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Pandemic Over? COVID-19 World data Amateur Python Analysis

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From an educational perspective, we review current COVID-19 data and arrive look at lockdowns and population density appears to have no numerical effect currently on COVID-19. In any case, this is more about exploring the code from a beginner’s standpoint with Python and DataFrames. #covid19#pandemicover#coviddata CSV files found here: https://ourworldindata.org/coronaviru… Code: (Had to remove the angle brackets) import numpy as np import pandas as pd from scipy import stats import statsmodels.api as sm import matplotlib.pyplot as plt import pandas as pd from scipy.stats import spearmanr from scipy.stats import kendalltau from scipy.stats import pearsonr from scipy import stats import seaborn as sns import warnings warnings.filterwarnings(“ignore”) #Pandemic Claim Currently Invalid —Ralph Turchiano data = pd.read_csv(‘owid-covid-data-19SEP2020.csv’) data.info() pd.set_option(‘max_columns’, None) data.tail(5) data[‘date’] = pd.to_datetime(data[‘date’]) data.info() data_18SEP = data[data[‘date’]==’2020-09-18′] data_ind = data_18SEP[data_18SEP[‘human_development_index’]=.8] data_ind.head(10) data_ind.drop([‘iso_code’,’continent’,’handwashing_facilities’,’stringency_index’,], axis=1, inplace=True) data_ind.columns data_ind[‘extreme_poverty’].fillna(0, inplace=True) data_compare = pd.DataFrame([data.loc[37991],data.loc[41736]]) data_compare data_compare.set_index(‘location’,inplace=True) data_compare[‘total_cases_per_million’] data_Swe_USA=pd.DataFrame(data_compare[[‘total_cases_per_million’,’new_cases_per_million’,’new_deaths_per_million’]]) data_Swe_USApd.DataFrame(data_compare[[‘total_cases_per_million’,’new_cases_per_million’,’new_deaths_per_million’]]) data_Swe_USA data_ind.drop([‘date’,’new_cases’,’new_deaths’,’total_tests’, ‘total_tests_per_thousand’, ‘new_tests_per_thousand’, ‘new_tests_smoothed’, ‘new_tests’, ‘new_tests_smoothed_per_thousand’, ‘tests_per_case’,’tests_units’,’new_deaths_per_million’,’positive_rate’ ], axis=1, inplace=True) data_ind.tail() data_ind.dropna(inplace=True) data_ind.corr(“kendall”) data_18SEP.tail() data_18SEP.loc[44310] data_18SEP.loc[44310,[‘new_cases_smoothed_per_million’,’new_deaths_smoothed_per_million’]] New =pd.DataFrame(data[[‘new_cases_smoothed_per_million’,’new_deaths_smoothed_per_million’]]) New.corr(‘kendall’) dataw = data.loc[data[‘iso_code’] == ‘OWID_WRL’] dataw dataw.datetime = pd.to_datetime(data.date) dataw.set_index(‘date’, inplace=True) data_cl = pd.DataFrame(dataw[[‘new_deaths_smoothed’,’new_cases_smoothed’]]) data_cl.dropna(inplace=True) data_cl.plot(figsize=(30,12)) data_cl.tail(20)SHOW LESS

About Post Author

Ralph Turchiano

I have a strong affinity for the sciences which led me to create my sites. My compulsion for the past decade has been reviewing literally every peer-reviewed research article. Which can easily be validated by following my posts. To me, science is where the real news is, as it will mold our destiny beyond that of politics or economics. 😉 Please feel free to e-mail: 161803p314159@gmail.com
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