COVID Vaccines not being tested to work, CBD a COVID Lung Saver?, Shoes thee COVID carrier and Data.

This week we review disturbing vaccine study requirements, CBD an incredible gem if possibly protecting the lungs and restoring oxygen levels, and a strong correlation as to shoes being an unrecognized major disease vector. In addition to looking at COVID data correlations to which countries are locking down in response Sars-COV-2 to those which have not or have done little. #covidvaccine #covidvector #covidnews Data Sources API for DataFrames: The COVID Tracking Project Our wold in Data (Oxford) Links:

COVID19 Analytics – Mask Trash and Shoes a Major Spreader, Newsom & Fauci Being Odd, Florida Wins

Our weekly review of the current COVID data and country comparisons as well as other oddities such as Mask Litter, Trash Cans, and Shoes being unintended spreaders. All this under the guise of Amateur Python Analytics. Brief CSV File Request Code below (Pandas). That will allow you to pull Oxford University Data up to the current date. Enjoy 😉

This is a long one, next week I will make it A LOT shorter.

#covid19 #sarscov2 #data

Code Snippet:
import pandas as pd
import csv
import requests
younameit = pd.read_csv(‘’)

Pandemic Charting – Weaponizing Uncertainty – Countries Do better with a Light touch – Python Data

Lockdowns a Complete Failure compared to controls – Countries that did not? Python Analysis Part 2

Part 2 as promised. We compare cases and death per million from industrialized countries which did little to nothing to Great Britain and the United States. The Data extrapolated is from:…
#covid19 #lockdown #socialdistancing

(Volume is kind of Choppy midpoint)
Additional Code: From Part 1:
datasw = data.loc[data.iso_code==’SWE’, :]
datagb = data.loc[data.iso_code==’GBR’, :]
dataus = data.loc[data.iso_code==’USA’, :]
datasg = data.loc[data.iso_code==’SGP’, :]
datajp = data.loc[data.iso_code==’JPN’, :]
datako = data.loc[data.iso_code==’KOR’, :]
datatw = data.loc[data.iso_code==’TWN’, :]
dataall = [datagb,dataus,datasw,datasg,datajp,datako, datatw]
dataall = pd.concat(dataall)
dataall.datetime = pd.to_datetime(
dataall.set_index(‘date’, inplace=True)
fig, ax = plt.subplots(figsize=(50,25))
dataall.groupby(‘iso_code’)[‘new_cases_smoothed_per_million’].plot(legend=True,fontsize = 20, linewidth=7.0)
ax.legend([‘Great Britain = Lockdown’,’Japan = No LD’, ‘South Korea = No LD’, ‘Singapore = LD JUNE -Migrant LD HIghest POP Density’, ‘Sweden = No LD’, ‘Taiwan = No LD’,’USA = Lockdown’],prop=dict(size=50))
comp = dataall.loc[‘2020-09-18’]
comp.set_index(“iso_code”, inplace=True)
comp= pd.DataFrame(comp[[‘total_cases_per_million’,’total_deaths_per_million’]])
plt.rc(‘legend’, fontsize=50), figsize=(20,20),fontsize=30)

Pandemic Over? COVID-19 World data Amateur Python Analysis

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:…
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
Claim Currently Invalid —Ralph Turchiano
data = pd.read_csv(‘owid-covid-data-19SEP2020.csv’)
pd.set_option(‘max_columns’, None)
data[‘date’] = pd.to_datetime(data[‘date’])
data_18SEP = data[data[‘date’]==’2020-09-18′]
data_ind = data_18SEP[data_18SEP[‘human_development_index’]=.8]
data_ind.drop([‘iso_code’,’continent’,’handwashing_facilities’,’stringency_index’,], axis=1, inplace=True)
data_ind[‘extreme_poverty’].fillna(0, inplace=True)
data_compare = pd.DataFrame([data.loc[37991],data.loc[41736]])
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)
New =pd.DataFrame(data[[‘new_cases_smoothed_per_million’,’new_deaths_smoothed_per_million’]])
dataw = data.loc[data[‘iso_code’] == ‘OWID_WRL’]
dataw.datetime = pd.to_datetime(
dataw.set_index(‘date’, inplace=True)
data_cl = pd.DataFrame(dataw[[‘new_deaths_smoothed’,’new_cases_smoothed’]])

COVID-19 Tracking Data API and Data Anomalies (No Correlations? Cases to Hospitalizations Increases)

Is there a correlation between Positive cases and Hospitalizations? Below is the API for python access, open to all who desire to filter the data. I want to just give easy access to all the beginner students data scientists out there, such as myself..Explore and Discover: **My Apologies It says High Def, but does not look High Def on video here**

Code: import matplotlib.pyplot as plt import pandas as pd from scipy import stats import statsmodels.api as sm import requests import time from IPython.display import clear_output response = requests.get(“”) covid = response.content ccc = open(“daily.csv”,”wb”) ccc.write(covid) ccc.close() df = pd.read_csv(“daily.csv”, index_col = ‘date’) df.head() data = df[[‘positiveIncrease’,’hospitalizedIncrease’]] dataT = df[[‘positiveIncrease’,’hospitalizedIncrease’,’hospitalizedCurrently’]] dataD = df[[‘hospitalizedIncrease’,’deathIncrease’]] dataT.head(20) plt.figure(figsize=(20,10)) Y = data[‘positiveIncrease’] X = data[‘hospitalizedIncrease’] plt.scatter(X,Y) plt.ylabel(“Tested Positive Increase”) plt.xlabel(“Hospitalization Increase”) Y1 = sm.add_constant(Y) reg = sm.OLS(X, Y1).fit() reg.summary() data.plot(y=[‘hospitalizedIncrease’,’positiveIncrease’],xticks=data.index[0:len(data):30], rot=90, figsize=(20,10) ) for x in range(len(data)): plt.figure(figsize=(20,10)) plt.xticks( data.index.values[0:len(data):30], rotation = 90, fontsize=20 ) plt.plot(data.tail(x))

Security researcher Cédric ‘Sid’ Blancher dead at 37

Skydiving accident claims Wifitap author

By  Richard Chirgwin

Posted in Security, 12th November 2013 00:59 GMT

Security researcher Cédric “Sid” Blancher has reportedly been killed in a skydiving accident in France.

At the time of writing, details of the accident remain sketchy. However, the Courrier-Picard says he died instantly after “a heavy fall on the landing zone” at the Frétoy-le-Chateau airfield.

Among other things, the 37-year-old Blancher was a sought-after speaker on WiFi security, and in 2005 published a Python-based WiFi traffic injection tool called Wifitap.

In 2006, while working for the EADS Corporate Research centre, he also put together a paper on how to exploit Skype to act as a botnet.

Cedric Blancher
Cedric Blancher, fromhis Vimeo profile

In addition to his corporate employment, Blancher had held lecturing posts in computer security at ESIEA and Limoges University.

The president of the Picardy Skydiving League, Marcel Hénique, says Blancher seems to have made an error attempting a maneuver which translates from the French as a “turn down”. ®

Original URL: