{
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   "cell_type": "code",
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   "id": "3ea82a12-74d3-40b5-919a-8e89d88e8d63",
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    "tags": [
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   "source": [
    "# Install the necessary dependencies\n",
    "\n",
    "import os\n",
    "import sys\n",
    "!{sys.executable} -m pip install --quiet pandas scikit-learn numpy matplotlib jupyterlab_myst ipython"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30074321",
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "source": [
    "---\n",
    "license:\n",
    "    code: MIT\n",
    "    content: CC-BY-4.0\n",
    "github: https://github.com/ocademy-ai/machine-learning\n",
    "venue: By Ocademy\n",
    "open_access: true\n",
    "bibliography:\n",
    "  - https://raw.githubusercontent.com/ocademy-ai/machine-learning/main/open-machine-learning-jupyter-book/references.bib\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe9eb643",
   "metadata": {},
   "source": [
    "\n",
    "# Visualizing relationships: all about honey 🍯\n",
    "\n",
    "Continuing with the nature focus of our research, let's discover interesting visualizations to show the relationships between various types of honey, according to a dataset derived from the [United States Department of Agriculture](https://www.nass.usda.gov/About_NASS/index.php). \n",
    "\n",
    "This dataset of about 600 items displays honey production in many U.S. states. So, for example, you can look at the number of colonies, yield per colony, total production, stocks, price per pound, and value of the honey produced in a given state from 1998-2012, with one row per year for each state. \n",
    "\n",
    "It will be interesting to visualize the relationship between a given state's production per year and, for example, the price of honey in that state. Alternatively, you could visualize the relationship between states' honey yield per colony. This year's span covers the devastating 'CCD' or '[Colony Collapse Disorder](http://npic.orst.edu/envir/ccd.html)' first seen in 2006, so it is a poignant dataset to study. 🐝\n",
    "\n",
    "In this section, you can use Seaborn, which you have used before, as a good library to visualize relationships between variables. Particularly interesting is the use of Seaborn's `relplot` function that allows scatter plots and line plots to quickly visualize '[statistical relationships](https://seaborn.pydata.org/tutorial/relational.html?highlight=relationships)', which allows the data scientist to better understand how variables relate to each other.\n",
    "\n",
    "## Scatterplots\n",
    "\n",
    "Use a scatterplot to show how the price of honey has evolved, year over year, per state. Seaborn, using `relplot`, conveniently groups the state data and displays data points for both categorical and numeric data. \n",
    "\n",
    "Let's start by importing the data and Seaborn:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8348f573",
   "metadata": {
    "attributes": {
     "classes": [
      "code-cell"
     ],
     "id": ""
    },
    "tags": [
     "output-scoll",
     "hide-input"
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   },
   "outputs": [
    {
     "data": {
      "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",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</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",
       "    <tr>\n",
       "      <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>"
      ],
      "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  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "honey = pd.read_csv('https://static-1300131294.cos.accelerate.myqcloud.com/data/honey.csv')\n",
    "honey.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe05ad8f",
   "metadata": {},
   "source": [
    "You notice that the honey data has several interesting columns, including year and price per pound. Let's explore this data, grouped by U.S. state\n",
    "\n",
    "Create a basic scatterplot to show the relationship between the price per pound of honey and its U.S. state of origin. Make the `y` axis tall enough to display all the states:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "45765c3a",
   "metadata": {
    "attributes": {
     "classes": [
      "code-cell"
     ],
     "id": ""
    },
    "tags": [
     "output-scoll",
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 750x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(x=\"priceperlb\", y=\"state\", data=honey, height=15, aspect=.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e00a01b5",
   "metadata": {},
   "source": [
    "Now, show the same data with a honey color scheme to show how the price evolves over the years. You can do this by adding a 'hue' parameter to show the change, year over year:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "caab1b61-c017-4c61-99e8-ef950440b1a8",
   "metadata": {
    "attributes": {
     "classes": [
      "seealso"
     ],
     "id": ""
    }
   },
   "source": [
    "Learn more about the [color palettes you can use in Seaborn](https://seaborn.pydata.org/tutorial/color_palettes.html) - try a beautiful rainbow color scheme!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fbc8e40c",
   "metadata": {
    "attributes": {
     "classes": [
      "code-cell"
     ],
     "id": ""
    },
    "tags": [
     "hide-input",
     "output-scoll"
    ]
   },
   "outputs": [],
   "source": [
    "sns.relplot(x=\"priceperlb\", y=\"state\", hue=\"year\", palette=\"YlOrBr\", data=honey, height=15, aspect=.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf32eac4",
   "metadata": {},
   "source": [
    "With this color scheme change, you can see that there's obviously a strong progression over the years in terms of honey price per pound. Indeed, if you look at a sample set in the data to verify (pick a given state, Arizona for example) you can see a pattern of price increases year over year, with few exceptions:\n",
    "\n",
    "| state | numcol | yieldpercol | totalprod | stocks  | priceperlb | prodvalue | year |\n",
    "| ----- | ------ | ----------- | --------- | ------- | ---------- | --------- | ---- |\n",
    "| AZ    | 55000  | 60          | 3300000   | 1485000 | 0.64       | 2112000   | 1998 |\n",
    "| AZ    | 52000  | 62          | 3224000   | 1548000 | 0.62       | 1999000   | 1999 |\n",
    "| AZ    | 40000  | 59          | 2360000   | 1322000 | 0.73       | 1723000   | 2000 |\n",
    "| AZ    | 43000  | 59          | 2537000   | 1142000 | 0.72       | 1827000   | 2001 |\n",
    "| AZ    | 38000  | 63          | 2394000   | 1197000 | 1.08       | 2586000   | 2002 |\n",
    "| AZ    | 35000  | 72          | 2520000   | 983000  | 1.34       | 3377000   | 2003 |\n",
    "| AZ    | 32000  | 55          | 1760000   | 774000  | 1.11       | 1954000   | 2004 |\n",
    "| AZ    | 36000  | 50          | 1800000   | 720000  | 1.04       | 1872000   | 2005 |\n",
    "| AZ    | 30000  | 65          | 1950000   | 839000  | 0.91       | 1775000   | 2006 |\n",
    "| AZ    | 30000  | 64          | 1920000   | 902000  | 1.26       | 2419000   | 2007 |\n",
    "| AZ    | 25000  | 64          | 1600000   | 336000  | 1.26       | 2016000   | 2008 |\n",
    "| AZ    | 20000  | 52          | 1040000   | 562000  | 1.45       | 1508000   | 2009 |\n",
    "| AZ    | 24000  | 77          | 1848000   | 665000  | 1.52       | 2809000   | 2010 |\n",
    "| AZ    | 23000  | 53          | 1219000   | 427000  | 1.55       | 1889000   | 2011 |\n",
    "| AZ    | 22000  | 46          | 1012000   | 253000  | 1.79       | 1811000   | 2012 |\n",
    "\n",
    "Another way to visualize this progression is to use size, rather than color. For colorblind users, this might be a better option. Edit your visualization to show an increase of price by an increase in dot circumference:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "66ce557a",
   "metadata": {
    "attributes": {
     "classes": [
      "code-cell"
     ],
     "id": ""
    },
    "tags": [
     "output-scoll",
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "sns.relplot(x=\"priceperlb\", y=\"state\", size=\"year\", data=honey, height=15, aspect=.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e7bc1d4",
   "metadata": {},
   "source": [
    "You can see the size of the dots gradually increasing.\n",
    "\n",
    "Is this a simple case of supply and demand? Due to factors such as climate change and colony collapse, is there less honey available for purchase year over year, and thus the price increases?\n",
    "\n",
    "To discover a correlation between some of the variables in this dataset, let's explore some line charts.\n",
    "\n",
    "## Line charts\n",
    "\n",
    "Question: Is there a clear rise in price of honey per pound year over year? You can most easily discover that by creating a single line chart:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "30721163",
   "metadata": {
    "attributes": {
     "classes": [
      "code-cell"
     ],
     "id": ""
    },
    "tags": [
     "output-scoll",
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "sns.relplot(x=\"year\", y=\"priceperlb\", kind=\"line\", data=honey);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ca0700a",
   "metadata": {},
   "source": [
    "Answer: Yes, with some exceptions around the year 2003:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "828486f0-3d23-4971-9ae4-ef4d35b53a43",
   "metadata": {
    "attributes": {
     "classes": [
      "note"
     ],
     "id": ""
    }
   },
   "source": [
    "Because Seaborn is aggregating data around one line, it displays \"the multiple measurements at each x value by plotting the mean and the 95% confidence interval around the mean\". [Source](https://seaborn.pydata.org/tutorial/relational.html). This time-consuming behavior can be disabled by adding `ci=None`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e258b148",
   "metadata": {},
   "source": [
    "Question: Well, in 2003 can we also see a spike in the honey supply? What if you look at total production year over year?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e9b1228c",
   "metadata": {
    "attributes": {
     "classes": [
      "code-cell"
     ],
     "id": ""
    },
    "tags": [
     "hide-input",
     "output-scoll"
    ]
   },
   "outputs": [],
   "source": [
    "sns.relplot(x=\"year\", y=\"totalprod\", kind=\"line\", data=honey);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25b136d3",
   "metadata": {},
   "source": [
    "Answer: Not really. If you look at total production, it actually seems to have increased in that particular year, even though generally speaking the amount of honey being produced is in decline during these years.\n",
    "\n",
    "Question: In that case, what could have caused that spike in the price of honey around 2003? \n",
    "\n",
    "To discover this, you can explore a facet grid.\n",
    "\n",
    "## Facet grids\n",
    "\n",
    "Facet grids take one facet of your dataset (in our case, you can choose 'year' to avoid having too many facets produced). Seaborn can then make a plot for each of those facets of your chosen x and y coordinates for more easy visual comparison. Does 2003 stand out in this type of comparison?\n",
    "\n",
    "Create a facet grid by continuing to use `relplot` as recommended by [Seaborn's documentation](https://seaborn.pydata.org/generated/seaborn.FacetGrid.html?highlight=facetgrid#seaborn.FacetGrid)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5c42d0ad",
   "metadata": {
    "attributes": {
     "classes": [
      "code-cell"
     ],
     "id": ""
    },
    "tags": [
     "output-scoll",
     "hide-input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x272486965e0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.relplot(\n",
    "    data=honey, \n",
    "    x=\"yieldpercol\", y=\"numcol\",\n",
    "    col=\"year\", \n",
    "    col_wrap=3,\n",
    "    kind=\"line\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ded0cf19",
   "metadata": {},
   "source": [
    "In this visualization, you can compare the yield per colony and number of colonies year over year, side by side with a wrap set at 3 for the columns\n",
    "\n",
    "For this dataset, nothing particularly stands out with regards to the number of colonies and their yield, year over year and state over state. Is there a different way to look at finding a correlation between these two variables?\n",
    "\n",
    "## Dual-line Plots\n",
    "\n",
    "Try a multiline plot by superimposing two lineplots on top of each other, using Seaborn's `despine` to remove their top and right spines, and using `ax.twinx` [derived from Matplotlib](https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.twinx.html). Twinx allows a chart to share the x axis and display two y axes. So, display the yield per colony and the number of colonies, superimposed:"
   ]
  },
  {
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   "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();"
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    "While nothing jumps out to the eye around the year 2003, it does allow us to end this section on a little happier note: while there are overall a declining number of colonies, the number of colonies is stabilizing even if their yield per colony is decreasing.\n",
    "\n",
    "Go, bees, go!\n",
    "\n",
    "🐝❤️\n",
    "\n",
    "## Self study\n",
    "\n",
    "Line plots can be simple or quite complex. Do a bit of reading in the [Seaborn documentation](https://seaborn.pydata.org/generated/seaborn.lineplot.html) on the various ways you can build them. Try to enhance the line charts you built in this section with other methods listed in the docs.\n",
    "\n",
    "## Your turn! 🚀\n",
    "\n",
    "In this section, you learned a bit more about other uses of scatterplots and line grids, including facet grids. Challenge yourself to create a facet grid using a different dataset, maybe one you used prior to these sections. Note how long they take to create and how you need to be careful about how many grids you need to draw using these techniques.\n",
    "\n",
    "Assignment - [Dive into the beehive](https://static-1300131294.cos.ap-shanghai.myqcloud.com/assignments/data-science/dive-into-the-beehive.md)\n",
    "\n",
    "## Acknowledgments\n",
    "\n",
    "Thanks to Microsoft for creating the open-source course [Data Science for Beginners](https://github.com/microsoft/Data-Science-For-Beginners). It inspires the majority of the content in this chapter."
   ]
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