{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3b84ddbc-2079-48c9-a38b-b0a34abd6d27",
   "metadata": {},
   "source": [
    "# Plotting Distributions with Seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66ebc29e-9f74-4333-ab93-9e163d1d5ce7",
   "metadata": {},
   "source": [
    "With Seaborn, it is also very practical to plot data distributions such as boxplots, bar graphs, histograms and kernel density estimation plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ec137599-b8bd-4666-b5f7-13e94243b50e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88932903-b980-42a3-b6e8-3f42327735c6",
   "metadata": {},
   "source": [
    "We start by loading a table of measurements into a pandas DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c0a502d8-9181-47b4-8e6b-63b37ed3a8d4",
   "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>area</th>\n",
       "      <th>intensity_mean</th>\n",
       "      <th>major_axis_length</th>\n",
       "      <th>minor_axis_length</th>\n",
       "      <th>aspect_ratio</th>\n",
       "      <th>file_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>139</td>\n",
       "      <td>96.546763</td>\n",
       "      <td>17.504104</td>\n",
       "      <td>10.292770</td>\n",
       "      <td>1.700621</td>\n",
       "      <td>20P1_POS0010_D_1UL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>360</td>\n",
       "      <td>86.613889</td>\n",
       "      <td>35.746808</td>\n",
       "      <td>14.983124</td>\n",
       "      <td>2.385805</td>\n",
       "      <td>20P1_POS0010_D_1UL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>43</td>\n",
       "      <td>91.488372</td>\n",
       "      <td>12.967884</td>\n",
       "      <td>4.351573</td>\n",
       "      <td>2.980045</td>\n",
       "      <td>20P1_POS0010_D_1UL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>140</td>\n",
       "      <td>73.742857</td>\n",
       "      <td>18.940508</td>\n",
       "      <td>10.314404</td>\n",
       "      <td>1.836316</td>\n",
       "      <td>20P1_POS0010_D_1UL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>144</td>\n",
       "      <td>89.375000</td>\n",
       "      <td>13.639308</td>\n",
       "      <td>13.458532</td>\n",
       "      <td>1.013432</td>\n",
       "      <td>20P1_POS0010_D_1UL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>305</td>\n",
       "      <td>88.252459</td>\n",
       "      <td>20.226532</td>\n",
       "      <td>19.244210</td>\n",
       "      <td>1.051045</td>\n",
       "      <td>20P1_POS0007_D_1UL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>593</td>\n",
       "      <td>89.905565</td>\n",
       "      <td>36.508370</td>\n",
       "      <td>21.365394</td>\n",
       "      <td>1.708762</td>\n",
       "      <td>20P1_POS0007_D_1UL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>289</td>\n",
       "      <td>106.851211</td>\n",
       "      <td>20.427809</td>\n",
       "      <td>18.221452</td>\n",
       "      <td>1.121086</td>\n",
       "      <td>20P1_POS0007_D_1UL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>277</td>\n",
       "      <td>100.664260</td>\n",
       "      <td>20.307965</td>\n",
       "      <td>17.432920</td>\n",
       "      <td>1.164920</td>\n",
       "      <td>20P1_POS0007_D_1UL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>46</td>\n",
       "      <td>70.869565</td>\n",
       "      <td>11.648895</td>\n",
       "      <td>5.298003</td>\n",
       "      <td>2.198733</td>\n",
       "      <td>20P1_POS0007_D_1UL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>111 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     area  intensity_mean  major_axis_length  minor_axis_length  aspect_ratio  \\\n",
       "0     139       96.546763          17.504104          10.292770      1.700621   \n",
       "1     360       86.613889          35.746808          14.983124      2.385805   \n",
       "2      43       91.488372          12.967884           4.351573      2.980045   \n",
       "3     140       73.742857          18.940508          10.314404      1.836316   \n",
       "4     144       89.375000          13.639308          13.458532      1.013432   \n",
       "..    ...             ...                ...                ...           ...   \n",
       "106   305       88.252459          20.226532          19.244210      1.051045   \n",
       "107   593       89.905565          36.508370          21.365394      1.708762   \n",
       "108   289      106.851211          20.427809          18.221452      1.121086   \n",
       "109   277      100.664260          20.307965          17.432920      1.164920   \n",
       "110    46       70.869565          11.648895           5.298003      2.198733   \n",
       "\n",
       "              file_name  \n",
       "0    20P1_POS0010_D_1UL  \n",
       "1    20P1_POS0010_D_1UL  \n",
       "2    20P1_POS0010_D_1UL  \n",
       "3    20P1_POS0010_D_1UL  \n",
       "4    20P1_POS0010_D_1UL  \n",
       "..                  ...  \n",
       "106  20P1_POS0007_D_1UL  \n",
       "107  20P1_POS0007_D_1UL  \n",
       "108  20P1_POS0007_D_1UL  \n",
       "109  20P1_POS0007_D_1UL  \n",
       "110  20P1_POS0007_D_1UL  \n",
       "\n",
       "[111 rows x 6 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"../../data/BBBC007_analysis.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed4b3c99-c91c-46e5-b83f-0767205dfca5",
   "metadata": {},
   "source": [
    "## Boxplots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b78a5029-eab9-4b2f-af69-2009535172d5",
   "metadata": {},
   "source": [
    "The axes function for plotting boxplots is `boxplot`.\n",
    "\n",
    "Seaborn already identified `file_name` as a categorical value and `ìntensity_mean` as a numerical value. Thus, it plots boxplots for the intensity variable. If we invert x and y, we still get the same graph, but as vertical bosplots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3e9fcfc2-8634-4f80-b361-538c5e084117",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='intensity_mean', ylabel='file_name'>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=df, x=\"intensity_mean\", y=\"file_name\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84b6a793-7829-45a7-9ecf-8c824ca5aed3",
   "metadata": {},
   "source": [
    "The figure-level, and more general, version of this kind of plot is `catplot`. We just have to provide `kind` as `box`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c9b2cd94-b738-4c18-b30a-c2b5bcb5fc32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x27775d754f0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(data=df, x=\"intensity_mean\", y=\"file_name\", kind=\"box\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5da21648-38e7-4e1c-90f5-e1f2d0fa83d2",
   "metadata": {},
   "source": [
    "There are other kinds available, like a `bar` graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e95df95a-b4e0-4ca5-b94d-0512fb09058f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2777b1abb80>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(data=df, x=\"file_name\", y=\"intensity_mean\", kind=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e2c1579-8df5-4e70-bd10-8a6095f2d564",
   "metadata": {},
   "source": [
    "## Histograms and Distribution Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e05666b2-84b9-4c07-a531-17af8496c28d",
   "metadata": {},
   "source": [
    "The axes-level function for plotting histograms is `histplot`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "467d56e3-4e6e-4e01-b67b-c0319734a7e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='intensity_mean', ylabel='Count'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data = df, x=\"intensity_mean\", hue=\"file_name\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d7b511b-8e8c-4f7d-97d5-f25cc0bcf8fa",
   "metadata": {},
   "source": [
    "We can instead plot the kernel density estimation (kde) with `kdeplot` function. Just be careful while interpreting these plots (check some pitfalls [here](https://seaborn.pydata.org/tutorial/distributions.html#kernel-density-estimation-pitfalls))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "450fe3a4-3069-4370-8b63-c4142ae77713",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='intensity_mean', ylabel='Density'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(data=df, x=\"intensity_mean\", hue=\"file_name\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4992a72-2d5a-4382-b44c-91050e53408e",
   "metadata": {},
   "source": [
    "The figure-level function for distributions is `distplot`. With it, you can have histograms and kde in the same plot, or other kinds of plots, like the empirical cumulative distribution function (ecdf)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0f61843d-2f1a-4602-8a7e-295ff64c650a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2777b77c910>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 707.375x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(data = df, x=\"intensity_mean\", hue=\"file_name\", kde=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05dd40e3-4b9a-44b8-8bfc-610f79809d06",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Exercise"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4acf97f1-efcb-4f27-851f-643e3bfa0bae",
   "metadata": {},
   "source": [
    "Plot two empirical cumulative distribution functions for 'area' from different files on a same graph with different colors.\n",
    "\n",
    "Repeat this for the property 'intensity_mean' on a second figure. Infer whether you would expect these properties to be different or not.\n",
    "\n",
    "*Hint: look for the `kind` parameter of `displot`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ee0c368-7b49-4f24-86a7-8ab65873f72f",
   "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": "3.9.15"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}