Aketch  Rachel

Aketch Rachel

1624964760

Altair: Interactive Data Visualizations in Python Made Easy

Data visualization helps us make sense of overwhelming and messy collections of tabular data. As a go-to language for data science, Python has several excellent data visualization libraries. One of them is Altair, and you’ll learn the basics of it today.

As a data scientist, visual representation of data is a must, especially when presenting data and insights to a less technical audience. You should also pay attention to the aesthetics of your visualizations, and that’s where Altair shines. You can easily make stunning interactive charts, something not easily achievable with Matplotlib.

Altair can also be used to build and deploy interactive reports. This article explains how

Today’s article is structured as follows:

  • Altair Installation and Dataset Introduction
  • Altair Basics — Simple Area Chart
  • Basic Styles With Altair
  • Advanced Style With Altair — Gradients
  • Adding Interactivity
  • Saving Charts
  • Final Words

Altair Installation and Dataset Introduction

You can install Altair as any other Python library. We’ll install it in a separate virtual environment called altair_env, based on Python version 3.8.

Numpy and Pandas are dependencies of Altair, so you don’t have to install them manually. If you’re using Anaconda, the following set of shell commands will set up the environment, activate it, and install everything needed:

conda create — name altair_env python=3.8
conda activate altair_env

conda install -c conda-forge altair altair_saver
conda install -c anaconda pandas-datareader
conda install jupyter jupyterlab

Awesome! You can now launch Jupyter Lab and create a new empty notebook.

#data-visualization #towards-data-science #data-science #python #altair

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Altair: Interactive Data Visualizations in Python Made Easy
Aketch  Rachel

Aketch Rachel

1624964760

Altair: Interactive Data Visualizations in Python Made Easy

Data visualization helps us make sense of overwhelming and messy collections of tabular data. As a go-to language for data science, Python has several excellent data visualization libraries. One of them is Altair, and you’ll learn the basics of it today.

As a data scientist, visual representation of data is a must, especially when presenting data and insights to a less technical audience. You should also pay attention to the aesthetics of your visualizations, and that’s where Altair shines. You can easily make stunning interactive charts, something not easily achievable with Matplotlib.

Altair can also be used to build and deploy interactive reports. This article explains how

Today’s article is structured as follows:

  • Altair Installation and Dataset Introduction
  • Altair Basics — Simple Area Chart
  • Basic Styles With Altair
  • Advanced Style With Altair — Gradients
  • Adding Interactivity
  • Saving Charts
  • Final Words

Altair Installation and Dataset Introduction

You can install Altair as any other Python library. We’ll install it in a separate virtual environment called altair_env, based on Python version 3.8.

Numpy and Pandas are dependencies of Altair, so you don’t have to install them manually. If you’re using Anaconda, the following set of shell commands will set up the environment, activate it, and install everything needed:

conda create — name altair_env python=3.8
conda activate altair_env

conda install -c conda-forge altair altair_saver
conda install -c anaconda pandas-datareader
conda install jupyter jupyterlab

Awesome! You can now launch Jupyter Lab and create a new empty notebook.

#data-visualization #towards-data-science #data-science #python #altair

Ray  Patel

Ray Patel

1623088800

Making Interactive Visualizations with Python Altair

A comprehensive practical guide

Data visualization is a fundamental piece of data science. If used in exploratory data analysis, data visualizations are highly effective at unveiling the underlying structure within a dataset or discovering relationships among variables.

Another common use case of data visualizations is to deliver results or findings. They carry much more informative power than plain numbers. Thus, we often use data visualization in storytelling, a critical part of the data science pipeline.

We can enhance the capabilities of data visualizations by adding interactivity. The Altair library for Python is highly efficient at creating interactive visualizations.

In this article, we will go over the basic components of interactivity in Altair. We will also do examples to put these components into action. Let’s start by importing the libraries.

#python #artificial-intelligence #data-visualization #machine-learning #creating interactive visualizations #making interactive visualizations with python altair

 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