Matplotlib and Seaborn Demo Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# In this demo, we'll introduce you to matplotlib and seaborn, packages in python that are used to plot and visualize data. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll first start by importing pyplot from matplotlib. When using matplotlib, we usually use pyplot to generate data visualizations. In addition to matplotlib, we'll also use the numpy and math packages for this tutorial. If you have python installed through anaconda, you should already have the package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our first example, let's try plotting a quadratic function y = x^2 + 6x + 9 for 20 values of x ranging from -13 to 7. To do this, we first create a list/array of x values using the linspace function in matlab (for those that are not familiar with linspace, you essentially input three values into the function: (start value, end value, number of values). So, for our list, we'll use np.linspace(0, 10, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.linspace(-13,7,20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we we'll create a function to represent our quadratic function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def quadratic(x):\n",
    "    y = x**2 + 6*x + 9\n",
    "    return y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we have our x-values and function, we can start plotting our function. To plot using pyplot, we'll use the command plt.plot (or pyplot.plot if you don't import pyplot as plt). Through plt.plot, we'll pass two inputs: (x-values, y-values). We plot our function below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a22daead0>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, quadratic(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, we have successfully plotted our function. Although visualizing data is the primary goal of matplotlib, our plot is not yet presentable. To be presentable, we would have to add a title, legend, x- and y-axis labels. Furthermore, we might also want to change the size of our plot. We show you how to do so here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# To add a label/legend, we pass a third input to our plot function:\n",
    "# Let's name our label \"function\"\n",
    "plt.plot(x, quadratic(x), label = 'function')\n",
    "\n",
    "# To show a legend/label, we simply use plt.legend().\n",
    "plt.legend()\n",
    "\n",
    "# To add a title to our plot, we simply use plt.title(\"Insert title here\"). Let's name our plot \"Function Plot\".\n",
    "plt.title('Function Plot')\n",
    "\n",
    "# To add x- and y-labels, we simply use plt.xlabel(\"Insert label here\") and plt.ylabel(\"Insert label here\"). \n",
    "# Let's name our x-label \"x-values\" and our y-label \"y-values\"\n",
    "plt.xlabel('x-values')\n",
    "plt.ylabel('y-values')\n",
    "\n",
    "# To change plot size, we can use plt.figsize(width, height) or plt.rcParams['figure.figsize'] = (width, height)\n",
    "# Both work well, but I prefer to use plt.rcParams (you might have to run the code twice for plot size to change)\n",
    "# so let's use that to create a 20 by 10 plot:\n",
    "plt.rcParams['figure.figsize'] = (20,10)\n",
    "\n",
    "# Finally, let's show our plot:\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Additionally, if we wanted to plot another function on the same plot we can simply add another plt.plot(x values, y values) line. Let's visualize this below using our original function and a new function (y = x^2 + 5x +6):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def second_quadratic(x):\n",
    "    y = x**2 + 5*x + 6\n",
    "    return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# To add a label/legend, we pass a third input to our plot function:\n",
    "# Let's name our first function's label \"first function\" and add \"second function\" for our second function\n",
    "plt.plot(x, quadratic(x), label = 'first function')\n",
    "plt.plot(x, second_quadratic(x), label = 'second function')\n",
    "\n",
    "# To show a legend/labem, we simply use plt.legend().\n",
    "plt.legend()\n",
    "\n",
    "# To add a title to our plot, we simply use plt.title(\"Insert title here\"). Let's name our plot \"Function Plot\".\n",
    "plt.title('Function Plot')\n",
    "\n",
    "# To add x- and y-labels, we simply use plt.xlabel(\"Insert label here\") and plt.ylabel(\"Insert label here\"). \n",
    "# Let's name our x-label \"x-values\" and our y-label \"y-values\"\n",
    "plt.xlabel('x-values')\n",
    "plt.ylabel('y-values')\n",
    "\n",
    "# To change plot size, we can use plt.figsize(width, height) or plt.rcParams['figure.figsize'] = (width, height)\n",
    "# Both work well, but I prefer to use plt.rcParams (you might have to run it twice for plot size to change)\n",
    "# so let's use that to create a 20 by 10 plot:\n",
    "plt.rcParams['figure.figsize'] = (20,10)\n",
    "\n",
    "# Finally, let's show our plot:\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another interesting topic we'll look at is subplots: in matplotlib, we can plot and visualize multiple plots. This can be helpful when it's not particularly useful to visualize multiple data in one plot or if the x- and y-ranges do not correspond to each other well. Let's create a plot with four subplots. We already a quadratic function, so let's also create a line graph, cubic function, and a quartic function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def linear(x):\n",
    "    y = 4*x + 7\n",
    "    return y\n",
    "\n",
    "def cubic(x):\n",
    "    y = x**3 + 9*x**2 + 4*x\n",
    "    return y\n",
    "\n",
    "def quartic(x):\n",
    "    y = 7*x**4 + 3*x - 4\n",
    "    return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# To create subplots, we use the plt.subplots command. In this function, we pass the number of rows and columns\n",
    "# of plots we want as (rows, columns)\n",
    "fig, ax = plt.subplots(2,2)\n",
    "\n",
    "# We can call and plot each specific subplot using the command ax[row, column]\n",
    "# Thus, to plot our first subplot, we'll plot our function in the zeroth row and zeroth column (recall that python\n",
    "# indexing begins at 0 and not 1)\n",
    "# This wasn't noted earlier, but to change the color of the plot, we can pass another input to indicate our desired color\n",
    "ax[0,0].plot(x, linear(x), color = 'blue')\n",
    "ax[0,0].set_title('Linear Function')\n",
    "\n",
    "# We repeat the following process for all four functions. \n",
    "ax[0,1].plot(x, quadratic(x), color = 'orange')\n",
    "ax[0,1].set_title('Quadratic Function')\n",
    "#ax[0,1].set(xlabel = 'x-values', ylabel = 'y-values')\n",
    "\n",
    "ax[1,0].plot(x, cubic(x), color = 'green')\n",
    "ax[1,0].set_title('Cubic Function')\n",
    "\n",
    "ax[1,1].plot(x, quartic(x), color = 'purple')\n",
    "ax[1,1].set_title('Quartic Function')\n",
    "\n",
    "# Finally, to add in labels, we can use ax.set(xlabel = 'insert label', ylabel = 'insert label') \n",
    "# Since we want to use the same labels for all plots, we can also use the following for loop:\n",
    "# This for loop goes through each subplot and sets it's labels to our choosing:\n",
    "for i in ax.flat:\n",
    "    i.set(xlabel = 'x-values', ylabel = 'y-values')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay!! So we now how to plot (multiple or single) functions/data using matplotlib as well as how to customize our plots. Let's now move on to an example involving pandas and how we can plot data from dataframes. In our first example, let's plot each student and their GPA. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our first order of business will be to import pandas and read the dataframe. To read the dataframe, make sure you know it's directory in your computer and make sure that this notebook is in a higher directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "gpa_data = pd.read_csv('dataset/dataset/education/grades.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's first look at the dataframe, specifically the column names and data types. As you have seen from previous tutorials, to briefly view the dataframe, we use the command dataframe.head(n) where dataframe represents the name of your dataframe and n represents the rows of data you want to see."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "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>uid</th>\n",
       "      <th>gpa all</th>\n",
       "      <th>gpa 13s</th>\n",
       "      <th>cs 65</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>u01</td>\n",
       "      <td>2.863</td>\n",
       "      <td>1.777778</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>u02</td>\n",
       "      <td>3.505</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>u04</td>\n",
       "      <td>3.029</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>u05</td>\n",
       "      <td>3.679</td>\n",
       "      <td>3.777778</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>u07</td>\n",
       "      <td>3.474</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   uid   gpa all   gpa 13s   cs 65\n",
       "0  u01     2.863  1.777778     3.0\n",
       "1  u02     3.505  4.000000     4.0\n",
       "2  u04     3.029  3.500000     4.0\n",
       "3  u05     3.679  3.777778     4.0\n",
       "4  u07     3.474  4.000000     4.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gpa_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['uid', ' gpa all', ' gpa 13s', ' cs 65'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(gpa_data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "uid          object\n",
      " gpa all    float64\n",
      " gpa 13s    float64\n",
      " cs 65      float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(gpa_data.dtypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Upon viewing the dataframe, we can see that the column 'uid' is object column and that the data we want to plot, the gpa all column, consists of floats and numerical values. This will be a problem since matplotlib cannot use strings as x-values. However, can resolve this issue. The first order of business will be to extract the ' gpa all' column and plot it. Since we can't use the 'uid' column as x-values, we'll use a placeholder. I typically use range(0, len(y-values)) so that the x-values range from 0 to the however large the set of data we're plotting is. Let's do this now:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(0, len(gpa_data[' gpa all'])), gpa_data[' gpa all'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once again, although we have plotted our data, the visualization is incomplete. Furthermore, we can't tell who's gpa is what and it's kinda hard to pin point the gpa with a line graph - this calls for a scatter plot and for a grid-style plot! Let's add these improvements now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# As usual, let's start by plotting the data. However, instead of a regular line plot, let's use a scatter plot.\n",
    "# To plot a scatter plot, the only change to our command will be using plt.scatter instead of plt.plot.\n",
    "# With the plt.scatter function, we can also indicate the size of our markers. Let's use s=100 for better \n",
    "# visualization.\n",
    "plt.scatter(range(0, len(gpa_data[' gpa all'])), gpa_data[' gpa all'], s=100)\n",
    "\n",
    "# Now let's change the x-ticks to student id. This will make it much easier to identify each student in the plot.\n",
    "# To set the x-ticks, we use plt.xticks(x-values, insert ticks list here) and pass in a set of ticks. \n",
    "# x-values in the command will the original (placeholder) x-values. For us, it's range(0, len(gpa_data[' gpa all']))\n",
    "# The ticks we want to use are the uids in the dataframe. Let's pass them in!\n",
    "plt.xticks(range(0, len(gpa_data[' gpa all'])), list(gpa_data['uid']))\n",
    "\n",
    "# Let's now add in x- and y-labels and a title.\n",
    "plt.title('Student GPA')\n",
    "plt.xlabel('Student ID')\n",
    "plt.ylabel('GPA')\n",
    "\n",
    "# Finally, we'll add in grids to make our plot easier to read:\n",
    "# To add in grids, we use plt.grid(color = 'desired color', linestyle = 'desired linestyle')\n",
    "plt.grid()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you now observe, the figure looks much more polished and is easier to read and understand. This particular process was a bit different from before when we were working with x and y data that were purely numerical as we now worked with dataframes that often have a mix of strings and integers. This process is therefore especially useful when trying to plot data from dataframes. However, as useful as pyplot has been, there's a much faster, more powerful way to plot data from dataframes: seaborn."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's start out by first importing seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To create a scatter plot in seaborn, we use sns.scatterplot (or seaborn.scatterplot if you didn't import seaborn as sns). When using seaborn to plot data from a pandas dataframe, there is a covenient way of doing so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a24c244d0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x = 'uid', y = ' gpa all', data = gpa_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, with just one line of code, we can plot student ID vs. GPA without having to manually set the x-axis ticks to the student IDs. However, the plot is incomplete: let's add in a grid style and increase the marker size for better visability as well as a title for our graph. We don't need to add in x- and y-axis labels since seaborn automatically uses the column names passed through the function. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To customize our plot, we can use matplotlib commands:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Student GPA')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "\n",
    "sns.scatterplot(x = 'uid', y = ' gpa all', data = gpa_data, s = 100)\n",
    "\n",
    "plt.title('Student GPA')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to plot customization in matplotlib, we can set the colors, sizes, and other features of our plot by customizating more parameters in the function. For a full list of parameters, refer to the documentation for seaborn and matplotlib."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When plotting using seaborn, we used the following line of code: sns.scatterplot(x='first column', y='second column', data = dataframe_used). However, we could've also used this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Student GPA')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "\n",
    "sns.scatterplot(x = gpa_data['uid'], y = gpa_data[' gpa all'], s = 100)\n",
    "\n",
    "plt.title('Student GPA')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although both graphs are equivalent, the second method of plotting becomes especially useful if you want to plot data from two different dataframes (i.e. if the x-values come from one dataframe and the y-values come from another dataframe). Let's look at an example: we'll stick with plotting student GPA, but let's vary the size of the markers by how the students scored on the PHQ-9 Depression Survey (in this example, a higher score = more severe symptoms of depression = larger marker size)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's first start by reading the PHQ-9 survey dataframe and merging this with the gpa dataframe. When working with two dataframes, it's always a good idea to merge them: if you, say, want to plot scores on the PHQ-9 survey on the x-axis and GPA on the y-axis, you might get an error since there might have been some students who reported their GPA but did not report/take the PHQ-9 (or vice-versa)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "phq9 = pd.read_csv('dataset/dataset/survey/PHQ-9.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "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>uid</th>\n",
       "      <th>type</th>\n",
       "      <th>Little interest or pleasure in doing things</th>\n",
       "      <th>Feeling down, depressed, hopeless.</th>\n",
       "      <th>Trouble falling or staying asleep, or sleeping too much.</th>\n",
       "      <th>Feeling tired or having little energy</th>\n",
       "      <th>Poor appetite or overeating</th>\n",
       "      <th>Feeling bad about yourself or that you are a failure or have let yourself or your family down</th>\n",
       "      <th>Trouble concentrating on things, such as reading the newspaper or watching television</th>\n",
       "      <th>Moving or speaking so slowly that other people could have noticed. Or the opposite being so figety or restless that you have been moving around a lot more than usual</th>\n",
       "      <th>Thoughts that you would be better off dead, or of hurting yourself</th>\n",
       "      <th>Response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>u00</td>\n",
       "      <td>pre</td>\n",
       "      <td>Not at all</td>\n",
       "      <td>Several days</td>\n",
       "      <td>Not at all</td>\n",
       "      <td>Several days</td>\n",
       "      <td>Not at all</td>\n",
       "      <td>Not at all</td>\n",
       "      <td>Not at all</td>\n",
       "      <td>Not at all</td>\n",
       "      <td>Not at all</td>\n",
       "      <td>Not difficult at all</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>u01</td>\n",
       "      <td>pre</td>\n",
       "      <td>Several days</td>\n",
       "      <td>Several days</td>\n",
       "      <td>Several days</td>\n",
       "      <td>Several days</td>\n",
       "      <td>Not at all</td>\n",
       "      <td>Several days</td>\n",
       "      <td>Not at all</td>\n",
       "      <td>Not at all</td>\n",
       "      <td>Not at all</td>\n",
       "      <td>Very difficult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>u02</td>\n",
       "      <td>pre</td>\n",
       "      <td>More than half the days</td>\n",
       "      <td>Several days</td>\n",
       "      <td>More than half the days</td>\n",
       "      <td>More than half the days</td>\n",
       "      <td>More than half the days</td>\n",
       "      <td>Several days</td>\n",
       "      <td>Several days</td>\n",
       "      <td>More than half the days</td>\n",
       "      <td>Not at all</td>\n",
       "      <td>Somewhat difficult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>u03</td>\n",
       "      <td>pre</td>\n",
       "      <td>Not at all</td>\n",
       "      <td>Several days</td>\n",
       "      <td>Not at all</td>\n",
       "      <td>Not at all</td>\n",
       "      <td>Not at all</td>\n",
       "      <td>Not at all</td>\n",
       "      <td>Not at all</td>\n",
       "      <td>Several days</td>\n",
       "      <td>Not at all</td>\n",
       "      <td>Somewhat difficult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>u04</td>\n",
       "      <td>pre</td>\n",
       "      <td>Several days</td>\n",
       "      <td>Several days</td>\n",
       "      <td>Not at all</td>\n",
       "      <td>Several days</td>\n",
       "      <td>Several days</td>\n",
       "      <td>Several days</td>\n",
       "      <td>Several days</td>\n",
       "      <td>Not at all</td>\n",
       "      <td>Not at all</td>\n",
       "      <td>Somewhat difficult</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   uid type Little interest or pleasure in doing things  \\\n",
       "0  u00  pre                                  Not at all   \n",
       "1  u01  pre                                Several days   \n",
       "2  u02  pre                     More than half the days   \n",
       "3  u03  pre                                  Not at all   \n",
       "4  u04  pre                                Several days   \n",
       "\n",
       "  Feeling down, depressed, hopeless.  \\\n",
       "0                       Several days   \n",
       "1                       Several days   \n",
       "2                       Several days   \n",
       "3                       Several days   \n",
       "4                       Several days   \n",
       "\n",
       "  Trouble falling or staying asleep, or sleeping too much.  \\\n",
       "0                                         Not at all         \n",
       "1                                       Several days         \n",
       "2                            More than half the days         \n",
       "3                                         Not at all         \n",
       "4                                         Not at all         \n",
       "\n",
       "  Feeling tired or having little energy Poor appetite or overeating  \\\n",
       "0                          Several days                  Not at all   \n",
       "1                          Several days                  Not at all   \n",
       "2               More than half the days     More than half the days   \n",
       "3                            Not at all                  Not at all   \n",
       "4                          Several days                Several days   \n",
       "\n",
       "  Feeling bad about yourself or that you are a failure or have let yourself or your family down  \\\n",
       "0                                         Not at all                                              \n",
       "1                                       Several days                                              \n",
       "2                                       Several days                                              \n",
       "3                                         Not at all                                              \n",
       "4                                       Several days                                              \n",
       "\n",
       "  Trouble concentrating on things, such as reading the newspaper or watching television  \\\n",
       "0                                         Not at all                                      \n",
       "1                                         Not at all                                      \n",
       "2                                       Several days                                      \n",
       "3                                         Not at all                                      \n",
       "4                                       Several days                                      \n",
       "\n",
       "  Moving or speaking so slowly that other people could have noticed. Or the opposite being so figety or restless that you have been moving around a lot more than usual  \\\n",
       "0                                         Not at all                                                                                                                      \n",
       "1                                         Not at all                                                                                                                      \n",
       "2                            More than half the days                                                                                                                      \n",
       "3                                       Several days                                                                                                                      \n",
       "4                                         Not at all                                                                                                                      \n",
       "\n",
       "  Thoughts that you would be better off dead, or of hurting yourself  \\\n",
       "0                                         Not at all                   \n",
       "1                                         Not at all                   \n",
       "2                                         Not at all                   \n",
       "3                                         Not at all                   \n",
       "4                                         Not at all                   \n",
       "\n",
       "               Response  \n",
       "0  Not difficult at all  \n",
       "1        Very difficult  \n",
       "2    Somewhat difficult  \n",
       "3    Somewhat difficult  \n",
       "4    Somewhat difficult  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phq9.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the PHQ-9 dataframe has two types of surveys: pre and post. For our purposes, we'll be using the pre-survey. Let's only look at that data. Also, as you'll realize, although the PHQ-9 survey is supposed to return a score, we have object values that we cannot add up. To fix this, we can replace the object values with their corresponding scores (ranging from 0 for \"Not at all\" to 3 for \"Nearly every day\")."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "phq9 = phq9[phq9['type'] == 'pre']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def corresponding_score(data):\n",
    "    columns = data.columns\n",
    "    for i in columns:\n",
    "        data[i] = data[i].replace('Not at all', 0)\n",
    "        data[i] = data[i].replace('Several days', 1)\n",
    "        data[i] = data[i].replace('More than half the days', 2)\n",
    "        data[i] = data[i].replace('Nearly every day', 3)\n",
    "        \n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "phq9_edited = corresponding_score(phq9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now have a look at our new dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Little interest or pleasure in doing things</th>\n",
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       "      <th>Trouble falling or staying asleep, or sleeping too much.</th>\n",
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       "      <th>Response</th>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>Somewhat difficult</td>\n",
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       "      <td>u03</td>\n",
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       "      <td>Somewhat difficult</td>\n",
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       "      <td>4</td>\n",
       "      <td>u04</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Somewhat difficult</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   uid type  Little interest or pleasure in doing things  \\\n",
       "0  u00  pre                                            0   \n",
       "1  u01  pre                                            1   \n",
       "2  u02  pre                                            2   \n",
       "3  u03  pre                                            0   \n",
       "4  u04  pre                                            1   \n",
       "\n",
       "   Feeling down, depressed, hopeless.  \\\n",
       "0                                   1   \n",
       "1                                   1   \n",
       "2                                   1   \n",
       "3                                   1   \n",
       "4                                   1   \n",
       "\n",
       "   Trouble falling or staying asleep, or sleeping too much.  \\\n",
       "0                                                  0          \n",
       "1                                                  1          \n",
       "2                                                  2          \n",
       "3                                                  0          \n",
       "4                                                  0          \n",
       "\n",
       "   Feeling tired or having little energy  Poor appetite or overeating  \\\n",
       "0                                      1                            0   \n",
       "1                                      1                            0   \n",
       "2                                      2                            2   \n",
       "3                                      0                            0   \n",
       "4                                      1                            1   \n",
       "\n",
       "   Feeling bad about yourself or that you are a failure or have let yourself or your family down  \\\n",
       "0                                                  0                                               \n",
       "1                                                  1                                               \n",
       "2                                                  1                                               \n",
       "3                                                  0                                               \n",
       "4                                                  1                                               \n",
       "\n",
       "   Trouble concentrating on things, such as reading the newspaper or watching television  \\\n",
       "0                                                  0                                       \n",
       "1                                                  0                                       \n",
       "2                                                  1                                       \n",
       "3                                                  0                                       \n",
       "4                                                  1                                       \n",
       "\n",
       "   Moving or speaking so slowly that other people could have noticed. Or the opposite being so figety or restless that you have been moving around a lot more than usual  \\\n",
       "0                                                  0                                                                                                                       \n",
       "1                                                  0                                                                                                                       \n",
       "2                                                  2                                                                                                                       \n",
       "3                                                  1                                                                                                                       \n",
       "4                                                  0                                                                                                                       \n",
       "\n",
       "   Thoughts that you would be better off dead, or of hurting yourself  \\\n",
       "0                                                  0                    \n",
       "1                                                  0                    \n",
       "2                                                  0                    \n",
       "3                                                  0                    \n",
       "4                                                  0                    \n",
       "\n",
       "               Response  \n",
       "0  Not difficult at all  \n",
       "1        Very difficult  \n",
       "2    Somewhat difficult  \n",
       "3    Somewhat difficult  \n",
       "4    Somewhat difficult  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phq9_edited.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, there are still three columns that have object values, but these columns are not relevant for the PHQ-9 depression score. So, we can go ahead and find the scores for each student by summing each row (note: although each row has an object value, when summing each row, pandas ignores the object values for you):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "phq9_edited['phq9 score'] = phq9_edited.sum(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now have a look at our dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>uid</th>\n",
       "      <th>type</th>\n",
       "      <th>Little interest or pleasure in doing things</th>\n",
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       "      <th>Trouble falling or staying asleep, or sleeping too much.</th>\n",
       "      <th>Feeling tired or having little energy</th>\n",
       "      <th>Poor appetite or overeating</th>\n",
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       "      <th>Trouble concentrating on things, such as reading the newspaper or watching television</th>\n",
       "      <th>Moving or speaking so slowly that other people could have noticed. Or the opposite being so figety or restless that you have been moving around a lot more than usual</th>\n",
       "      <th>Thoughts that you would be better off dead, or of hurting yourself</th>\n",
       "      <th>Response</th>\n",
       "      <th>phq9 score</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
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       "      <td>2</td>\n",
       "      <td>u02</td>\n",
       "      <td>pre</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Somewhat difficult</td>\n",
       "      <td>13</td>\n",
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       "      <td>3</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Somewhat difficult</td>\n",
       "      <td>6</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "   uid type  Little interest or pleasure in doing things  \\\n",
       "0  u00  pre                                            0   \n",
       "1  u01  pre                                            1   \n",
       "2  u02  pre                                            2   \n",
       "3  u03  pre                                            0   \n",
       "4  u04  pre                                            1   \n",
       "\n",
       "   Feeling down, depressed, hopeless.  \\\n",
       "0                                   1   \n",
       "1                                   1   \n",
       "2                                   1   \n",
       "3                                   1   \n",
       "4                                   1   \n",
       "\n",
       "   Trouble falling or staying asleep, or sleeping too much.  \\\n",
       "0                                                  0          \n",
       "1                                                  1          \n",
       "2                                                  2          \n",
       "3                                                  0          \n",
       "4                                                  0          \n",
       "\n",
       "   Feeling tired or having little energy  Poor appetite or overeating  \\\n",
       "0                                      1                            0   \n",
       "1                                      1                            0   \n",
       "2                                      2                            2   \n",
       "3                                      0                            0   \n",
       "4                                      1                            1   \n",
       "\n",
       "   Feeling bad about yourself or that you are a failure or have let yourself or your family down  \\\n",
       "0                                                  0                                               \n",
       "1                                                  1                                               \n",
       "2                                                  1                                               \n",
       "3                                                  0                                               \n",
       "4                                                  1                                               \n",
       "\n",
       "   Trouble concentrating on things, such as reading the newspaper or watching television  \\\n",
       "0                                                  0                                       \n",
       "1                                                  0                                       \n",
       "2                                                  1                                       \n",
       "3                                                  0                                       \n",
       "4                                                  1                                       \n",
       "\n",
       "   Moving or speaking so slowly that other people could have noticed. Or the opposite being so figety or restless that you have been moving around a lot more than usual  \\\n",
       "0                                                  0                                                                                                                       \n",
       "1                                                  0                                                                                                                       \n",
       "2                                                  2                                                                                                                       \n",
       "3                                                  1                                                                                                                       \n",
       "4                                                  0                                                                                                                       \n",
       "\n",
       "   Thoughts that you would be better off dead, or of hurting yourself  \\\n",
       "0                                                  0                    \n",
       "1                                                  0                    \n",
       "2                                                  0                    \n",
       "3                                                  0                    \n",
       "4                                                  0                    \n",
       "\n",
       "               Response  phq9 score  \n",
       "0  Not difficult at all           2  \n",
       "1        Very difficult           5  \n",
       "2    Somewhat difficult          13  \n",
       "3    Somewhat difficult           2  \n",
       "4    Somewhat difficult           6  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phq9_edited.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This looks good! Now, all we have to do is merge this dataframe with the gpa dataframe. Since we would only like to merge on two columns (uid and phq9 score), we can specify this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "gpa_and_phq9 = pd.merge(gpa_data, phq9_edited[['uid', 'phq9 score']], how='inner', on='uid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>uid</th>\n",
       "      <th>gpa all</th>\n",
       "      <th>gpa 13s</th>\n",
       "      <th>cs 65</th>\n",
       "      <th>phq9 score</th>\n",
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       "      <td>0</td>\n",
       "      <td>u01</td>\n",
       "      <td>2.863</td>\n",
       "      <td>1.777778</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>u02</td>\n",
       "      <td>3.505</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>u04</td>\n",
       "      <td>3.029</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>u05</td>\n",
       "      <td>3.679</td>\n",
       "      <td>3.777778</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>u07</td>\n",
       "      <td>3.474</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   uid   gpa all   gpa 13s   cs 65  phq9 score\n",
       "0  u01     2.863  1.777778     3.0           5\n",
       "1  u02     3.505  4.000000     4.0          13\n",
       "2  u04     3.029  3.500000     4.0           6\n",
       "3  u05     3.679  3.777778     4.0           2\n",
       "4  u07     3.474  4.000000     4.0           7"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gpa_and_phq9.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay!! So we've successfully merged the two dataframes. Although we have merged them, there are far too many unique PHQ-9 scores. However, we can fix this by assigning levels for the scores: since the PHQ-9 scores range from 0-27, we can make 4 'levels' of PHQ-9 scores: 0-7, 8-14, 15-21, and 22-27."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def assign_phq9_labels(data):\n",
    "    \n",
    "    phq9_level = []\n",
    "    for i in data['phq9 score']:\n",
    "        \n",
    "        if 0 <= i <= 7:\n",
    "            phq9_level.append(1)\n",
    "        if 8 <= i <= 14:\n",
    "            phq9_level.append(2)\n",
    "        if 15 <= i <= 21:\n",
    "            phq9_level.append(3)\n",
    "        if 22 <= i <= 27:\n",
    "            phq9_level.append(4)\n",
    "    \n",
    "    data['phq9 level'] = phq9_level\n",
    "    \n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "gpa_and_phq9_final = assign_phq9_labels(gpa_and_phq9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "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>uid</th>\n",
       "      <th>gpa all</th>\n",
       "      <th>gpa 13s</th>\n",
       "      <th>cs 65</th>\n",
       "      <th>phq9 score</th>\n",
       "      <th>phq9 level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>u01</td>\n",
       "      <td>2.863</td>\n",
       "      <td>1.777778</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>u02</td>\n",
       "      <td>3.505</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>u04</td>\n",
       "      <td>3.029</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>u05</td>\n",
       "      <td>3.679</td>\n",
       "      <td>3.777778</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>u07</td>\n",
       "      <td>3.474</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>u08</td>\n",
       "      <td>3.705</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>u09</td>\n",
       "      <td>3.806</td>\n",
       "      <td>3.777778</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>u10</td>\n",
       "      <td>3.667</td>\n",
       "      <td>3.777778</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>u12</td>\n",
       "      <td>3.245</td>\n",
       "      <td>2.583333</td>\n",
       "      <td>2.333333</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>u14</td>\n",
       "      <td>3.293</td>\n",
       "      <td>3.888889</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>u15</td>\n",
       "      <td>2.815</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>u16</td>\n",
       "      <td>3.373</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>u17</td>\n",
       "      <td>3.476</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>u18</td>\n",
       "      <td>3.474</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>u19</td>\n",
       "      <td>3.947</td>\n",
       "      <td>3.777778</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>u22</td>\n",
       "      <td>3.889</td>\n",
       "      <td>3.916667</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>u24</td>\n",
       "      <td>2.987</td>\n",
       "      <td>3.222222</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>u27</td>\n",
       "      <td>3.719</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>u30</td>\n",
       "      <td>3.930</td>\n",
       "      <td>3.916667</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>u32</td>\n",
       "      <td>3.826</td>\n",
       "      <td>3.888889</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>u33</td>\n",
       "      <td>2.815</td>\n",
       "      <td>2.777778</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>23</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>u43</td>\n",
       "      <td>3.790</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>u46</td>\n",
       "      <td>3.646</td>\n",
       "      <td>1.111111</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>u49</td>\n",
       "      <td>3.625</td>\n",
       "      <td>3.777778</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>u52</td>\n",
       "      <td>2.400</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>u57</td>\n",
       "      <td>3.389</td>\n",
       "      <td>2.666667</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>u59</td>\n",
       "      <td>3.519</td>\n",
       "      <td>3.555556</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    uid   gpa all   gpa 13s     cs 65  phq9 score  phq9 level\n",
       "0   u01     2.863  1.777778  3.000000           5           1\n",
       "1   u02     3.505  4.000000  4.000000          13           2\n",
       "2   u04     3.029  3.500000  4.000000           6           1\n",
       "3   u05     3.679  3.777778  4.000000           2           1\n",
       "4   u07     3.474  4.000000  4.000000           7           1\n",
       "5   u08     3.705  3.333333  4.000000           5           1\n",
       "6   u09     3.806  3.777778  3.666667           4           1\n",
       "7   u10     3.667  3.777778  4.000000           0           1\n",
       "8   u12     3.245  2.583333  2.333333           1           1\n",
       "9   u14     3.293  3.888889  3.666667           1           1\n",
       "10  u15     2.815  3.333333  3.666667           3           1\n",
       "11  u16     3.373  4.000000  4.000000           6           1\n",
       "12  u17     3.476  3.333333  4.000000          13           2\n",
       "13  u18     3.474  3.333333  3.333333          15           3\n",
       "14  u19     3.947  3.777778  3.333333           5           1\n",
       "15  u22     3.889  3.916667  4.000000           3           1\n",
       "16  u24     2.987  3.222222  3.333333           5           1\n",
       "17  u27     3.719  3.333333  3.000000           5           1\n",
       "18  u30     3.930  3.916667  4.000000           1           1\n",
       "19  u32     3.826  3.888889  4.000000           4           1\n",
       "20  u33     2.815  2.777778  4.000000          23           4\n",
       "21  u43     3.790  4.000000  4.000000           7           1\n",
       "22  u46     3.646  1.111111  4.000000          10           2\n",
       "23  u49     3.625  3.777778  4.000000           2           1\n",
       "24  u52     2.400  1.000000  0.000000          12           2\n",
       "25  u57     3.389  2.666667  4.000000           0           1\n",
       "26  u59     3.519  3.555556  3.666667           5           1"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gpa_and_phq9_final"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataframe looks good! Let's move to plotting this. The code is the exact same as before. The only difference? Instead of passing an integer for the size parameter, we pass the dataframe values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Student GPA')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "                                                               # This the only difference in the code.\n",
    "sns.scatterplot(x = gpa_data['uid'], y = gpa_data[' gpa all'], s = gpa_and_phq9_final['phq9 level']*40)\n",
    "                                                               # The only reason why we multiply by 40 is because \n",
    "                                                               # the size otherwise would be 1-4, which is too small to see\n",
    "plt.title('Student GPA')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, now when looking at the GPA of each student, we can get an idea of how depressed they were at the start of the semester. We can also have the same effect by instead of changing the size of the markers, we change the color. To do this, we change the hue of the plot. The code is still very much the same:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Student GPA')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "                                                               \n",
    "sns.scatterplot(x = gpa_data['uid'], y = gpa_data[' gpa all'], s = 100, hue = gpa_and_phq9_final['phq9 level'])\n",
    "                                                              \n",
    "plt.title('Student GPA')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although there no clear correlations between GPA and PHQ-9 score, it does serve as a good example to show you how you can customize plots using seaborn to showcase data from your dataset more powerfully."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This final example wraps up this demo! This demo is by no means a comprehensive overview of matplotlib and seaborn, but does provide a great startpoint for your data science adventures. For more information about matplotlib and seaborn, as well as the different plot customization options and types, check out the documentation for each: matplotlib: https://matplotlib.org/3.3.3/contents.html and seaborn: https://seaborn.pydata.org/\n",
    "\n",
    "In addition to the documentations, there are a multitude of other websites, guides and forums available for assistance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As always, if you need any help, don't hesitate to reach out to us!"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}