1622880455
Building a Data Story with Python and Dash
“Some books are to be tasted, others to be swallowed, and some few to be chewed and digested; that is, some books are to be read only in parts; others to be read, but not curiously; and some few are to be read wholly, and with diligence and attention.” ~ Sir Francis Bacon
The vast majority of our data exploration is relatively simple. We need to compare two values or we need to see how our data appears in context. So we quickly whip up a bar chart or line graph in our notebook of choice, nod our head with a knowing grunt, and move on to the next item on our list.
Sometimes we need to dive deeper. We need to wring just a little more out of the information delivered. We need to offer up our visualization to someone else and not waste their time with it. In other cases we just need to make the most use of limited space.
In any case, it requires us to think a bit more permanently about what we’re doing. It may be with us for a while.
For this article, I’m going to take you on a little journey of putting together a slightly more complex visualization that tells a story. Don’t worry. Although I’ve used that keyword “complex”, this will be useful. Maybe not all-in-one as a direct copy and paste, but at the very least as a mechanism to build out your own tool kit and maybe churn up some creative ideas.
Just to manage expectations, this is not a complete application walk-through. That would be a huge undertaking. What this does is walk you through the process to get to a functional end result using one of my own specific applications. The goal is to highlight some key concepts I believe are missing in the most common locations for searching out answers. I can only hope it helps someone even if it’s just in one small way.
#data-analysis #data-visualization #python #data-science #dash
What is GEEK
Buddha Community
1619518440
top 30 Python Tips and Tricks for Beginners
Welcome to my Blog , In this article, you are going to learn the top 10 python tips and tricks.
1) swap two numbers.
2) Reversing a string in Python.
3) Create a single string from all the elements in list.
4) Chaining Of Comparison Operators.
5) Print The File Path Of Imported Modules.
6) Return Multiple Values From Functions.
7) Find The Most Frequent Value In A List.
8) Check The Memory Usage Of An Object.
…
#python #python hacks tricks #python learning tips #python programming tricks #python tips #python tips and tricks #python tips and tricks advanced #python tips and tricks for beginners #python tips tricks and techniques #python tutorial #tips and tricks in python #tips to learn python #top 30 python tips and tricks for beginners
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
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
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
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
1622880455
Building a Data Story with Python and Dash
“Some books are to be tasted, others to be swallowed, and some few to be chewed and digested; that is, some books are to be read only in parts; others to be read, but not curiously; and some few are to be read wholly, and with diligence and attention.” ~ Sir Francis Bacon
The vast majority of our data exploration is relatively simple. We need to compare two values or we need to see how our data appears in context. So we quickly whip up a bar chart or line graph in our notebook of choice, nod our head with a knowing grunt, and move on to the next item on our list.
Sometimes we need to dive deeper. We need to wring just a little more out of the information delivered. We need to offer up our visualization to someone else and not waste their time with it. In other cases we just need to make the most use of limited space.
In any case, it requires us to think a bit more permanently about what we’re doing. It may be with us for a while.
For this article, I’m going to take you on a little journey of putting together a slightly more complex visualization that tells a story. Don’t worry. Although I’ve used that keyword “complex”, this will be useful. Maybe not all-in-one as a direct copy and paste, but at the very least as a mechanism to build out your own tool kit and maybe churn up some creative ideas.
Just to manage expectations, this is not a complete application walk-through. That would be a huge undertaking. What this does is walk you through the process to get to a functional end result using one of my own specific applications. The goal is to highlight some key concepts I believe are missing in the most common locations for searching out answers. I can only hope it helps someone even if it’s just in one small way.
#data-analysis #data-visualization #python #data-science #dash