Anna Yusef

Anna Yusef

1613990400

Visualizing Climate Change Data with Python

It is an undeniable fact that climate change poses the biggest challenge for humanity in the current era. The global mean temperature is constantly rising, and the IPCC has pointed out the increase should be limited to 1.5 °C above pre-industrial levels, to have any hope of mitigating the harmful effects of climate change. This goal was set in the Paris Agreement, which was adopted by almost every country in the world on 2015. Unfortunately though, little action is being taken to deal with the problem, highlighted by the alarming scientific research that is constantly published.

The COVID-19 pandemic led to a drop in most aspects of human activity, and the subsequent decrease of fossil fuel burning and CO2 emissions. Regardless of that, 2020 was one of the hottest years in history, with extreme weather events being recorded all over the globe. Furthermore, according to research published in the Cryosphere journal, ice sheets are melting at record rates across the planet, being in line with the worst-case scenarios of IPCC¹. Other scientists claim that climate change and biodiversity loss pose an unprecedented existential threat for humanity, on a scale that is difficult to grasp even for experts².

According to surveys conducted by the Pew Research Center, about 70% of people worldwide believe that climate change is a major threat to their country. This percentage has increased significantly compared to 2013, but it should still be considered low, as everyone must understand the magnitude of the problem. One way to inform the public about climate change, is by creating informative and aesthetically appealing visualizations of the associated data. In this article, I am going to show you how to create map charts and animations of temperature variability, using Python.

#data-visualization #data-science #climate-change #python

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Visualizing Climate Change Data with Python
 iOS App Dev

iOS App Dev

1620466520

Your Data Architecture: Simple Best Practices for Your Data Strategy

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

Arvel  Parker

Arvel Parker

1593156510

Basic Data Types in Python | Python Web Development For Beginners

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

I Mutable objects

II Immutable objects

III Built-in data types in Python

Mutable objects

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

Immutable objects

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

Built-in data types in Python

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 (int,Float,Complex)

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).

String

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

Ray  Patel

Ray Patel

1619510796

Lambda, Map, Filter functions in python

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

Sid  Schuppe

Sid Schuppe

1617988080

How To Blend Data in Google Data Studio For Better Data Analysis

Using data to inform decisions is essential to product management, or anything really. And thankfully, we aren’t short of it. Any online application generates an abundance of data and it’s up to us to collect it and then make sense of it.

Google Data Studio helps us understand the meaning behind data, enabling us to build beautiful visualizations and dashboards that transform data into stories. If it wasn’t already, data literacy is as much a fundamental skill as learning to read or write. Or it certainly will be.

Nothing is more powerful than data democracy, where anyone in your organization can regularly make decisions informed with data. As part of enabling this, we need to be able to visualize data in a way that brings it to life and makes it more accessible. I’ve recently been learning how to do this and wanted to share some of the cool ways you can do this in Google Data Studio.

#google-data-studio #blending-data #dashboard #data-visualization #creating-visualizations #how-to-visualize-data #data-analysis #data-visualisation

Anna Yusef

Anna Yusef

1613990400

Visualizing Climate Change Data with Python

It is an undeniable fact that climate change poses the biggest challenge for humanity in the current era. The global mean temperature is constantly rising, and the IPCC has pointed out the increase should be limited to 1.5 °C above pre-industrial levels, to have any hope of mitigating the harmful effects of climate change. This goal was set in the Paris Agreement, which was adopted by almost every country in the world on 2015. Unfortunately though, little action is being taken to deal with the problem, highlighted by the alarming scientific research that is constantly published.

The COVID-19 pandemic led to a drop in most aspects of human activity, and the subsequent decrease of fossil fuel burning and CO2 emissions. Regardless of that, 2020 was one of the hottest years in history, with extreme weather events being recorded all over the globe. Furthermore, according to research published in the Cryosphere journal, ice sheets are melting at record rates across the planet, being in line with the worst-case scenarios of IPCC¹. Other scientists claim that climate change and biodiversity loss pose an unprecedented existential threat for humanity, on a scale that is difficult to grasp even for experts².

According to surveys conducted by the Pew Research Center, about 70% of people worldwide believe that climate change is a major threat to their country. This percentage has increased significantly compared to 2013, but it should still be considered low, as everyone must understand the magnitude of the problem. One way to inform the public about climate change, is by creating informative and aesthetically appealing visualizations of the associated data. In this article, I am going to show you how to create map charts and animations of temperature variability, using Python.

#data-visualization #data-science #climate-change #python