01 - Fitting scaling laws and ranking cities.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "743e6541",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ebba5a54",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "414fd251",
   "metadata": {},
   "outputs": [],
   "source": [
    "import scaling_fit"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa180863",
   "metadata": {},
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "76c94fe7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_theft = pd.read_csv(\"./data/italy/italy_theft.csv\")\n",
    "df_theft.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ed579c4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_xy = np.array(df_theft.population.values)*1.0, np.array(df_theft.crime.values)*1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8fc47493",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 300x270 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 272,
       "width": 306
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(3, 2.7))\n",
    "plt.plot(*data_xy, ls=\"\", marker='o', ms=2)\n",
    "plt.xscale('log')\n",
    "plt.yscale('log')\n",
    "plt.xlabel(\"Population\")\n",
    "plt.ylabel(\"Crime\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47d377c5",
   "metadata": {},
   "source": [
    "# Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f5fb9380",
   "metadata": {},
   "outputs": [],
   "source": [
    "required_successes = 2\n",
    "df, _, _, _ = scaling_fit.fit_models(data_xy, required_successes=required_successes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b4e3089c",
   "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>beta</th>\n",
       "      <th>beta_ci</th>\n",
       "      <th>bic</th>\n",
       "      <th>p_value</th>\n",
       "      <th>alpha</th>\n",
       "      <th>beta_</th>\n",
       "      <th>gamma</th>\n",
       "      <th>delta</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>lognormal_delta_fixed</th>\n",
       "      <td>1.331309</td>\n",
       "      <td>0.137427</td>\n",
       "      <td>2.090247e+03</td>\n",
       "      <td>9.599789e-01</td>\n",
       "      <td>0.000349</td>\n",
       "      <td>1.331309</td>\n",
       "      <td>0.168516</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lognormal_delta_fixed_beta_fixed</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.118405e+03</td>\n",
       "      <td>5.468698e-04</td>\n",
       "      <td>0.026284</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.235641</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lognormal</th>\n",
       "      <td>1.293474</td>\n",
       "      <td>0.133376</td>\n",
       "      <td>2.093981e+03</td>\n",
       "      <td>8.057138e-01</td>\n",
       "      <td>0.000573</td>\n",
       "      <td>1.293474</td>\n",
       "      <td>3.350852</td>\n",
       "      <td>1.679164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lognormal_beta_fixed</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.120411e+03</td>\n",
       "      <td>9.930496e-06</td>\n",
       "      <td>0.027523</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.535706</td>\n",
       "      <td>1.712911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gaussian_delta_fixed</th>\n",
       "      <td>1.370797</td>\n",
       "      <td>0.129987</td>\n",
       "      <td>2.140162e+03</td>\n",
       "      <td>1.443909e-04</td>\n",
       "      <td>0.000198</td>\n",
       "      <td>1.370797</td>\n",
       "      <td>49.346534</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gaussian_delta_fixed_beta_fixed</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.224209e+03</td>\n",
       "      <td>9.674646e-23</td>\n",
       "      <td>0.032584</td>\n",
       "      <td>1.0</td>\n",
       "      <td>64.331081</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gaussian</th>\n",
       "      <td>1.306332</td>\n",
       "      <td>0.119232</td>\n",
       "      <td>2.115112e+03</td>\n",
       "      <td>8.999250e-08</td>\n",
       "      <td>0.000482</td>\n",
       "      <td>1.306332</td>\n",
       "      <td>1.038086</td>\n",
       "      <td>0.8977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gaussian_beta_fixed</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.146514e+03</td>\n",
       "      <td>5.196704e-10</td>\n",
       "      <td>0.026199</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.46319</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>person</th>\n",
       "      <td>1.325422</td>\n",
       "      <td>0.139205</td>\n",
       "      <td>3.183073e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.325422</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>person_beta_fixed</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.200680e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      beta   beta_ci           bic  \\\n",
       "model                                                                \n",
       "lognormal_delta_fixed             1.331309  0.137427  2.090247e+03   \n",
       "lognormal_delta_fixed_beta_fixed  1.000000  0.000000  2.118405e+03   \n",
       "lognormal                         1.293474  0.133376  2.093981e+03   \n",
       "lognormal_beta_fixed              1.000000  0.000000  2.120411e+03   \n",
       "gaussian_delta_fixed              1.370797  0.129987  2.140162e+03   \n",
       "gaussian_delta_fixed_beta_fixed   1.000000  0.000000  2.224209e+03   \n",
       "gaussian                          1.306332  0.119232  2.115112e+03   \n",
       "gaussian_beta_fixed               1.000000  0.000000  2.146514e+03   \n",
       "person                            1.325422  0.139205  3.183073e+07   \n",
       "person_beta_fixed                 1.000000  0.000000  3.200680e+07   \n",
       "\n",
       "                                       p_value     alpha     beta_      gamma  \\\n",
       "model                                                                           \n",
       "lognormal_delta_fixed             9.599789e-01  0.000349  1.331309   0.168516   \n",
       "lognormal_delta_fixed_beta_fixed  5.468698e-04  0.026284       1.0   0.235641   \n",
       "lognormal                         8.057138e-01  0.000573  1.293474   3.350852   \n",
       "lognormal_beta_fixed              9.930496e-06  0.027523       1.0   3.535706   \n",
       "gaussian_delta_fixed              1.443909e-04  0.000198  1.370797  49.346534   \n",
       "gaussian_delta_fixed_beta_fixed   9.674646e-23  0.032584       1.0  64.331081   \n",
       "gaussian                          8.999250e-08  0.000482  1.306332   1.038086   \n",
       "gaussian_beta_fixed               5.196704e-10  0.026199       1.0    0.46319   \n",
       "person                            0.000000e+00  1.325422                        \n",
       "person_beta_fixed                 0.000000e+00  1.000000                        \n",
       "\n",
       "                                     delta  \n",
       "model                                       \n",
       "lognormal_delta_fixed                  2.0  \n",
       "lognormal_delta_fixed_beta_fixed       2.0  \n",
       "lognormal                         1.679164  \n",
       "lognormal_beta_fixed              1.712911  \n",
       "gaussian_delta_fixed                   0.5  \n",
       "gaussian_delta_fixed_beta_fixed        0.5  \n",
       "gaussian                            0.8977  \n",
       "gaussian_beta_fixed                    1.0  \n",
       "person                                      \n",
       "person_beta_fixed                           "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3041e6e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('lognormal_delta_fixed', 1.331308742358201)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaling_law = scaling_fit.get_best_model(df)\n",
    "scaling_law.model_name, scaling_law.beta"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c9ff8ed",
   "metadata": {},
   "source": [
    "Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b541444d",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 10**np.linspace(np.log10(data_xy[0].min()*0.8), np.log10(data_xy[0].max()*1.2), 300)\n",
    "y = scaling_law.mean(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "44dfef39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 300x270 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 272,
       "width": 306
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(3, 2.7))\n",
    "plt.plot(*data_xy, ls=\"\", marker='o', ms=1.5)\n",
    "\n",
    "color = \"gray\"\n",
    "plt.fill_between(x, *scaling_law.std(x, 2.0), color=color, alpha=0.1, linewidth=0.0)\n",
    "plt.fill_between(x, *scaling_law.std(x, 1.5), color=color, alpha=0.2, linewidth=0.0)\n",
    "plt.fill_between(x, *scaling_law.std(x, 1.0), color=color, alpha=0.3, linewidth=0.0)\n",
    "plt.fill_between(x, *scaling_law.std(x, 0.5), color=color, alpha=0.8, linewidth=0.0)\n",
    "\n",
    "plt.ylim(min(y), max(y))\n",
    "plt.xlim(min(x), max(x))\n",
    "\n",
    "plt.plot(x, y, color='k', lw=1)\n",
    "plt.xscale('log')\n",
    "plt.yscale('log')\n",
    "plt.xlabel(\"Population\")\n",
    "plt.ylabel(\"Crime\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfe80ce5",
   "metadata": {},
   "source": [
    "# ranking cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e1f5db6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_theft = scaling_law.rank_cities(df_theft)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5eb2a85e",
   "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>region</th>\n",
       "      <th>population</th>\n",
       "      <th>crime</th>\n",
       "      <th>per_capita</th>\n",
       "      <th>z_score</th>\n",
       "      <th>per_capita_ranking</th>\n",
       "      <th>adjusted_ranking</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Novara</td>\n",
       "      <td>371686</td>\n",
       "      <td>10565.0</td>\n",
       "      <td>2842.453038</td>\n",
       "      <td>1.462659</td>\n",
       "      <td>33</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Torino</td>\n",
       "      <td>2297917</td>\n",
       "      <td>120124.0</td>\n",
       "      <td>5227.516921</td>\n",
       "      <td>1.499393</td>\n",
       "      <td>4</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Como</td>\n",
       "      <td>598810</td>\n",
       "      <td>13673.0</td>\n",
       "      <td>2283.362001</td>\n",
       "      <td>-0.958245</td>\n",
       "      <td>61</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lecco</td>\n",
       "      <td>340814</td>\n",
       "      <td>7530.0</td>\n",
       "      <td>2209.416280</td>\n",
       "      <td>0.029382</td>\n",
       "      <td>65</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Trento</td>\n",
       "      <td>536237</td>\n",
       "      <td>11652.0</td>\n",
       "      <td>2172.919810</td>\n",
       "      <td>-1.041793</td>\n",
       "      <td>66</td>\n",
       "      <td>73</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>Pesaro e Urbino</td>\n",
       "      <td>364385</td>\n",
       "      <td>7892.0</td>\n",
       "      <td>2165.841075</td>\n",
       "      <td>-0.240793</td>\n",
       "      <td>67</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>Napoli</td>\n",
       "      <td>3127390</td>\n",
       "      <td>82566.0</td>\n",
       "      <td>2640.092857</td>\n",
       "      <td>-3.542719</td>\n",
       "      <td>42</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>Salerno</td>\n",
       "      <td>1105485</td>\n",
       "      <td>23004.0</td>\n",
       "      <td>2080.896620</td>\n",
       "      <td>-2.858749</td>\n",
       "      <td>71</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>Matera</td>\n",
       "      <td>201133</td>\n",
       "      <td>2333.0</td>\n",
       "      <td>1159.929002</td>\n",
       "      <td>-2.986311</td>\n",
       "      <td>96</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>Monza e della Brianza</td>\n",
       "      <td>862684</td>\n",
       "      <td>28605.0</td>\n",
       "      <td>3315.814365</td>\n",
       "      <td>0.660486</td>\n",
       "      <td>23</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>107 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    region  population     crime   per_capita   z_score  \\\n",
       "0                   Novara      371686   10565.0  2842.453038  1.462659   \n",
       "1                   Torino     2297917  120124.0  5227.516921  1.499393   \n",
       "2                     Como      598810   13673.0  2283.362001 -0.958245   \n",
       "3                    Lecco      340814    7530.0  2209.416280  0.029382   \n",
       "4                   Trento      536237   11652.0  2172.919810 -1.041793   \n",
       "..                     ...         ...       ...          ...       ...   \n",
       "104        Pesaro e Urbino      364385    7892.0  2165.841075 -0.240793   \n",
       "105                 Napoli     3127390   82566.0  2640.092857 -3.542719   \n",
       "106                Salerno     1105485   23004.0  2080.896620 -2.858749   \n",
       "107                 Matera      201133    2333.0  1159.929002 -2.986311   \n",
       "110  Monza e della Brianza      862684   28605.0  3315.814365  0.660486   \n",
       "\n",
       "     per_capita_ranking  adjusted_ranking  \n",
       "0                    33                29  \n",
       "1                     4                28  \n",
       "2                    61                72  \n",
       "3                    65                53  \n",
       "4                    66                73  \n",
       "..                  ...               ...  \n",
       "104                  67                61  \n",
       "105                  42                94  \n",
       "106                  71                90  \n",
       "107                  96                92  \n",
       "110                  23                40  \n",
       "\n",
       "[107 rows x 7 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_theft"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7b302eb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 300x270 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 271,
       "width": 308
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(3, 2.7))\n",
    "plt.plot(df_theft.per_capita_ranking, df_theft.adjusted_ranking, ls=\"\", marker='o', ms=1.5)\n",
    "plt.plot([1, len(df_theft)], [1, len(df_theft)], color='k', lw=1)\n",
    "plt.xlim(1, len(df_theft))\n",
    "plt.ylim(1, len(df_theft))\n",
    "\n",
    "plt.xlabel(\"Per-capita ranking\")\n",
    "plt.ylabel(\"Adjusted ranking\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c7708058",
   "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>region</th>\n",
       "      <th>population</th>\n",
       "      <th>crime</th>\n",
       "      <th>per_capita</th>\n",
       "      <th>z_score</th>\n",
       "      <th>per_capita_ranking</th>\n",
       "      <th>adjusted_ranking</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Rimini</td>\n",
       "      <td>334254</td>\n",
       "      <td>23491.0</td>\n",
       "      <td>7027.888971</td>\n",
       "      <td>7.501060</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Milano</td>\n",
       "      <td>3176180</td>\n",
       "      <td>218888.0</td>\n",
       "      <td>6891.548968</td>\n",
       "      <td>2.585363</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Bologna</td>\n",
       "      <td>1001170</td>\n",
       "      <td>61240.0</td>\n",
       "      <td>6116.843293</td>\n",
       "      <td>4.275738</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Roma</td>\n",
       "      <td>4321244</td>\n",
       "      <td>240124.0</td>\n",
       "      <td>5556.825766</td>\n",
       "      <td>0.548132</td>\n",
       "      <td>3</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Torino</td>\n",
       "      <td>2297917</td>\n",
       "      <td>120124.0</td>\n",
       "      <td>5227.516921</td>\n",
       "      <td>1.499393</td>\n",
       "      <td>4</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>Firenze</td>\n",
       "      <td>1007252</td>\n",
       "      <td>49030.0</td>\n",
       "      <td>4867.699444</td>\n",
       "      <td>2.796094</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Ravenna</td>\n",
       "      <td>392358</td>\n",
       "      <td>18350.0</td>\n",
       "      <td>4676.851243</td>\n",
       "      <td>4.544993</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Venezia</td>\n",
       "      <td>857841</td>\n",
       "      <td>39371.0</td>\n",
       "      <td>4589.545149</td>\n",
       "      <td>2.759848</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Lucca</td>\n",
       "      <td>394600</td>\n",
       "      <td>17811.0</td>\n",
       "      <td>4513.684744</td>\n",
       "      <td>4.304847</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Pisa</td>\n",
       "      <td>420254</td>\n",
       "      <td>18233.0</td>\n",
       "      <td>4338.566676</td>\n",
       "      <td>3.916764</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      region  population     crime   per_capita   z_score  per_capita_ranking  \\\n",
       "72    Rimini      334254   23491.0  7027.888971  7.501060                   0   \n",
       "83    Milano     3176180  218888.0  6891.548968  2.585363                   1   \n",
       "49   Bologna     1001170   61240.0  6116.843293  4.275738                   2   \n",
       "88      Roma     4321244  240124.0  5556.825766  0.548132                   3   \n",
       "1     Torino     2297917  120124.0  5227.516921  1.499393                   4   \n",
       "100  Firenze     1007252   49030.0  4867.699444  2.796094                   5   \n",
       "37   Ravenna      392358   18350.0  4676.851243  4.544993                   6   \n",
       "99   Venezia      857841   39371.0  4589.545149  2.759848                   7   \n",
       "12     Lucca      394600   17811.0  4513.684744  4.304847                   8   \n",
       "38      Pisa      420254   18233.0  4338.566676  3.916764                   9   \n",
       "\n",
       "     adjusted_ranking  \n",
       "72                  0  \n",
       "83                 15  \n",
       "49                  4  \n",
       "88                 43  \n",
       "1                  28  \n",
       "100                12  \n",
       "37                  2  \n",
       "99                 13  \n",
       "12                  3  \n",
       "38                  5  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_theft[df_theft.per_capita_ranking < 10].sort_values(\"per_capita_ranking\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1ec1e1e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>region</th>\n",
       "      <th>population</th>\n",
       "      <th>crime</th>\n",
       "      <th>per_capita</th>\n",
       "      <th>z_score</th>\n",
       "      <th>per_capita_ranking</th>\n",
       "      <th>adjusted_ranking</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Rimini</td>\n",
       "      <td>334254</td>\n",
       "      <td>23491.0</td>\n",
       "      <td>7027.888971</td>\n",
       "      <td>7.501060</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Prato</td>\n",
       "      <td>253245</td>\n",
       "      <td>10828.0</td>\n",
       "      <td>4275.701396</td>\n",
       "      <td>4.900571</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Ravenna</td>\n",
       "      <td>392358</td>\n",
       "      <td>18350.0</td>\n",
       "      <td>4676.851243</td>\n",
       "      <td>4.544993</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Lucca</td>\n",
       "      <td>394600</td>\n",
       "      <td>17811.0</td>\n",
       "      <td>4513.684744</td>\n",
       "      <td>4.304847</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Bologna</td>\n",
       "      <td>1001170</td>\n",
       "      <td>61240.0</td>\n",
       "      <td>6116.843293</td>\n",
       "      <td>4.275738</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Pisa</td>\n",
       "      <td>420254</td>\n",
       "      <td>18233.0</td>\n",
       "      <td>4338.566676</td>\n",
       "      <td>3.916764</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Imperia</td>\n",
       "      <td>217703</td>\n",
       "      <td>7340.0</td>\n",
       "      <td>3371.565849</td>\n",
       "      <td>3.696801</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Savona</td>\n",
       "      <td>283813</td>\n",
       "      <td>10320.0</td>\n",
       "      <td>3636.197073</td>\n",
       "      <td>3.617842</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>La Spezia</td>\n",
       "      <td>222377</td>\n",
       "      <td>6919.0</td>\n",
       "      <td>3111.382922</td>\n",
       "      <td>3.135924</td>\n",
       "      <td>28</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Pescara</td>\n",
       "      <td>322401</td>\n",
       "      <td>11301.0</td>\n",
       "      <td>3505.262080</td>\n",
       "      <td>3.111155</td>\n",
       "      <td>18</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       region  population    crime   per_capita   z_score  per_capita_ranking  \\\n",
       "72     Rimini      334254  23491.0  7027.888971  7.501060                   0   \n",
       "73      Prato      253245  10828.0  4275.701396  4.900571                  10   \n",
       "37    Ravenna      392358  18350.0  4676.851243  4.544993                   6   \n",
       "12      Lucca      394600  17811.0  4513.684744  4.304847                   8   \n",
       "49    Bologna     1001170  61240.0  6116.843293  4.275738                   2   \n",
       "38       Pisa      420254  18233.0  4338.566676  3.916764                   9   \n",
       "81    Imperia      217703   7340.0  3371.565849  3.696801                  20   \n",
       "67     Savona      283813  10320.0  3636.197073  3.617842                  14   \n",
       "33  La Spezia      222377   6919.0  3111.382922  3.135924                  28   \n",
       "76    Pescara      322401  11301.0  3505.262080  3.111155                  18   \n",
       "\n",
       "    adjusted_ranking  \n",
       "72                 0  \n",
       "73                 1  \n",
       "37                 2  \n",
       "12                 3  \n",
       "49                 4  \n",
       "38                 5  \n",
       "81                 6  \n",
       "67                 7  \n",
       "33                 8  \n",
       "76                 9  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_theft[df_theft.adjusted_ranking < 10].sort_values(\"adjusted_ranking\")"
   ]
  }
 ],
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