1612362000
Charting provides a powerful way to visualize and explore your data by helping to uncover patterns, trends, relationships, and structures that might not be apparent when looking at a table or map. The COVID-19 pandemic has created voluminous streams of data for scientists, researchers, and decision-makers to visualize, analyze, and understand through a variety of data analysis packages and tools.
This blog walks through visualizing characteristics and trends of the COVID-19 pandemic in the United States during 2020 using the integration between Python and ArcGIS Platform.
To get started, I’ll load and prepare the data using pandas, but you can use whatever Python tools you prefer. I’m acquiring the data from the New York Times COVID-19 data repository (publicly accessible here), and I’m filtering the data to include only dates from the complete year of 2020.
import pandas as pd
from arcgis.features import GeoAccessor
import arcpy
arcpy.env.workspace = 'memory'
DATA_URL = 'https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv'
# load data with pandas, create new fields, and filter
daily_df = (
pd.read_csv(DATA_URL, parse_dates=['date'])
.sort_values(['state', 'date'])
.rename(columns={
'cases': 'cases_total',
'deaths': 'deaths_total'
})
.assign(
cases_new = lambda df: df.groupby('state')['cases_total'].diff().clip(lower=0),
deaths_new = lambda df: df.groupby('state')['deaths_total'].diff().clip(lower=0)
)
.query("'2020-01-01' <= date <= '2020-12-31'")
.reset_index(drop=True)
)
Here’s a quick look at the prepared dataset. Notice that there is an individual row for each date and state combination. These rows will be summarized and aggregated when I visualize this data with charts.
#python #covid-19 #covid 19 #charting
1620127560
Bhavesh Bhatt, Data Scientist from Fractal Analytics posted that he has created a Python script that checks the available slots for Covid-19 vaccination centres from CoWIN API in India. He has also shared the GitHub link to the script.
The YouTube content creator posted, “Tracking available slots for Covid-19 Vaccination Centers in India on the CoWIN website can be a bit strenuous.” “I have created a Python script which checks the available slots for Covid-19 vaccination centres from CoWIN API in India. I also plan to add features in this script of booking a slot using the API directly,” he added.
We asked Bhatt how did the idea come to fruition, he said, “Registration for Covid vaccines for those above 18 started on 28th of April. When I was going through the CoWIN website – https://www.cowin.gov.in/home, I found it hard to navigate and find empty slots across different pin codes near my residence. On the site itself, I discovered public APIs shared by the government [https://apisetu.gov.in/public/marketplace/api/cowin] so I decided to play around with it and that’s how I came up with the script.”
Talking about the Python script, Bhatt mentioned that he used just 2 simple python libraries to create the Python script, which is datetime and requests. The first part of the code helps the end-user to discover a unique district_id. “Once he has the district_id, he has to input the data range for which he wants to check availability which is where the 2nd part of the script comes in handy,” Bhatt added.
#news #covid centre #covid news #covid news india #covid python #covid tracing #covid tracker #covid vaccine #covid-19 news #data scientist #python #python script
1620466520
If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition
1593156510
At the end of 2019, Python is one of the fastest-growing programming languages. More than 10% of developers have opted for Python development.
In the programming world, Data types play an important role. Each Variable is stored in different data types and responsible for various functions. Python had two different objects, and They are mutable and immutable objects.
Table of Contents hide
III Built-in data types in Python
The Size and declared value and its sequence of the object can able to be modified called mutable objects.
Mutable Data Types are list, dict, set, byte array
The Size and declared value and its sequence of the object can able to be modified.
Immutable data types are int, float, complex, String, tuples, bytes, and frozen sets.
id() and type() is used to know the Identity and data type of the object
a**=25+**85j
type**(a)**
output**:<class’complex’>**
b**={1:10,2:“Pinky”****}**
id**(b)**
output**:**238989244168
a**=str(“Hello python world”)****#str**
b**=int(18)****#int**
c**=float(20482.5)****#float**
d**=complex(5+85j)****#complex**
e**=list((“python”,“fast”,“growing”,“in”,2018))****#list**
f**=tuple((“python”,“easy”,“learning”))****#tuple**
g**=range(10)****#range**
h**=dict(name=“Vidu”,age=36)****#dict**
i**=set((“python”,“fast”,“growing”,“in”,2018))****#set**
j**=frozenset((“python”,“fast”,“growing”,“in”,2018))****#frozenset**
k**=bool(18)****#bool**
l**=bytes(8)****#bytes**
m**=bytearray(8)****#bytearray**
n**=memoryview(bytes(18))****#memoryview**
Numbers are stored in numeric Types. when a number is assigned to a variable, Python creates Number objects.
#signed interger
age**=**18
print**(age)**
Output**:**18
Python supports 3 types of numeric data.
int (signed integers like 20, 2, 225, etc.)
float (float is used to store floating-point numbers like 9.8, 3.1444, 89.52, etc.)
complex (complex numbers like 8.94j, 4.0 + 7.3j, etc.)
A complex number contains an ordered pair, i.e., a + ib where a and b denote the real and imaginary parts respectively).
The string can be represented as the sequence of characters in the quotation marks. In python, to define strings we can use single, double, or triple quotes.
# String Handling
‘Hello Python’
#single (') Quoted String
“Hello Python”
# Double (") Quoted String
“”“Hello Python”“”
‘’‘Hello Python’‘’
# triple (‘’') (“”") Quoted String
In python, string handling is a straightforward task, and python provides various built-in functions and operators for representing strings.
The operator “+” is used to concatenate strings and “*” is used to repeat the string.
“Hello”+“python”
output**:****‘Hello python’**
"python "*****2
'Output : Python python ’
#python web development #data types in python #list of all python data types #python data types #python datatypes #python types #python variable type
1612362000
Charting provides a powerful way to visualize and explore your data by helping to uncover patterns, trends, relationships, and structures that might not be apparent when looking at a table or map. The COVID-19 pandemic has created voluminous streams of data for scientists, researchers, and decision-makers to visualize, analyze, and understand through a variety of data analysis packages and tools.
This blog walks through visualizing characteristics and trends of the COVID-19 pandemic in the United States during 2020 using the integration between Python and ArcGIS Platform.
To get started, I’ll load and prepare the data using pandas, but you can use whatever Python tools you prefer. I’m acquiring the data from the New York Times COVID-19 data repository (publicly accessible here), and I’m filtering the data to include only dates from the complete year of 2020.
import pandas as pd
from arcgis.features import GeoAccessor
import arcpy
arcpy.env.workspace = 'memory'
DATA_URL = 'https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv'
# load data with pandas, create new fields, and filter
daily_df = (
pd.read_csv(DATA_URL, parse_dates=['date'])
.sort_values(['state', 'date'])
.rename(columns={
'cases': 'cases_total',
'deaths': 'deaths_total'
})
.assign(
cases_new = lambda df: df.groupby('state')['cases_total'].diff().clip(lower=0),
deaths_new = lambda df: df.groupby('state')['deaths_total'].diff().clip(lower=0)
)
.query("'2020-01-01' <= date <= '2020-12-31'")
.reset_index(drop=True)
)
Here’s a quick look at the prepared dataset. Notice that there is an individual row for each date and state combination. These rows will be summarized and aggregated when I visualize this data with charts.
#python #covid-19 #covid 19 #charting
1619510796
Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.
Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is
Syntax: x = lambda arguments : expression
Now i will show you some python lambda function examples:
#python #anonymous function python #filter function in python #lambda #lambda python 3 #map python #python filter #python filter lambda #python lambda #python lambda examples #python map