visualization/statsannotation_example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7a82c0e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-10T15:35:17.637911Z",
     "start_time": "2022-08-10T15:35:15.629917Z"
    }
   },
   "outputs": [],
   "source": [
    "!pip install statannotations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5e87ca4c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-10T18:06:45.451036Z",
     "start_time": "2022-08-10T18:06:45.442232Z"
    }
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "from statannotations.Annotator import Annotator\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "db8c7978",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-10T18:07:52.211552Z",
     "start_time": "2022-08-10T18:07:52.203207Z"
    }
   },
   "outputs": [],
   "source": [
    "sns.set(rc={\"figure.figsize\": (14, 8)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "543de873",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-10T18:07:52.508028Z",
     "start_time": "2022-08-10T18:07:52.465698Z"
    }
   },
   "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>pickup</th>\n",
       "      <th>dropoff</th>\n",
       "      <th>passengers</th>\n",
       "      <th>distance</th>\n",
       "      <th>fare</th>\n",
       "      <th>tip</th>\n",
       "      <th>tolls</th>\n",
       "      <th>total</th>\n",
       "      <th>color</th>\n",
       "      <th>payment</th>\n",
       "      <th>pickup_zone</th>\n",
       "      <th>dropoff_zone</th>\n",
       "      <th>pickup_borough</th>\n",
       "      <th>dropoff_borough</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-03-23 20:21:09</td>\n",
       "      <td>2019-03-23 20:27:24</td>\n",
       "      <td>1</td>\n",
       "      <td>1.60</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.95</td>\n",
       "      <td>yellow</td>\n",
       "      <td>credit card</td>\n",
       "      <td>Lenox Hill West</td>\n",
       "      <td>UN/Turtle Bay South</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Manhattan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-03-04 16:11:55</td>\n",
       "      <td>2019-03-04 16:19:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.79</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.30</td>\n",
       "      <td>yellow</td>\n",
       "      <td>cash</td>\n",
       "      <td>Upper West Side South</td>\n",
       "      <td>Upper West Side South</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Manhattan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-03-27 17:53:01</td>\n",
       "      <td>2019-03-27 18:00:25</td>\n",
       "      <td>1</td>\n",
       "      <td>1.37</td>\n",
       "      <td>7.5</td>\n",
       "      <td>2.36</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.16</td>\n",
       "      <td>yellow</td>\n",
       "      <td>credit card</td>\n",
       "      <td>Alphabet City</td>\n",
       "      <td>West Village</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Manhattan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-03-10 01:23:59</td>\n",
       "      <td>2019-03-10 01:49:51</td>\n",
       "      <td>1</td>\n",
       "      <td>7.70</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36.95</td>\n",
       "      <td>yellow</td>\n",
       "      <td>credit card</td>\n",
       "      <td>Hudson Sq</td>\n",
       "      <td>Yorkville West</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Manhattan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-03-30 13:27:42</td>\n",
       "      <td>2019-03-30 13:37:14</td>\n",
       "      <td>3</td>\n",
       "      <td>2.16</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.40</td>\n",
       "      <td>yellow</td>\n",
       "      <td>credit card</td>\n",
       "      <td>Midtown East</td>\n",
       "      <td>Yorkville West</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Manhattan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019-03-11 10:37:23</td>\n",
       "      <td>2019-03-11 10:47:31</td>\n",
       "      <td>1</td>\n",
       "      <td>0.49</td>\n",
       "      <td>7.5</td>\n",
       "      <td>2.16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.96</td>\n",
       "      <td>yellow</td>\n",
       "      <td>credit card</td>\n",
       "      <td>Times Sq/Theatre District</td>\n",
       "      <td>Midtown East</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Manhattan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019-03-26 21:07:31</td>\n",
       "      <td>2019-03-26 21:17:29</td>\n",
       "      <td>1</td>\n",
       "      <td>3.65</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.80</td>\n",
       "      <td>yellow</td>\n",
       "      <td>credit card</td>\n",
       "      <td>Battery Park City</td>\n",
       "      <td>Two Bridges/Seward Park</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Manhattan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2019-03-22 12:47:13</td>\n",
       "      <td>2019-03-22 12:58:17</td>\n",
       "      <td>0</td>\n",
       "      <td>1.40</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.80</td>\n",
       "      <td>yellow</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Murray Hill</td>\n",
       "      <td>Flatiron</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Manhattan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2019-03-23 11:48:50</td>\n",
       "      <td>2019-03-23 12:06:14</td>\n",
       "      <td>1</td>\n",
       "      <td>3.63</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.30</td>\n",
       "      <td>yellow</td>\n",
       "      <td>credit card</td>\n",
       "      <td>East Harlem South</td>\n",
       "      <td>Midtown Center</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Manhattan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2019-03-08 16:18:37</td>\n",
       "      <td>2019-03-08 16:26:57</td>\n",
       "      <td>1</td>\n",
       "      <td>1.52</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.30</td>\n",
       "      <td>yellow</td>\n",
       "      <td>credit card</td>\n",
       "      <td>Lincoln Square East</td>\n",
       "      <td>Central Park</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Manhattan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                pickup              dropoff  passengers  distance  fare   tip  \\\n",
       "0  2019-03-23 20:21:09  2019-03-23 20:27:24           1      1.60   7.0  2.15   \n",
       "1  2019-03-04 16:11:55  2019-03-04 16:19:00           1      0.79   5.0  0.00   \n",
       "2  2019-03-27 17:53:01  2019-03-27 18:00:25           1      1.37   7.5  2.36   \n",
       "3  2019-03-10 01:23:59  2019-03-10 01:49:51           1      7.70  27.0  6.15   \n",
       "4  2019-03-30 13:27:42  2019-03-30 13:37:14           3      2.16   9.0  1.10   \n",
       "5  2019-03-11 10:37:23  2019-03-11 10:47:31           1      0.49   7.5  2.16   \n",
       "6  2019-03-26 21:07:31  2019-03-26 21:17:29           1      3.65  13.0  2.00   \n",
       "7  2019-03-22 12:47:13  2019-03-22 12:58:17           0      1.40   8.5  0.00   \n",
       "8  2019-03-23 11:48:50  2019-03-23 12:06:14           1      3.63  15.0  1.00   \n",
       "9  2019-03-08 16:18:37  2019-03-08 16:26:57           1      1.52   8.0  1.00   \n",
       "\n",
       "   tolls  total   color      payment                pickup_zone  \\\n",
       "0    0.0  12.95  yellow  credit card            Lenox Hill West   \n",
       "1    0.0   9.30  yellow         cash      Upper West Side South   \n",
       "2    0.0  14.16  yellow  credit card              Alphabet City   \n",
       "3    0.0  36.95  yellow  credit card                  Hudson Sq   \n",
       "4    0.0  13.40  yellow  credit card               Midtown East   \n",
       "5    0.0  12.96  yellow  credit card  Times Sq/Theatre District   \n",
       "6    0.0  18.80  yellow  credit card          Battery Park City   \n",
       "7    0.0  11.80  yellow          NaN                Murray Hill   \n",
       "8    0.0  19.30  yellow  credit card          East Harlem South   \n",
       "9    0.0  13.30  yellow  credit card        Lincoln Square East   \n",
       "\n",
       "              dropoff_zone pickup_borough dropoff_borough  \n",
       "0      UN/Turtle Bay South      Manhattan       Manhattan  \n",
       "1    Upper West Side South      Manhattan       Manhattan  \n",
       "2             West Village      Manhattan       Manhattan  \n",
       "3           Yorkville West      Manhattan       Manhattan  \n",
       "4           Yorkville West      Manhattan       Manhattan  \n",
       "5             Midtown East      Manhattan       Manhattan  \n",
       "6  Two Bridges/Seward Park      Manhattan       Manhattan  \n",
       "7                 Flatiron      Manhattan       Manhattan  \n",
       "8           Midtown Center      Manhattan       Manhattan  \n",
       "9             Central Park      Manhattan       Manhattan  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = sns.load_dataset(\"taxis\")\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9e85588f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-10T18:28:50.308346Z",
     "start_time": "2022-08-10T18:28:50.289956Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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>median_total_fare</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pickup_borough</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Bronx</th>\n",
       "      <td>16.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brooklyn</th>\n",
       "      <td>14.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Manhattan</th>\n",
       "      <td>13.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Queens</th>\n",
       "      <td>23.80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                median_total_fare\n",
       "pickup_borough                   \n",
       "Bronx                       16.80\n",
       "Brooklyn                    14.69\n",
       "Manhattan                   13.80\n",
       "Queens                      23.80"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(by=\"pickup_borough\").agg(median_total_fare=(\"total\", \"median\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f010b583",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-10T18:07:53.458357Z",
     "start_time": "2022-08-10T18:07:53.345468Z"
    }
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = \"pickup_borough\"\n",
    "y = \"total\"\n",
    "cities = [\"Manhattan\", \"Brooklyn\", \"Bronx\", \"Queens\"]\n",
    "ax = sns.boxplot(data=df, x=x, y=y, order=cities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0a65f424",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-10T18:29:01.352379Z",
     "start_time": "2022-08-10T18:29:01.340840Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Manhattan', 'Brooklyn'),\n",
       " ('Manhattan', 'Bronx'),\n",
       " ('Manhattan', 'Queens'),\n",
       " ('Brooklyn', 'Bronx'),\n",
       " ('Brooklyn', 'Queens'),\n",
       " ('Bronx', 'Queens')]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from itertools import combinations\n",
    "\n",
    "cities = [\"Manhattan\", \"Brooklyn\", \"Bronx\", \"Queens\"]\n",
    "pairs = list(combinations(cities, r=2))\n",
    "pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a7eafcf1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-10T18:29:02.958965Z",
     "start_time": "2022-08-10T18:29:02.787937Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value annotation legend:\n",
      "      ns: p <= 1.00e+00\n",
      "       *: 1.00e-02 < p <= 5.00e-02\n",
      "      **: 1.00e-03 < p <= 1.00e-02\n",
      "     ***: 1.00e-04 < p <= 1.00e-03\n",
      "    ****: p <= 1.00e-04\n",
      "\n",
      "Manhattan vs. Brooklyn: Mann-Whitney-Wilcoxon test two-sided, P_val:7.225e-01 U_stat=9.979e+05\n",
      "Brooklyn vs. Bronx: Mann-Whitney-Wilcoxon test two-sided, P_val:1.992e-02 U_stat=1.608e+04\n",
      "Bronx vs. Queens: Mann-Whitney-Wilcoxon test two-sided, P_val:1.676e-02 U_stat=2.768e+04\n",
      "Manhattan vs. Bronx: Mann-Whitney-Wilcoxon test two-sided, P_val:5.785e-04 U_stat=2.082e+05\n",
      "Brooklyn vs. Queens: Mann-Whitney-Wilcoxon test two-sided, P_val:3.666e-12 U_stat=9.335e+04\n",
      "Manhattan vs. Queens: Mann-Whitney-Wilcoxon test two-sided, P_val:2.929e-30 U_stat=1.258e+06\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statannotations.Annotator import Annotator\n",
    "\n",
    "ax = sns.boxplot(data=df, x=x, y=y, order=cities)\n",
    "annotator = Annotator(ax, pairs, data=df, x=x, y=y, order=cities)\n",
    "annotator.configure(test=\"Mann-Whitney\")\n",
    "annotator.apply_and_annotate()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c4b75ae7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-08-10T18:29:04.358433Z",
     "start_time": "2022-08-10T18:29:04.345406Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Manhattan vs. Brooklyn: Mann-Whitney-Wilcoxon test two-sided, P_val:7.225e-01 U_stat=9.979e+05\n",
      "Brooklyn vs. Bronx: Mann-Whitney-Wilcoxon test two-sided, P_val:1.992e-02 U_stat=1.608e+04\n",
      "Bronx vs. Queens: Mann-Whitney-Wilcoxon test two-sided, P_val:1.676e-02 U_stat=2.768e+04\n",
      "Manhattan vs. Bronx: Mann-Whitney-Wilcoxon test two-sided, P_val:5.785e-04 U_stat=2.082e+05\n",
      "Brooklyn vs. Queens: Mann-Whitney-Wilcoxon test two-sided, P_val:3.666e-12 U_stat=9.335e+04\n",
      "Manhattan vs. Queens: Mann-Whitney-Wilcoxon test two-sided, P_val:2.929e-30 U_stat=1.258e+06\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot(data=df, x=x, y=y, order=cities)\n",
    "annotator = Annotator(ax, pairs, data=df, x=x, y=y, order=cities)\n",
    "annotator.configure(test=\"Mann-Whitney\", text_format=\"simple\")\n",
    "annotator.apply_and_annotate()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3.9.13 ('venv': venv)",
   "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"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "vscode": {
   "interpreter": {
    "hash": "478cc5ef0f338997730d9103983e193fe6c3c8e58abb3c8c0481de8a2f51bddc"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}