{ "cells": [ { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "plt.rcParams.update({ 'axes.spines.left': False, 'axes.spines.right': False, 'axes.spines.top': False, 'axes.spines.bottom': False })\n", "\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Adjusting for population\n", "\n", "## The \"Texas/California Problem\"\n", "\n", "> \"When you have states with big populations or places with a large population of one specific type, **your data may simply be representing population and nothing else.** So normalizing for population or understanding your data, to begin with, are both important steps. It's not enough to scrape it and chart it, you need to step back and understand what you are looking at. Just watch out for big populations.\"\n", "\n", "Let's say we're graphing how many **central libraries** are in each state. We pull in the data, it has the state and the number of libraries, and we graph it.\n", "\n", "## Adjusting for population with long (normal) data" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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statelibraries
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" ], "text/plain": [ " state libraries\n", "0 Alabama 284\n", "1 Alaska 102\n", "2 Arizona 185\n", "3 Arkansas 210\n", "4 California 1084" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv(\"libraries.csv\")\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 97, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,1,'Libraries per state')" ] }, "execution_count": 97, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = df.sort_values(by='libraries').tail(10).plot(x='state', y='libraries', kind='barh', color='#F28FBE')\n", "ax.set_title('Libraries per state', loc='left')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "According to this map, **California, New York, and Texas all have the most libraries.** There's something we're forgetting, though, and it's that **CA, NY and TX *also* all have the most people.**\n", "\n", "More people = more libraries. We're basically making a graph of libraries that's a proxy for population!\n", "\n", "To fix this, we need to **adjust for population**, and make it a per-capita (per person) graph." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 1: Read in your population data\n", "\n", "Make sure it has a **matching column to your original data,** whether it's state name, country name, borough name, census code, etc." ] }, { "cell_type": "code", "execution_count": 98, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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State or territoryPopulation
0California39250017
1Texas27862596
2Florida20612439
3New York19745289
4Illinois12801539
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" ], "text/plain": [ " State or territory Population\n", "0 California 39250017\n", "1 Texas 27862596\n", "2 Florida 20612439\n", "3 New York 19745289\n", "4 Illinois 12801539" ] }, "execution_count": 98, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pop = pd.read_csv(\"population.csv\")\n", "pop.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 2: Join the population data to your original data\n", "\n", "Using `.merge` and `left_on` + `right_on` (the column names they have in common) you can easily join your population data to your original data." ] }, { "cell_type": "code", "execution_count": 99, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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statelibrariesState or territoryPopulation
0Alabama284Alabama4863300
1Alaska102Alaska741894
2Arizona185Arizona6931071
3Arkansas210Arkansas2988248
4California1084California39250017
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" ], "text/plain": [ " state libraries State or territory Population\n", "0 Alabama 284 Alabama 4863300\n", "1 Alaska 102 Alaska 741894\n", "2 Arizona 185 Arizona 6931071\n", "3 Arkansas 210 Arkansas 2988248\n", "4 California 1084 California 39250017" ] }, "execution_count": 99, "metadata": {}, "output_type": "execute_result" } ], "source": [ "merged = df.merge(pop, left_on='state', right_on='State or territory')\n", "merged.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 3: Do the math" ] }, { "cell_type": "code", "execution_count": 100, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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statelibrariesState or territoryPopulationlibraries_per_capita
0Alabama284Alabama48633000.000058
1Alaska102Alaska7418940.000137
2Arizona185Arizona69310710.000027
3Arkansas210Arkansas29882480.000070
4California1084California392500170.000028
\n", "
" ], "text/plain": [ " state libraries State or territory Population libraries_per_capita\n", "0 Alabama 284 Alabama 4863300 0.000058\n", "1 Alaska 102 Alaska 741894 0.000137\n", "2 Arizona 185 Arizona 6931071 0.000027\n", "3 Arkansas 210 Arkansas 2988248 0.000070\n", "4 California 1084 California 39250017 0.000028" ] }, "execution_count": 100, "metadata": {}, "output_type": "execute_result" } ], "source": [ "merged['libraries_per_capita'] = merged['libraries'] / merged['Population']\n", "merged.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 4: Adjust the math\n", "\n", "`0.000058` libraries per person\" just doesn't sound interesting! For smaller numbers, you'll want to make it \"per 100 people,\" per 10,000 people,\" or even per *million people*." ] }, { "cell_type": "code", "execution_count": 101, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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statelibrariesState or territoryPopulationlibraries_per_capitalibraries_per_100k_people
0Alabama284Alabama48633000.0000585.839656
1Alaska102Alaska7418940.00013713.748595
2Arizona185Arizona69310710.0000272.669140
3Arkansas210Arkansas29882480.0000707.027529
4California1084California392500170.0000282.761782
\n", "
" ], "text/plain": [ " state libraries State or territory Population libraries_per_capita \\\n", "0 Alabama 284 Alabama 4863300 0.000058 \n", "1 Alaska 102 Alaska 741894 0.000137 \n", "2 Arizona 185 Arizona 6931071 0.000027 \n", "3 Arkansas 210 Arkansas 2988248 0.000070 \n", "4 California 1084 California 39250017 0.000028 \n", "\n", " libraries_per_100k_people \n", "0 5.839656 \n", "1 13.748595 \n", "2 2.669140 \n", "3 7.027529 \n", "4 2.761782 " ] }, "execution_count": 101, "metadata": {}, "output_type": "execute_result" } ], "source": [ "merged['libraries_per_100k_people'] = merged['libraries'] / merged['Population'] * 100000\n", "merged.head()" ] }, { "cell_type": "code", "execution_count": 104, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,1,'Libraries per 100,000 residents')" ] }, "execution_count": 104, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = merged.sort_values(by='libraries_per_100k_people').tail(10).plot(x='state', y='libraries_per_100k_people', kind='barh', color='#F28FBE', figsize=(5,3.5))\n", "ax.set_title(\"Libraries per 100,000 residents\", loc='left')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 5: Celebrate\n", "\n", "Look at that, we've actually got a somewhat-interesting story! Instead of New York, California and Texas having the most, it looks like Vermont is *way ahead of everyone else.* Something to investigate, perhaps?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Adjusting for population with wide data (or adjusting for multiple places over multiple years)\n", "\n", "Adjusting for population with *wide data* is a little bit different. Not just because you'll need to adjust for multiple places over multiple years, but because you need to do it across *multiple columns*.\n", "\n", "If you had long data you could just join on multiple columns and do simple math, but with wide data you gotta get a little fancy.\n", "\n", "### Step 1: Read in your data\n", "\n", "This is how many ice cream shops were in Staten Island, Manhattan, and Brooklyn in 1850, 1900, 1950 and 2000 (not real data, sadly)." ] }, { "cell_type": "code", "execution_count": 168, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " year Staten Island Brooklyn Manhattan\n", "0 1850 2 8 10\n", "1 1900 4 44 34\n", "2 1950 10 34 44\n", "3 2000 50 250 100" ] }, "execution_count": 168, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv(\"borough-ice-cream.csv\")\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 169, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,1,'Number of ice cream shops')" ] }, "execution_count": 169, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = df.plot(x='year', kind='bar')\n", "ax.set_title(\"Number of ice cream shops\", loc='left')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "But of course, **if there are more people there should be more ice cream shops,** so we should adjust for population. It's kind of weird since we have wide data, but we can do it!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 2: Prepare your population data\n", "\n", "Because we have multiple areas over multiple years, we can't just say \"I want the population of Staten Island in 1850, 1900, 1950 and 2000\" - we need the populations of *every borough* in *each year*. Our data will look like this:" ] }, { "cell_type": "code", "execution_count": 170, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
yearStaten IslandBrooklynManhattan
0185015061138822515547
119006702111665821850093
2195019155527381751960101
3200044372824653261537195
\n", "
" ], "text/plain": [ " year Staten Island Brooklyn Manhattan\n", "0 1850 15061 138822 515547\n", "1 1900 67021 1166582 1850093\n", "2 1950 191555 2738175 1960101\n", "3 2000 443728 2465326 1537195" ] }, "execution_count": 170, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pop = pd.read_csv(\"borough-population.csv\")\n", "pop.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I actually had to make this data by hand! You probably will, too. **Make the rows and columns match up with your dataframe.** Notice how I have the same years and the same column headers." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 3: Divide your data.\n", "\n", "Just like we did before, we divide. But this time we need to divide each cell by the same cell in the other dataframe!\n", "\n", "To make this work best, we're going to **change the index of the dataframes**. When we do our math, it's going to do it to **every single cell**, and we don't want to divide the years, so we keep them safe in the index." ] }, { "cell_type": "code", "execution_count": 171, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Staten IslandBrooklynManhattan
year
18502810
190044434
1950103444
200050250100
\n", "
" ], "text/plain": [ " Staten Island Brooklyn Manhattan\n", "year \n", "1850 2 8 10\n", "1900 4 44 34\n", "1950 10 34 44\n", "2000 50 250 100" ] }, "execution_count": 171, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Ice cream shops\n", "df.set_index('year', inplace=True)\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 172, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Staten IslandBrooklynManhattan
year
185015061138822515547
19006702111665821850093
195019155527381751960101
200044372824653261537195
\n", "
" ], "text/plain": [ " Staten Island Brooklyn Manhattan\n", "year \n", "1850 15061 138822 515547\n", "1900 67021 1166582 1850093\n", "1950 191555 2738175 1960101\n", "2000 443728 2465326 1537195" ] }, "execution_count": 172, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Population\n", "pop.set_index('year', inplace=True)\n", "pop.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once we've made sure they match, we'll do `df.div` to divide a dataframe by another dataframe. This divides **each cell by the matching cell in the other dataframe**." ] }, { "cell_type": "code", "execution_count": 173, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Staten IslandBrooklynManhattan
year
18500.0001330.0000580.000019
19000.0000600.0000380.000018
19500.0000520.0000120.000022
20000.0001130.0001010.000065
\n", "
" ], "text/plain": [ " Staten Island Brooklyn Manhattan\n", "year \n", "1850 0.000133 0.000058 0.000019\n", "1900 0.000060 0.000038 0.000018\n", "1950 0.000052 0.000012 0.000022\n", "2000 0.000113 0.000101 0.000065" ] }, "execution_count": 173, "metadata": {}, "output_type": "execute_result" } ], "source": [ "per_capita = df.div(pop)\n", "per_capita" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looking pretty good! Let's multiply by 100000 to make it per 100000 people." ] }, { "cell_type": "code", "execution_count": 174, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Staten IslandBrooklynManhattan
year
185013.2793315.7627751.939687
19005.9682793.7717021.837745
19505.2204331.2417032.244782
200011.26816410.1406476.505356
\n", "
" ], "text/plain": [ " Staten Island Brooklyn Manhattan\n", "year \n", "1850 13.279331 5.762775 1.939687\n", "1900 5.968279 3.771702 1.837745\n", "1950 5.220433 1.241703 2.244782\n", "2000 11.268164 10.140647 6.505356" ] }, "execution_count": 174, "metadata": {}, "output_type": "execute_result" } ], "source": [ "per_capita = df.div(pop) * 100000\n", "per_capita" ] }, { "cell_type": "code", "execution_count": 175, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 175, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "per_capita.plot(kind='bar')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "While our original chart had Brooklyn as loving ice cream the most, it looks like Staten Island wins if we look at things per-capita!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 4: Getting our old data back\n", "\n", "Remember how we set the index to be the year?" ] }, { "cell_type": "code", "execution_count": 151, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Staten Island Brooklyn Manhattan\n", "year \n", "1850 5 8 10\n", "1900 4 44 34\n", "1950 10 34 44\n", "2000 15 250 100" ] }, "execution_count": 151, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we want the year back as a normal column, we just do a `.reset_index(inplace=True)`" ] }, { "cell_type": "code", "execution_count": 153, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " year Staten Island Brooklyn Manhattan\n", "0 1850 5 8 10\n", "1 1900 4 44 34\n", "2 1950 10 34 44\n", "3 2000 15 250 100" ] }, "execution_count": 153, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.reset_index(inplace=True)\n", "df.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "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": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }