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Data-Science-For-Beginners/3-Data-Visualization/12-visualization-relationships/solution/notebook.ipynb

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renumber viz lessons
3 years ago
{
"cells": [
{
"cell_type": "markdown",
"source": [
"# Visualizing Honey Production 🍯 🐝"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 177,
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"honey = pd.read_csv('../../../data/honey.csv')\n",
"honey.head()"
],
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" state numcol yieldpercol totalprod stocks priceperlb \\\n",
"0 AL 16000.0 71 1136000.0 159000.0 0.72 \n",
"1 AZ 55000.0 60 3300000.0 1485000.0 0.64 \n",
"2 AR 53000.0 65 3445000.0 1688000.0 0.59 \n",
"3 CA 450000.0 83 37350000.0 12326000.0 0.62 \n",
"4 CO 27000.0 72 1944000.0 1594000.0 0.70 \n",
"\n",
" prodvalue year \n",
"0 818000.0 1998 \n",
"1 2112000.0 1998 \n",
"2 2033000.0 1998 \n",
"3 23157000.0 1998 \n",
"4 1361000.0 1998 "
],
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
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" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>state</th>\n",
" <th>numcol</th>\n",
" <th>yieldpercol</th>\n",
" <th>totalprod</th>\n",
" <th>stocks</th>\n",
" <th>priceperlb</th>\n",
" <th>prodvalue</th>\n",
" <th>year</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
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" <th>0</th>\n",
" <td>AL</td>\n",
" <td>16000.0</td>\n",
" <td>71</td>\n",
" <td>1136000.0</td>\n",
" <td>159000.0</td>\n",
" <td>0.72</td>\n",
" <td>818000.0</td>\n",
" <td>1998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>AZ</td>\n",
" <td>55000.0</td>\n",
" <td>60</td>\n",
" <td>3300000.0</td>\n",
" <td>1485000.0</td>\n",
" <td>0.64</td>\n",
" <td>2112000.0</td>\n",
" <td>1998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>AR</td>\n",
" <td>53000.0</td>\n",
" <td>65</td>\n",
" <td>3445000.0</td>\n",
" <td>1688000.0</td>\n",
" <td>0.59</td>\n",
" <td>2033000.0</td>\n",
" <td>1998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>CA</td>\n",
" <td>450000.0</td>\n",
" <td>83</td>\n",
" <td>37350000.0</td>\n",
" <td>12326000.0</td>\n",
" <td>0.62</td>\n",
" <td>23157000.0</td>\n",
" <td>1998</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>CO</td>\n",
" <td>27000.0</td>\n",
" <td>72</td>\n",
" <td>1944000.0</td>\n",
" <td>1594000.0</td>\n",
" <td>0.70</td>\n",
" <td>1361000.0</td>\n",
" <td>1998</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
]
},
"metadata": {},
"execution_count": 177
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"Use a scatterplot to show the relationship between a state and its price per pound for local honey"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 178,
"source": [
"sns.relplot(x=\"priceperlb\", y=\"state\", data=honey, height=15, aspect=.5);\n"
],
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 540x1080 with 1 Axes>"
],
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},
"metadata": {}
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"Show this relationship plus a new yellow-colored scatterplot with hue determined by year."
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 179,
"source": [
"sns.relplot(x=\"priceperlb\", y=\"state\", hue=\"year\", palette=\"YlOrBr\", data=honey, height=15, aspect=.5);\n"
],
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
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},
"metadata": {}
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"Try this plot one more time, this time showing the price change by size of the dot in the scatterplot"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 180,
"source": [
"sns.relplot(x=\"priceperlb\", y=\"state\", size=\"year\", data=honey, height=15, aspect=.5);\n"
],
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 604.1x1080 with 1 Axes>"
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},
"metadata": {}
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"Explore some line charting options"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 181,
"source": [
"sns.relplot(x=\"year\", y=\"priceperlb\", kind=\"line\", data=honey);"
],
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
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},
"metadata": {}
}
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 201,
"source": [
"sns.relplot(x=\"year\", y=\"totalprod\", kind=\"line\", data=honey);"
],
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
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},
"metadata": {}
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"Build a facet grid"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 189,
"source": [
"\n",
"sns.relplot(\n",
" data=honey, \n",
" x=\"yieldpercol\", y=\"numcol\",\n",
" col=\"year\", \n",
" col_wrap=3,\n",
" kind=\"line\"\n",
")\n"
],
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x7fec8d8419b0>"
]
},
"metadata": {},
"execution_count": 189
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1080x1800 with 15 Axes>"
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},
"metadata": {}
}
],
"metadata": {}
},
{
"cell_type": "markdown",
"source": [
"Create a dual line plot (this solution is suggested by Kedar Ghule: https://kedar.hashnode.dev/how-to-combine-two-line-charts-in-seaborn-and-python)"
],
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 200,
"source": [
"fig, ax = plt.subplots(figsize=(12,6))\n",
"lineplot = sns.lineplot(x=honey['year'], y=honey['numcol'], data=honey, \n",
" label = 'Number of bee colonies', legend=False)\n",
"sns.despine()\n",
"plt.ylabel('# colonies')\n",
"plt.title('Honey Production Year over Year');\n",
"\n",
"ax2 = ax.twinx()\n",
"lineplot2 = sns.lineplot(x=honey['year'], y=honey['yieldpercol'], ax=ax2, color=\"r\", \n",
" label ='Yield per colony', legend=False) \n",
"sns.despine(right=False)\n",
"plt.ylabel('colony yield')\n",
"ax.figure.legend();"
],
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 864x432 with 2 Axes>"
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