Rachel Wood

Rachel Wood


Demo: Visualizing data with Matplotlib | Even More Python for Beginners - Data Tools 30/31

In this series, “Even More Python for Beginners - Data Tools”, we’re going to help you build your toolkit for getting into data science and machine learning using Python.

If a picture is worth 1,000 words, then a chart is worth thousands of data points. By creating a chart we can better visualize what’s happening with our data. By using Matplotlib we can chart our data, and see the results from the models we’ve trained.

See how to use Matplotlib to create charts.

#python #machine-learning #data-science

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Demo: Visualizing data with Matplotlib | Even More Python for Beginners - Data Tools 30/31
Ray  Patel

Ray Patel


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

Arvel  Parker

Arvel Parker


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







Built-in data types in Python

a**=str(“Hello python world”)****#str**














Numbers (int,Float,Complex)

Numbers are stored in numeric Types. when a number is assigned to a variable, Python creates Number objects.

#signed interger




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.


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

Tia  Gottlieb

Tia Gottlieb


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 
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')
# 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.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

Siphiwe  Nair

Siphiwe Nair


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

Ian  Robinson

Ian Robinson


Top 10 Big Data Tools for Data Management and Analytics

Introduction to Big Data

What exactly is Big Data? Big Data is nothing but large and complex data sets, which can be both structured and unstructured. Its concept encompasses the infrastructures, technologies, and Big Data Tools created to manage this large amount of information.

To fulfill the need to achieve high-performance, Big Data Analytics tools play a vital role. Further, various Big Data tools and frameworks are responsible for retrieving meaningful information from a huge set of data.

List of Big Data Tools & Frameworks

The most important as well as popular Big Data Analytics Open Source Tools which are used in 2020 are as follows:

  1. Big Data Framework
  2. Data Storage Tools
  3. Data Visualization Tools
  4. Big Data Processing Tools
  5. Data Preprocessing Tools
  6. Data Wrangling Tools
  7. Big Data Testing Tools
  8. Data Governance Tools
  9. Security Management Tools
  10. Real-Time Data Streaming Tools

#big data engineering #top 10 big data tools for data management and analytics #big data tools for data management and analytics #tools for data management #analytics #top big data tools for data management and analytics