th>\n",
" <td>2908015</td>\n",
" <td>us</td>\n",
" <td>bessemer</td>\n",
" <td>AL</td>\n",
" <td>28628.0</td>\n",
" <td>33.401667</td>\n",
" <td>-86.954444</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Birmingham</th>\n",
" <td>2908046</td>\n",
" <td>us</td>\n",
" <td>birmingham</td>\n",
" <td>AL</td>\n",
" <td>231621.0</td>\n",
" <td>33.520556</td>\n",
" <td>-86.802500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cullman</th>\n",
" <td>2908668</td>\n",
" <td>us</td>\n",
" <td>cullman</td>\n",
" <td>AL</td>\n",
" <td>14796.0</td>\n",
" <td>34.174722</td>\n",
" <td>-86.843611</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Daphne</th>\n",
" <td>2908701</td>\n",
" <td>us</td>\n",
" <td>daphne</td>\n",
" <td>AL</td>\n",
" <td>18581.0</td>\n",
" <td>30.603333</td>\n",
" <td>-87.903611</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Decatur</th>\n",
" <td>2908730</td>\n",
" <td>us</td>\n",
" <td>decatur</td>\n",
" <td>AL</td>\n",
" <td>54621.0</td>\n",
" <td>34.605833</td>\n",
" <td>-86.983333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Dothan</th>\n",
" <td>2908787</td>\n",
" <td>us</td>\n",
" <td>dothan</td>\n",
" <td>AL</td>\n",
" <td>61741.0</td>\n",
" <td>31.223056</td>\n",
" <td>-85.390556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Enterprise</th>\n",
" <td>2908939</td>\n",
" <td>us</td>\n",
" <td>enterprise</td>\n",
" <td>AL</td>\n",
" <td>22572.0</td>\n",
" <td>31.315000</td>\n",
" <td>-85.855278</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Eufaula</th>\n",
" <td>2908954</td>\n",
" <td>us</td>\n",
" <td>eufaula</td>\n",
" <td>AL</td>\n",
" <td>13453.0</td>\n",
" <td>31.891111</td>\n",
" <td>-85.145556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Fairfield</th>\n",
" <td>2908982</td>\n",
" <td>us</td>\n",
" <td>fairfield</td>\n",
" <td>AL</td>\n",
" <td>11641.0</td>\n",
" <td>33.485833</td>\n",
" <td>-86.911944</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Fairhope</th>\n",
" <td>2908986</td>\n",
" <td>us</td>\n",
" <td>fairhope</td>\n",
" <td>AL</td>\n",
" <td>15419.0</td>\n",
" <td>30.522778</td>\n",
" <td>-87.903333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Florence</th>\n",
" <td>2909053</td>\n",
" <td>us</td>\n",
" <td>florence</td>\n",
" <td>AL</td>\n",
" <td>35733.0</td>\n",
" <td>34.799722</td>\n",
" <td>-87.677222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Fort Payne</th>\n",
" <td>2909087</td>\n",
" <td>us</td>\n",
" <td>fort payne</td>\n",
" <td>AL</td>\n",
" <td>13586.0</td>\n",
" <td>34.444167</td>\n",
" <td>-85.719722</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Gadsden</th>\n",
" <td>2909142</td>\n",
" <td>us</td>\n",
" <td>gadsden</td>\n",
" <td>AL</td>\n",
" <td>36821.0</td>\n",
" <td>34.014167</td>\n",
" <td>-86.006667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Gardendale</th>\n",
" <td>2909161</td>\n",
" <td>us</td>\n",
" <td>gardendale</td>\n",
" <td>AL</td>\n",
" <td>12370.0</td>\n",
" <td>33.660000</td>\n",
" <td>-86.812778</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Hartselle</th>\n",
" <td>2909380</td>\n",
" <td>us</td>\n",
" <td>hartselle</td>\n",
" <td>AL</td>\n",
" <td>13089.0</td>\n",
" <td>34.443333</td>\n",
" <td>-86.935278</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Helena</th>\n",
" <td>2909416</td>\n",
" <td>us</td>\n",
" <td>helena</td>\n",
" <td>AL</td>\n",
" <td>13119.0</td>\n",
" <td>33.296111</td>\n",
" <td>-86.843611</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Homewood</th>\n",
" <td>2909519</td>\n",
" <td>us</td>\n",
" <td>homewood</td>\n",
" <td>AL</td>\n",
" <td>23815.0</td>\n",
" <td>33.471667</td>\n",
" <td>-86.800833</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Hoover</th>\n",
" <td>2909526</td>\n",
" <td>us</td>\n",
" <td>hoover</td>\n",
" <td>AL</td>\n",
" <td>66752.0</td>\n",
" <td>33.405278</td>\n",
" <td>-86.811389</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Hueytown</th>\n",
" <td>2909557</td>\n",
" <td>us</td>\n",
" <td>hueytown</td>\n",
" <td>AL</td>\n",
" <td>15308.0</td>\n",
" <td>33.451111</td>\n",
" <td>-86.996667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Huntsville</th>\n",
" <td>2909571</td>\n",
" <td>us</td>\n",
" <td>huntsville</td>\n",
" <td>AL</td>\n",
" <td>167528.0</td>\n",
" <td>34.730278</td>\n",
" <td>-86.586111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Irondale</th>\n",
" <td>2909609</td>\n",
" <td>us</td>\n",
" <td>irondale</td>\n",
" <td>AL</td>\n",
" <td>9652.0</td>\n",
" <td>33.538056</td>\n",
" <td>-86.707222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Jacksonville</th>\n",
" <td>2909632</td>\n",
" <td>us</td>\n",
" <td>jacksonville</td>\n",
" <td>AL</td>\n",
" <td>8603.0</td>\n",
" <td>33.813611</td>\n",
" <td>-85.761389</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Jasper</th>\n",
" <td>2909638</td>\n",
" <td>us</td>\n",
" <td>jasper</td>\n",
" <td>AL</td>\n",
" <td>13831.0</td>\n",
" <td>33.831111</td>\n",
" <td>-87.277500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Leeds</th>\n",
" <td>2909843</td>\n",
" <td>us</td>\n",
" <td>leeds</td>\n",
" <td>AL</td>\n",
" <td>11167.0</td>\n",
" <td>33.548056</td>\n",
" <td>-86.544444</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Verona</th>\n",
" <td>3048989</td>\n",
" <td>us</td>\n",
" <td>verona</td>\n",
" <td>WI</td>\n",
" <td>10649.0</td>\n",
" <td>42.990833</td>\n",
" <td>-89.533056</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Watertown</th>\n",
" <td>3049019</td>\n",
" <td>us</td>\n",
" <td>watertown</td>\n",
" <td>WI</td>\n",
" <td>23323.0</td>\n",
" <td>43.194722</td>\n",
" <td>-88.728889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Waukesha</th>\n",
" <td>3049024</td>\n",
" <td>us</td>\n",
" <td>waukesha</td>\n",
" <td>WI</td>\n",
" <td>68112.0</td>\n",
" <td>43.011667</td>\n",
" <td>-88.231389</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Waunakee</th>\n",
" <td>3049026</td>\n",
" <td>us</td>\n",
" <td>waunakee</td>\n",
" <td>WI</td>\n",
" <td>10366.0</td>\n",
" <td>43.191944</td>\n",
" <td>-89.455556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Waupun</th>\n",
" <td>3049028</td>\n",
" <td>us</td>\n",
" <td>waupun</td>\n",
" <td>WI</td>\n",
" <td>10480.0</td>\n",
" <td>43.633333</td>\n",
" <td>-88.729444</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Wausau</th>\n",
" <td>3049029</td>\n",
" <td>us</td>\n",
" <td>wausau</td>\n",
" <td>WI</td>\n",
" <td>36779.0</td>\n",
" <td>44.959167</td>\n",
" <td>-89.630000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Wauwatosa</th>\n",
" <td>3049033</td>\n",
" <td>us</td>\n",
" <td>wauwatosa</td>\n",
" <td>WI</td>\n",
" <td>45453.0</td>\n",
" <td>43.049444</td>\n",
" <td>-88.007500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>West Allis</th>\n",
" <td>3049050</td>\n",
" <td>us</td>\n",
" <td>west allis</td>\n",
" <td>WI</td>\n",
" <td>59363.0</td>\n",
" <td>43.016667</td>\n",
" <td>-88.006944</td>\n",
" </tr>\n",
" <tr>\n",
" <th>West Bend</th>\n",
" <td>3049054</td>\n",
" <td>us</td>\n",
" <td>west bend</td>\n",
" <td>WI</td>\n",
" <td>29431.0</td>\n",
" <td>43.425278</td>\n",
" <td>-88.183333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Weston</th>\n",
" <td>3049071</td>\n",
" <td>us</td>\n",
" <td>weston</td>\n",
" <td>WI</td>\n",
" <td>13080.0</td>\n",
" <td>44.890833</td>\n",
" <td>-89.547500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Whitefish Bay</th>\n",
" <td>3049086</td>\n",
" <td>us</td>\n",
" <td>whitefish bay</td>\n",
" <td>WI</td>\n",
" <td>13611.0</td>\n",
" <td>43.113333</td>\n",
" <td>-87.900000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Whitewater</th>\n",
" <td>3049089</td>\n",
" <td>us</td>\n",
" <td>whitewater</td>\n",
" <td>WI</td>\n",
" <td>14536.0</td>\n",
" <td>42.833611</td>\n",
" <td>-88.732222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Wisconsin Rapids</th>\n",
" <td>3049120</td>\n",
" <td>us</td>\n",
" <td>wisconsin rapids</td>\n",
" <td>WI</td>\n",
" <td>17724.0</td>\n",
" <td>44.383611</td>\n",
" <td>-89.817222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Casper</th>\n",
" <td>3049260</td>\n",
" <td>us</td>\n",
" <td>casper</td>\n",
" <td>WY</td>\n",
" <td>51507.0</td>\n",
" <td>42.866667</td>\n",
" <td>-106.312500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cheyenne</th>\n",
" <td>3049264</td>\n",
" <td>us</td>\n",
" <td>cheyenne</td>\n",
" <td>WY</td>\n",
" <td>55443.0</td>\n",
" <td>41.140000</td>\n",
" <td>-104.819722</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Cody</th>\n",
" <td>3049273</td>\n",
" <td>us</td>\n",
" <td>cody</td>\n",
" <td>WY</td>\n",
" <td>9161.0</td>\n",
" <td>44.526389</td>\n",
" <td>-109.055833</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Douglas</th>\n",
" <td>3049305</td>\n",
" <td>us</td>\n",
" <td>douglas</td>\n",
" <td>WY</td>\n",
" <td>5378.0</td>\n",
" <td>42.759722</td>\n",
" <td>-105.381667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Evanston</th>\n",
" <td>3049335</td>\n",
" <td>us</td>\n",
" <td>evanston</td>\n",
" <td>WY</td>\n",
" <td>11258.0</td>\n",
" <td>41.268333</td>\n",
" <td>-110.962500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Gillette</th>\n",
" <td>3049358</td>\n",
" <td>us</td>\n",
" <td>gillette</td>\n",
" <td>WY</td>\n",
" <td>23101.0</td>\n",
" <td>44.291111</td>\n",
" <td>-105.501667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Green River</th>\n",
" <td>3049368</td>\n",
" <td>us</td>\n",
" <td>green river</td>\n",
" <td>WY</td>\n",
" <td>11358.0</td>\n",
" <td>41.528611</td>\n",
" <td>-109.465556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Jackson</th>\n",
" <td>3049409</td>\n",
" <td>us</td>\n",
" <td>jackson</td>\n",
" <td>WY</td>\n",
" <td>8989.0</td>\n",
" <td>43.480000</td>\n",
" <td>-110.761667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Lander</th>\n",
" <td>3049439</td>\n",
" <td>us</td>\n",
" <td>lander</td>\n",
" <td>WY</td>\n",
" <td>6821.0</td>\n",
" <td>42.833056</td>\n",
" <td>-108.730000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Laramie</th>\n",
" <td>3049440</td>\n",
" <td>us</td>\n",
" <td>laramie</td>\n",
" <td>WY</td>\n",
" <td>26934.0</td>\n",
" <td>41.311389</td>\n",
" <td>-105.590556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Powell</th>\n",
" <td>3049544</td>\n",
" <td>us</td>\n",
" <td>powell</td>\n",
" <td>WY</td>\n",
" <td>5213.0</td>\n",
" <td>44.753889</td>\n",
" <td>-108.756667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Rawlins</th>\n",
" <td>3049552</td>\n",
" <td>us</td>\n",
" <td>rawlins</td>\n",
" <td>WY</td>\n",
" <td>8533.0</td>\n",
" <td>41.791111</td>\n",
" <td>-107.238056</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Riverton</th>\n",
" <td>3049568</td>\n",
" <td>us</td>\n",
" <td>riverton</td>\n",
" <td>WY</td>\n",
" <td>9192.0</td>\n",
" <td>43.025000</td>\n",
" <td>-108.379444</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Rock Springs</th>\n",
" <td>3049572</td>\n",
" <td>us</td>\n",
" <td>rock springs</td>\n",
" <td>WY</td>\n",
" <td>18226.0</td>\n",
" <td>41.587500</td>\n",
" <td>-109.202222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sheridan</th>\n",
" <td>3049595</td>\n",
" <td>us</td>\n",
" <td>sheridan</td>\n",
" <td>WY</td>\n",
" <td>16154.0</td>\n",
" <td>44.797222</td>\n",
" <td>-106.955556</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Torrington</th>\n",
" <td>3049648</td>\n",
" <td>us</td>\n",
" <td>torrington</td>\n",
" <td>WY</td>\n",
" <td>5483.0</td>\n",
" <td>42.065000</td>\n",
" <td>-104.181111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Worland</th>\n",
" <td>3049696</td>\n",
" <td>us</td>\n",
" <td>worland</td>\n",
" <td>WY</td>\n",
" <td>4729.0</td>\n",
" <td>44.016944</td>\n",
" <td>-107.954722</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>4175 rows × 7 columns</p>\n",
"</div>"
],
"text/plain": [
" id Country City Region Population \\\n",
"AccentCity \n",
"Alabaster 2907755 us alabaster AL 26738.0 \n",
"Albertville 2907759 us albertville AL 18368.0 \n",
"Alexander City 2907765 us alexander city AL 14993.0 \n",
"Anniston 2907804 us anniston AL 23423.0 \n",
"Athens 2907848 us athens AL 20470.0 \n",
"Auburn 2907855 us auburn AL 49833.0 \n",
"Bessemer 2908015 us bessemer AL 28628.0 \n",
"Birmingham 2908046 us birmingham AL 231621.0 \n",
"Cullman 2908668 us cullman AL 14796.0 \n",
"Daphne 2908701 us daphne AL 18581.0 \n",
"Decatur 2908730 us decatur AL 54621.0 \n",
"Dothan 2908787 us dothan AL 61741.0 \n",
"Enterprise 2908939 us enterprise AL 22572.0 \n",
"Eufaula 2908954 us eufaula AL 13453.0 \n",
"Fairfield 2908982 us fairfield AL 11641.0 \n",
"Fairhope 2908986 us fairhope AL 15419.0 \n",
"Florence 2909053 us florence AL 35733.0 \n",
"Fort Payne 2909087 us fort payne AL 13586.0 \n",
"Gadsden 2909142 us gadsden AL 36821.0 \n",
"Gardendale 2909161 us gardendale AL 12370.0 \n",
"Hartselle 2909380 us hartselle AL 13089.0 \n",
"Helena 2909416 us helena AL 13119.0 \n",
"Homewood 2909519 us homewood AL 23815.0 \n",
"Hoover 2909526 us hoover AL 66752.0 \n",
"Hueytown 2909557 us hueytown AL 15308.0 \n",
"Huntsville 2909571 us huntsville AL 167528.0 \n",
"Irondale 2909609 us irondale AL 9652.0 \n",
"Jacksonville 2909632 us jacksonville AL 8603.0 \n",
"Jasper 2909638 us jasper AL 13831.0 \n",
"Leeds 2909843 us leeds AL 11167.0 \n",
"... ... ... ... ... ... \n",
"Verona 3048989 us verona WI 10649.0 \n",
"Watertown 3049019 us watertown WI 23323.0 \n",
"Waukesha 3049024 us waukesha WI 68112.0 \n",
"Waunakee 3049026 us waunakee WI 10366.0 \n",
"Waupun 3049028 us waupun WI 10480.0 \n",
"Wausau 3049029 us wausau WI 36779.0 \n",
"Wauwatosa 3049033 us wauwatosa WI 45453.0 \n",
"West Allis 3049050 us west allis WI 59363.0 \n",
"West Bend 3049054 us west bend WI 29431.0 \n",
"Weston 3049071 us weston WI 13080.0 \n",
"Whitefish Bay 3049086 us whitefish bay WI 13611.0 \n",
"Whitewater 3049089 us whitewater WI 14536.0 \n",
"Wisconsin Rapids 3049120 us wisconsin rapids WI 17724.0 \n",
"Casper 3049260 us casper WY 51507.0 \n",
"Cheyenne 3049264 us cheyenne WY 55443.0 \n",
"Cody 3049273 us cody WY 9161.0 \n",
"Douglas 3049305 us douglas WY 5378.0 \n",
"Evanston 3049335 us evanston WY 11258.0 \n",
"Gillette 3049358 us gillette WY 23101.0 \n",
"Green River 3049368 us green river WY 11358.0 \n",
"Jackson 3049409 us jackson WY 8989.0 \n",
"Lander 3049439 us lander WY 6821.0 \n",
"Laramie 3049440 us laramie WY 26934.0 \n",
"Powell 3049544 us powell WY 5213.0 \n",
"Rawlins 3049552 us rawlins WY 8533.0 \n",
"Riverton 3049568 us riverton WY 9192.0 \n",
"Rock Springs 3049572 us rock springs WY 18226.0 \n",
"Sheridan 3049595 us sheridan WY 16154.0 \n",
"Torrington 3049648 us torrington WY 5483.0 \n",
"Worland 3049696 us worland WY 4729.0 \n",
"\n",
" Latitude Longitude \n",
"AccentCity \n",
"Alabaster 33.244167 -86.816389 \n",
"Albertville 34.267500 -86.208889 \n",
"Alexander City 32.943889 -85.953889 \n",
"Anniston 33.659722 -85.831667 \n",
"Athens 34.802778 -86.971667 \n",
"Auburn 32.609722 -85.480833 \n",
"Bessemer 33.401667 -86.954444 \n",
"Birmingham 33.520556 -86.802500 \n",
"Cullman 34.174722 -86.843611 \n",
"Daphne 30.603333 -87.903611 \n",
"Decatur 34.605833 -86.983333 \n",
"Dothan 31.223056 -85.390556 \n",
"Enterprise 31.315000 -85.855278 \n",
"Eufaula 31.891111 -85.145556 \n",
"Fairfield 33.485833 -86.911944 \n",
"Fairhope 30.522778 -87.903333 \n",
"Florence 34.799722 -87.677222 \n",
"Fort Payne 34.444167 -85.719722 \n",
"Gadsden 34.014167 -86.006667 \n",
"Gardendale 33.660000 -86.812778 \n",
"Hartselle 34.443333 -86.935278 \n",
"Helena 33.296111 -86.843611 \n",
"Homewood 33.471667 -86.800833 \n",
"Hoover 33.405278 -86.811389 \n",
"Hueytown 33.451111 -86.996667 \n",
"Huntsville 34.730278 -86.586111 \n",
"Irondale 33.538056 -86.707222 \n",
"Jacksonville 33.813611 -85.761389 \n",
"Jasper 33.831111 -87.277500 \n",
"Leeds 33.548056 -86.544444 \n",
"... ... ... \n",
"Verona 42.990833 -89.533056 \n",
"Watertown 43.194722 -88.728889 \n",
"Waukesha 43.011667 -88.231389 \n",
"Waunakee 43.191944 -89.455556 \n",
"Waupun 43.633333 -88.729444 \n",
"Wausau 44.959167 -89.630000 \n",
"Wauwatosa 43.049444 -88.007500 \n",
"West Allis 43.016667 -88.006944 \n",
"West Bend 43.425278 -88.183333 \n",
"Weston 44.890833 -89.547500 \n",
"Whitefish Bay 43.113333 -87.900000 \n",
"Whitewater 42.833611 -88.732222 \n",
"Wisconsin Rapids 44.383611 -89.817222 \n",
"Casper 42.866667 -106.312500 \n",
"Cheyenne 41.140000 -104.819722 \n",
"Cody 44.526389 -109.055833 \n",
"Douglas 42.759722 -105.381667 \n",
"Evanston 41.268333 -110.962500 \n",
"Gillette 44.291111 -105.501667 \n",
"Green River 41.528611 -109.465556 \n",
"Jackson 43.480000 -110.761667 \n",
"Lander 42.833056 -108.730000 \n",
"Laramie 41.311389 -105.590556 \n",
"Powell 44.753889 -108.756667 \n",
"Rawlins 41.791111 -107.238056 \n",
"Riverton 43.025000 -108.379444 \n",
"Rock Springs 41.587500 -109.202222 \n",
"Sheridan 44.797222 -106.955556 \n",
"Torrington 42.065000 -104.181111 \n",
"Worland 44.016944 -107.954722 \n",
"\n",
"[4175 rows x 7 columns]"
]
},
"execution_count": 89,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
}
],
"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": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}