2416 lines
376 KiB
Plaintext
2416 lines
376 KiB
Plaintext
{
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"cells": [
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||
{
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||
"cell_type": "code",
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||
"execution_count": 108,
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||
"id": "c8ee3886-80ba-40be-b83a-4567c06e61fb",
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||
"metadata": {},
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||
"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/tmp/ipykernel_101408/3055773264.py:25: UserWarning:\n",
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"\n",
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"pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
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"\n"
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]
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}
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],
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"source": [
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"import pandas as pd\n",
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"import psycopg2 as pg\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"from sklearn.model_selection import train_test_split\n",
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"import xgboost as xgb\n",
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"from sklearn.metrics import confusion_matrix,matthews_corrcoef,accuracy_score\n",
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"import optuna\n",
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"import pickle\n",
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"from sklearn.feature_selection import SequentialFeatureSelector\n",
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"reload_data = True\n",
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"solo_trentino = True\n",
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"all_seasons = False\n",
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"import plotly.io as pio\n",
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"pio.renderers.default = 'iframe'\n",
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"from datetime import datetime\n",
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"def norm(x):\n",
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" if len(x)==1 and x[0]=='':\n",
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" return []\n",
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" else:\n",
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" return x\n",
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"if solo_trentino:\n",
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" engine = pg.connect(\"dbname='safeidx' user='fbk_mpba' host='172.104.247.67' port='5432' password='fbk2024$'\")\n",
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" if all_seasons is False:\n",
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" df = pd.read_sql('select * from fbk_export_08052024', con=engine) \n",
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" else:\n",
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" df = pd.read_sql('select * from fbk_export_09052024', con=engine) \n",
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"\n",
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"else:\n",
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" if reload_data:\n",
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" #fbk_export_08052024\n",
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" engine = pg.connect(\"dbname='safeidx' user='fbk_mpba' host='172.104.247.67' port='5432' password='fbk2024$'\")\n",
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" df = pd.read_sql('select * from fbk_export_20240212', con=engine)\n",
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" with open('../src/data.pkl','wb') as f:\n",
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" pickle.dump(df,f)\n",
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" else:\n",
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" with open('../src/data.pkl','rb') as f:\n",
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" df = pickle.load(f)\n",
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"\n",
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"\n",
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" df = df[df.year>2015]\n",
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"df['iii'] = list(range(df.shape[0]))\n",
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"df['hour'] = df.dateandtime.apply(lambda x: x.hour)\n",
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"df['dow'] = df.dateandtime.apply(lambda x: x.weekday())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 109,
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"id": "ed3c0dad-8da1-4fd3-99a4-f1d781ca643e",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
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||
" }\n",
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"\n",
|
||
" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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||
" }\n",
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"\n",
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||
" .dataframe thead th {\n",
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" text-align: right;\n",
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||
" }\n",
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||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
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||
" <thead>\n",
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||
" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>id</th>\n",
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" <th>dateandtime</th>\n",
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" <th>skiarea_id</th>\n",
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" <th>skiarea_name</th>\n",
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||
" <th>day_of_year</th>\n",
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" <th>minute_of_day</th>\n",
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" <th>year</th>\n",
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" <th>season</th>\n",
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" <th>difficulty</th>\n",
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" <th>cause</th>\n",
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||
" <th>...</th>\n",
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" <th>diagnosis</th>\n",
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" <th>india</th>\n",
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" <th>age</th>\n",
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" <th>country</th>\n",
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" <th>injury_side</th>\n",
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" <th>injury_general_location</th>\n",
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" <th>evacuation_vehicles</th>\n",
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" <th>iii</th>\n",
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" <th>hour</th>\n",
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" <th>dow</th>\n",
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" </tr>\n",
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" </thead>\n",
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||
" <tbody>\n",
|
||
" <tr>\n",
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||
" <th>0</th>\n",
|
||
" <td>627685195</td>\n",
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||
" <td>2024-04-27 08:00:00+00:00</td>\n",
|
||
" <td>8</td>\n",
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||
" <td>Passo Tonale - Presena</td>\n",
|
||
" <td>118</td>\n",
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||
" <td>480</td>\n",
|
||
" <td>2024</td>\n",
|
||
" <td>2024</td>\n",
|
||
" <td>advanced</td>\n",
|
||
" <td>fall_alone</td>\n",
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||
" <td>...</td>\n",
|
||
" <td>fracture</td>\n",
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||
" <td>None</td>\n",
|
||
" <td>52.0</td>\n",
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||
" <td>Italia</td>\n",
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||
" <td>R</td>\n",
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||
" <td>lower_limbs</td>\n",
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||
" <td>[ambulance, akja]</td>\n",
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||
" <td>0</td>\n",
|
||
" <td>8</td>\n",
|
||
" <td>5</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>627685151</td>\n",
|
||
" <td>2024-04-20 10:40:00+00:00</td>\n",
|
||
" <td>8</td>\n",
|
||
" <td>Passo Tonale - Presena</td>\n",
|
||
" <td>111</td>\n",
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||
" <td>640</td>\n",
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||
" <td>2024</td>\n",
|
||
" <td>2024</td>\n",
|
||
" <td>intermediate</td>\n",
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||
" <td>illness</td>\n",
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||
" <td>...</td>\n",
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||
" <td>malaise</td>\n",
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||
" <td>None</td>\n",
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||
" <td>51.0</td>\n",
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||
" <td>Italia</td>\n",
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||
" <td>None</td>\n",
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||
" <td>None</td>\n",
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||
" <td>[ski_lift, indipendently]</td>\n",
|
||
" <td>1</td>\n",
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||
" <td>10</td>\n",
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||
" <td>5</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>627685150</td>\n",
|
||
" <td>2024-04-20 09:00:00+00:00</td>\n",
|
||
" <td>8</td>\n",
|
||
" <td>Passo Tonale - Presena</td>\n",
|
||
" <td>111</td>\n",
|
||
" <td>540</td>\n",
|
||
" <td>2024</td>\n",
|
||
" <td>2024</td>\n",
|
||
" <td>advanced</td>\n",
|
||
" <td>fall_alone</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>distortion</td>\n",
|
||
" <td>None</td>\n",
|
||
" <td>48.0</td>\n",
|
||
" <td>Italia</td>\n",
|
||
" <td>R</td>\n",
|
||
" <td>lower_limbs</td>\n",
|
||
" <td>[akja, car]</td>\n",
|
||
" <td>2</td>\n",
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||
" <td>9</td>\n",
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||
" <td>5</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>627685033</td>\n",
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||
" <td>2024-04-14 12:30:00+00:00</td>\n",
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||
" <td>6</td>\n",
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||
" <td>Pampeago</td>\n",
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||
" <td>105</td>\n",
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||
" <td>750</td>\n",
|
||
" <td>2024</td>\n",
|
||
" <td>2024</td>\n",
|
||
" <td>intermediate</td>\n",
|
||
" <td>illness</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>other</td>\n",
|
||
" <td>None</td>\n",
|
||
" <td>26.0</td>\n",
|
||
" <td>Italia</td>\n",
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||
" <td>None</td>\n",
|
||
" <td>None</td>\n",
|
||
" <td>[snowmobile]</td>\n",
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||
" <td>3</td>\n",
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||
" <td>12</td>\n",
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||
" <td>6</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
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||
" <th>4</th>\n",
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||
" <td>627685024</td>\n",
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||
" <td>2024-04-14 09:15:00+00:00</td>\n",
|
||
" <td>8</td>\n",
|
||
" <td>Passo Tonale - Presena</td>\n",
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||
" <td>105</td>\n",
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||
" <td>555</td>\n",
|
||
" <td>2024</td>\n",
|
||
" <td>2024</td>\n",
|
||
" <td>easy</td>\n",
|
||
" <td>fall_alone</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>bruise</td>\n",
|
||
" <td>None</td>\n",
|
||
" <td>9.0</td>\n",
|
||
" <td>Italia</td>\n",
|
||
" <td>L</td>\n",
|
||
" <td>lower_limbs</td>\n",
|
||
" <td>[snowmobile]</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>9</td>\n",
|
||
" <td>6</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>5 rows × 26 columns</p>\n",
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||
"</div>"
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||
],
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"text/plain": [
|
||
" id dateandtime skiarea_id skiarea_name \\\n",
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||
"0 627685195 2024-04-27 08:00:00+00:00 8 Passo Tonale - Presena \n",
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||
"1 627685151 2024-04-20 10:40:00+00:00 8 Passo Tonale - Presena \n",
|
||
"2 627685150 2024-04-20 09:00:00+00:00 8 Passo Tonale - Presena \n",
|
||
"3 627685033 2024-04-14 12:30:00+00:00 6 Pampeago \n",
|
||
"4 627685024 2024-04-14 09:15:00+00:00 8 Passo Tonale - Presena \n",
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||
"\n",
|
||
" day_of_year minute_of_day year season difficulty cause ... \\\n",
|
||
"0 118 480 2024 2024 advanced fall_alone ... \n",
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||
"1 111 640 2024 2024 intermediate illness ... \n",
|
||
"2 111 540 2024 2024 advanced fall_alone ... \n",
|
||
"3 105 750 2024 2024 intermediate illness ... \n",
|
||
"4 105 555 2024 2024 easy fall_alone ... \n",
|
||
"\n",
|
||
" diagnosis india age country injury_side injury_general_location \\\n",
|
||
"0 fracture None 52.0 Italia R lower_limbs \n",
|
||
"1 malaise None 51.0 Italia None None \n",
|
||
"2 distortion None 48.0 Italia R lower_limbs \n",
|
||
"3 other None 26.0 Italia None None \n",
|
||
"4 bruise None 9.0 Italia L lower_limbs \n",
|
||
"\n",
|
||
" evacuation_vehicles iii hour dow \n",
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"0 [ambulance, akja] 0 8 5 \n",
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"1 [ski_lift, indipendently] 1 10 5 \n",
|
||
"2 [akja, car] 2 9 5 \n",
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||
"3 [snowmobile] 3 12 6 \n",
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||
"4 [snowmobile] 4 9 6 \n",
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||
"\n",
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||
"[5 rows x 26 columns]"
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||
]
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||
},
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||
"execution_count": 109,
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||
"metadata": {},
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||
"output_type": "execute_result"
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||
}
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||
],
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"source": [
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||
"df.head() ## provare ad aggiungere ora\n",
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" ## aggiungere regione? e lavorare solo trentino ##chiesto\n",
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" ## chiedere meteo ##chiesto\n",
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" ## numero incidenti giornalieri e gravita\n",
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" ## uso improprio delle ambulanze \n",
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" ## max un mese (meta' maggio) --> 6 giugno campiglio 17.50 \n",
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" ## ingressi meteo e pista (MARCO)\n",
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"\n",
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" ## "
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]
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||
},
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||
{
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||
"cell_type": "code",
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||
"execution_count": 110,
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||
"id": "258eaf97-d3fd-4737-ad3b-bc0f8fc04ce8",
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||
"metadata": {},
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||
"outputs": [
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||
{
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||
"data": {
|
||
"text/plain": [
|
||
"(22324, 26)"
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||
]
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||
},
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||
"execution_count": 110,
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||
"metadata": {},
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||
"output_type": "execute_result"
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||
}
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||
],
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||
"source": [
|
||
"df.shape"
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||
]
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||
},
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{
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||
"cell_type": "code",
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||
"execution_count": 111,
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||
"id": "22a0b715-d8e2-4de5-bf50-7c7171ce242a",
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||
"metadata": {},
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||
"outputs": [],
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||
"source": [
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||
"aa = df.groupby('diagnosis').age.count().reset_index()\n",
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"aa.sort_values(by='age',ascending=True,inplace=True)\n",
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"\n",
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"import plotly.express as px\n",
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"fig = px.bar(aa.rename(columns={'age':'count'}), y='diagnosis', x='count',width=800,height=1200)\n",
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"fig.update_layout(\n",
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" xaxis_title=\"Counts\",\n",
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" yaxis_title=\"Diagnosis\",\n",
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" title = {\n",
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" 'text': \"Distribution of rescues by diagnosis\",\n",
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" #'y':0.9, # new\n",
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" 'x':0.5,\n",
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" 'xanchor': 'center',\n",
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" 'yanchor': 'top' # new\n",
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" },\n",
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"\n",
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" font=dict(\n",
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" #family=\"Courier New, monospace\",\n",
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" size=18,\n",
|
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" #color=\"RebeccaPurple\"\n",
|
||
" )\n",
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")\n",
|
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"if all_seasons:\n",
|
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" fig.write_image(\"/home/agobbi/Projects/PID/datanalytics/PID/materiale_pres_all_seasons/fig1.png\") \n",
|
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"else:\n",
|
||
" fig.write_image(\"/home/agobbi/Projects/PID/datanalytics/PID/materiale_pres/fig1.png\") \n",
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"fig = px.bar(aa.rename(columns={'age':'count'}), y='diagnosis', x='count',width=800,height=1200,log_x=True)\n",
|
||
"fig.update_layout(\n",
|
||
" xaxis_title=\"Counts (log scale)\",\n",
|
||
" yaxis_title=\"Diagnosis\",\n",
|
||
" title = {\n",
|
||
" 'text': \"Distribution of rescues by diagnosis\",\n",
|
||
" #'y':0.9, # new\n",
|
||
" 'x':0.5,\n",
|
||
" 'xanchor': 'center',\n",
|
||
" 'yanchor': 'top' # new\n",
|
||
" },\n",
|
||
"\n",
|
||
" font=dict(\n",
|
||
" #family=\"Courier New, monospace\",\n",
|
||
" size=18,\n",
|
||
" #color=\"RebeccaPurple\"\n",
|
||
" )\n",
|
||
")\n",
|
||
"if all_seasons:\n",
|
||
" fig.write_image(\"/home/agobbi/Projects/PID/datanalytics/PID/materiale_pres_all_seasons/fig1_log.png\") \n",
|
||
"else:\n",
|
||
" fig.write_image(\"/home/agobbi/Projects/PID/datanalytics/PID/materiale_pres/fig1_log.png\") \n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 112,
|
||
"id": "a78b9ccd-e8bb-44f9-a096-a917fa0caed8",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Text(0, 0.5, 'Diagnosi')"
|
||
]
|
||
},
|
||
"execution_count": 112,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 500x700 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"aa = df.groupby('diagnosis').age.count().reset_index()\n",
|
||
"aa.sort_values(by='age',ascending=True,inplace=True)\n",
|
||
"plt.figure(figsize = (5,7))\n",
|
||
"plt.barh(aa.diagnosis, aa.age)\n",
|
||
"plt.xlabel('Numero di soccorsi')\n",
|
||
"plt.ylabel('Diagnosi')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 113,
|
||
"id": "0d54fbe1-9a36-4162-836f-5f45061070bb",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"colors = [\n",
|
||
" \"rgb(0, 48, 143)\",\n",
|
||
" \"rgb(65, 175, 26)\",\n",
|
||
" #\"rgb(168, 227, 0)\",\n",
|
||
" \"rgb(255, 201, 77)\",\n",
|
||
" \"rgb(255, 107, 0)\",\n",
|
||
" \"rgb(214, 11, 67)\",\n",
|
||
" ]\n",
|
||
"\n",
|
||
"\n",
|
||
"color_list=[[0, colors[0]],\n",
|
||
" [1/10, colors[1]], \n",
|
||
" [5/10, colors[2]], \n",
|
||
" #[5/10, colors[3]], \n",
|
||
" [10/10, colors[4]]]\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 114,
|
||
"id": "ca2eaf74-60e5-4798-9f51-e08da69eb6d5",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def plot_rr(df,c1,c2):\n",
|
||
" diagnosis = df.groupby([c1,c2]).iii.count().reset_index()\n",
|
||
" diagnosis = diagnosis.pivot(columns=c2,values='iii',index=c1).reset_index().fillna(0)\n",
|
||
" diagnosis.columns.name = None\n",
|
||
" diagnosis.index.name = None\n",
|
||
" if c1=='skiarea_id':\n",
|
||
" diagnosis.loc[:,c1]=diagnosis[c1].apply(lambda x:str(int(float(x))))\n",
|
||
" diagnosis = diagnosis.set_index(c1)\n",
|
||
" diagnosis['tot'] = diagnosis.sum(axis=1)\n",
|
||
" diagnosis = diagnosis[diagnosis.tot>100]\n",
|
||
" sus=[]\n",
|
||
" tots = pd.DataFrame(diagnosis.sum(axis=0),columns=['n'])\n",
|
||
" from scipy.stats.contingency import relative_risk\n",
|
||
" for i in range(diagnosis.shape[0]):\n",
|
||
" for j in range(diagnosis.shape[1]-1):\n",
|
||
" result = relative_risk(int(diagnosis.values[i,j]), int(diagnosis.values[i,-1]), int(tots.values[j][0]), int(tots.values[-1][0]))\n",
|
||
" ci = result.confidence_interval(confidence_level=0.95)\n",
|
||
" if ((ci[0]>1) & (ci[1]>1)) | ((ci[0]<1) & (ci[1]<1)):\n",
|
||
" sus.append({c1:diagnosis.index[i],c2:diagnosis.columns[j],'rr':np.round(result.relative_risk,2)})\n",
|
||
" else:\n",
|
||
" sus.append({c1:diagnosis.index[i],c2:diagnosis.columns[j],'rr':np.nan})\n",
|
||
" sus = pd.DataFrame(sus) \n",
|
||
" import matplotlib.pyplot as plt\n",
|
||
" import plotly.express as px\n",
|
||
" sus.loc[sus.rr>10,'rr'] = 10\n",
|
||
" ss = sus.pivot(columns=c2,values='rr',index=c1).reset_index().fillna(1)\n",
|
||
" #plt.imshow(ss.values[:,2:].astype(float),aspect='auto')\n",
|
||
" ss.index = ss[c1]\n",
|
||
" ss = ss.drop(columns=c1)\n",
|
||
" ss[ss==1] = np.nan\n",
|
||
"\n",
|
||
" #plt.colorbar()\n",
|
||
" #x = ss.columns[2:]\n",
|
||
" #y = ss.skiarea.values\n",
|
||
" #plt.xticks(range(len(x)), x, fontsize=12);\n",
|
||
" #plt.yticks(range(len(y)), y, fontsize=12);\n",
|
||
" \n",
|
||
" #fig.show()\n",
|
||
" return diagnosis, ss"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 115,
|
||
"id": "d5c1aa3f-15f4-460a-a115-ff54e8036356",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/tmp/ipykernel_101408/941372287.py:7: FutureWarning:\n",
|
||
"\n",
|
||
"Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['1' '3' '4' '6' '7' '8' '9' '10' '11' '12' '17' '20' '24' '28' '31' '32'\n",
|
||
" '36' '37' '51' '55' '58' '59' '61' '64' '65' '78' '81' '170']' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<iframe\n",
|
||
" scrolling=\"no\"\n",
|
||
" width=\"1220px\"\n",
|
||
" height=\"1220\"\n",
|
||
" src=\"iframe_figures/figure_115.html\"\n",
|
||
" frameborder=\"0\"\n",
|
||
" allowfullscreen\n",
|
||
"></iframe>\n"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"diagnosis,ss = plot_rr(df,'skiarea_id','diagnosis')\n",
|
||
"fig = px.imshow(ss.T,width=1200, height=1200, aspect=\"auto\", color_continuous_scale=color_list)\n",
|
||
"fig.update_coloraxes(showscale=True)\n",
|
||
"\n",
|
||
"fig.update_layout(\n",
|
||
" yaxis_title=\"Diagnosis\",\n",
|
||
" xaxis_title=\"Skiarea ID\",\n",
|
||
" title = {\n",
|
||
" 'text': \"Relative risk Diagnosis-Skiarea\",\n",
|
||
" #'y':0.9, # new\n",
|
||
" 'x':0.5,\n",
|
||
" 'xanchor': 'center',\n",
|
||
" 'yanchor': 'top' # new\n",
|
||
" },\n",
|
||
" font=dict(\n",
|
||
" #family=\"Courier New, monospace\",\n",
|
||
" size=18,\n",
|
||
" #color=\"RebeccaPurple\"\n",
|
||
" )\n",
|
||
")\n",
|
||
"fig.show()\n",
|
||
"if all_seasons:\n",
|
||
" fig.write_image(\"/home/agobbi/Projects/PID/datanalytics/PID/materiale_pres_all_seasons/fig2.png\") \n",
|
||
"else:\n",
|
||
" fig.write_image(\"/home/agobbi/Projects/PID/datanalytics/PID/materiale_pres/fig2.png\") "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 116,
|
||
"id": "1810c816-6e31-44c2-aec3-585556ecb6a0",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/tmp/ipykernel_101408/941372287.py:7: FutureWarning:\n",
|
||
"\n",
|
||
"Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['1' '3' '4' '6' '7' '8' '9' '10' '11' '12' '17' '20' '24' '28' '31' '32'\n",
|
||
" '36' '37' '51' '55' '58' '59' '61' '64' '65' '78' '81' '170']' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<iframe\n",
|
||
" scrolling=\"no\"\n",
|
||
" width=\"1220px\"\n",
|
||
" height=\"1220\"\n",
|
||
" src=\"iframe_figures/figure_116.html\"\n",
|
||
" frameborder=\"0\"\n",
|
||
" allowfullscreen\n",
|
||
"></iframe>\n"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"diagnosis,ss = plot_rr(df,'skiarea_id','dow')\n",
|
||
"fig = px.imshow(ss,width=1200, height=1200, aspect=\"auto\", color_continuous_scale=color_list)\n",
|
||
"fig.update_coloraxes(showscale=True)\n",
|
||
"\n",
|
||
"fig.update_layout(\n",
|
||
" xaxis_title=\"Day of the week\",\n",
|
||
" yaxis_title=\"Skiarea ID\",\n",
|
||
" title = {\n",
|
||
" 'text': \"Relative risk Diagnosis-DOW\",\n",
|
||
" #'y':0.9, # new\n",
|
||
" 'x':0.5,\n",
|
||
" 'xanchor': 'center',\n",
|
||
" 'yanchor': 'top' # new\n",
|
||
" },\n",
|
||
" font=dict(\n",
|
||
" #family=\"Courier New, monospace\",\n",
|
||
" size=18,\n",
|
||
" #color=\"RebeccaPurple\"\n",
|
||
" )\n",
|
||
")\n",
|
||
"fig.show()\n",
|
||
"if all_seasons:\n",
|
||
" fig.write_image(\"/home/agobbi/Projects/PID/datanalytics/PID/materiale_pres_all_seasons/fig3.png\") \n",
|
||
"else:\n",
|
||
" fig.write_image(\"/home/agobbi/Projects/PID/datanalytics/PID/materiale_pres/fig3.png\") \n",
|
||
"aa = df.groupby('dow').age.count().reset_index()\n",
|
||
"days = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n",
|
||
"\n",
|
||
"aa.dow = aa.dow.apply(lambda x: days[x])\n",
|
||
"import plotly.express as px\n",
|
||
"fig = px.bar(aa.rename(columns={'age':'count'}), y='count', x='dow',width=800,height=800)\n",
|
||
"fig.update_layout(\n",
|
||
" yaxis_title=\"Counts\",\n",
|
||
" xaxis_title=\"Day of the week\",\n",
|
||
" title = {\n",
|
||
" 'text': \"Distribution of rescues by day of the week\",\n",
|
||
" #'y':0.9, # new\n",
|
||
" 'x':0.5,\n",
|
||
" 'xanchor': 'center',\n",
|
||
" 'yanchor': 'top' # new\n",
|
||
" },\n",
|
||
"\n",
|
||
" font=dict(\n",
|
||
" #family=\"Courier New, monospace\",\n",
|
||
" size=18,\n",
|
||
" #color=\"RebeccaPurple\"\n",
|
||
" )\n",
|
||
")\n",
|
||
"if all_seasons:\n",
|
||
" fig.write_image(\"/home/agobbi/Projects/PID/datanalytics/PID/materiale_pres_all_seasons/fig4.png\") \n",
|
||
"else:\n",
|
||
" fig.write_image(\"/home/agobbi/Projects/PID/datanalytics/PID/materiale_pres/fig4.png\") "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 117,
|
||
"id": "ef4c4a52-1be0-4261-9eda-552977c6a1d4",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/tmp/ipykernel_101408/941372287.py:7: FutureWarning:\n",
|
||
"\n",
|
||
"Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '['1' '3' '4' '6' '7' '8' '9' '10' '11' '12' '17' '20' '24' '28' '31' '32'\n",
|
||
" '36' '37' '51' '55' '58' '59' '61' '64' '65' '78' '81' '170']' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<iframe\n",
|
||
" scrolling=\"no\"\n",
|
||
" width=\"1220px\"\n",
|
||
" height=\"1220\"\n",
|
||
" src=\"iframe_figures/figure_117.html\"\n",
|
||
" frameborder=\"0\"\n",
|
||
" allowfullscreen\n",
|
||
"></iframe>\n"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"\n",
|
||
"diagnosis,ss = plot_rr(df,'skiarea_id','hour')\n",
|
||
"fig = px.imshow(ss,width=1200, height=1200, aspect=\"auto\", color_continuous_scale=color_list)\n",
|
||
"fig.update_coloraxes(showscale=True)\n",
|
||
"fig.update_layout(\n",
|
||
" xaxis_title=\"Hour\",\n",
|
||
" yaxis_title=\"Skiarea ID\",\n",
|
||
" title = {\n",
|
||
" 'text': \"Relative risk Diagnosis-Hour\",\n",
|
||
" #'y':0.9, # new\n",
|
||
" 'x':0.5,\n",
|
||
" 'xanchor': 'center',\n",
|
||
" 'yanchor': 'top' # new\n",
|
||
" },\n",
|
||
" font=dict(\n",
|
||
" #family=\"Courier New, monospace\",\n",
|
||
" size=18,\n",
|
||
" #color=\"RebeccaPurple\"\n",
|
||
" )\n",
|
||
")\n",
|
||
"fig.show()\n",
|
||
"if all_seasons:\n",
|
||
" fig.write_image(\"/home/agobbi/Projects/PID/datanalytics/PID/materiale_pres_all_seasons/fig5.png\") \n",
|
||
"else:\n",
|
||
" fig.write_image(\"/home/agobbi/Projects/PID/datanalytics/PID/materiale_pres/fig5.png\") \n",
|
||
"\n",
|
||
"aa = df.groupby('hour').age.count().reset_index()\n",
|
||
"\n",
|
||
"import plotly.express as px\n",
|
||
"fig = px.bar(aa.rename(columns={'age':'count'}), y='count', x='hour',width=1000,height=600)\n",
|
||
"fig.update_layout(\n",
|
||
" yaxis_title=\"Counts\",\n",
|
||
" xaxis_title=\"Hour\",\n",
|
||
" title = {\n",
|
||
" 'text': \"Distribution of rescues by hour\",\n",
|
||
" #'y':0.9, # new\n",
|
||
" 'x':0.5,\n",
|
||
" 'xanchor': 'center',\n",
|
||
" 'yanchor': 'top' # new\n",
|
||
" },\n",
|
||
"\n",
|
||
" font=dict(\n",
|
||
" #family=\"Courier New, monospace\",\n",
|
||
" size=18,\n",
|
||
" #color=\"RebeccaPurple\"\n",
|
||
" )\n",
|
||
")\n",
|
||
"if all_seasons:\n",
|
||
" fig.write_image(\"/home/agobbi/Projects/PID/datanalytics/PID/materiale_pres_all_seasons/fig6.png\") \n",
|
||
"else:\n",
|
||
" fig.write_image(\"/home/agobbi/Projects/PID/datanalytics/PID/materiale_pres/fig6.png\") "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 118,
|
||
"id": "aeb4bee2-213f-430a-a66d-35bcee5f4ca1",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<iframe\n",
|
||
" scrolling=\"no\"\n",
|
||
" width=\"620px\"\n",
|
||
" height=\"620\"\n",
|
||
" src=\"iframe_figures/figure_118.html\"\n",
|
||
" frameborder=\"0\"\n",
|
||
" allowfullscreen\n",
|
||
"></iframe>\n"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"\n",
|
||
"diagnosis,ss = plot_rr(df,'hour','dow')\n",
|
||
"fig = px.imshow(ss,width=600, height=600, aspect=\"auto\", color_continuous_scale=color_list)\n",
|
||
"fig.update_coloraxes(showscale=True)\n",
|
||
"fig.update_layout(\n",
|
||
" yaxis_title=\"Hour\",\n",
|
||
" xaxis_title=\"Dow\",\n",
|
||
" title = {\n",
|
||
" 'text': \"Relative risk Dow-Hour\",\n",
|
||
" #'y':0.9, # new\n",
|
||
" 'x':0.5,\n",
|
||
" 'xanchor': 'center',\n",
|
||
" 'yanchor': 'top' # new\n",
|
||
" },\n",
|
||
" font=dict(\n",
|
||
" #family=\"Courier New, monospace\",\n",
|
||
" size=18,\n",
|
||
" #color=\"RebeccaPurple\"\n",
|
||
" )\n",
|
||
")\n",
|
||
"fig.show()\n",
|
||
"if all_seasons:\n",
|
||
" fig.write_image(\"/home/agobbi/Projects/PID/datanalytics/PID/materiale_pres_all_seasons/fig7.png\") \n",
|
||
"else:\n",
|
||
" fig.write_image(\"/home/agobbi/Projects/PID/datanalytics/PID/materiale_pres/fig7.png\") "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 119,
|
||
"id": "f2b7a2cc-4a7d-44e2-9f4e-23e0005e399c",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<iframe\n",
|
||
" scrolling=\"no\"\n",
|
||
" width=\"920px\"\n",
|
||
" height=\"920\"\n",
|
||
" src=\"iframe_figures/figure_119.html\"\n",
|
||
" frameborder=\"0\"\n",
|
||
" allowfullscreen\n",
|
||
"></iframe>\n"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"\n",
|
||
"diagnosis,ss = plot_rr(df,'diagnosis','dow')\n",
|
||
"fig = px.imshow(ss,width=900, height=900, aspect=\"auto\", color_continuous_scale=color_list)\n",
|
||
"fig.update_coloraxes(showscale=True)\n",
|
||
"fig.update_layout(\n",
|
||
" xaxis_title=\"Hour\",\n",
|
||
" yaxis_title=\"Diagnosis\",\n",
|
||
" title = {\n",
|
||
" 'text': \"Relative risk Diagnosis-Hour\",\n",
|
||
" #'y':0.9, # new\n",
|
||
" 'x':0.5,\n",
|
||
" 'xanchor': 'center',\n",
|
||
" 'yanchor': 'top' # new\n",
|
||
" },\n",
|
||
" font=dict(\n",
|
||
" #family=\"Courier New, monospace\",\n",
|
||
" size=18,\n",
|
||
" #color=\"RebeccaPurple\"\n",
|
||
" )\n",
|
||
")\n",
|
||
"fig.show()\n",
|
||
"if all_seasons:\n",
|
||
" fig.write_image(\"/home/agobbi/Projects/PID/datanalytics/PID/materiale_pres_all_seasons/fig8.png\") \n",
|
||
"else:\n",
|
||
" fig.write_image(\"/home/agobbi/Projects/PID/datanalytics/PID/materiale_pres/fig8.png\") \n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 120,
|
||
"id": "7f2d54a0-f869-4504-9b78-0b964b9e23c2",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"if all_seasons:\n",
|
||
" # 'weather', 'snow_condition',\n",
|
||
" diagnosis,ss = plot_rr(df,'skiarea_id','weather')\n",
|
||
" fig = px.imshow(ss,width=1200, height=1200, aspect=\"auto\", color_continuous_scale=color_list)\n",
|
||
" fig.update_coloraxes(showscale=True)\n",
|
||
" \n",
|
||
" fig.update_layout(\n",
|
||
" xaxis_title=\"Weather condition\",\n",
|
||
" yaxis_title=\"Skiarea ID\",\n",
|
||
" title = {\n",
|
||
" 'text': \"Relative risk Diagnosis-DOW\",\n",
|
||
" #'y':0.9, # new\n",
|
||
" 'x':0.5,\n",
|
||
" 'xanchor': 'center',\n",
|
||
" 'yanchor': 'top' # new\n",
|
||
" },\n",
|
||
" font=dict(\n",
|
||
" #family=\"Courier New, monospace\",\n",
|
||
" size=18,\n",
|
||
" #color=\"RebeccaPurple\"\n",
|
||
" )\n",
|
||
" )\n",
|
||
" fig.show()\n",
|
||
" if all_seasons:\n",
|
||
" fig.write_image(\"/home/agobbi/Projects/PID/datanalytics/PID/materiale_pres_all_seasons/fig9.png\") \n",
|
||
" else:\n",
|
||
" fig.write_image(\"/home/agobbi/Projects/PID/datanalytics/PID/materiale_pres/fig9.png\") \n",
|
||
" aa = df.groupby('weather').age.count().reset_index()\n",
|
||
"\n",
|
||
" import plotly.express as px\n",
|
||
" fig = px.bar(aa.rename(columns={'age':'count'}), y='count', x='weather',width=800,height=800,log_y=True)\n",
|
||
" fig.update_layout(\n",
|
||
" yaxis_title=\"Counts (log scale)\",\n",
|
||
" xaxis_title=\"Weather condition\",\n",
|
||
" title = {\n",
|
||
" 'text': \"Distribution of rescues by weather condition\",\n",
|
||
" #'y':0.9, # new\n",
|
||
" 'x':0.5,\n",
|
||
" 'xanchor': 'center',\n",
|
||
" 'yanchor': 'top' # new\n",
|
||
" },\n",
|
||
" \n",
|
||
" font=dict(\n",
|
||
" #family=\"Courier New, monospace\",\n",
|
||
" size=18,\n",
|
||
" #color=\"RebeccaPurple\"\n",
|
||
" )\n",
|
||
" )\n",
|
||
" if all_seasons:\n",
|
||
" fig.write_image(\"/home/agobbi/Projects/PID/datanalytics/PID/materiale_pres_all_seasons/fig10.png\") \n",
|
||
" else:\n",
|
||
" fig.write_image(\"/home/agobbi/Projects/PID/datanalytics/PID/materiale_pres/fig10.png\") \n",
|
||
"\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 121,
|
||
"id": "a5ac856e-db76-42bd-a088-f9cd4211ad16",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"if all_seasons:\n",
|
||
" # 'weather', 'snow_condition',\n",
|
||
" diagnosis,ss = plot_rr(df,'skiarea_id','snow_condition')\n",
|
||
" fig = px.imshow(ss,width=1200, height=1200, aspect=\"auto\", color_continuous_scale=color_list)\n",
|
||
" fig.update_coloraxes(showscale=True)\n",
|
||
" \n",
|
||
" fig.update_layout(\n",
|
||
" xaxis_title=\"Snow condition\",\n",
|
||
" yaxis_title=\"Skiarea ID\",\n",
|
||
" title = {\n",
|
||
" 'text': \"Relative risk Diagnosis-DOW\",\n",
|
||
" #'y':0.9, # new\n",
|
||
" 'x':0.5,\n",
|
||
" 'xanchor': 'center',\n",
|
||
" 'yanchor': 'top' # new\n",
|
||
" },\n",
|
||
" font=dict(\n",
|
||
" #family=\"Courier New, monospace\",\n",
|
||
" size=18,\n",
|
||
" #color=\"RebeccaPurple\"\n",
|
||
" )\n",
|
||
" )\n",
|
||
" fig.show()\n",
|
||
" if all_seasons:\n",
|
||
" fig.write_image(\"/home/agobbi/Projects/PID/datanalytics/PID/materiale_pres_all_seasons/fig11.png\") \n",
|
||
" else:\n",
|
||
" fig.write_image(\"/home/agobbi/Projects/PID/datanalytics/PID/materiale_pres/fig11.png\") \n",
|
||
" aa = df.groupby('snow_condition').age.count().reset_index()\n",
|
||
"\n",
|
||
" import plotly.express as px\n",
|
||
" fig = px.bar(aa.rename(columns={'age':'count'}), y='count', x='snow_condition',width=800,height=800,log_y=True)\n",
|
||
" fig.update_layout(\n",
|
||
" yaxis_title=\"Counts (log scale)\",\n",
|
||
" xaxis_title=\"Snow condition\",\n",
|
||
" title = {\n",
|
||
" 'text': \"Distribution of rescues by weather condition\",\n",
|
||
" #'y':0.9, # new\n",
|
||
" 'x':0.5,\n",
|
||
" 'xanchor': 'center',\n",
|
||
" 'yanchor': 'top' # new\n",
|
||
" },\n",
|
||
" \n",
|
||
" font=dict(\n",
|
||
" #family=\"Courier New, monospace\",\n",
|
||
" size=18,\n",
|
||
" #color=\"RebeccaPurple\"\n",
|
||
" )\n",
|
||
" )\n",
|
||
" if all_seasons:\n",
|
||
" fig.write_image(\"/home/agobbi/Projects/PID/datanalytics/PID/materiale_pres_all_seasons/fig12.png\") \n",
|
||
" else:\n",
|
||
" fig.write_image(\"/home/agobbi/Projects/PID/datanalytics/PID/materiale_pres/fig12.png\") \n",
|
||
"\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 102,
|
||
"id": "82cba189-591d-4498-a606-32dd9c2d6e8d",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Index(['id', 'dateandtime', 'skiarea_id', 'skiarea_name', 'day_of_year',\n",
|
||
" 'minute_of_day', 'year', 'season', 'difficulty', 'cause', 'town',\n",
|
||
" 'province', 'gender', 'equipment', 'helmet', 'destination', 'diagnosis',\n",
|
||
" 'india', 'age', 'country', 'injury_side', 'injury_general_location',\n",
|
||
" 'evacuation_vehicles', 'weather', 'snow_condition', 'iii', 'hour',\n",
|
||
" 'dow'],\n",
|
||
" dtype='object')"
|
||
]
|
||
},
|
||
"execution_count": 102,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df.columns"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "555d73b3-8b99-4cd3-92e3-216be4b4d459",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 95,
|
||
"id": "14363528-3d2b-4c4f-ae4b-a3abb5943b57",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"df_orig = df.copy()\n",
|
||
"with open('../src/test/metadata.pkl','rb') as f:\n",
|
||
" to_remove,use_small,evacuations,encoders = pickle.load(f)\n",
|
||
"\n",
|
||
"for c in evacuations:\n",
|
||
" df[c] = False\n",
|
||
"df['other'] = False\n",
|
||
"for i,row in df.iterrows():\n",
|
||
" evacuation = row.evacuation_vehicles\n",
|
||
" for c in evacuation:\n",
|
||
" df.loc[i,c] = True\n",
|
||
" \n",
|
||
" for c in evacuation:\n",
|
||
" if c not in evacuations:\n",
|
||
" df.loc[i,'other'] = True\n",
|
||
" brea\n",
|
||
"\n",
|
||
"df.drop(columns='evacuation_vehicles', inplace=True)\n",
|
||
"\n",
|
||
"\n",
|
||
"df['age'] = df['age'].astype(np.float32)\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"for c in df.columns:\n",
|
||
" if c not in ['india','age','season','skiarea_name','destination']:\n",
|
||
" df[c] = df[c].astype('str') \n",
|
||
"if use_small:\n",
|
||
" for c in to_remove.keys():\n",
|
||
" for k in to_remove[c]:\n",
|
||
" df.loc[df[c]==k,c] = 'other'\n",
|
||
"if use_small:\n",
|
||
" for c in encoders['small']:\n",
|
||
" df.loc[~df[c].isin( encoders['small'][c].classes_),c] = 'other'\n",
|
||
"else:\n",
|
||
" for c in encoders['normal']:\n",
|
||
" df.loc[~df[c].isin( encoders['normal'][c].classes_),c] = 'other'\n",
|
||
"\n",
|
||
"bst_FS = xgb.Booster()\n",
|
||
"bst_FS.load_model(\"../src/test/model.json\")\n",
|
||
"for c in df.columns:\n",
|
||
" if c not in ['age','season','skiarea_name','india']:\n",
|
||
" df[c] = df[c].fillna('None')\n",
|
||
" if use_small:\n",
|
||
" if c in bst_FS.feature_names:\n",
|
||
" df[c] = pd.Categorical( encoders['small'][c].transform(df[c]), categories=list(range(len(encoders['small'][c].classes_))), ordered=False)\n",
|
||
" else:\n",
|
||
" if c in bst_FS.feature_names:\n",
|
||
" df[c] = pd.Categorical( encoders['normal'][c].transform(df[c]), categories=list(range(len(encoders['normal'][c].classes_))), ordered=False)\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"dtest_FS = xgb.DMatrix(df[bst_FS.feature_names],enable_categorical=True)\n",
|
||
"preds = bst_FS.predict(dtest_FS)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 96,
|
||
"id": "656a1519-7f4d-4e22-a8c0-408cff1d8e90",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"df['computed_SI'] = preds.argmax(1)\n",
|
||
"df_orig['computed_SI'] = preds.argmax(1)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "3c736c86-78c9-42bf-a7f7-47e18b5f3210",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 97,
|
||
"id": "839561a1-5482-4675-bb53-4e2ce55409f6",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/tmp/ipykernel_101408/3067476809.py:2: SettingWithCopyWarning:\n",
|
||
"\n",
|
||
"\n",
|
||
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
|
||
"Try using .loc[row_indexer,col_indexer] = value instead\n",
|
||
"\n",
|
||
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"0.8162839248434238"
|
||
]
|
||
},
|
||
"execution_count": 97,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"tmp = df[~pd.isnull(df.india)]\n",
|
||
"tmp.india = tmp.india.apply(lambda x:int(x[1]))\n",
|
||
"accuracy_score(tmp.india, tmp.computed_SI)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "89dad89d-b425-4969-b647-6a4f8ced343f",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 35,
|
||
"id": "9fef4151-e178-439b-b2c4-b4ffb93c1309",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/tmp/ipykernel_71942/1056303979.py:25: FutureWarning:\n",
|
||
"\n",
|
||
"ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
|
||
"You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
|
||
"A typical example is when you are setting values in a column of a DataFrame, like:\n",
|
||
"\n",
|
||
"df[\"col\"][row_indexer] = value\n",
|
||
"\n",
|
||
"Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n",
|
||
"\n",
|
||
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
|
||
"\n",
|
||
"\n",
|
||
"/tmp/ipykernel_71942/1056303979.py:25: SettingWithCopyWarning:\n",
|
||
"\n",
|
||
"\n",
|
||
"A value is trying to be set on a copy of a slice from a DataFrame\n",
|
||
"\n",
|
||
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<iframe\n",
|
||
" scrolling=\"no\"\n",
|
||
" width=\"1020px\"\n",
|
||
" height=\"1020\"\n",
|
||
" src=\"iframe_figures/figure_35.html\"\n",
|
||
" frameborder=\"0\"\n",
|
||
" allowfullscreen\n",
|
||
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|
||
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|
||
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|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"diagnosis = plot_rr(df_orig,'skiarea_id','computed_SI')\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 36,
|
||
"id": "170e8f70-ec31-4989-a534-5411c15465ef",
|
||
"metadata": {},
|
||
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|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/tmp/ipykernel_71942/1056303979.py:25: FutureWarning:\n",
|
||
"\n",
|
||
"ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
|
||
"You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
|
||
"A typical example is when you are setting values in a column of a DataFrame, like:\n",
|
||
"\n",
|
||
"df[\"col\"][row_indexer] = value\n",
|
||
"\n",
|
||
"Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n",
|
||
"\n",
|
||
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
|
||
"\n",
|
||
"\n",
|
||
"/tmp/ipykernel_71942/1056303979.py:25: SettingWithCopyWarning:\n",
|
||
"\n",
|
||
"\n",
|
||
"A value is trying to be set on a copy of a slice from a DataFrame\n",
|
||
"\n",
|
||
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<iframe\n",
|
||
" scrolling=\"no\"\n",
|
||
" width=\"1020px\"\n",
|
||
" height=\"1020\"\n",
|
||
" src=\"iframe_figures/figure_36.html\"\n",
|
||
" frameborder=\"0\"\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
],
|
||
"source": [
|
||
"diagnosis = plot_rr(df_orig,'dow','computed_SI')\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
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|
||
"id": "2f1d3beb-241b-4eb0-8fec-379603db2bcd",
|
||
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|
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|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/tmp/ipykernel_71942/1056303979.py:2: FutureWarning:\n",
|
||
"\n",
|
||
"The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
|
||
"\n",
|
||
"/tmp/ipykernel_71942/1056303979.py:25: FutureWarning:\n",
|
||
"\n",
|
||
"ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
|
||
"You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
|
||
"A typical example is when you are setting values in a column of a DataFrame, like:\n",
|
||
"\n",
|
||
"df[\"col\"][row_indexer] = value\n",
|
||
"\n",
|
||
"Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n",
|
||
"\n",
|
||
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
|
||
"\n",
|
||
"\n",
|
||
"/tmp/ipykernel_71942/1056303979.py:25: SettingWithCopyWarning:\n",
|
||
"\n",
|
||
"\n",
|
||
"A value is trying to be set on a copy of a slice from a DataFrame\n",
|
||
"\n",
|
||
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
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|
||
"<iframe\n",
|
||
" scrolling=\"no\"\n",
|
||
" width=\"1020px\"\n",
|
||
" height=\"1020\"\n",
|
||
" src=\"iframe_figures/figure_37.html\"\n",
|
||
" frameborder=\"0\"\n",
|
||
" allowfullscreen\n",
|
||
"></iframe>\n"
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
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|
||
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|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
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|
||
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|
||
"\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",
|
||
" <th>1</th>\n",
|
||
" <th>2</th>\n",
|
||
" <th>3</th>\n",
|
||
" <th>tot</th>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>helicopter</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>0</th>\n",
|
||
" <td>16</td>\n",
|
||
" <td>184</td>\n",
|
||
" <td>52</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>252</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" 0 1 2 3 tot\n",
|
||
"helicopter \n",
|
||
"0 16 184 52 0 252"
|
||
]
|
||
},
|
||
"execution_count": 37,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"diagnosis = plot_rr(tmp,'helicopter','computed_SI')\n",
|
||
"diagnosis"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 38,
|
||
"id": "a067c790-be3f-4abd-b67b-3a8957fae201",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/tmp/ipykernel_71942/1056303979.py:2: FutureWarning:\n",
|
||
"\n",
|
||
"The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
|
||
"\n",
|
||
"/tmp/ipykernel_71942/1056303979.py:25: FutureWarning:\n",
|
||
"\n",
|
||
"ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
|
||
"You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
|
||
"A typical example is when you are setting values in a column of a DataFrame, like:\n",
|
||
"\n",
|
||
"df[\"col\"][row_indexer] = value\n",
|
||
"\n",
|
||
"Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n",
|
||
"\n",
|
||
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
|
||
"\n",
|
||
"\n",
|
||
"/tmp/ipykernel_71942/1056303979.py:25: SettingWithCopyWarning:\n",
|
||
"\n",
|
||
"\n",
|
||
"A value is trying to be set on a copy of a slice from a DataFrame\n",
|
||
"\n",
|
||
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
|
||
"\n"
|
||
]
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
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|
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|
||
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|
||
" <th>3</th>\n",
|
||
" <th>4</th>\n",
|
||
" <th>tot</th>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>helicopter</th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
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|
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||
" </thead>\n",
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" <tbody>\n",
|
||
" <tr>\n",
|
||
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||
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|
||
" <td>13926</td>\n",
|
||
" <td>4049</td>\n",
|
||
" <td>13</td>\n",
|
||
" <td>19</td>\n",
|
||
" <td>21754</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>3</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>428</td>\n",
|
||
" <td>138</td>\n",
|
||
" <td>0</td>\n",
|
||
" <td>570</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" 0 1 2 3 4 tot\n",
|
||
"helicopter \n",
|
||
"0 3747 13926 4049 13 19 21754\n",
|
||
"1 3 1 428 138 0 570"
|
||
]
|
||
},
|
||
"execution_count": 38,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"diagnosis = plot_rr(df,'helicopter','computed_SI')\n",
|
||
"diagnosis"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
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|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"0\t1243\t11696\t4646\t57\t5\t17647\n",
|
||
"1\t5\t0\t621\t144\t2\t772"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 99,
|
||
"id": "d90d65eb-12a6-40a8-a68b-131a8a4070c5",
|
||
"metadata": {},
|
||
"outputs": [
|
||
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|
||
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|
||
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||
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|
||
"<style scoped>\n",
|
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|
||
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|
||
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|
||
" <th></th>\n",
|
||
" <th>computed_SI</th>\n",
|
||
" <th>skiarea_id</th>\n",
|
||
" </tr>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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|
||
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|
||
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|
||
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|
||
" <th>2</th>\n",
|
||
" <td>2</td>\n",
|
||
" <td>13569</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>3</td>\n",
|
||
" <td>497</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>4</td>\n",
|
||
" <td>62</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" computed_SI skiarea_id\n",
|
||
"0 0 10385\n",
|
||
"1 1 40284\n",
|
||
"2 2 13569\n",
|
||
"3 3 497\n",
|
||
"4 4 62"
|
||
]
|
||
},
|
||
"execution_count": 99,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df.groupby('computed_SI').skiarea_id.count().reset_index()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 100,
|
||
"id": "4dc947a9-5b16-41e5-adc4-88da08501314",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
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|
||
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|
||
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|
||
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|
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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||
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|
||
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|
||
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|
||
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|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>india</th>\n",
|
||
" <th>skiarea_id</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>i0</td>\n",
|
||
" <td>15</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>i1</td>\n",
|
||
" <td>355</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>i2</td>\n",
|
||
" <td>105</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>i3</td>\n",
|
||
" <td>4</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" india skiarea_id\n",
|
||
"0 i0 15\n",
|
||
"1 i1 355\n",
|
||
"2 i2 105\n",
|
||
"3 i3 4"
|
||
]
|
||
},
|
||
"execution_count": 100,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df.groupby('india').skiarea_id.count().reset_index()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "2ed8a05a-7128-496e-943f-38630fc783ca",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "4ec40f03-bf09-44a1-8322-1e778ab30f23",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "9cafaf56-8819-478f-bd7e-0a1abd07e5f0",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 179,
|
||
"id": "a7994f45-b384-4551-9d29-4f221d26592e",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"## posso anche vedere se ci sono delle aree sciistiche con pattern simili!\n",
|
||
"diagnosis = df[df.year>2015].groupby(['skiarea_id','diagnosis']).age.count().reset_index()\n",
|
||
"diagnosis = diagnosis.pivot(columns='diagnosis',values='age',index='skiarea_id').reset_index().fillna(0)\n",
|
||
"diagnosis.columns.name = None\n",
|
||
"diagnosis.index.name = None\n",
|
||
"diagnosis.skiarea_id=diagnosis.skiarea_id.apply(lambda x:str(int(float(x))))\n",
|
||
"diagnosis = diagnosis.set_index('skiarea_id')\n",
|
||
"diagnosis['tot'] = diagnosis.sum(axis=1)\n",
|
||
"diagnosis = diagnosis[diagnosis.tot>20]\n",
|
||
"diagnosis = diagnosis.apply(lambda x:x/x.tot,axis=1)\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 180,
|
||
"id": "9b28f525-d393-4d54-aaef-434a045252fa",
|
||
"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",
|
||
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|
||
" .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>abdominal_pain</th>\n",
|
||
" <th>anterior_cruciate_ligament</th>\n",
|
||
" <th>bruise</th>\n",
|
||
" <th>bruised_wound</th>\n",
|
||
" <th>burn</th>\n",
|
||
" <th>cardiovascular_problem</th>\n",
|
||
" <th>chest_pain</th>\n",
|
||
" <th>compound_fracture</th>\n",
|
||
" <th>concussion</th>\n",
|
||
" <th>crush</th>\n",
|
||
" <th>...</th>\n",
|
||
" <th>other</th>\n",
|
||
" <th>paralysis</th>\n",
|
||
" <th>penetrating_wound</th>\n",
|
||
" <th>pulse_alteration</th>\n",
|
||
" <th>respiratory_problems</th>\n",
|
||
" <th>trauma</th>\n",
|
||
" <th>trauma_crane</th>\n",
|
||
" <th>unharmed</th>\n",
|
||
" <th>wound</th>\n",
|
||
" <th>tot</th>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>skiarea_id</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",
|
||
" <th></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",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000743</td>\n",
|
||
" <td>0.215825</td>\n",
|
||
" <td>0.001114</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.001857</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0.151560</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000743</td>\n",
|
||
" <td>0.000371</td>\n",
|
||
" <td>0.000371</td>\n",
|
||
" <td>0.007058</td>\n",
|
||
" <td>0.008172</td>\n",
|
||
" <td>0.073923</td>\n",
|
||
" <td>0.052006</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>0.001193</td>\n",
|
||
" <td>0.008948</td>\n",
|
||
" <td>0.241996</td>\n",
|
||
" <td>0.003778</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.000994</td>\n",
|
||
" <td>0.001193</td>\n",
|
||
" <td>0.000199</td>\n",
|
||
" <td>0.000994</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0.117916</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.001193</td>\n",
|
||
" <td>0.000398</td>\n",
|
||
" <td>0.000398</td>\n",
|
||
" <td>0.005170</td>\n",
|
||
" <td>0.035594</td>\n",
|
||
" <td>0.053887</td>\n",
|
||
" <td>0.053887</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>0.000576</td>\n",
|
||
" <td>0.000863</td>\n",
|
||
" <td>0.241151</td>\n",
|
||
" <td>0.002158</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.000144</td>\n",
|
||
" <td>0.000432</td>\n",
|
||
" <td>0.003022</td>\n",
|
||
" <td>0.000719</td>\n",
|
||
" <td>0.000144</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0.054532</td>\n",
|
||
" <td>0.000144</td>\n",
|
||
" <td>0.000576</td>\n",
|
||
" <td>0.000288</td>\n",
|
||
" <td>0.000144</td>\n",
|
||
" <td>0.003309</td>\n",
|
||
" <td>0.012950</td>\n",
|
||
" <td>0.021439</td>\n",
|
||
" <td>0.032086</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.004695</td>\n",
|
||
" <td>0.247261</td>\n",
|
||
" <td>0.000782</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.002347</td>\n",
|
||
" <td>0.003912</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0.091549</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.001565</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.007825</td>\n",
|
||
" <td>0.007825</td>\n",
|
||
" <td>0.045383</td>\n",
|
||
" <td>0.034429</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000242</td>\n",
|
||
" <td>0.246496</td>\n",
|
||
" <td>0.001450</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.000242</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000967</td>\n",
|
||
" <td>0.000483</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0.041808</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000242</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.007975</td>\n",
|
||
" <td>0.024891</td>\n",
|
||
" <td>0.044949</td>\n",
|
||
" <td>0.048574</td>\n",
|
||
" <td>1.0</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",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\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>159</th>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.123288</td>\n",
|
||
" <td>0.027397</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.006849</td>\n",
|
||
" <td>0.006849</td>\n",
|
||
" <td>0.020548</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0.150685</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.034247</td>\n",
|
||
" <td>0.239726</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>162</th>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.295455</td>\n",
|
||
" <td>0.007576</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.007576</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0.015152</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.007576</td>\n",
|
||
" <td>0.037879</td>\n",
|
||
" <td>0.022727</td>\n",
|
||
" <td>0.068182</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>166</th>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.181818</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.045455</td>\n",
|
||
" <td>0.090909</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.045455</td>\n",
|
||
" <td>0.045455</td>\n",
|
||
" <td>0.045455</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>167</th>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.027149</td>\n",
|
||
" <td>0.176471</td>\n",
|
||
" <td>0.018100</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.004525</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.018100</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0.013575</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.027149</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.067873</td>\n",
|
||
" <td>0.004525</td>\n",
|
||
" <td>0.018100</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>170</th>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.235772</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.0</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.008130</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>0.048780</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.000000</td>\n",
|
||
" <td>0.016260</td>\n",
|
||
" <td>0.130081</td>\n",
|
||
" <td>0.016260</td>\n",
|
||
" <td>1.0</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>71 rows × 33 columns</p>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" abdominal_pain anterior_cruciate_ligament bruise \\\n",
|
||
"skiarea_id \n",
|
||
"1 0.000000 0.000743 0.215825 \n",
|
||
"2 0.001193 0.008948 0.241996 \n",
|
||
"3 0.000576 0.000863 0.241151 \n",
|
||
"4 0.000000 0.004695 0.247261 \n",
|
||
"5 0.000000 0.000242 0.246496 \n",
|
||
"... ... ... ... \n",
|
||
"159 0.000000 0.000000 0.123288 \n",
|
||
"162 0.000000 0.000000 0.295455 \n",
|
||
"166 0.000000 0.000000 0.181818 \n",
|
||
"167 0.000000 0.027149 0.176471 \n",
|
||
"170 0.000000 0.000000 0.235772 \n",
|
||
"\n",
|
||
" bruised_wound burn cardiovascular_problem chest_pain \\\n",
|
||
"skiarea_id \n",
|
||
"1 0.001114 0.0 0.000000 0.000000 \n",
|
||
"2 0.003778 0.0 0.000994 0.001193 \n",
|
||
"3 0.002158 0.0 0.000144 0.000432 \n",
|
||
"4 0.000782 0.0 0.000000 0.002347 \n",
|
||
"5 0.001450 0.0 0.000242 0.000000 \n",
|
||
"... ... ... ... ... \n",
|
||
"159 0.027397 0.0 0.006849 0.006849 \n",
|
||
"162 0.007576 0.0 0.000000 0.000000 \n",
|
||
"166 0.000000 0.0 0.000000 0.045455 \n",
|
||
"167 0.018100 0.0 0.000000 0.004525 \n",
|
||
"170 0.000000 0.0 0.000000 0.008130 \n",
|
||
"\n",
|
||
" compound_fracture concussion crush ... other paralysis \\\n",
|
||
"skiarea_id ... \n",
|
||
"1 0.001857 0.000000 0.000000 ... 0.151560 0.000000 \n",
|
||
"2 0.000199 0.000994 0.000000 ... 0.117916 0.000000 \n",
|
||
"3 0.003022 0.000719 0.000144 ... 0.054532 0.000144 \n",
|
||
"4 0.003912 0.000000 0.000000 ... 0.091549 0.000000 \n",
|
||
"5 0.000967 0.000483 0.000000 ... 0.041808 0.000000 \n",
|
||
"... ... ... ... ... ... ... \n",
|
||
"159 0.020548 0.000000 0.000000 ... 0.150685 0.000000 \n",
|
||
"162 0.000000 0.007576 0.000000 ... 0.015152 0.000000 \n",
|
||
"166 0.090909 0.000000 0.000000 ... 0.000000 0.000000 \n",
|
||
"167 0.000000 0.018100 0.000000 ... 0.013575 0.000000 \n",
|
||
"170 0.000000 0.000000 0.000000 ... 0.048780 0.000000 \n",
|
||
"\n",
|
||
" penetrating_wound pulse_alteration respiratory_problems \\\n",
|
||
"skiarea_id \n",
|
||
"1 0.000743 0.000371 0.000371 \n",
|
||
"2 0.001193 0.000398 0.000398 \n",
|
||
"3 0.000576 0.000288 0.000144 \n",
|
||
"4 0.000000 0.001565 0.000000 \n",
|
||
"5 0.000242 0.000000 0.000000 \n",
|
||
"... ... ... ... \n",
|
||
"159 0.000000 0.000000 0.000000 \n",
|
||
"162 0.000000 0.000000 0.000000 \n",
|
||
"166 0.000000 0.000000 0.000000 \n",
|
||
"167 0.027149 0.000000 0.000000 \n",
|
||
"170 0.000000 0.000000 0.000000 \n",
|
||
"\n",
|
||
" trauma trauma_crane unharmed wound tot \n",
|
||
"skiarea_id \n",
|
||
"1 0.007058 0.008172 0.073923 0.052006 1.0 \n",
|
||
"2 0.005170 0.035594 0.053887 0.053887 1.0 \n",
|
||
"3 0.003309 0.012950 0.021439 0.032086 1.0 \n",
|
||
"4 0.007825 0.007825 0.045383 0.034429 1.0 \n",
|
||
"5 0.007975 0.024891 0.044949 0.048574 1.0 \n",
|
||
"... ... ... ... ... ... \n",
|
||
"159 0.000000 0.034247 0.239726 0.000000 1.0 \n",
|
||
"162 0.007576 0.037879 0.022727 0.068182 1.0 \n",
|
||
"166 0.000000 0.045455 0.045455 0.045455 1.0 \n",
|
||
"167 0.000000 0.067873 0.004525 0.018100 1.0 \n",
|
||
"170 0.000000 0.016260 0.130081 0.016260 1.0 \n",
|
||
"\n",
|
||
"[71 rows x 33 columns]"
|
||
]
|
||
},
|
||
"execution_count": 180,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"diagnosis"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 182,
|
||
"id": "9e81dcdb-146f-4f12-a78a-1bdc40d05750",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<seaborn.matrix.ClusterGrid at 0x7f7f4502e8a0>"
|
||
]
|
||
},
|
||
"execution_count": 182,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x1000 with 4 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import seaborn as sns\n",
|
||
"sns.clustermap(diagnosis)\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 205,
|
||
"id": "5ce83040-3807-4f1e-af7e-0b8414fa525d",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/tmp/ipykernel_218931/2034767285.py:21: FutureWarning:\n",
|
||
"\n",
|
||
"ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
|
||
"You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
|
||
"A typical example is when you are setting values in a column of a DataFrame, like:\n",
|
||
"\n",
|
||
"df[\"col\"][row_indexer] = value\n",
|
||
"\n",
|
||
"Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n",
|
||
"\n",
|
||
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
|
||
"\n",
|
||
"\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"diagnosis = df.groupby(['skiarea_id','diagnosis']).age.count().reset_index()\n",
|
||
"diagnosis = diagnosis.pivot(columns='diagnosis',values='age',index='skiarea_id').reset_index().fillna(0)\n",
|
||
"diagnosis.columns.name = None\n",
|
||
"diagnosis.index.name = None\n",
|
||
"diagnosis.skiarea_id=diagnosis.skiarea_id.apply(lambda x:str(int(float(x))))\n",
|
||
"diagnosis = diagnosis.set_index('skiarea_id')\n",
|
||
"diagnosis['tot'] = diagnosis.sum(axis=1)\n",
|
||
"diagnosis = diagnosis[diagnosis.tot>100]\n",
|
||
"sus=[]\n",
|
||
"tots = pd.DataFrame(diagnosis.sum(axis=0),columns=['n'])\n",
|
||
"from scipy.stats.contingency import relative_risk\n",
|
||
"for i in range(diagnosis.shape[0]):\n",
|
||
" for j in range(diagnosis.shape[1]-1):\n",
|
||
" result = relative_risk(int(diagnosis.values[i,j]), int(diagnosis.values[i,-1]), int(tots.values[j][0]), int(tots.values[-1][0]))\n",
|
||
" ci = result.confidence_interval(confidence_level=0.95)\n",
|
||
" if ((ci[0]>1) & (ci[1]>1)) | ((ci[0]<1) & (ci[1]<1)):\n",
|
||
" sus.append({'skiarea':diagnosis.index[i],'diagnosis':diagnosis.columns[j],'rr':np.round(result.relative_risk,2)})\n",
|
||
"sus = pd.DataFrame(sus) \n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import plotly.express as px\n",
|
||
"sus.rr[sus.rr>10] = 10\n",
|
||
"ss = sus.pivot(columns='diagnosis',values='rr',index='skiarea').reset_index().fillna(1)\n",
|
||
"#plt.imshow(ss.values[:,2:].astype(float),aspect='auto')\n",
|
||
"ss.index = ss.skiarea\n",
|
||
"ss = ss.drop(columns='skiarea')\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 190,
|
||
"id": "b8afc3be-c951-4982-b1bc-af8b006f3ba8",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<seaborn.matrix.ClusterGrid at 0x7f7f56cbaa80>"
|
||
]
|
||
},
|
||
"execution_count": 190,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x1000 with 4 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"sns.clustermap(ss)\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 206,
|
||
"id": "64415581-e2c8-49a2-ae9b-0a60b2a10eef",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<iframe\n",
|
||
" scrolling=\"no\"\n",
|
||
" width=\"1220px\"\n",
|
||
" height=\"1220\"\n",
|
||
" src=\"iframe_figures/figure_206.html\"\n",
|
||
" frameborder=\"0\"\n",
|
||
" allowfullscreen\n",
|
||
"></iframe>\n"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import dash_bio\n",
|
||
"\n",
|
||
"dash_bio.Clustergram(\n",
|
||
" data=ss,\n",
|
||
" center_values=False,\n",
|
||
"\n",
|
||
" column_labels=list(ss.columns.values),\n",
|
||
" row_labels=list(ss.index),\n",
|
||
" height=1200,\n",
|
||
" width=1200,\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 196,
|
||
"id": "46682368-1321-424b-a979-276805c8f27b",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/tmp/ipykernel_218931/2465247991.py:22: FutureWarning:\n",
|
||
"\n",
|
||
"ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
|
||
"You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
|
||
"A typical example is when you are setting values in a column of a DataFrame, like:\n",
|
||
"\n",
|
||
"df[\"col\"][row_indexer] = value\n",
|
||
"\n",
|
||
"Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n",
|
||
"\n",
|
||
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
|
||
"\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<iframe\n",
|
||
" scrolling=\"no\"\n",
|
||
" width=\"1220px\"\n",
|
||
" height=\"1220\"\n",
|
||
" src=\"iframe_figures/figure_196.html\"\n",
|
||
" frameborder=\"0\"\n",
|
||
" allowfullscreen\n",
|
||
"></iframe>\n"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"diagnosis = df_orig.groupby(['skiarea_id','computed_SI']).age.count().reset_index()\n",
|
||
"diagnosis = diagnosis.pivot(columns='computed_SI',values='age',index='skiarea_id').reset_index().fillna(0)\n",
|
||
"diagnosis.columns.name = None\n",
|
||
"diagnosis.index.name = None\n",
|
||
"diagnosis.skiarea_id=diagnosis.skiarea_id.apply(lambda x:str(int(float(x))))\n",
|
||
"\n",
|
||
"diagnosis = diagnosis.set_index('skiarea_id')\n",
|
||
"diagnosis['tot'] = diagnosis.sum(axis=1)\n",
|
||
"diagnosis = diagnosis[diagnosis.tot>100]\n",
|
||
"sus = []\n",
|
||
"tots = pd.DataFrame(diagnosis.sum(axis=0),columns=['n'])\n",
|
||
"from scipy.stats.contingency import relative_risk\n",
|
||
"for i in range(diagnosis.shape[0]):\n",
|
||
" for j in range(diagnosis.shape[1]-1):\n",
|
||
" result = relative_risk(int(diagnosis.values[i,j]), int(diagnosis.values[i,-1]), int(tots.values[j][0]), int(tots.values[-1][0]))\n",
|
||
" ci = result.confidence_interval(confidence_level=0.95)\n",
|
||
" if ((ci[0]>1) & (ci[1]>1)) | ((ci[0]<1) & (ci[1]<1)):\n",
|
||
" sus.append({'skiarea':diagnosis.index[i],'computed_SI':diagnosis.columns[j],'rr':result.relative_risk})\n",
|
||
"sus = pd.DataFrame(sus) \n",
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import plotly.express as px\n",
|
||
"sus.rr[sus.rr>10] = 10\n",
|
||
"ss = sus.pivot(columns='computed_SI',values='rr',index='skiarea').reset_index().fillna(1)\n",
|
||
"#plt.imshow(ss.values[:,2:].astype(float),aspect='auto')\n",
|
||
"ss.index = ss.skiarea\n",
|
||
"ss = ss.drop(columns='skiarea')\n",
|
||
"fig = px.imshow(ss,width=1200, height=1200, aspect=\"auto\", color_continuous_scale=color_list)\n",
|
||
"fig.update_coloraxes(showscale=True)\n",
|
||
"#plt.colorbar()\n",
|
||
"#x = ss.columns[2:]\n",
|
||
"#y = ss.skiarea.values\n",
|
||
"#plt.xticks(range(len(x)), x, fontsize=12);\n",
|
||
"#plt.yticks(range(len(y)), y, fontsize=12);\n",
|
||
"import plotly.io as pio\n",
|
||
"pio.renderers.default = 'iframe'\n",
|
||
"fig.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 203,
|
||
"id": "f14d0b7e-140e-4726-bdf9-9ee1db706728",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"0.2031521389902816"
|
||
]
|
||
},
|
||
"execution_count": 203,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"ss.values.min()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 204,
|
||
"id": "8ccde0a2-bca0-4d10-90da-933c4c211a28",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<iframe\n",
|
||
" scrolling=\"no\"\n",
|
||
" width=\"1220px\"\n",
|
||
" height=\"1220\"\n",
|
||
" src=\"iframe_figures/figure_204.html\"\n",
|
||
" frameborder=\"0\"\n",
|
||
" allowfullscreen\n",
|
||
"></iframe>\n"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import dash_bio\n",
|
||
"\n",
|
||
"dash_bio.Clustergram(\n",
|
||
" data=ss,\n",
|
||
" center_values=False,\n",
|
||
" column_labels=list(ss.columns.values),\n",
|
||
" row_labels=list(ss.index),\n",
|
||
" height=1200,\n",
|
||
" width=1200,\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "25f5206d-f605-43a3-b247-5ac51f37dc08",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3 (ipykernel)",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.12.2"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|