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@tylerkkp
Created September 16, 2020 05:26
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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "brewery_visualization",
"provenance": [],
"collapsed_sections": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "kO93CdhXi4aG",
"colab_type": "text"
},
"source": [
"### Brewery Dataset Preliminary Analysis\n",
"\n",
"We'll start this preliminary analysis by looking at the number of breweries in each state, as found in the dataset found here: [openbrewerydb](https://www.openbrewerydb.org/)\n",
"\n",
"Note: This analysis was done primarily done as a test of the Google Colaboratory system, and is therefore not fully commented or documented. Please feel free to reach out to me if you have any questions or comments."
]
},
{
"cell_type": "code",
"metadata": {
"id": "EUZRGfKHZfoD",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 974
},
"outputId": "720cdd99-f490-4318-b9ed-6a215192d362"
},
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"\n",
"breweries = pd.read_csv('/content/drive/My Drive/Colab Notebooks/breweries.csv')\n",
"\n",
"# need to see what attributes are available to be analyzed\n",
"print(breweries.info())\n",
"\n",
"fig, ax = plt.subplots()\n",
"\n",
"color = 'tab:blue'\n",
"\n",
"# need to count the number of breweries in each state\n",
"brew_counts = pd.DataFrame({\n",
" 'count': breweries['state'].value_counts()\n",
"})\n",
"\n",
"#change index (currently holding state names) into a column\n",
"brew_counts.reset_index(inplace=True)\n",
"\n",
"#rename 'index' column id to be 'state'\n",
"brew_counts = brew_counts.rename(columns = {'index':'state'})\n",
"\n",
"# print out some info to see what the data looks like\n",
"print(brew_counts.info())\n",
"print(brew_counts.head())\n",
"\n",
"bars = ax.bar(brew_counts['state'], brew_counts['count'])\n",
"plt.yticks(fontsize=8, color=color)\n",
"plt.xticks(fontsize=6, rotation=270)\n",
"plt.ylabel('Number of Breweries', color=color)\n",
"plt.xlabel('State')\n",
"plt.title('Breweries per State')\n",
"\n",
"# see matplotlib barchart docs for more info about this function\n",
"def autolabel(rects, xpos='center'):\n",
" \"\"\"\n",
" Attach a text label above each bar in *rects*, displaying its height.\n",
"\n",
" *xpos* indicates which side to place the text w.r.t. the center of\n",
" the bar. It can be one of the following {'center', 'right', 'left'}.\n",
" \"\"\"\n",
"\n",
" xpos = xpos.lower() # normalize the case of the parameter\n",
" ha = {'center': 'center', 'right': 'left', 'left': 'right'}\n",
" offset = {'center': 0.5, 'right': 0.57, 'left': 0.43} # x_txt = x + w*off\n",
"\n",
" for rect in rects:\n",
" height = rect.get_height()\n",
" ax.text(rect.get_x() + rect.get_width()*offset[xpos], 1.01*height,\n",
" '{}'.format(height), ha=ha[xpos], va='bottom', fontsize=4, rotation=270)\n",
"\n",
"autolabel(bars, \"center\")\n",
"\n",
"\n",
"# plt.tight_layout is used here to prevent the x-axis labels from getting cut\n",
"# off at the edge of the figure\n",
"plt.tight_layout()\n",
"\n",
"# ***Only uncomment the below when a plotted image is required***\n",
"# plt.savefig('breweries.png', dpi=300)\n",
"\n",
"plt.show()\n",
"\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 7923 entries, 0 to 7922\n",
"Data columns (total 15 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 id 7923 non-null object \n",
" 1 name 7923 non-null object \n",
" 2 brewery_type 7923 non-null object \n",
" 3 street 6845 non-null object \n",
" 4 city 7923 non-null object \n",
" 5 state 7923 non-null object \n",
" 6 postal_code 7923 non-null object \n",
" 7 website_url 6489 non-null object \n",
" 8 phone 6980 non-null float64\n",
" 9 created_at 7923 non-null object \n",
" 10 updated_at 7923 non-null object \n",
" 11 country 7923 non-null object \n",
" 12 longitude 4737 non-null float64\n",
" 13 latitude 4737 non-null float64\n",
" 14 tags 0 non-null float64\n",
"dtypes: float64(4), object(11)\n",
"memory usage: 928.6+ KB\n",
"None\n",
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 51 entries, 0 to 50\n",
"Data columns (total 2 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 state 51 non-null object\n",
" 1 count 51 non-null int64 \n",
"dtypes: int64(1), object(1)\n",
"memory usage: 944.0+ bytes\n",
"None\n",
" state count\n",
"0 California 915\n",
"1 Colorado 439\n",
"2 New York 428\n",
"3 Washington 414\n",
"4 Michigan 381\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SCyC1PY-qwy5",
"colab_type": "text"
},
"source": [
"Alright, so now we have a nice little bar graph showing the number of breweries in each state (and Washington D.C.). I assume Puerto Rico also has breweries, but they are not included in the dataset. Note to self: reach out to openbrewerydb offering help adding Puerto Rican breweries.\n",
"\n",
"It is very clear from the visualization that there are waaay more breweries in California than in any other state. It makes sense - California is big, populous, and wealthy. It may make more sense to take a look at how the states compare using a metric which is normalized to be more meaningful. First, let's see how the states compare by incorporating population. \n",
"\n",
"I've uploaded the state_pops.csv containing populations of all 50 states (and the District of Columbia) to my google Drive, which we can now add to the pandas dataframe brew_counts. The data for the populations is taken from census.gov, and the populations used are the projected estimates for 2019 (the most current populations available at time of writing)."
]
},
{
"cell_type": "code",
"metadata": {
"id": "xJ0uxnKesHC6",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 295
},
"outputId": "6fd89ef6-bb71-41ac-fbcc-6d18e5306b41"
},
"source": [
"populations = pd.read_csv('/content/drive/My Drive/Colab Notebooks/state_pops.csv')\n",
"\n",
"print(populations.info())\n",
"print(populations.head())"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 51 entries, 0 to 50\n",
"Data columns (total 2 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 state 51 non-null object\n",
" 1 population 51 non-null int64 \n",
"dtypes: int64(1), object(1)\n",
"memory usage: 944.0+ bytes\n",
"None\n",
" state population\n",
"0 Alabama 4903185\n",
"1 Alaska 731545\n",
"2 Arizona 7278717\n",
"3 Arkansas 3017804\n",
"4 California 39512223\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cvBO6xKnxdCl",
"colab_type": "text"
},
"source": [
"Now that we've got the population data loaded, we can append that data to our brew_counts dataframe."
]
},
{
"cell_type": "code",
"metadata": {
"id": "Er15Ouk8xnJz",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 121
},
"outputId": "1e8363cf-847b-456f-e73a-7d0bae6c4b65"
},
"source": [
"brew_counts = brew_counts.join(populations.set_index('state'), how='left', on='state')\n",
"#brew_counts.merge(populations, on='state')\n",
"\n",
"print(brew_counts.head())"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
" state count population\n",
"0 California 915 39512223\n",
"1 Colorado 439 5758736\n",
"2 New York 428 19453561\n",
"3 Washington 414 7614893\n",
"4 Michigan 381 9986857\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3_hfxxD11qbU",
"colab_type": "text"
},
"source": [
"Colorado and Washington are looking pretty good now! Let's take a look with a visualization, keeping the total breweries per state on one y-axis, and adding a second y-axis for the breweries per capita calculation."
]
},
{
"cell_type": "code",
"metadata": {
"id": "aPEHO_Cc1-T3",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 303
},
"outputId": "e0ea7aa7-4230-4005-811e-71f2eae87863"
},
"source": [
"fig, ax = plt.subplots()\n",
"\n",
"color = 'tab:blue'\n",
"ax.set_xlabel('State')\n",
"ax.set_ylabel('Number of Breweries', color=color)\n",
"ax.bar(brew_counts['state'], brew_counts['count'], color=color, alpha=0.25, label='Number of Breweries')\n",
"ax.tick_params(axis='y', size=8, labelcolor=color)\n",
"plt.xticks(fontsize=6, rotation=270)\n",
"plt.title('Breweries per State')\n",
"\n",
"\n",
"\n",
"# we'll need a second axes that shares the same y-axis\n",
"ax2 = ax.twinx()\n",
"\n",
"color = 'tab:red'\n",
"ax2.set_ylabel('Breweries per Capita', color=color)\n",
"ax2.bar(brew_counts['state'], brew_counts['count']/brew_counts['population'], color=color, alpha=0.25, label='Breweries per Capita')\n",
"ax2.tick_params(axis='y', size=8, labelcolor=color)\n",
"\n",
"fig.legend()\n",
"\n",
"# plt.tight_layout is used here to prevent the x-axis labels from getting cut\n",
"# off at the edge of the figure\n",
"plt.tight_layout()\n",
"\n",
"# ***Only uncomment the below when a plotted image is required***\n",
"# plt.savefig('breweries_with_per_capita.png', dpi=300)\n",
"\n",
"plt.show()\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Nzu3tQn95Qt4",
"colab_type": "text"
},
"source": [
"Nice! It's a pretty nice looking graph, but those tiny numbers on the right hand axis are not very intuitive. Let's scale it to 'Breweries per 100,000 People'. Also, that Legend is does not look great. It's too big and overlaps the border of the graph."
]
},
{
"cell_type": "code",
"metadata": {
"id": "5HQdB9N158ae",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 305
},
"outputId": "0da10228-c3e6-4089-f38d-7189aead7a9a"
},
"source": [
"fig, ax = plt.subplots()\n",
"\n",
"color = 'tab:blue'\n",
"ax.set_xlabel('State')\n",
"ax.set_ylabel('Number of Breweries', color=color)\n",
"ax.bar(brew_counts['state'], brew_counts['count'], color=color, alpha=0.25, label='Number of Breweries')\n",
"ax.tick_params(axis='y', size=8, labelcolor=color)\n",
"plt.xticks(fontsize=6, rotation=270)\n",
"plt.title('Breweries per State')\n",
"\n",
"\n",
"\n",
"# we'll need a second axes that shares the same y-axis\n",
"ax2 = ax.twinx()\n",
"\n",
"color = 'tab:red'\n",
"ax2.set_ylabel('Breweries per 100,000 People', color=color)\n",
"ax2.bar(brew_counts['state'], brew_counts['count']/brew_counts['population']*100000, color=color, alpha=0.25, label='Breweries per 100,000 People')\n",
"ax2.tick_params(axis='y', size=8, labelcolor=color)\n",
"\n",
"fig.legend(loc='upper right', fontsize='x-small')\n",
"\n",
"# plt.tight_layout is used here to prevent the x-axis labels from getting cut\n",
"# off at the edge of the figure\n",
"plt.tight_layout()\n",
"\n",
"# ***Only uncomment the below when a plotted image is required***\n",
"# plt.savefig('breweries_with_per_100k.png', dpi=300)\n",
"\n",
"plt.show()\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZghsYD7ySUsU",
"colab_type": "text"
},
"source": [
"### Wrap-up\n",
"\n",
"That's a reasonable enough looking graph for a quick look at this brewery data. We'll need to look deeper to find big insights, but there are a few takeaways here:\n",
"\n",
"* California is a very populous state, with a lot of breweries.\n",
"* Mississippi is not a very fun state to live in, if you like beer.\n",
"* Vermont is, surprisingly (at least to me), the state with the highest number of breweries per capita.\n",
"* Utah, while low on the rankings, is not lowest in total breweries or breweries per capita, despite having some of the most restrictive liquor laws.\n",
"\n",
"Although California can claim the greatest number of breweries, I think it is a much better thing to have a higher brewery/capita rank. The top five states are:\n",
"\n",
"1. Vermont\n",
"2. Montana\n",
"3. Maine\n",
"4. Colorado\n",
"5. Oregon\n",
"\n",
"Although my home state of Washington didn't make the top 5, I am happy to see that we safely made the top 10, along with New Mexico, New Hampshire, Alaska, and Wyoming. \n",
"\n",
"Cheers,\n",
"\n",
"Tyler\n",
"\n",
"\n",
"\n"
]
}
]
}
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