{
"cells": [
{
"cell_type": "code",
"execution_count": 180,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
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" \n",
" \n",
" | \n",
" dateRep | \n",
" day | \n",
" month | \n",
" year | \n",
" cases | \n",
" deaths | \n",
" countriesAndTerritories | \n",
" geoId | \n",
" countryterritoryCode | \n",
" popData2019 | \n",
" continentExp | \n",
" Cumulative_number_for_14_days_of_COVID-19_cases_per_100000 | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 11/23/2020 | \n",
" 23 | \n",
" 11 | \n",
" 2020 | \n",
" 252 | \n",
" 8 | \n",
" Afghanistan | \n",
" AF | \n",
" AFG | \n",
" 38041757.0 | \n",
" Asia | \n",
" 6.655844 | \n",
"
\n",
" \n",
" 1 | \n",
" 11/22/2020 | \n",
" 22 | \n",
" 11 | \n",
" 2020 | \n",
" 154 | \n",
" 12 | \n",
" Afghanistan | \n",
" AF | \n",
" AFG | \n",
" 38041757.0 | \n",
" Asia | \n",
" 6.203709 | \n",
"
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" \n",
" 2 | \n",
" 11/21/2020 | \n",
" 21 | \n",
" 11 | \n",
" 2020 | \n",
" 232 | \n",
" 25 | \n",
" Afghanistan | \n",
" AF | \n",
" AFG | \n",
" 38041757.0 | \n",
" Asia | \n",
" 6.130106 | \n",
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" 3 | \n",
" 11/20/2020 | \n",
" 20 | \n",
" 11 | \n",
" 2020 | \n",
" 282 | \n",
" 5 | \n",
" Afghanistan | \n",
" AF | \n",
" AFG | \n",
" 38041757.0 | \n",
" Asia | \n",
" 5.672714 | \n",
"
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" \n",
" 4 | \n",
" 11/19/2020 | \n",
" 19 | \n",
" 11 | \n",
" 2020 | \n",
" 0 | \n",
" 0 | \n",
" Afghanistan | \n",
" AF | \n",
" AFG | \n",
" 38041757.0 | \n",
" Asia | \n",
" 5.036571 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" dateRep day month year cases deaths countriesAndTerritories geoId \\\n",
"0 11/23/2020 23 11 2020 252 8 Afghanistan AF \n",
"1 11/22/2020 22 11 2020 154 12 Afghanistan AF \n",
"2 11/21/2020 21 11 2020 232 25 Afghanistan AF \n",
"3 11/20/2020 20 11 2020 282 5 Afghanistan AF \n",
"4 11/19/2020 19 11 2020 0 0 Afghanistan AF \n",
"\n",
" countryterritoryCode popData2019 continentExp \\\n",
"0 AFG 38041757.0 Asia \n",
"1 AFG 38041757.0 Asia \n",
"2 AFG 38041757.0 Asia \n",
"3 AFG 38041757.0 Asia \n",
"4 AFG 38041757.0 Asia \n",
"\n",
" Cumulative_number_for_14_days_of_COVID-19_cases_per_100000 \n",
"0 6.655844 \n",
"1 6.203709 \n",
"2 6.130106 \n",
"3 5.672714 \n",
"4 5.036571 "
]
},
"execution_count": 180,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.dates as mdates\n",
"from matplotlib.dates import DateFormatter\n",
"from matplotlib.ticker import (MultipleLocator, FormatStrFormatter,\n",
" AutoMinorLocator) #gia ta ticks\n",
"import numpy as np\n",
"import seaborn as sns\n",
"# Use white grid plot background from seaborn\n",
"#sns.set(font_scale=1.5, style=\"whitegrid\")\n",
"#sns.set_style('darkgrid')\n",
"#sns.set_context(\"poster\")\n",
"from scipy.optimize import curve_fit\n",
"from lmfit import Model\n",
"import scipy.optimize as scopt\n",
"\n",
"#years = mdates.YearLocator() # every year\n",
"months = mdates.MonthLocator() # every month\n",
"days = mdates.DayLocator() #every day\n",
"#yearsFmt = mdates.DateFormatter('%Y')\n",
"\n",
"# Handle date time conversions between pandas and matplotlib\n",
"from pandas.plotting import register_matplotlib_converters\n",
"register_matplotlib_converters()\n",
"\n",
"from datetime import datetime\n",
"\n",
"\n",
"df = pd.read_csv('COVID_19_geographic_disbtribution_worldwide.csv')#,index_col='dateRep',parse_dates=True)\n",
"#df.describe()\n",
"df.head()\n",
"#df.columns"
]
},
{
"cell_type": "code",
"execution_count": 181,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'United_States_of_America'"
]
},
"execution_count": 181,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['countriesAndTerritories'][54900]"
]
},
{
"cell_type": "code",
"execution_count": 182,
"metadata": {},
"outputs": [],
"source": [
"SWE=df.loc[df['countriesAndTerritories'] == 'Sweden'] #country selection"
]
},
{
"cell_type": "code",
"execution_count": 183,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" dateRep | \n",
" day | \n",
" month | \n",
" year | \n",
" cases | \n",
" deaths | \n",
" countriesAndTerritories | \n",
" geoId | \n",
" countryterritoryCode | \n",
" popData2019 | \n",
" continentExp | \n",
" Cumulative_number_for_14_days_of_COVID-19_cases_per_100000 | \n",
"
\n",
" \n",
" \n",
" \n",
" 50095 | \n",
" 11/23/2020 | \n",
" 23 | \n",
" 11 | \n",
" 2020 | \n",
" 0 | \n",
" 0 | \n",
" Sweden | \n",
" SE | \n",
" SWE | \n",
" 10230185.0 | \n",
" Europe | \n",
" 577.311163 | \n",
"
\n",
" \n",
" 50096 | \n",
" 11/22/2020 | \n",
" 22 | \n",
" 11 | \n",
" 2020 | \n",
" 0 | \n",
" 0 | \n",
" Sweden | \n",
" SE | \n",
" SWE | \n",
" 10230185.0 | \n",
" Europe | \n",
" 577.311163 | \n",
"
\n",
" \n",
" 50097 | \n",
" 11/21/2020 | \n",
" 21 | \n",
" 11 | \n",
" 2020 | \n",
" 6243 | \n",
" 6 | \n",
" Sweden | \n",
" SE | \n",
" SWE | \n",
" 10230185.0 | \n",
" Europe | \n",
" 577.311163 | \n",
"
\n",
" \n",
" 50098 | \n",
" 11/20/2020 | \n",
" 20 | \n",
" 11 | \n",
" 2020 | \n",
" 4960 | \n",
" 11 | \n",
" Sweden | \n",
" SE | \n",
" SWE | \n",
" 10230185.0 | \n",
" Europe | \n",
" 572.677816 | \n",
"
\n",
" \n",
" 50099 | \n",
" 11/19/2020 | \n",
" 19 | \n",
" 11 | \n",
" 2020 | \n",
" 4460 | \n",
" 20 | \n",
" Sweden | \n",
" SE | \n",
" SWE | \n",
" 10230185.0 | \n",
" Europe | \n",
" 564.359296 | \n",
"
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" \n",
"
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"
"
],
"text/plain": [
" dateRep day month year cases deaths countriesAndTerritories \\\n",
"50095 11/23/2020 23 11 2020 0 0 Sweden \n",
"50096 11/22/2020 22 11 2020 0 0 Sweden \n",
"50097 11/21/2020 21 11 2020 6243 6 Sweden \n",
"50098 11/20/2020 20 11 2020 4960 11 Sweden \n",
"50099 11/19/2020 19 11 2020 4460 20 Sweden \n",
"\n",
" geoId countryterritoryCode popData2019 continentExp \\\n",
"50095 SE SWE 10230185.0 Europe \n",
"50096 SE SWE 10230185.0 Europe \n",
"50097 SE SWE 10230185.0 Europe \n",
"50098 SE SWE 10230185.0 Europe \n",
"50099 SE SWE 10230185.0 Europe \n",
"\n",
" Cumulative_number_for_14_days_of_COVID-19_cases_per_100000 \n",
"50095 577.311163 \n",
"50096 577.311163 \n",
"50097 577.311163 \n",
"50098 572.677816 \n",
"50099 564.359296 "
]
},
"execution_count": 183,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"SWE.head() #decide which columns to select "
]
},
{
"cell_type": "code",
"execution_count": 184,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"10230185.0\n"
]
}
],
"source": [
"population=df['popData2019'][50095]\n",
"print(population)"
]
},
{
"cell_type": "code",
"execution_count": 185,
"metadata": {},
"outputs": [],
"source": [
"SWE=SWE[['dateRep','cases']] #select columns"
]
},
{
"cell_type": "code",
"execution_count": 186,
"metadata": {},
"outputs": [],
"source": [
"SWE= SWE.rename(index = lambda x: x - 50094).sort_index(axis=0 ,ascending=False) #concise indexing"
]
},
{
"cell_type": "code",
"execution_count": 187,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" dateRep | \n",
" cases | \n",
"
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" \n",
" \n",
" \n",
" 329 | \n",
" 12/31/2019 | \n",
" 0 | \n",
"
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" \n",
" 328 | \n",
" 1/1/2020 | \n",
" 0 | \n",
"
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" \n",
" 327 | \n",
" 1/2/2020 | \n",
" 0 | \n",
"
\n",
" \n",
" 326 | \n",
" 1/3/2020 | \n",
" 0 | \n",
"
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" \n",
" 325 | \n",
" 1/4/2020 | \n",
" 0 | \n",
"
\n",
" \n",
" ... | \n",
" ... | \n",
" ... | \n",
"
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" \n",
" 5 | \n",
" 11/19/2020 | \n",
" 4460 | \n",
"
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" \n",
" 4 | \n",
" 11/20/2020 | \n",
" 4960 | \n",
"
\n",
" \n",
" 3 | \n",
" 11/21/2020 | \n",
" 6243 | \n",
"
\n",
" \n",
" 2 | \n",
" 11/22/2020 | \n",
" 0 | \n",
"
\n",
" \n",
" 1 | \n",
" 11/23/2020 | \n",
" 0 | \n",
"
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" \n",
"
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"
329 rows × 2 columns
\n",
"
"
],
"text/plain": [
" dateRep cases\n",
"329 12/31/2019 0\n",
"328 1/1/2020 0\n",
"327 1/2/2020 0\n",
"326 1/3/2020 0\n",
"325 1/4/2020 0\n",
".. ... ...\n",
"5 11/19/2020 4460\n",
"4 11/20/2020 4960\n",
"3 11/21/2020 6243\n",
"2 11/22/2020 0\n",
"1 11/23/2020 0\n",
"\n",
"[329 rows x 2 columns]"
]
},
"execution_count": 187,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"SWE"
]
},
{
"cell_type": "code",
"execution_count": 188,
"metadata": {},
"outputs": [],
"source": [
"SWE= SWE.rename({'dateRep': 'Date'}, axis=1) # change the name of the column of dates"
]
},
{
"cell_type": "code",
"execution_count": 189,
"metadata": {},
"outputs": [],
"source": [
"SWE.index=range(329) #reverse the numbering of indexes beginning from last day of 2019"
]
},
{
"cell_type": "code",
"execution_count": 190,
"metadata": {},
"outputs": [],
"source": [
"SWE['Date'] = pd.to_datetime(SWE['Date'],format=\"%m/%d/%Y\")"
]
},
{
"cell_type": "code",
"execution_count": 191,
"metadata": {},
"outputs": [],
"source": [
"SWE['cumulative_cases']=SWE['cases']\n",
"for i in range(1,329):\n",
" SWE.loc[i, 'cumulative_cases'] = SWE.loc[i, 'cases'] + SWE.loc[i-1, 'cumulative_cases'] #create df column of cumulative cases\n"
]
},
{
"cell_type": "code",
"execution_count": 192,
"metadata": {},
"outputs": [],
"source": [
"# convert the Date column to a datetime type\n",
"SWE.Date = pd.to_datetime(SWE.Date)\n",
"\n",
"# set the column of Date as the index\n",
"SWE.set_index('Date', inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 197,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
"The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
]
},
{
"data": {
"image/png": 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\n",
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