Seaborn Library for Data Visualization

Seaborn

The human minds are more versatile and adaptable to visual graphics than to textual information. Data visualization is a technique that expresses, analyzes, and represents the massive amount of data in the form of a graph, chart, or animations instead of using the textual representation. Data visualization translates complex information into digestible insights for non-technical audiences.  There are many libraries in Python for data visualization, but seaborn is one of the most powerful tools for data visualization in Python. Seaborn

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Python Statistics Fundamentals

Python Statistics Fundamentals

Python Statistics Fundamentals: Math and statistics are essential for data science because these disciples form the solid foundation of all the machine learning algorithms. Mathematics is a part of every aspect of our lives. Mathematics and Statistics, in fact, are behind everything that surrounds us, from shapes, patterns, and colors to counting petals in flower. A key aspect of working with data is the ability to describe, summarize and represent data visually. Python Statistics Libraries are thorough, accessible,and commonly used

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Matplotlib for Data Visualization

Matplotlib for Data Visualization

What is data visualization? Human minds are more flexible and adaptable to the graphic illustration of data than to textual data. We can comprehend things when they are visualized. It is better to depict the information through a graph in which we can analyze the data more efficiently and make a specific decision on the basis of data analysis. Before learning the matplotlib, we need to understand data visualization and why data visualization is essential. Graphics provides an excellent approach

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Matrix Operations with Python NumPy-II

Matrix Operations with Python NumPy-2

Trace of a Matrix Calculations Trace of a Matrix is the sum of diagonal elements of the Matrix. >>> import numpy as np >>> matrix = np.array ( [ [ 4, 5, 6 ], [ 7, 8, 9 ], [ 10, 11, 12 ] ] ) >>> print ( ” Matrix is : \n “, matrix)  Matrix is :   [[ 4  5  6]  [ 7  8  9]  [10 11 12]] >>> print ( ” Trace of the matrix :

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Array with Python Numpy

Array with Python NumPy

About Numpy: NumPy is a module for Python. The name is an acronym for “Numeric Python” or “Numerical Python”. It is an extension module for Python, mostly written in C. It is a successor for two earlier computing Python libraries such as Numeric and numarray. It is the fundamental package for scientific computing. It is a general-purpose array-processing or manipulating python package. It provides a high-level performance on multidimensional array object and tools for working with these arrays. It is

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Selenium Python

selenium python

Selenium Python : Today, companies and applications are widely structured on web-based systems. The information that these systems contain is dense and requires extensive processing. Various tasks are repeated, tedious and take a lot of time and money. These repeated tasks can be taken care by Web Automation. The common tasks in web automation includes form filling, screen scraping, data collection, transfer between applications, website testing and periodic reports generation. Web Automation Tool There are many tools available for automation. A

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Python vs. R

Python Vs. R

Python vs. R : R and Python are the most popular programming languages used by data analysts and data scientists. Both are free and open source and were developed in the early 1990s—R for statistical analysis and Python as a general-purpose programming language. In the first, I will cover the vital aspect of python language after that covers important aspect in R language. About Python: Python is a high-level, interpreted, general-purpose programming language. Python programming language has been created by

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Shallow Copy and Deep Copy

Copy in python

Copy in python : Shallow Copy and Deep Copy  In Python programming, many times we need to make a copy of variable(s). In orders to make copy different methods are available in the Python programming.  Let’s take the example of the integer. >>> a = 10 >>> b = 10 >>> id(a) 140730625348928 >>> id(b) 140730625348928 >>> >>> x = 257 >>> y = 257 >>> id(x) 2067355160528 >>> id(y) 2067355160432 >>> If an integer value is less than 256

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Python Threading – Part 2

Python threading

Python Threading In the previous article, you have seen the threading methods. In this article, you will see daemon threads and locks. Daemon Thread: So far, we have created the non-daemon thread. What is the daemon thread? When the main thread exits, it attempts to terminate all of its daemonic child threads. Consider an example of GUI as shown below Consider, by GUI input some calculation is being performed in the background and the calculation is taking its time. If you

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Python Thread – Part 1

Python Thread

Python Thread A Python thread is like a process, and may even be a process, depending on the Python thread system. In fact, Python threads are sometimes called “lightweight” processes, because threads occupy much less memory, and take less time to create than do processes. Threads allow applications to perform multiple tasks at once. Multi-threading is important in many applications. In python, threading module is used to create the threads. In order to create threads, threading modules can be used

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