{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5a391686-4c42-446a-9895-25d27117ffc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import dask.dataframe\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import tqdm.notebook as tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e108ebcf-1cbd-42b5-ac84-a12ebb2fd2b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 47.8 s, sys: 39.7 s, total: 1min 27s\n",
      "Wall time: 1min 56s\n",
      "CPU times: user 53.3 ms, sys: 3.23 ms, total: 56.6 ms\n",
      "Wall time: 113 ms\n",
      "CPU times: user 99 ms, sys: 0 ns, total: 99 ms\n",
      "Wall time: 98.5 ms\n",
      "CPU times: user 22.5 ms, sys: 0 ns, total: 22.5 ms\n",
      "Wall time: 22.3 ms\n",
      "CPU times: user 2min 34s, sys: 1min 40s, total: 4min 14s\n",
      "Wall time: 2min 43s\n"
     ]
    }
   ],
   "source": [
    "%time standardized_form = dask.dataframe.read_parquet(\"../../urbangrammar_samba/spatial_signatures/clustering_data/form/standardized/\").set_index('hindex')\n",
    "%time stand_fn = dask.dataframe.read_parquet(\"../../urbangrammar_samba/spatial_signatures/clustering_data/function/standardized/\")\n",
    "%time data = dask.dataframe.multi.concat([standardized_form, stand_fn], axis=1).replace([np.inf, -np.inf], np.nan).fillna(0)\n",
    "%time data = data.drop(columns=[\"keep_q1\", \"keep_q2\", \"keep_q3\"])\n",
    "%time data = data.compute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f1202f0a-2bbe-4575-bd36-4d865e48e142",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels_l1 = pd.read_parquet(\"../../urbangrammar_samba/spatial_signatures/clustering_data/KMeans10GB.pq\")\n",
    "labels_l2_9 = pd.read_parquet(\"../../urbangrammar_samba/spatial_signatures/clustering_data/clustergram_cl9_labels.pq\")\n",
    "labels_l2_2 = pd.read_parquet(\"../../urbangrammar_samba/spatial_signatures/clustering_data/subclustering_cluster2_k3.pq\")\n",
    "labels = labels_l1.copy()\n",
    "labels.loc[labels.kmeans10gb == 9, 'kmeans10gb'] = labels_l2_9['9'].values + 90\n",
    "labels.loc[labels.kmeans10gb == 2, 'kmeans10gb'] = labels_l2_2['subclustering_cluster2_k3'].values + 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "449bb1a6-3de1-4030-97a0-c0765a3cf483",
   "metadata": {},
   "outputs": [],
   "source": [
    "outliers = [98, 93, 96, 97]\n",
    "mask = ~labels.kmeans10gb.isin(outliers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9a20d8ed-7510-4ad6-a98c-e336daa08fcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "from statsmodels.formula.api import ols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "380a89be-ec0d-4f76-945f-f33442d3db42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['sdbAre_q1', 'sdbAre_q2', 'sdbAre_q3', 'sdbPer_q1', 'sdbPer_q2',\n",
       "       'sdbPer_q3', 'sdbCoA_q1', 'sdbCoA_q2', 'sdbCoA_q3', 'ssbCCo_q1'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b2938187-50ad-476f-904c-f21f0dc78e34",
   "metadata": {},
   "outputs": [],
   "source": [
    "dummies = pd.get_dummies(labels.astype('str'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "93ba57bc-9c62-4662-b70c-122edde16646",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Length of values (1) does not match length of index (14539578)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.9/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36massign\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m   4484\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4485\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4486\u001b[0;31m             \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4487\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4488\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.9/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   3610\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3611\u001b[0m             \u001b[0;31m# set column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3612\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_item\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3613\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3614\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_setitem_slice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mslice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.9/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_set_item\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   3782\u001b[0m         \u001b[0mensure\u001b[0m \u001b[0mhomogeneity\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3783\u001b[0m         \"\"\"\n\u001b[0;32m-> 3784\u001b[0;31m         \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sanitize_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3785\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3786\u001b[0m         if (\n",
      "\u001b[0;32m/opt/conda/lib/python3.9/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_sanitize_column\u001b[0;34m(self, value)\u001b[0m\n\u001b[1;32m   4507\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4508\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_list_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4509\u001b[0;31m             \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequire_length_match\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4510\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0msanitize_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4511\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.9/site-packages/pandas/core/common.py\u001b[0m in \u001b[0;36mrequire_length_match\u001b[0;34m(data, index)\u001b[0m\n\u001b[1;32m    529\u001b[0m     \"\"\"\n\u001b[1;32m    530\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 531\u001b[0;31m         raise ValueError(\n\u001b[0m\u001b[1;32m    532\u001b[0m             \u001b[0;34m\"Length of values \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    533\u001b[0m             \u001b[0;34mf\"({len(data)}) \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Length of values (1) does not match length of index (14539578)"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "model = ols(\"sdbAre_q1 ~ labels\", data=data.assign(labels=pd.Categorical(labels)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "35da590c-7bd2-4b5f-899b-d549e73ce018",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 2s, sys: 1min 2s, total: 2min 5s\n",
      "Wall time: 23.7 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "model = sm.OLS(data[\"sdbAre_q1\"], dummies).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "bbac93d0-b8bc-4133-a6bc-6a725df58b6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "p = model.f_pvalue\n",
    "f = model.fvalue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "36e967c9-c346-4045-9cd7-ccbd4c944e2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "959104d1053743d297d262e285139e89",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/328 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.9/site-packages/statsmodels/regression/linear_model.py:1827: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  return self.mse_model/self.mse_resid\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 5h 51min 10s, sys: 5h 52min 9s, total: 11h 43min 20s\n",
      "Wall time: 2h 11min 46s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "results = {}\n",
    "for c in tqdm.tqdm(data.columns, total=328):\n",
    "    model = sm.OLS(data[c], dummies).fit()\n",
    "    results[c] = (model.f_pvalue, model.fvalue)\n",
    "results_df = pd.DataFrame(results).T\n",
    "results_df.columns = [\"p\", \"F\"]\n",
    "results_df.to_parquet(\"../../urbangrammar_samba/spatial_signatures/f_stats.pq\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "8624c7db-ec31-4354-9eab-dfe3d7eeae1a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 328 entries, sdbAre_q1 to Code_18_521_q3\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   p       310 non-null    float64\n",
      " 1   F       310 non-null    float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 15.8+ KB\n"
     ]
    }
   ],
   "source": [
    "results_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "a7bf2a81-51a4-435a-beaa-98c3eee764b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14539578, 328)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "77e63efd-2e5d-4e55-96d2-bba764682860",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Code_18_141_q1', 'Code_18_122_q2', 'Code_18_512_q2', 'Code_18_324_q1',\n",
       "       'Code_18_312_q1'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df[results_df.p > 0.05].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d91d585-c884-4eed-9ee6-c25f28778fc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "Land cover [Green urban areas] (Q1), Land cover [Road and rail networks and associated land] (Q2), "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "51de74bc-378f-4e92-bb6f-5cfff7244f01",
   "metadata": {},
   "outputs": [],
   "source": [
    "key = { \n",
    "    '124': 'Land cover [Airports]',\n",
    "    '211': 'Land cover [Non-irrigated arable land]',\n",
    "    '121': 'Land cover [Industrial or commercial units]',\n",
    "    '421': 'Land cover [Salt marshes]',\n",
    "    '522': 'Land cover [Estuaries]',\n",
    "    '142': 'Land cover [Sport and leisure facilities]',\n",
    "    '141': 'Land cover [Green urban areas]',\n",
    "    '112': 'Land cover [Discontinuous urban fabric]',\n",
    "    '231': 'Land cover [Pastures]',\n",
    "    '311': 'Land cover [Broad-leaved forest]',\n",
    "    '131': 'Land cover [Mineral extraction sites]',\n",
    "    '123': 'Land cover [Port areas]',\n",
    "    '122': 'Land cover [Road and rail networks and associated land]',\n",
    "    '512': 'Land cover [Water bodies]',\n",
    "    '243': 'Land cover [Land principally occupied by agriculture, with significant areas of natural vegetation]',\n",
    "    '313': 'Land cover [Mixed forest]',\n",
    "    '412': 'Land cover [Peat bogs]',\n",
    "    '321': 'Land cover [Natural grasslands]',\n",
    "    '322': 'Land cover [Moors and heathland]',\n",
    "    '324': 'Land cover [Transitional woodland-shrub]',\n",
    "    '111': 'Land cover [Continuous urban fabric]',\n",
    "    '423': 'Land cover [Intertidal flats]',\n",
    "    '523': 'Land cover [Sea and ocean]',\n",
    "    '312': 'Land cover [Coniferous forest]',\n",
    "    '133': 'Land cover [Construction sites]',\n",
    "    '333': 'Land cover [Sparsely vegetated areas]',\n",
    "    '332': 'Land cover [Bare rocks]',\n",
    "    '411': 'Land cover [Inland marshes]',\n",
    "    '132': 'Land cover [Dump sites]',\n",
    "    '222': 'Land cover [Fruit trees and berry plantations]',\n",
    "    '242': 'Land cover [Complex cultivation patterns]',\n",
    "    '331': 'Land cover [Beaches, dunes, sands]',\n",
    "    '511': 'Land cover [Water courses]',\n",
    "    '334': 'Land cover [Burnt areas]',\n",
    "    '244': 'Land cover [Agro-forestry areas]',\n",
    "    '521': 'Land cover [Coastal lagoons]',\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "5d4c447b-01c0-4957-bbd6-497fec16ccfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Land cover [Airports] Q1, Land cover [Mineral extraction sites] Q1, Land cover [Road and rail networks and associated land] Q1, Land cover [Water bodies] Q1, Land cover [Inland marshes] Q1, Land cover [Dump sites] Q1, Land cover [Water courses] Q2, Land cover [Burnt areas] Q2, Land cover [Water courses] Q1, Land cover [Burnt areas] Q1, Land cover [Agro-forestry areas] Q3, Land cover [Coastal lagoons] Q2, Land cover [Burnt areas] Q3, Land cover [Agro-forestry areas] Q1, Land cover [Agro-forestry areas] Q2, Land cover [Dump sites] Q2, Land cover [Coastal lagoons] Q1, Land cover [Coastal lagoons] Q3, '"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = \"\"\n",
    "for ch in results_df[results_df.p.isna()].index:\n",
    "    s += f\"{key[ch[8:11]]} {ch[12:].upper()}, \"\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "098fa24b-dd9c-41f4-8314-71a1e288daa8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df.p.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5fdbd9a-52cc-4a25-be92-c8aebde58e99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEDCAYAAAA4FgP0AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAATYklEQVR4nO3dfZBdd33f8fcH2QY7mDqgTerKFhKMC1UZG8za0CbhKU+SO6C6ebLChMS1o3piM810OmPnYYAO05nQTMLDYFAE1TimqdUSHMdJRFxIm7gz4NpyYxsJY6PYTryYiWRcYp4GR+bbP+4RXFa7q7vaPffe9e/9mrmz557zu+d+9oykj87DPTdVhSSpXc+adABJ0mRZBJLUOItAkhpnEUhS4ywCSWqcRSBJjVuTRZBkT5LDSQ6MMPbdSe7pHg8m+fIYIkrSmpG1+DmCJK8BvgrcWFUvW8br3gq8oqr+dW/hJGmNWZN7BFV1O/DE8LwkL07yp0nuTvK/k7x0gZfuAG4aS0hJWiNOmXSAVbQbuKqqPp/kVcAHgDccW5jkhcBm4H9OKJ8kTaVnRBEkeS7wz4GPJjk2+9nzhl0G/H5VPT3ObJI07Z4RRcDgENeXq+rlS4y5DLh6PHEkae1Yk+cI5quqJ4GHk/wUQAYuOLY8yUuA7wU+PaGIkjS11mQRJLmJwT/qL0kyl+QK4M3AFUnuBQ4C24desgPYW2vxEilJ6tmavHxUkrR61uQegSRp9ay5k8Xr16+vTZs2TTqGJK0pd9999+NVNbPQsjVXBJs2bWL//v2TjiFJa0qSv15smYeGJKlxFoEkNc4ikKTGWQSS1DiLQJIaZxFIUuMsAklqnEUgSY2zCCSpcWvuk8Ursem6P5nYez/yG/9iYu8tSUtxj0CSGmcRSFLjLAJJalxvRZBkT5LDSQ6cYNxFSZ5O8pN9ZZEkLa7PPYIbgK1LDUiyDngXcFuPOSRJS+itCKrqduCJEwx7K/Ax4HBfOSRJS5vYOYIkG4BLgV0jjN2ZZH+S/UeOHOk/nCQ1ZJIni98DXFtVT59oYFXtrqrZqpqdmVnwm9YkSSdpkh8omwX2JgFYD1yS5GhV3TLBTJLUnIkVQVVtPjad5Abgjy0BSRq/3oogyU3A64D1SeaAtwOnAlTVCc8LSJLGo7ciqKodyxj7C33lkCQtzU8WS1LjLAJJapxFIEmNswgkqXEWgSQ1ziKQpMZZBJLUOItAkhpnEUhS4ywCSWqcRSBJjbMIJKlxFoEkNc4ikKTGWQSS1DiLQJIaZxFIUuMsAklqnEUgSY2zCCSpcb0VQZI9SQ4nObDI8jcnua97fCrJBX1lkSQtrs89ghuArUssfxh4bVWdD7wT2N1jFknSIk7pa8VVdXuSTUss/9TQ0zuAc/rKIkla3LScI7gC+PhiC5PsTLI/yf4jR46MMZYkPfNNvAiSvJ5BEVy72Jiq2l1Vs1U1OzMzM75wktSA3g4NjSLJ+cCHgW1V9aVJZpGkVk1sjyDJRuBm4Oeq6sFJ5ZCk1vW2R5DkJuB1wPokc8DbgVMBqmoX8DbgBcAHkgAcrarZvvJIkhbW51VDO06w/Ergyr7eX5I0momfLJYkTZZFIEmNswgkqXEWgSQ1ziKQpMZZBJLUOItAkhpnEUhS4ywCSWqcRSBJjbMIJKlxFoEkNc4ikKTGWQSS1DiLQJIaZxFIUuMsAklqnEUgSY2zCCSpcb0VQZI9SQ4nObDI8iR5X5JDSe5LcmFfWSRJi+tzj+AGYOsSy7cB53WPncAHe8wiSVpEb0VQVbcDTywxZDtwYw3cAZyV5Oy+8kiSFjbJcwQbgEeHns91846TZGeS/Un2HzlyZCzhJKkVkyyCLDCvFhpYVburaraqZmdmZnqOJUltmWQRzAHnDj0/B3hsQlkkqVmTLIJbgbd0Vw+9Gvi7qvriBPNIUpNO6WvFSW4CXgesTzIHvB04FaCqdgH7gEuAQ8DXgcv7yiJJWlxvRVBVO06wvICr+3p/SdJo/GSxJDXOIpCkxlkEktQ4i0CSGmcRSFLjRiqCJC/rO4gkaTJG3SPYleTOJL+U5Kw+A0mSxmukIqiqHwTezOCWEPuT/NckP9prMknSWIx8jqCqPg/8OnAt8FrgfUk+l+Rf9RVOktS/Uc8RnJ/k3cD9wBuAN1bVP+mm391jPklSz0a9xcT7gQ8Bv1pV3zg2s6oeS/LrvSSTJI3FqEVwCfCNqnoaIMmzgOdU1der6iO9pZMk9W7UcwSfBE4fen5GN0+StMaNWgTPqaqvHnvSTZ/RTyRJ0jiNWgRfS3LhsSdJXgl8Y4nxkqQ1YtRzBL8MfDTJsa+SPBv4mV4SSZLGaqQiqKq7krwUeAmDL53/XFX9fa/JJEljsZxvKLsI2NS95hVJqKobe0klSRqbkYogyUeAFwP3AE93swuwCCRpjRt1j2AW2NJ9z7Ak6Rlk1KuGDgD/cLkrT7I1yQNJDiW5boHl/yDJHyW5N8nBJJcv9z0kSSsz6h7BeuCzSe4EvnlsZlW9abEXJFkHXA/8KDAH3JXk1qr67NCwq4HPVtUbk8wADyT5vap6arm/iCTp5IxaBO84iXVfDByqqocAkuwFtgPDRVDAmUkCPBd4Ajh6Eu8lSTpJo14++hdJXgicV1WfTHIGsO4EL9sAPDr0fA541bwx7wduBR4DzgR+pqq+NX9FSXYCOwE2btw4SmRJ0ohGvQ31LwK/D/xON2sDcMuJXrbAvPknm3+cwZVI/wh4OfD+JM877kVVu6tqtqpmZ2ZmRoksSRrRqCeLrwZ+AHgSvv0lNd93gtfMMfhGs2POYfA//2GXAzfXwCHgYeClI2aSJK2CUYvgm8MncJOcwvH/u5/vLuC8JJuTnAZcxuAw0LC/AX64W+f3M/jk8kMjZpIkrYJRTxb/RZJfBU7vvqv4l4A/WuoFVXU0yTXAbQzOJ+ypqoNJruqW7wLeCdyQ5DMMDiVdW1WPn+TvIkk6CaMWwXXAFcBngH8D7AM+fKIXVdW+buzwvF1D048BPzZqWEnS6hv1qqFvMfiqyg/1G0eSNG6j3mvoYRY4J1BVL1r1RJKksVrOvYaOeQ7wU8DzVz+OJGncRrpqqKq+NPT4QlW9B3hDv9EkSeMw6qGhC4eePovBHsKZvSSSJI3VqIeGfmto+ijwCPDTq55GkjR2o1419Pq+g0iSJmPUQ0P/bqnlVfXbqxNHkjRuy7lq6CK+c4uINwK38913F5UkrUHL+WKaC6vqKwBJ3gF8tKqu7CuYJGk8Rr3p3EZg+FvDngI2rXoaSdLYjbpH8BHgziR/wOATxpcCN/aWSpI0NqNeNfQfk3wc+KFu1uVV9Zf9xZIkjcuoh4YAzgCerKr3AnNJNveUSZI0RqN+VeXbgWuBX+lmnQr8l75CSZLGZ9Q9gkuBNwFfg29/j4C3mJCkZ4BRi+Cpqiq6W1En+Z7+IkmSxmnUIvjvSX4HOCvJLwKfxC+pkaRnhBNeNZQkwH8DXgo8yeAL5t9WVZ/oOZskaQxOWARVVUluqapXAsv6xz/JVuC9DL68/sNV9RsLjHkd8B4GJ6Afr6rXLuc9JEkrM+qhoTuSXLScFSdZB1wPbAO2ADuSbJk35izgA8CbquqfMvjmM0nSGI36yeLXA1cleYTBlUNhsLNw/hKvuRg4VFUPASTZC2wHPjs05meBm6vqbxis8PDy4kuSVmrJIkiysftHettJrHsD33130jngVfPG/GPg1CR/zuBy1PdW1XG3rkiyE9gJsHHjxpOIIklazIn2CG5hcNfRv07ysar6iWWsOwvMqwXe/5XADwOnA59OckdVPfhdL6raDewGmJ2dnb8OSdIKnKgIhv8xf9Ey1z0HnDv0/BzgsQXGPF5VXwO+luR24ALgQSRJY3Gik8W1yPQo7gLOS7I5yWnAZXzni22O+UPgh5KckuQMBoeO7l/m+0iSVuBEewQXJHmSwZ7B6d00fOdk8fMWe2FVHU1yDXAbg8tH91TVwSRXdct3VdX9Sf4UuA/4FoNLTA+s8HeSJC3DkkVQVetWsvKq2gfsmzdv17znvwn85kreR5J08pZzG2pJ0jOQRSBJjbMIJKlxFoEkNc4ikKTGWQSS1DiLQJIaZxFIUuMsAklqnEUgSY2zCCSpcRaBJDXOIpCkxlkEktQ4i0CSGmcRSFLjLAJJapxFIEmNswgkqXEWgSQ1rtciSLI1yQNJDiW5bolxFyV5OslP9plHknS83oogyTrgemAbsAXYkWTLIuPeBdzWVxZJ0uL63CO4GDhUVQ9V1VPAXmD7AuPeCnwMONxjFknSIvosgg3Ao0PP57p535ZkA3ApsGupFSXZmWR/kv1HjhxZ9aCS1LI+iyALzKt5z98DXFtVTy+1oqraXVWzVTU7MzOzWvkkScApPa57Djh36Pk5wGPzxswCe5MArAcuSXK0qm7pMZckaUifRXAXcF6SzcAXgMuAnx0eUFWbj00nuQH4Y0tAksartyKoqqNJrmFwNdA6YE9VHUxyVbd8yfMCkqTx6HOPgKraB+ybN2/BAqiqX+gziyRpYX6yWJIaZxFIUuMsAklqnEUgSY2zCCSpcRaBJDXOIpCkxlkEktQ4i0CSGmcRSFLjLAJJapxFIEmNswgkqXEWgSQ1ziKQpMZZBJLUOItAkhpnEUhS4ywCSWpcr0WQZGuSB5IcSnLdAsvfnOS+7vGpJBf0mUeSdLzeiiDJOuB6YBuwBdiRZMu8YQ8Dr62q84F3Arv7yiNJWlifewQXA4eq6qGqegrYC2wfHlBVn6qq/9c9vQM4p8c8kqQF9FkEG4BHh57PdfMWcwXw8YUWJNmZZH+S/UeOHFnFiJKkPosgC8yrBQcmr2dQBNcutLyqdlfVbFXNzszMrGJESdIpPa57Djh36Pk5wGPzByU5H/gwsK2qvtRjHknSAvrcI7gLOC/J5iSnAZcBtw4PSLIRuBn4uap6sMcskqRF9LZHUFVHk1wD3AasA/ZU1cEkV3XLdwFvA14AfCAJwNGqmu0rkyTpeH0eGqKq9gH75s3bNTR9JXBlnxkkSUvzk8WS1DiLQJIaZxFIUuMsAklqnEUgSY2zCCSpcRaBJDXOIpCkxlkEktQ4i0CSGmcRSFLjLAJJapxFIEmNswgkqXEWgSQ1ziKQpMZZBJLUOItAkhpnEUhS4ywCSWpcr0WQZGuSB5IcSnLdAsuT5H3d8vuSXNhnHknS8XorgiTrgOuBbcAWYEeSLfOGbQPO6x47gQ/2lUeStLA+9wguBg5V1UNV9RSwF9g+b8x24MYauAM4K8nZPWaSJM1zSo/r3gA8OvR8DnjVCGM2AF8cHpRkJ4M9BoCvJnngJDOtBx4/ydeuSN410rCJ5RuR+VbGfCtjvpV54WIL+iyCLDCvTmIMVbUb2L3iQMn+qppd6Xr6Yr6VMd/KmG9lpj3fUvo8NDQHnDv0/BzgsZMYI0nqUZ9FcBdwXpLNSU4DLgNunTfmVuAt3dVDrwb+rqq+OH9FkqT+9HZoqKqOJrkGuA1YB+ypqoNJruqW7wL2AZcAh4CvA5f3laez4sNLPTPfyphvZcy3MtOeb1GpOu6QvCSpIX6yWJIaZxFIUuOaKYIT3e5ijDkeSfKZJPck2d/Ne36STyT5fPfze4fG/0qX+YEkP95Dnj1JDic5MDRv2XmSvLL7vQ51tw1Z6NLg1cr3jiRf6LbhPUkumUS+JOcm+V9J7k9yMMm/7eZPxfZbIt+0bL/nJLkzyb1dvv/QzZ+W7bdYvqnYfquqqp7xDwYnq/8KeBFwGnAvsGVCWR4B1s+b95+A67rp64B3ddNbuqzPBjZ3v8O6Vc7zGuBC4MBK8gB3Av+MwWdDPg5s6zHfO4B/v8DYseYDzgYu7KbPBB7sMkzF9lsi37RsvwDP7aZPBf4P8Oop2n6L5ZuK7beaj1b2CEa53cUkbQd+t5v+XeBfDs3fW1XfrKqHGVxddfFqvnFV3Q48sZI8GdwW5HlV9eka/Km/ceg1feRbzFjzVdUXq+r/dtNfAe5n8Mn4qdh+S+RbzLjzVVV9tXt6avcopmf7LZZvMWP/+7FaWimCxW5lMQkF/I8kd2dw6wyA76/u8xPdz+/r5k8q93LzbOimx5nzmgzuWLtn6NDBxPIl2QS8gsH/Gqdu+83LB1Oy/ZKsS3IPcBj4RFVN1fZbJB9MyfZbLa0UwUi3shiTH6iqCxncefXqJK9ZYuw05YbF84w75weBFwMvZ3Bfqt/q5k8kX5LnAh8Dfrmqnlxq6CI5xp1varZfVT1dVS9ncFeBi5O8bInh05JvarbfammlCKbmVhZV9Vj38zDwBwwO9fxtt/tI9/NwN3xSuZebZ66bHkvOqvrb7i/ot4AP8Z3DZWPPl+RUBv/I/l5V3dzNnprtt1C+adp+x1TVl4E/B7YyRdtvoXzTuP1WqpUiGOV2F71L8j1Jzjw2DfwYcKDL8vPdsJ8H/rCbvhW4LMmzk2xm8L0Nd44h6rLydLvvX0ny6u5qiLcMvWbV5btvVX4pg2049nzduv4zcH9V/fbQoqnYfovlm6LtN5PkrG76dOBHgM8xPdtvwXzTsv1W1aTPVo/rweBWFg8yOJP/axPK8CIGVxXcCxw8lgN4AfBnwOe7n88fes2vdZkfoIcrDYCbGOze/j2D/7lccTJ5gFkGfyH+Cng/3afWe8r3EeAzwH0M/vKdPYl8wA8y2MW/D7ine1wyLdtviXzTsv3OB/6yy3EAeNvJ/n0Yc76p2H6r+fAWE5LUuFYODUmSFmERSFLjLAJJapxFIEmNswgkqXEWgSQ1ziKQpMb9f2mvyTcEWk9aAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[\"Code_18_141_q1\"].plot.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8d21ff2f-5638-46a9-a48c-7737d790e8a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "for char in results_df[results_df.isna().any(axis=1)].index:\n",
    "    assert data[char].min() == 0\n",
    "    assert data[char].max() == 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "89759382-a596-4b4d-a3d0-0bbc2188e44a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sdbAre_q1         1.164105e+06\n",
       "sdbAre_q2         2.713511e+06\n",
       "sdbAre_q3         2.829579e+06\n",
       "sdbPer_q1         9.943499e+05\n",
       "sdbPer_q2         2.135257e+06\n",
       "                      ...     \n",
       "Code_18_132_q2             NaN\n",
       "Code_18_132_q3    5.033400e+00\n",
       "Code_18_521_q1             NaN\n",
       "Code_18_222_q2    1.679105e+02\n",
       "Code_18_521_q3             NaN\n",
       "Name: F, Length: 328, dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df[\"F\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "6b13a969-0bdc-4876-9724-34ef56b5e439",
   "metadata": {},
   "outputs": [],
   "source": [
    "importance = pd.read_csv(\"../../urbangrammar_samba/spatial_signatures/clustering_data/spsig_feature_importance.csv\", index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "97be55df-52b3-4213-ac4c-9670bb876b37",
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sicCAR_q1</th>\n",
       "      <td>0.036944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sicCAR_q2</th>\n",
       "      <td>0.031717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mtcWNe_q2</th>\n",
       "      <td>0.023476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ltbIBD_q2</th>\n",
       "      <td>0.016662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sdcAre_q3</th>\n",
       "      <td>0.016005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Code_18_334_q2</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Code_18_141_q1</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Code_18_124_q1</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Code_18_511_q2</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Code_18_521_q3</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>328 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       0\n",
       "sicCAR_q1       0.036944\n",
       "sicCAR_q2       0.031717\n",
       "mtcWNe_q2       0.023476\n",
       "ltbIBD_q2       0.016662\n",
       "sdcAre_q3       0.016005\n",
       "...                  ...\n",
       "Code_18_334_q2  0.000000\n",
       "Code_18_141_q1  0.000000\n",
       "Code_18_124_q1  0.000000\n",
       "Code_18_511_q2  0.000000\n",
       "Code_18_521_q3  0.000000\n",
       "\n",
       "[328 rows x 1 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "73c2afad-be18-4d5d-a78e-05500ccf271b",
   "metadata": {},
   "outputs": [],
   "source": [
    "results_df[\"importance\"] = importance['0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "24e9ba6c-cc40-42ca-b832-c038707391ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='F', ylabel='importance'>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_df.plot.scatter(\"F\", \"importance\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "8d4e57e0-2395-42dd-8545-104ccc561d78",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "f4b58f1d-875b-4919-ae4b-5aedcb0b8816",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='F', ylabel='importance'>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(\"F\", \"importance\", data=results_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "c1a22186-0752-4b68-af30-1ddc16803d56",
   "metadata": {},
   "outputs": [],
   "source": [
    "results_df[\"sig\"] = results_df.p < 0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "089a4ae6-b8c3-4a7e-9354-7dae1668887f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.9/site-packages/seaborn/categorical.py:1296: UserWarning: 35.4% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sig', ylabel='importance'>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.swarmplot(y=\"importance\", x='sig', data=results_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "c8e00dc2-6e73-4fff-9d78-0df0c6e864a3",
   "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>p</th>\n",
       "      <th>F</th>\n",
       "      <th>importance</th>\n",
       "      <th>sig</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ssbCor_q1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>7.031875e+06</td>\n",
       "      <td>1.004750e-07</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ssbCCD_q2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5.784977e+06</td>\n",
       "      <td>4.943006e-03</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sicCAR_q2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5.586707e+06</td>\n",
       "      <td>3.171676e-02</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ssbSqu_q1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5.260049e+06</td>\n",
       "      <td>2.004136e-03</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sdsSPO_q2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4.555403e+06</td>\n",
       "      <td>9.863778e-03</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ssbCCD_q3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4.464720e+06</td>\n",
       "      <td>3.564785e-03</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sdsSPO_q1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4.453836e+06</td>\n",
       "      <td>4.645283e-03</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R, S, T, U. Other_q2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4.113209e+06</td>\n",
       "      <td>1.432705e-03</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sicCAR_q1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4.039581e+06</td>\n",
       "      <td>3.694416e-02</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ssbERI_q1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.946964e+06</td>\n",
       "      <td>7.240188e-03</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ssbCor_q2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.928673e+06</td>\n",
       "      <td>2.848661e-05</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R, S, T, U. Other_q3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.884124e+06</td>\n",
       "      <td>2.055444e-03</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sicCAR_q3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.799049e+06</td>\n",
       "      <td>9.460556e-03</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mtcWNe_q1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.795199e+06</td>\n",
       "      <td>9.559132e-03</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K, L, M, N. Financial, real estate, professional and administrative activities_q3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.775361e+06</td>\n",
       "      <td>1.921642e-03</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mtcWNe_q2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.706665e+06</td>\n",
       "      <td>2.347565e-02</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>H, J. Transport and communication_q3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.607226e+06</td>\n",
       "      <td>1.674766e-03</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ssbCCD_q1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.603939e+06</td>\n",
       "      <td>3.043970e-03</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ssbERI_q2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.578647e+06</td>\n",
       "      <td>2.690754e-03</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G, I. Distribution, hotels and restaurants_q2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.530780e+06</td>\n",
       "      <td>1.797167e-03</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      p             F  \\\n",
       "ssbCor_q1                                           0.0  7.031875e+06   \n",
       "ssbCCD_q2                                           0.0  5.784977e+06   \n",
       "sicCAR_q2                                           0.0  5.586707e+06   \n",
       "ssbSqu_q1                                           0.0  5.260049e+06   \n",
       "sdsSPO_q2                                           0.0  4.555403e+06   \n",
       "ssbCCD_q3                                           0.0  4.464720e+06   \n",
       "sdsSPO_q1                                           0.0  4.453836e+06   \n",
       "R, S, T, U. Other_q2                                0.0  4.113209e+06   \n",
       "sicCAR_q1                                           0.0  4.039581e+06   \n",
       "ssbERI_q1                                           0.0  3.946964e+06   \n",
       "ssbCor_q2                                           0.0  3.928673e+06   \n",
       "R, S, T, U. Other_q3                                0.0  3.884124e+06   \n",
       "sicCAR_q3                                           0.0  3.799049e+06   \n",
       "mtcWNe_q1                                           0.0  3.795199e+06   \n",
       "K, L, M, N. Financial, real estate, professiona...  0.0  3.775361e+06   \n",
       "mtcWNe_q2                                           0.0  3.706665e+06   \n",
       "H, J. Transport and communication_q3                0.0  3.607226e+06   \n",
       "ssbCCD_q1                                           0.0  3.603939e+06   \n",
       "ssbERI_q2                                           0.0  3.578647e+06   \n",
       "G, I. Distribution, hotels and restaurants_q2       0.0  3.530780e+06   \n",
       "\n",
       "                                                      importance   sig  \n",
       "ssbCor_q1                                           1.004750e-07  True  \n",
       "ssbCCD_q2                                           4.943006e-03  True  \n",
       "sicCAR_q2                                           3.171676e-02  True  \n",
       "ssbSqu_q1                                           2.004136e-03  True  \n",
       "sdsSPO_q2                                           9.863778e-03  True  \n",
       "ssbCCD_q3                                           3.564785e-03  True  \n",
       "sdsSPO_q1                                           4.645283e-03  True  \n",
       "R, S, T, U. Other_q2                                1.432705e-03  True  \n",
       "sicCAR_q1                                           3.694416e-02  True  \n",
       "ssbERI_q1                                           7.240188e-03  True  \n",
       "ssbCor_q2                                           2.848661e-05  True  \n",
       "R, S, T, U. Other_q3                                2.055444e-03  True  \n",
       "sicCAR_q3                                           9.460556e-03  True  \n",
       "mtcWNe_q1                                           9.559132e-03  True  \n",
       "K, L, M, N. Financial, real estate, professiona...  1.921642e-03  True  \n",
       "mtcWNe_q2                                           2.347565e-02  True  \n",
       "H, J. Transport and communication_q3                1.674766e-03  True  \n",
       "ssbCCD_q1                                           3.043970e-03  True  \n",
       "ssbERI_q2                                           2.690754e-03  True  \n",
       "G, I. Distribution, hotels and restaurants_q2       1.797167e-03  True  "
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df.query('sig').sort_values(\"F\", ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ebd097f4-6bbc-4ac9-812c-dde46b0f22a0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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