1213 lines
35 KiB
Plaintext
1213 lines
35 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<a href=\"https://www.nvidia.com/dli\"><img src=\"images/DLI_Header.png\" alt=\"Header\" style=\"width: 400px;\"/></a>"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Week 3: Identify Risk Factors for Infection"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<span style=\"color:red\">\n",
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"**UPDATE**\n",
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"\n",
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"Thank you again for the previous analysis. We will next be publishing a public health advisory that warns of specific infection risk factors of which individuals should be aware. Please advise as to which population characteristics are associated with higher infection rates. \n",
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"</span>\n",
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"\n",
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"Your goal for this notebook will be to identify key potential demographic and economic risk factors for infection by comparing the infected and uninfected populations."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Imports"
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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": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"%load_ext cudf.pandas\n",
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"import pandas as pd\n",
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"import cuml"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Load Data"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Begin by loading the data you've received about week 3 of the outbreak into a cuDF-accelerated pandas DataFrame. The data is located at `./data/week3.csv`. For this notebook you will need all columns of the data."
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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": 2,
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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",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .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",
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"<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>age</th>\n",
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" <th>sex</th>\n",
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" <th>employment</th>\n",
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" <th>infected</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>0</td>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>0</td>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>0</td>\n",
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" <td>U</td>\n",
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" <td>0.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>0</td>\n",
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" <td>m</td>\n",
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" <td>U</td>\n",
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" <td>0.0</td>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>0</td>\n",
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" <td>m</td>\n",
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" <td>U</td>\n",
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" <td>0.0</td>\n",
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" <tr>\n",
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" <th>...</th>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>58479889</th>\n",
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" <td>90</td>\n",
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" <td>f</td>\n",
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" <td>V</td>\n",
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" <td>0.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>58479890</th>\n",
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" <td>90</td>\n",
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" <td>f</td>\n",
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" <td>V</td>\n",
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" <td>0.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>58479891</th>\n",
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||
" <td>90</td>\n",
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" <td>f</td>\n",
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" <td>V</td>\n",
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" <td>0.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>58479892</th>\n",
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" <td>90</td>\n",
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||
" <td>f</td>\n",
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||
" <td>V</td>\n",
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" <td>0.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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||
" <th>58479893</th>\n",
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||
" <td>90</td>\n",
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||
" <td>f</td>\n",
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||
" <td>V</td>\n",
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||
" <td>0.0</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"<p>58479894 rows × 4 columns</p>\n",
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"</div>"
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],
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"text/plain": [
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" age sex employment infected\n",
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"0 0 m U 0.0\n",
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"1 0 m U 0.0\n",
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"2 0 m U 0.0\n",
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"3 0 m U 0.0\n",
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"4 0 m U 0.0\n",
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"... ... .. ... ...\n",
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"58479889 90 f V 0.0\n",
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||
"58479890 90 f V 0.0\n",
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||
"58479891 90 f V 0.0\n",
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||
"58479892 90 f V 0.0\n",
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"58479893 90 f V 0.0\n",
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"\n",
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||
"[58479894 rows x 4 columns]"
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]
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},
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"execution_count": 2,
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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 = pd.read_csv('./data/week3.csv')\n",
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"df"
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||
]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Calculate Infection Rates by Employment Code"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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||
"Convert the `infected` column to type `float32`. For people who are not infected, the float32 `infected` value should be `0.0`, and for infected people it should be `1.0`."
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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": 5,
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||
"metadata": {},
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"outputs": [
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{
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"data": {
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"</style>\n",
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"<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",
|
||
" <th>age</th>\n",
|
||
" <th>sex</th>\n",
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||
" <th>employment</th>\n",
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||
" <th>infected</th>\n",
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||
" </tr>\n",
|
||
" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <td>0</td>\n",
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" <td>0.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>0</td>\n",
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" <td>m</td>\n",
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" <td>U</td>\n",
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" <td>0.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>0</td>\n",
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" <td>m</td>\n",
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" <td>U</td>\n",
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" <td>0.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>0</td>\n",
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" <td>m</td>\n",
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" <td>U</td>\n",
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" <td>0.0</td>\n",
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" </tr>\n",
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" <tr>\n",
|
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" <th>...</th>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>58479889</th>\n",
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||
" <td>90</td>\n",
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" <td>f</td>\n",
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" <td>V</td>\n",
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" <td>0.0</td>\n",
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" </tr>\n",
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" <tr>\n",
|
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" <th>58479890</th>\n",
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" <td>90</td>\n",
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" <td>f</td>\n",
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||
" <td>V</td>\n",
|
||
" <td>0.0</td>\n",
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" </tr>\n",
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" <tr>\n",
|
||
" <th>58479891</th>\n",
|
||
" <td>90</td>\n",
|
||
" <td>f</td>\n",
|
||
" <td>V</td>\n",
|
||
" <td>0.0</td>\n",
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||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>58479892</th>\n",
|
||
" <td>90</td>\n",
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" <td>f</td>\n",
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" <td>V</td>\n",
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||
" <td>0.0</td>\n",
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||
" </tr>\n",
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" <tr>\n",
|
||
" <th>58479893</th>\n",
|
||
" <td>90</td>\n",
|
||
" <td>f</td>\n",
|
||
" <td>V</td>\n",
|
||
" <td>0.0</td>\n",
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||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>58479894 rows × 4 columns</p>\n",
|
||
"</div>"
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||
],
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||
"text/plain": [
|
||
" age sex employment infected\n",
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||
"0 0 m U 0.0\n",
|
||
"1 0 m U 0.0\n",
|
||
"2 0 m U 0.0\n",
|
||
"3 0 m U 0.0\n",
|
||
"4 0 m U 0.0\n",
|
||
"... ... .. ... ...\n",
|
||
"58479889 90 f V 0.0\n",
|
||
"58479890 90 f V 0.0\n",
|
||
"58479891 90 f V 0.0\n",
|
||
"58479892 90 f V 0.0\n",
|
||
"58479893 90 f V 0.0\n",
|
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"\n",
|
||
"[58479894 rows x 4 columns]"
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||
]
|
||
},
|
||
"execution_count": 5,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df['infected'] = df['infected'].astype('float32')\n",
|
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"df"
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||
]
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||
},
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||
{
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||
"cell_type": "markdown",
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||
"metadata": {},
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||
"source": [
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||
"Now, produce a list of employment types and their associated **rates** of infection, sorted from highest to lowest rate of infection.\n",
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"\n",
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"**NOTE**: The infection **rate** for each employment type should be the percentage of total individuals within an employment type who are infected. Therefore, if employment type \"X\" has 1000 people, and 10 of them are infected, the infection **rate** would be .01. If employment type \"Z\" has 10,000 people, and 50 of them are infected, the infection rate would be .005, and would be **lower** than for type \"X\", even though more people within that employment type were infected."
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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": 6,
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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",
|
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"<style scoped>\n",
|
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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||
" }\n",
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"\n",
|
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" .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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" text-align: right;\n",
|
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" }\n",
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"</style>\n",
|
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"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>infected</th>\n",
|
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" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>employment</th>\n",
|
||
" <th></th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>A</th>\n",
|
||
" <td>0.003853</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>B, D, E</th>\n",
|
||
" <td>0.003774</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>C</th>\n",
|
||
" <td>0.003882</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>F</th>\n",
|
||
" <td>0.003182</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>G</th>\n",
|
||
" <td>0.004948</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>H</th>\n",
|
||
" <td>0.003388</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>I</th>\n",
|
||
" <td>0.010354</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>J</th>\n",
|
||
" <td>0.003939</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>K</th>\n",
|
||
" <td>0.004772</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>L</th>\n",
|
||
" <td>0.004970</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>M</th>\n",
|
||
" <td>0.004777</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>N</th>\n",
|
||
" <td>0.004784</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>O</th>\n",
|
||
" <td>0.005284</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>P</th>\n",
|
||
" <td>0.006190</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>Q</th>\n",
|
||
" <td>0.012756</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>R, S, T</th>\n",
|
||
" <td>0.005390</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>U</th>\n",
|
||
" <td>0.000217</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>V</th>\n",
|
||
" <td>0.007590</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>X</th>\n",
|
||
" <td>0.004539</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>Z</th>\n",
|
||
" <td>0.005655</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" infected\n",
|
||
"employment \n",
|
||
"A 0.003853\n",
|
||
"B, D, E 0.003774\n",
|
||
"C 0.003882\n",
|
||
"F 0.003182\n",
|
||
"G 0.004948\n",
|
||
"H 0.003388\n",
|
||
"I 0.010354\n",
|
||
"J 0.003939\n",
|
||
"K 0.004772\n",
|
||
"L 0.004970\n",
|
||
"M 0.004777\n",
|
||
"N 0.004784\n",
|
||
"O 0.005284\n",
|
||
"P 0.006190\n",
|
||
"Q 0.012756\n",
|
||
"R, S, T 0.005390\n",
|
||
"U 0.000217\n",
|
||
"V 0.007590\n",
|
||
"X 0.004539\n",
|
||
"Z 0.005655"
|
||
]
|
||
},
|
||
"execution_count": 6,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"emp_groups = df[['employment', 'infected']].groupby(['employment'])\n",
|
||
"emp_rate_df = emp_groups.mean()\n",
|
||
"emp_rate_df"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Finally, read in the employment codes guide from `./data/code_guide.csv` to interpret which employment types are seeing the highest rates of infection."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"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",
|
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|
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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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|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>Code</th>\n",
|
||
" <th>Field</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>A</td>\n",
|
||
" <td>Agriculture, forestry & fishing</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>B, D, E</td>\n",
|
||
" <td>Mining, energy and water supply</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>C</td>\n",
|
||
" <td>Manufacturing</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>F</td>\n",
|
||
" <td>Construction</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>G</td>\n",
|
||
" <td>Wholesale, retail & repair of motor vehicles</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>5</th>\n",
|
||
" <td>H</td>\n",
|
||
" <td>Transport & storage</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>6</th>\n",
|
||
" <td>I</td>\n",
|
||
" <td>Accommodation & food services</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>7</th>\n",
|
||
" <td>J</td>\n",
|
||
" <td>Information & communication</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>8</th>\n",
|
||
" <td>K</td>\n",
|
||
" <td>Financial & insurance activities</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>9</th>\n",
|
||
" <td>L</td>\n",
|
||
" <td>Real estate activities</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>10</th>\n",
|
||
" <td>M</td>\n",
|
||
" <td>Professional, scientific & technical activities</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>11</th>\n",
|
||
" <td>N</td>\n",
|
||
" <td>Administrative & support services</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>12</th>\n",
|
||
" <td>O</td>\n",
|
||
" <td>Public admin & defence; social security</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>13</th>\n",
|
||
" <td>P</td>\n",
|
||
" <td>Education</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>14</th>\n",
|
||
" <td>Q</td>\n",
|
||
" <td>Human health & social work activities</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>15</th>\n",
|
||
" <td>R, S, T</td>\n",
|
||
" <td>Other services</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>16</th>\n",
|
||
" <td>U</td>\n",
|
||
" <td>Student</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>17</th>\n",
|
||
" <td>V</td>\n",
|
||
" <td>Retired</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>18</th>\n",
|
||
" <td>X</td>\n",
|
||
" <td>Outside the UK or not specified</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>19</th>\n",
|
||
" <td>Y</td>\n",
|
||
" <td>Pre-school child</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>20</th>\n",
|
||
" <td>Z</td>\n",
|
||
" <td>Not formally employed</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" Code Field\n",
|
||
"0 A Agriculture, forestry & fishing\n",
|
||
"1 B, D, E Mining, energy and water supply\n",
|
||
"2 C Manufacturing\n",
|
||
"3 F Construction\n",
|
||
"4 G Wholesale, retail & repair of motor vehicles\n",
|
||
"5 H Transport & storage\n",
|
||
"6 I Accommodation & food services\n",
|
||
"7 J Information & communication\n",
|
||
"8 K Financial & insurance activities\n",
|
||
"9 L Real estate activities\n",
|
||
"10 M Professional, scientific & technical activities\n",
|
||
"11 N Administrative & support services\n",
|
||
"12 O Public admin & defence; social security\n",
|
||
"13 P Education\n",
|
||
"14 Q Human health & social work activities\n",
|
||
"15 R, S, T Other services\n",
|
||
"16 U Student\n",
|
||
"17 V Retired\n",
|
||
"18 X Outside the UK or not specified\n",
|
||
"19 Y Pre-school child\n",
|
||
"20 Z Not formally employed"
|
||
]
|
||
},
|
||
"execution_count": 8,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"emp_codes = pd.read_csv('./data/code_guide.csv')\n",
|
||
"emp_codes"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Get Top 2 Employment Type with Highest Rate of Infection ###"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Here we ask you to get the top two employment types that have the highest rate of infection. We start by using `.sort_values()` to sort `emp_rate_df` by the rate of infection. We then take the first 2 results. \n",
|
||
"\n",
|
||
"We will also need to index `emp_codes` to get the respeictve field name. "
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"6 Accommodation & food services\n",
|
||
"14 Human health & social work activities\n",
|
||
"Name: Field, dtype: object"
|
||
]
|
||
},
|
||
"execution_count": 11,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"top_inf_emp = emp_rate_df.sort_values('infected', ascending=False).iloc[[0, 1]].index\n",
|
||
"top_inf_emp_df = emp_codes.loc[emp_codes['Code'].isin(top_inf_emp), 'Field']\n",
|
||
"top_inf_emp_df"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"/opt/conda/lib/python3.10/site-packages/cudf/io/json.py:194: UserWarning: Using CPU via Pandas to write JSON dataset\n",
|
||
" warnings.warn(\"Using CPU via Pandas to write JSON dataset\")\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"top_inf_emp_df.to_json('my_assessment/question_3.json', orient='records')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Calculate Infection Rates by Employment Code and Sex"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"We want to see if there is an effect of `sex` on infection rate, either in addition to `employment` or confounding it. Group by both `employment` and `sex` simultaneously to get the infection rate for the intersection of those categories."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"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></th>\n",
|
||
" <th>age</th>\n",
|
||
" <th>infected</th>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>employment</th>\n",
|
||
" <th>sex</th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>I</th>\n",
|
||
" <th>f</th>\n",
|
||
" <td>41.377627</td>\n",
|
||
" <td>0.015064</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>Q</th>\n",
|
||
" <th>f</th>\n",
|
||
" <td>41.385400</td>\n",
|
||
" <td>0.014947</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>V</th>\n",
|
||
" <th>f</th>\n",
|
||
" <td>76.022214</td>\n",
|
||
" <td>0.010852</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>B, D, E</th>\n",
|
||
" <th>f</th>\n",
|
||
" <td>41.425618</td>\n",
|
||
" <td>0.007973</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>R, S, T</th>\n",
|
||
" <th>f</th>\n",
|
||
" <td>41.371672</td>\n",
|
||
" <td>0.007748</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>O</th>\n",
|
||
" <th>f</th>\n",
|
||
" <td>41.396246</td>\n",
|
||
" <td>0.007719</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>K</th>\n",
|
||
" <th>f</th>\n",
|
||
" <td>41.377495</td>\n",
|
||
" <td>0.007672</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>M</th>\n",
|
||
" <th>f</th>\n",
|
||
" <td>41.401898</td>\n",
|
||
" <td>0.007645</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>J</th>\n",
|
||
" <th>f</th>\n",
|
||
" <td>41.385772</td>\n",
|
||
" <td>0.007645</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>C</th>\n",
|
||
" <th>f</th>\n",
|
||
" <td>41.391365</td>\n",
|
||
" <td>0.007630</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>Z</th>\n",
|
||
" <th>f</th>\n",
|
||
" <td>41.384618</td>\n",
|
||
" <td>0.007629</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>P</th>\n",
|
||
" <th>f</th>\n",
|
||
" <td>41.399302</td>\n",
|
||
" <td>0.007584</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>F</th>\n",
|
||
" <th>f</th>\n",
|
||
" <td>41.415191</td>\n",
|
||
" <td>0.007577</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>G</th>\n",
|
||
" <th>f</th>\n",
|
||
" <td>41.394893</td>\n",
|
||
" <td>0.007556</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>A</th>\n",
|
||
" <th>f</th>\n",
|
||
" <td>41.385010</td>\n",
|
||
" <td>0.007491</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>X</th>\n",
|
||
" <th>f</th>\n",
|
||
" <td>41.401447</td>\n",
|
||
" <td>0.007391</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>N</th>\n",
|
||
" <th>f</th>\n",
|
||
" <td>41.386565</td>\n",
|
||
" <td>0.007389</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>H</th>\n",
|
||
" <th>f</th>\n",
|
||
" <td>41.416857</td>\n",
|
||
" <td>0.007385</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>L</th>\n",
|
||
" <th>f</th>\n",
|
||
" <td>41.412315</td>\n",
|
||
" <td>0.007221</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>Q</th>\n",
|
||
" <th>m</th>\n",
|
||
" <td>41.015871</td>\n",
|
||
" <td>0.005120</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>I</th>\n",
|
||
" <th>m</th>\n",
|
||
" <td>40.984340</td>\n",
|
||
" <td>0.005117</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>V</th>\n",
|
||
" <th>m</th>\n",
|
||
" <td>74.962331</td>\n",
|
||
" <td>0.003685</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>G</th>\n",
|
||
" <th>m</th>\n",
|
||
" <td>41.001163</td>\n",
|
||
" <td>0.002596</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>P</th>\n",
|
||
" <th>m</th>\n",
|
||
" <td>40.973641</td>\n",
|
||
" <td>0.002577</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>C</th>\n",
|
||
" <th>m</th>\n",
|
||
" <td>40.997851</td>\n",
|
||
" <td>0.002569</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>J</th>\n",
|
||
" <th>m</th>\n",
|
||
" <td>41.007800</td>\n",
|
||
" <td>0.002546</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>O</th>\n",
|
||
" <th>m</th>\n",
|
||
" <td>41.017959</td>\n",
|
||
" <td>0.002543</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>Z</th>\n",
|
||
" <th>m</th>\n",
|
||
" <td>41.010787</td>\n",
|
||
" <td>0.002543</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>R, S, T</th>\n",
|
||
" <th>m</th>\n",
|
||
" <td>41.021642</td>\n",
|
||
" <td>0.002542</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>N</th>\n",
|
||
" <th>m</th>\n",
|
||
" <td>40.975332</td>\n",
|
||
" <td>0.002538</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>F</th>\n",
|
||
" <th>m</th>\n",
|
||
" <td>41.007458</td>\n",
|
||
" <td>0.002535</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>M</th>\n",
|
||
" <th>m</th>\n",
|
||
" <td>40.989591</td>\n",
|
||
" <td>0.002520</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>A</th>\n",
|
||
" <th>m</th>\n",
|
||
" <td>40.992202</td>\n",
|
||
" <td>0.002514</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>K</th>\n",
|
||
" <th>m</th>\n",
|
||
" <td>40.988683</td>\n",
|
||
" <td>0.002490</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>H</th>\n",
|
||
" <th>m</th>\n",
|
||
" <td>40.978731</td>\n",
|
||
" <td>0.002482</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>B, D, E</th>\n",
|
||
" <th>m</th>\n",
|
||
" <td>41.014025</td>\n",
|
||
" <td>0.002462</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>X</th>\n",
|
||
" <th>m</th>\n",
|
||
" <td>41.024242</td>\n",
|
||
" <td>0.002435</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>L</th>\n",
|
||
" <th>m</th>\n",
|
||
" <td>40.953436</td>\n",
|
||
" <td>0.002197</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th rowspan=\"2\" valign=\"top\">U</th>\n",
|
||
" <th>f</th>\n",
|
||
" <td>8.330251</td>\n",
|
||
" <td>0.000329</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>m</th>\n",
|
||
" <td>8.331235</td>\n",
|
||
" <td>0.000110</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" age infected\n",
|
||
"employment sex \n",
|
||
"I f 41.377627 0.015064\n",
|
||
"Q f 41.385400 0.014947\n",
|
||
"V f 76.022214 0.010852\n",
|
||
"B, D, E f 41.425618 0.007973\n",
|
||
"R, S, T f 41.371672 0.007748\n",
|
||
"O f 41.396246 0.007719\n",
|
||
"K f 41.377495 0.007672\n",
|
||
"M f 41.401898 0.007645\n",
|
||
"J f 41.385772 0.007645\n",
|
||
"C f 41.391365 0.007630\n",
|
||
"Z f 41.384618 0.007629\n",
|
||
"P f 41.399302 0.007584\n",
|
||
"F f 41.415191 0.007577\n",
|
||
"G f 41.394893 0.007556\n",
|
||
"A f 41.385010 0.007491\n",
|
||
"X f 41.401447 0.007391\n",
|
||
"N f 41.386565 0.007389\n",
|
||
"H f 41.416857 0.007385\n",
|
||
"L f 41.412315 0.007221\n",
|
||
"Q m 41.015871 0.005120\n",
|
||
"I m 40.984340 0.005117\n",
|
||
"V m 74.962331 0.003685\n",
|
||
"G m 41.001163 0.002596\n",
|
||
"P m 40.973641 0.002577\n",
|
||
"C m 40.997851 0.002569\n",
|
||
"J m 41.007800 0.002546\n",
|
||
"O m 41.017959 0.002543\n",
|
||
"Z m 41.010787 0.002543\n",
|
||
"R, S, T m 41.021642 0.002542\n",
|
||
"N m 40.975332 0.002538\n",
|
||
"F m 41.007458 0.002535\n",
|
||
"M m 40.989591 0.002520\n",
|
||
"A m 40.992202 0.002514\n",
|
||
"K m 40.988683 0.002490\n",
|
||
"H m 40.978731 0.002482\n",
|
||
"B, D, E m 41.014025 0.002462\n",
|
||
"X m 41.024242 0.002435\n",
|
||
"L m 40.953436 0.002197\n",
|
||
"U f 8.330251 0.000329\n",
|
||
" m 8.331235 0.000110"
|
||
]
|
||
},
|
||
"execution_count": 13,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"simul_groups = df.groupby(['employment', 'sex'])\n",
|
||
"simul_groups.mean().sort_values('infected', ascending=False)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Check Submission ##"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[\"Accommodation & food services\",\"Human health & social work activities\"]"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"!cat my_assessment/question_3.json"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"**Tip**: Your submission file should contain one line of text, similar to: \n",
|
||
"\n",
|
||
"```\n",
|
||
"[\"Agriculture, forestry & fishing\",\"Mining, energy and water supply\"]"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"<div align=\"center\"><h2>Please Restart the Kernel</h2></div>"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"If you plan to continue work in other notebooks, please shutdown the kernel."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"{'status': 'ok', 'restart': True}"
|
||
]
|
||
},
|
||
"execution_count": 15,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"import IPython\n",
|
||
"app = IPython.Application.instance()\n",
|
||
"app.kernel.do_shutdown(True)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"<a href=\"https://www.nvidia.com/dli\"><img src=\"images/DLI_Header.png\" alt=\"Header\" style=\"width: 400px;\"/></a>"
|
||
]
|
||
}
|
||
],
|
||
"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": "3.10.15"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 4
|
||
}
|