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Data Visualization with Matplotlib in Python
Before you analyze data, one of the first things you have to perform is data visualization. In Python, there are a number of high-quality visualization libraries. The most popular general-purpose visualization library is Matplotlib¹, which mainly focuses on generating static publication-quality 2D and 3D graphs. We can use it for many different plots such as histograms, bar plots, heat maps, line plots, scatter plots, and many others.
Fundamentals of Matplotlib
There are some of the important concepts that you shall come across and use in Matplotlib, and their meanings or roles.
#python #matplotlib #towards-data-science #visualization #technology
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1594088160
Data Visualization With Python: Matplotlib
Data visualization is the graphical representation of data in a graph, chart or other visual formats. It shows relationships of the data with images.
Python offers multiple graphics libraries, with which you can create interactive, live or highly customizable plots with the given data.
To get a little overview here are a few popular plotting libraries:
In this article, we will learn about creating a different type of plots using the Matplotlib library.
Matplotlib is the most popular plotting library for python, which was designed to have a similar feel to MATLAB’s graphical plotting. It gives you control over every aspect of a plot.
Matplotlib allows you to create reproducible figures using a few lines of code. Let’s learn how to use it! I also encourage you to explore: http://matplotlib.org/.
Installing Matplotlib
Install it with pip or conda at your command line or the terminal with:-
pip install matplotlib
OR
conda install matplotlib
To quickly get started with Matplotlib without installing anything on your local machine, check out Google Colab. It provides Jupyter Notebooks hosted on the cloud for free which are associated with your Google Drive account and it comes with all the important packages pre-installed.
Necessary Imports
pyplot is a module of Matplotlib that makes this library work like MATLAB. Import the matplotlib.pyplot module under the name plt (the tidy way):
import matplotlib.pyplot as plt
import numpy as np # for working with arrays
Making a Simple Plot
We pass two NumPy arrays(x and y) and ‘r’ as arguments to Pyplot’s plot() function. Here ‘r’ is for red colour, x elements will appear on x-axis and y elements will appear on the y-axis.
import matplotlib.pyplot as plt
import numpy as np
x = np.array([0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5])
y = x ** 2 # y is now a list with elements of x to the power 2
plt.plot(x, y, 'r')
plt.xlabel('X Axis Title Here')
plt.ylabel('Y Axis Title Here')
plt.title('String Title Here')
plt.show()
# The plot below is the output of this program.
Creating Multiple Plots on The Same Canvas
subplot(): a method of pyplot, divides the canvas into nrows x ncols parts and using plot_number argument you can choose the plot.
Syntax: subplot(nrows, ncols, plot_number)
In the below example, using plt.plot(x, y, 'r--’)we plot a red coloured graph with line style ‘- -’ between x and y at plot_number=1.
import matplotlib.pyplot as plt
import numpy as np
x = np.array([0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5])
y = x ** 2
plt.subplot(1,2,1) # subplot(nrows, ncols, plot_number)
plt.plot(x, y, 'r--') # r-- meaning colour red with -- pattern
plt.subplot(1,2,2)
plt.plot(y, x, 'g*-') # g*- meaning colour green with *- pattern
# The plot below is the output of this program.
For making it more simple subplots() method can be used instead of subplot(). You will see its example in “Creating Multiple plots on The Same Canvas” under “Matplotlib Object-Oriented Method”.
#data-science #matplotlib #data-visualization #python #plotting-data #data analysis
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
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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
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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
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Must-Know Data Science Libraries in Python
Python is the most widespread and popular programming language in data science, software development, and related fields. The simplicity of codes in Python, which helps learners avoid any confusion, is the key to this popularity. Python has constantly been developing, and it keeps getting updated for more ease in using. With 137,000 plus libraries and tools, Python has always provided its users with the solutions to problems of any complexity level. This reason makes Python the ideal language for Data Science operations. This article focuses on some of the essential and must-learn libraries in Python used heavily by Data Scientists. I have tried to cover different libraries used in various stages of a data science cycle, such as Data Mining, processing and modeling, Data Visualization.
Learn Data Science in Python from here!
#data-visualization #data #data-science #python-programming #python #must-know data science libraries in python