tu021-Plotting-Seaborn-Introduction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1e6ca3f6",
   "metadata": {},
   "source": [
    "# Seaborn overview"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c08f3b0",
   "metadata": {},
   "source": [
    "We have seen before that `matplotlib` is powerful and flexible, but that power and flexibility means that there is also a lot to learn!\n",
    "\n",
    "`Seaborn` is meant to provide a facilitatated access to plotting data. Just like `Pandas` sits on top of `Numpy` to facilitate object oriented data applications, `seaborn` sits on top of `Pandas` to accelarate data exploration via visualiation."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1404b293",
   "metadata": {},
   "source": [
    "#### Learning goals\n",
    "\n",
    "  - `seaborn` - overview of functionality\n",
    "  - Plot distributions and histograms\n",
    "  - Explore data using visualization\n",
    "  \n",
    "#### Prerequisites\n",
    "  - Python and NumPy\n",
    "  - Pandas, DataFrames and Series\n",
    "  \n",
    " ---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3603631",
   "metadata": {},
   "source": [
    "`Seaborn` was written to:\n",
    "\n",
    "* make plots from `pandas` data frames\n",
    "* create good looking plots \"out of the box\"\n",
    "\n",
    "The `seaborn` package (which we've already used some) is a \"high level\" plotting package that calls various `matplotlib` functions for you while taking care of many details for you under the hood. The various `seaborn` functions are conceptually structured like this:\n",
    "![seaborn_overview](./assets/jpnb20/seabornOverview.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f1ddfa5",
   "metadata": {},
   "source": [
    "The three columns correspond to plot types: plots of relationships, plots of data distributions, and plots of categorical data. \n",
    "\n",
    "For each plot type, there is a \"figure level\" function, `relplot()`, `displot()`, and `catplot()`. The main advantage of these is that they make it easy to create figures with multiple axes on them."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc9c5840",
   "metadata": {},
   "source": [
    "In addition to the figure level functions, there are specific \"axes level\" functions for making each specific kind of plot directly. Each of these returns an `axes` object, which you can then modify if necessary just as you would had you created it with `plt.plot()`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6eecb6aa",
   "metadata": {},
   "source": [
    "Let's play around with these using some data we've played with before."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3797e0ca",
   "metadata": {},
   "source": [
    "### Preliminaries"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffe6c8da",
   "metadata": {},
   "source": [
    "First, let's import what we'll need:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4fa6c370",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "# import your_module as ym or whatever if you want"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9351200",
   "metadata": {},
   "source": [
    "If we don't have our module handy, we can copy our data loader and tidier function from before:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "287ad38d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def tidyMyData(filename) :\n",
    "    '''\n",
    "    tidyMyData() Takes one-column-per-cell rat reaction time data as input.\n",
    "    Returns tidy one-column-per-variable data.\n",
    "    User specifies a filename string.\n",
    "    '''\n",
    "    \n",
    "    import pandas as pd\n",
    "    import numpy as np\n",
    "\n",
    "    my_input_data = pd.read_csv(filename)  # read the data\n",
    "\n",
    "    raw_data = my_input_data.to_numpy()                      # convert to numpy array\n",
    "\n",
    "    obs, grps = raw_data.shape                               # get the number of rows and columns\n",
    "\n",
    "    new_length = obs*grps                                    # compute total number of observations\n",
    "\n",
    "    values_col = np.reshape(raw_data, (new_length, 1), \n",
    "                            order = 'F')                     # reshape the array\n",
    "    values_col = np.squeeze(values_col)                      # squeeze to make 1D\n",
    "\n",
    "    # construct the inner grouping variable\n",
    "    sexes = pd.Series(['male', 'female'])                    # define the levels\n",
    "    sexes = sexes.repeat(obs)                                # make one cycle of the levels\n",
    "    sexes = pd.concat([sexes]*2, ignore_index=True)     # and repeat the cycle, ditching the indexes\n",
    "\n",
    "    # construct the outer grouping variable\n",
    "    strain = pd.Series(['wildtype', 'mutant'])               # define the levels\n",
    "    strain = strain.repeat(2*obs)                            # make the one cycle\n",
    "    strain = strain.reset_index(drop=True)                   # drop the pesky index\n",
    "\n",
    "    # construct the data frame\n",
    "    my_new_tidy_data = pd.DataFrame(\n",
    "        {\n",
    "            \"RTs\": values_col,                               # make a column named RTs and put the values in\n",
    "            \"sex\": sexes,                                    # ditto for sex\n",
    "            \"strain\": strain                                 # and for genetic strain\n",
    "        }    \n",
    "    )\n",
    "    \n",
    "    return my_new_tidy_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "affcfab0",
   "metadata": {},
   "source": [
    "And now we can load and tidy our data with one simple call."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b4635eac",
   "metadata": {},
   "outputs": [],
   "source": [
    "our_data = tidyMyData(\"datasets/018DataFile2.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b33ef38f",
   "metadata": {},
   "source": [
    "Let's remind ourselves of what the data look like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e8958b0f",
   "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>RTs</th>\n",
       "      <th>sex</th>\n",
       "      <th>strain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12.577226</td>\n",
       "      <td>male</td>\n",
       "      <td>wildtype</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12.778183</td>\n",
       "      <td>male</td>\n",
       "      <td>wildtype</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.389130</td>\n",
       "      <td>male</td>\n",
       "      <td>wildtype</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12.747877</td>\n",
       "      <td>male</td>\n",
       "      <td>wildtype</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13.615121</td>\n",
       "      <td>male</td>\n",
       "      <td>wildtype</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>24.539374</td>\n",
       "      <td>female</td>\n",
       "      <td>mutant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>23.877924</td>\n",
       "      <td>female</td>\n",
       "      <td>mutant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>23.161896</td>\n",
       "      <td>female</td>\n",
       "      <td>mutant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>24.426455</td>\n",
       "      <td>female</td>\n",
       "      <td>mutant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>21.990136</td>\n",
       "      <td>female</td>\n",
       "      <td>mutant</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>168 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           RTs     sex    strain\n",
       "0    12.577226    male  wildtype\n",
       "1    12.778183    male  wildtype\n",
       "2    13.389130    male  wildtype\n",
       "3    12.747877    male  wildtype\n",
       "4    13.615121    male  wildtype\n",
       "..         ...     ...       ...\n",
       "163  24.539374  female    mutant\n",
       "164  23.877924  female    mutant\n",
       "165  23.161896  female    mutant\n",
       "166  24.426455  female    mutant\n",
       "167  21.990136  female    mutant\n",
       "\n",
       "[168 rows x 3 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "our_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c83ad95d",
   "metadata": {},
   "source": [
    "## Figure level plots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "137b9026",
   "metadata": {},
   "source": [
    " We'll start with some figure level plots."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c09ecee",
   "metadata": {},
   "source": [
    "### Distribution plots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff85ff73",
   "metadata": {},
   "source": [
    "#### Histogram of the RTs\n",
    "We'll start interrogating the data with a histogram of the lone numerical variable, the RTs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a90daf40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(x = \"RTs\", data = our_data);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ba0dddd",
   "metadata": {},
   "source": [
    "Okay, here we can see that there are two clumps of data. Let's see if they correspond to one or more of the categorical variables."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93aaa872",
   "metadata": {},
   "source": [
    "$\\color{blue}{\\text{Complete the following exercise.}}$\n",
    "\n",
    " - Use the cell below to report one parameters that can be passed to `sns`. Explain what the parameter does using your own words."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a44d1066",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "5f899a9d",
   "metadata": {},
   "source": [
    " - Use the cell below to show an example application of the paramter use described above. Use `data` to shwo the example (Please, pick a parameter that is not the one used in the next example!):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f9ac1c3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "fd53c80c",
   "metadata": {},
   "source": [
    "#### Histogram of RTs by one of the categorical variables\n",
    "We'll use color (\"hue\" in seaborn-speak) to code the categorical variable \"sex\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c0792db2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 430.5x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(x = \"RTs\", data = our_data, hue = \"sex\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25109188",
   "metadata": {},
   "source": [
    "Okay, there might be something going on with females being both faster (left) and slower (right) than males, but there's still something going on here that \"sex\" isn't capturing. Let's see if \"strain\" does."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4857ed13",
   "metadata": {},
   "source": [
    "#### Histogram of RTs by the other categorical variable\n",
    "Now we'll use color (\"hue\" in seaborn-speak) to code the categorical variable \"strain\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fd210b75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 438.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(x = \"RTs\", data = our_data, hue = \"strain\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "788e3b1c",
   "metadata": {},
   "source": [
    "**Aha!** Gotcha – it looks like strain is doing a pretty good job of explaining the two clumps in the histogram. But the histogram of RT x Sex still did look a litte weird. Let's see if we can crack out both variables."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72b55ab2",
   "metadata": {},
   "source": [
    "#### Creating a multi-axes figure with a figure level seaborn function"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b11ee39",
   "metadata": {},
   "source": [
    "This is where the figure level `seaborn` functions are really handy. We can simply assign a categorical variable to be represented by the columns or rows of a multi-panel figure.\n",
    "\n",
    "Let's assign \"sex\" to columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "585e1585",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 798.375x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(x = \"RTs\", data = our_data, hue = \"strain\", col=\"sex\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81ad8af4",
   "metadata": {},
   "source": [
    "Okay, that's great. Now we have males on the left and females on the right. Also, `displot()` has done something really nice, which is to make the x-axis limits the same in the two plots. So the bigger gap in the female data isn't just a visual artifact of the axis scaling.\n",
    "\n",
    "Still, since the data share a common x-axis, it would be nice to have the plots aligned vertically rather than horizontally. So let's assign sex to the rows rather than the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "41b46d15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 438.375x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(x = \"RTs\", data = our_data, hue = \"strain\", row=\"sex\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a8e5841",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1edf173d",
   "metadata": {},
   "source": [
    "$\\color{blue}{\\text{Complete the following exercise.}}$\n",
    "\n",
    " - The previosu example used `sex` to separate the data into histogram plots. Can you use the `strain` to create two separate hisograms? If yes, show the code in the next cell, if not, use the following MarkDown Cell to explain why not. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f24cee9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "be8474bf",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f1be656b",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42bb9431",
   "metadata": {},
   "source": [
    "Okay, that last plot we made above was much better in terms of making a visual comparison between the sexes. Still, these histrograms are a bit ugly. We could improve that by playing around with the bins. Or we could just ask `displot()` to give us kernel density estimates instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7f42c7cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 438.375x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(x = \"RTs\", data = our_data, hue = \"strain\", row=\"sex\", kind=\"kde\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86d9aa2b",
   "metadata": {},
   "source": [
    "Much better. Visually, however, filled KDE's are a bit nicer. Since these are probability densities, it's the area that's important anyway, and having them filled emphasizes the area rather than the height."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51fca667",
   "metadata": {},
   "source": [
    "We can easily do this by setting a `fill` argument to `True`. Strictly speaking, however, `fill` is not a valid argument to `displot()`. However, what displot will do is pass any named argument (called a \"keywork arguement\" or \"kwarg\" in Python) to the underlying axes level function.\n",
    "\n",
    "The only catch with these `**kwargs` is that they won't appear in the documentation for the figure level plots, only in the documentation for the axes level plots. The documentation for the figure level [plot](https://seaborn.pydata.org/generated/seaborn.displot.html#seaborn.displot), like `displot()` does helpfully tell us this at least:\n",
    "\n",
    "---\n",
    "\n",
    "![displot() kwargs](./assets/jpnb20/displotKwargs.png)\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5307f22",
   "metadata": {},
   "source": [
    "So now let's plot with `fill=True` and see if that works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6362cca2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 438.375x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(x = \"RTs\", data = our_data, hue = \"strain\", row=\"sex\", kind=\"kde\", fill=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d41cad8",
   "metadata": {},
   "source": [
    "Ah, much better!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec351b9e",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "$\\color{blue}{\\text{Complete the following exercise.}}$\n",
    "\n",
    " - Use the cell below to show an example plot that uses the data above in combination with the `seaborn` parameter `ecdf`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ad26de7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "19387586",
   "metadata": {},
   "source": [
    " - If you belive the parameter cannot be used in combination with the data at hand, use the cell below to explain the reason why the parameter cannot be properly used in this case."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26c84f21",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b17e8eb3",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "723dab1b",
   "metadata": {},
   "source": [
    "### Categorical plots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c51cb01",
   "metadata": {},
   "source": [
    "The categorical plots are nice because they allow us to separate both of our categorical variables within a single plot."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ee45ee3",
   "metadata": {},
   "source": [
    "Let's try playing with `catplot()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3a6264f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 430.5x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(y = \"RTs\", x=\"strain\", data = our_data, hue = \"sex\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbececa9",
   "metadata": {},
   "source": [
    "So a `stripplot` is the default axes-level plot (and notice that the default axes-level plots are the first ones listed under their corresponding figure-level counterparts. But we can have it call `boxplot()` for us by telling it that we want `kind=\"box\"`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "44c640bd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 430.5x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(y = \"RTs\", x=\"strain\", data = our_data, hue = \"sex\", kind=\"violin\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b7d7e61",
   "metadata": {},
   "source": [
    "### Axis level plots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "661e427b",
   "metadata": {},
   "source": [
    "We can call any of the axis-level functions directly, without going through the corresponding figure-level function. This gives us more control over single-panel plots should we need it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "da96d094",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "ax = sns.boxplot(y = \"RTs\", x=\"strain\", data = our_data, hue = \"sex\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "454e57d5",
   "metadata": {},
   "source": [
    "Notice that we've assigned the output of `sns.boxplot()` to `ax`. So we have an `axes` object (named `ax`), and thus have access to all the things an `axes` knows how to do. So if we type \"ax.\" and a tab, we'll see something like this:\n",
    "    \n",
    "---\n",
    "    \n",
    "![seaborn_overview](./assets/jpnb20/axesMethods.png)\n",
    "    \n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "881cd01e",
   "metadata": {},
   "source": [
    "So let's do that:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7eea0f46",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (1102313331.py, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"/var/folders/ym/g0t45zsx6kj122mw2d8vf7zm0000gq/T/ipykernel_22327/1102313331.py\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    ax.\u001b[0m\n\u001b[0m       ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "ax."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c612ac84",
   "metadata": {},
   "source": [
    "and now we can scroll around to find useful things. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00906779",
   "metadata": {},
   "source": [
    "Let's try inverting the y axis so that faster times plot visually higher!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "239e7af7",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "ax = sns.boxplot(y = \"RTs\", x=\"strain\", data = our_data, hue = \"sex\");\n",
    "ax.invert_yaxis();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "038ae729",
   "metadata": {},
   "source": [
    "And we can see a lot of useful stuff by typing `ax.set` and a tab. Among those is `ax.set_title` which we can use to... wait for it... add a title!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "768f6c86",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "ax = sns.boxplot(y = \"RTs\", x=\"strain\", data = our_data, hue = \"sex\");\n",
    "ax.invert_yaxis();\n",
    "ax.set_title(\"Response time by sex and strain - higher is faster\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5904e596",
   "metadata": {},
   "source": [
    "### Summary\n",
    "So `seaborn` is a nice way to make plots of data from `pandas` data frames. Its default values make good looking plots. It has two main kinds of plotting functions:\n",
    "\n",
    "* figure level functions that are handy for making multi axes panel figures\n",
    "* axes level functions that return an axes object handle to you, allowing for fine control over the plot's appearance\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "115fb8a8",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7340104e",
   "metadata": {},
   "source": [
    "$\\color{blue}{\\text{Complete the following exercise.}}$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67e10c02",
   "metadata": {},
   "source": [
    "Write a function that uses `seaborn` to visualize data as we need it!\n",
    "\n",
    "To flex both our plotting and function writing muscles, let's write a function to do some plotting! Your function should:\n",
    "\n",
    "* take as input a data frame as produced by the function above\n",
    "* allow the user to choose between a strip, violin, or box plot\n",
    "* set one of the above three be the default\n",
    "* have a docstr so users can get help() on it\n",
    "* produce the plot requested by the user (of course!)\n",
    "* provide a meanignful help"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a279fdca",
   "metadata": {},
   "source": [
    "Write your function here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5a4b6b1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "325a6c68",
   "metadata": {},
   "source": [
    "Now get help on it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4734c4eb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "807ee07e",
   "metadata": {},
   "source": [
    "And show that it works with an example call and a plot!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33f15c00",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}