diff --git a/covid-model.ipynb b/covid-model.ipynb
index 5a3279f..a3ac1ae 100644
--- a/covid-model.ipynb
+++ b/covid-model.ipynb
@@ -2,7 +2,49 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 150,
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Reading data\n",
+ "import os\n",
+ "import git\n",
+ "import shutil\n",
+ "import tempfile\n",
+ "\n",
+ "# Create temporary dir\n",
+ "t = tempfile.mkdtemp()\n",
+ "d = 'lwc/topics/covid19/covid-model'\n",
+ "# Clone into temporary dir\n",
+ "git.Repo.clone_from('http://gmarx.jumpingcrab.com:8088/COVID-19/covid19-data.git', \n",
+ " t, branch='master', depth=1)\n",
+ "# Delete files\n",
+ "#os.remove('README.txt')\n",
+ "#shutil.rmtree('firstTest')\n",
+ "#shutil.rmtree('secondTest')\n",
+ "# Copy desired file from temporary dir\n",
+ "shutil.move(os.path.join(t, 'data'), '.')\n",
+ "# Remove temporary dir\n",
+ "shutil.rmtree(t)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import os\n",
+ "def loadData(path, file):\n",
+ " csvPath=os.path.join(path, file)\n",
+ " return pd.read_csv(csvPath)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
"metadata": {},
"outputs": [
{
@@ -26,183 +68,103 @@
" \n",
" \n",
" | \n",
- " DateRep | \n",
- " Day | \n",
- " Month | \n",
- " Year | \n",
- " Cases | \n",
+ " Date | \n",
+ " Country/Region | \n",
+ " Province/State | \n",
+ " Lat | \n",
+ " Long | \n",
+ " Confirmed | \n",
+ " Recovered | \n",
" Deaths | \n",
- " Countries and territories | \n",
- " GeoId | \n",
"
\n",
" \n",
"
\n",
" \n",
" 0 | \n",
- " 24/03/2020 | \n",
- " 24 | \n",
- " 3 | \n",
- " 2020 | \n",
- " 6 | \n",
- " 1 | \n",
- " Afghanistan | \n",
- " AF | \n",
- "
\n",
- " \n",
- " 1 | \n",
- " 23/03/2020 | \n",
- " 23 | \n",
- " 3 | \n",
- " 2020 | \n",
- " 10 | \n",
- " 0 | \n",
+ " 2020-01-22 | \n",
" Afghanistan | \n",
- " AF | \n",
- "
\n",
- " \n",
- " 2 | \n",
- " 22/03/2020 | \n",
- " 22 | \n",
- " 3 | \n",
- " 2020 | \n",
+ " NaN | \n",
+ " 33.0 | \n",
+ " 65.0 | \n",
" 0 | \n",
+ " 0.0 | \n",
" 0 | \n",
- " Afghanistan | \n",
- " AF | \n",
"
\n",
" \n",
- " 3 | \n",
- " 21/03/2020 | \n",
- " 21 | \n",
- " 3 | \n",
- " 2020 | \n",
- " 2 | \n",
- " 0 | \n",
+ " 1 | \n",
+ " 2020-01-23 | \n",
" Afghanistan | \n",
- " AF | \n",
- "
\n",
- " \n",
- " 4 | \n",
- " 20/03/2020 | \n",
- " 20 | \n",
- " 3 | \n",
- " 2020 | \n",
+ " NaN | \n",
+ " 33.0 | \n",
+ " 65.0 | \n",
" 0 | \n",
+ " 0.0 | \n",
" 0 | \n",
- " Afghanistan | \n",
- " AF | \n",
- "
\n",
- " \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
- " ... | \n",
"
\n",
" \n",
- " 6546 | \n",
- " 19/03/2020 | \n",
- " 19 | \n",
- " 3 | \n",
- " 2020 | \n",
- " 2 | \n",
+ " 2 | \n",
+ " 2020-01-24 | \n",
+ " Afghanistan | \n",
+ " NaN | \n",
+ " 33.0 | \n",
+ " 65.0 | \n",
" 0 | \n",
- " Zambia | \n",
- " ZM | \n",
- "
\n",
- " \n",
- " 6547 | \n",
- " 24/03/2020 | \n",
- " 24 | \n",
- " 3 | \n",
- " 2020 | \n",
+ " 0.0 | \n",
" 0 | \n",
- " 1 | \n",
- " Zimbabwe | \n",
- " ZW | \n",
"
\n",
" \n",
- " 6548 | \n",
- " 23/03/2020 | \n",
- " 23 | \n",
- " 3 | \n",
- " 2020 | \n",
+ " 3 | \n",
+ " 2020-01-25 | \n",
+ " Afghanistan | \n",
+ " NaN | \n",
+ " 33.0 | \n",
+ " 65.0 | \n",
" 0 | \n",
+ " 0.0 | \n",
" 0 | \n",
- " Zimbabwe | \n",
- " ZW | \n",
"
\n",
" \n",
- " 6549 | \n",
- " 22/03/2020 | \n",
- " 22 | \n",
- " 3 | \n",
- " 2020 | \n",
- " 1 | \n",
+ " 4 | \n",
+ " 2020-01-26 | \n",
+ " Afghanistan | \n",
+ " NaN | \n",
+ " 33.0 | \n",
+ " 65.0 | \n",
" 0 | \n",
- " Zimbabwe | \n",
- " ZW | \n",
- "
\n",
- " \n",
- " 6550 | \n",
- " 21/03/2020 | \n",
- " 21 | \n",
- " 3 | \n",
- " 2020 | \n",
- " 1 | \n",
+ " 0.0 | \n",
" 0 | \n",
- " Zimbabwe | \n",
- " ZW | \n",
"
\n",
" \n",
"\n",
- "6551 rows × 8 columns
\n",
""
],
"text/plain": [
- " DateRep Day Month Year Cases Deaths Countries and territories \\\n",
- "0 24/03/2020 24 3 2020 6 1 Afghanistan \n",
- "1 23/03/2020 23 3 2020 10 0 Afghanistan \n",
- "2 22/03/2020 22 3 2020 0 0 Afghanistan \n",
- "3 21/03/2020 21 3 2020 2 0 Afghanistan \n",
- "4 20/03/2020 20 3 2020 0 0 Afghanistan \n",
- "... ... ... ... ... ... ... ... \n",
- "6546 19/03/2020 19 3 2020 2 0 Zambia \n",
- "6547 24/03/2020 24 3 2020 0 1 Zimbabwe \n",
- "6548 23/03/2020 23 3 2020 0 0 Zimbabwe \n",
- "6549 22/03/2020 22 3 2020 1 0 Zimbabwe \n",
- "6550 21/03/2020 21 3 2020 1 0 Zimbabwe \n",
+ " Date Country/Region Province/State Lat Long Confirmed Recovered \\\n",
+ "0 2020-01-22 Afghanistan NaN 33.0 65.0 0 0.0 \n",
+ "1 2020-01-23 Afghanistan NaN 33.0 65.0 0 0.0 \n",
+ "2 2020-01-24 Afghanistan NaN 33.0 65.0 0 0.0 \n",
+ "3 2020-01-25 Afghanistan NaN 33.0 65.0 0 0.0 \n",
+ "4 2020-01-26 Afghanistan NaN 33.0 65.0 0 0.0 \n",
"\n",
- " GeoId \n",
- "0 AF \n",
- "1 AF \n",
- "2 AF \n",
- "3 AF \n",
- "4 AF \n",
- "... ... \n",
- "6546 ZM \n",
- "6547 ZW \n",
- "6548 ZW \n",
- "6549 ZW \n",
- "6550 ZW \n",
- "\n",
- "[6551 rows x 8 columns]"
+ " Deaths \n",
+ "0 0 \n",
+ "1 0 \n",
+ "2 0 \n",
+ "3 0 \n",
+ "4 0 "
]
},
- "execution_count": 150,
+ "execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "# Reading data\n",
- "import pandas as pd\n",
- "covid_data = pd.read_csv('https://worldhealthorg-my.sharepoint.com/personal/garnicacarrenoj_who_int/_layouts/15/Doc.aspx?sourcedoc=%7B87BD9C0A-2E91-4BE3-8308-B0B545B6DFB6%7D&file=CSV%20as%20at%2024%20March%202020-Daily%20additions.csv&action=default&mobileredirect=true&CT=1585104198257&OR=ItemsView')\n",
- "covid_data"
+ "# import jtplot submodule from jupyterthemes\n",
+ "from jupyterthemes import jtplot\n",
+ "PATH=os.path.join(\"data\")\n",
+ "covid_data=loadData(PATH,\"time-series-19-covid-combined.csv\")\n",
+ "covid_data.head()"
]
},
{
@@ -218,28 +180,28 @@
},
{
"cell_type": "code",
- "execution_count": 152,
+ "execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "array([[,\n",
- " ],\n",
- " [,\n",
- " ],\n",
- " [,\n",
- " ]],\n",
+ "array([[,\n",
+ " ],\n",
+ " [,\n",
+ " ],\n",
+ " [,\n",
+ " ]],\n",
" dtype=object)"
]
},
- "execution_count": 152,
+ "execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
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