- COVID-19 Open Research Dataset (CORD-19)
- Novel Coronavirus (COVID-19) Cases, provided by JHU CSSE
- European Center for Disease Control & Prevention (ECDC) - COVID-19 Epidemiological Data
- U.S. Hospital Capacity Estimates (Harvard Global Health Institute)
- U.S. State COVID-19 Testing Data
- Italy COVID-19 Data
- ACAPS COVID-19: Government Measures Dataset
- World Bank Indicators (population health & healthcare systems) relevant to COVID-19
- GeneBank COVID-19 Genetic Sequences
- Next Strain - COVID-19 Genomics Database
- API of Scrapped Data from MoHFW
- COVID-19 Laboratories & Sample Collection Centers - Mapped by Health Analytics Asia
- COVID 19 India Network
- Code available for Covid19 India Cluster
- Health Related Dataset APIs available on OGD Platform
Solve Problems by Coding Solutions - A Complete solution for python programming
Public Datasets for Research & Innovation on Coronavirus
Python Programming for Quantitative Economics
This website presents a set of lectures on python programming for quantitative economics, designed and written by Thomas J. Sargent and John Stachurski.
Webiste
A python program for neural network trained with backpropagation with sigmoid function
import numpy as np
def nonlin(x,deriv=False):
if(deriv==True):
return x*(1-x)
return 1/(1+np.exp(-x))
X = np.array([ [0,0,1],
[0,1,1],
[1,0,1],
[1,1,1] ])
y = np.array([[0,0,1,1]]).T
np.random.seed(1)
syn0 = 2*np.random.random((3,1)) - 1
for iter in range(10000):
l0 = X
l1 = nonlin(np.dot(l0,syn0))
l1_error = y - l1
l1_delta = l1_error * nonlin(l1,True)
syn0 += np.dot(l0.T,l1_delta)
print ("Output After Training:")
print (l1)
def nonlin(x,deriv=False):
if(deriv==True):
return x*(1-x)
return 1/(1+np.exp(-x))
X = np.array([ [0,0,1],
[0,1,1],
[1,0,1],
[1,1,1] ])
y = np.array([[0,0,1,1]]).T
np.random.seed(1)
syn0 = 2*np.random.random((3,1)) - 1
for iter in range(10000):
l0 = X
l1 = nonlin(np.dot(l0,syn0))
l1_error = y - l1
l1_delta = l1_error * nonlin(l1,True)
syn0 += np.dot(l0.T,l1_delta)
print ("Output After Training:")
print (l1)
First Come First Serve Process scheduling using python
process = []
total_waiting_time = 0
n = int(raw_input('Enter the total no of processes: '))
for i in xrange(n):
process.append([])
process[i].append(raw_input('Enter process name: '))
process[i].append(int(raw_input('Enter process arrival time : ')))
total_waiting_time += process[i][1]
process[i].append(int(raw_input('Enter process burst time: ')))
print ''
process.sort(key = lambda process:process[1])
print 'Process Name\tArrival Time\tBurst Time'
for i in xrange(n):
print process[i][0],'\t\t',process[i][1],'\t\t',process[i][2]
print 'Total waiting time: ', total_waiting_time
print 'Average waiting time: ',(total_waiting_time/n)
total_waiting_time = 0
n = int(raw_input('Enter the total no of processes: '))
for i in xrange(n):
process.append([])
process[i].append(raw_input('Enter process name: '))
process[i].append(int(raw_input('Enter process arrival time : ')))
total_waiting_time += process[i][1]
process[i].append(int(raw_input('Enter process burst time: ')))
print ''
process.sort(key = lambda process:process[1])
print 'Process Name\tArrival Time\tBurst Time'
for i in xrange(n):
print process[i][0],'\t\t',process[i][1],'\t\t',process[i][2]
print 'Total waiting time: ', total_waiting_time
print 'Average waiting time: ',(total_waiting_time/n)
Python Any Where - Host, run, and code Python in the cloud
Basic plan gives you access to machines with a full Python environment already installed for free. You can develop and host your website or any other code directly from your browser without having to install software or manage your own server.
Python Any Where
Metaflow
Metaflow is a human-friendly Python library that helps scientists and engineers build and manage real-life data science projects. Metaflow was originally developed at Netflix to boost productivity of data scientists who work on a wide variety of projects from classical statistics to state-of-the-art deep learning.
pip install metaflow
Photon
Photon can extract the following data while crawling:
URLs (in-scope & out-of-scope)
URLs with parameters (example.com/gallery.php?id=2)
Intel (emails, social media accounts, amazon buckets etc.)
Files (pdf, png, xml etc.)
Secret keys (auth/API keys & hashes)
JavaScript files & Endpoints present in them
Strings matching custom regex pattern
Subdomains & DNS related data
The extracted information is saved in an organized manner or can be exported as json.
Python Regular Expression
A Regular Expression, is a sequence of characters that forms a search pattern. Python has a built-in package called re, which can be used to work with Regular Expressions.
(Example Code to remove symbols and numbers - compatible with Python 2.7.17)
import re
import string
input_str = "58597884|01:31:50|The rise of python stated by pythonforengineers blog"
print"Before Processing:",input_str
result = re.sub(r'\d+', '', input_str)
result = result.translate(string.maketrans("",""), string.punctuation)
print"After Processing:",result
(Example Code to remove symbols and numbers - compatible with Python 2.7.17)
import re
import string
input_str = "58597884|01:31:50|The rise of python stated by pythonforengineers blog"
print"Before Processing:",input_str
result = re.sub(r'\d+', '', input_str)
result = result.translate(string.maketrans("",""), string.punctuation)
print"After Processing:",result
Python Open Source Project - Manim
Manim is an animation engine for explanatory math videos. It's used to create precise animations programmatically.
Install it from PyPI via pip: pip3 install manimlib
Click here for more info
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