# Python and Data Science

## What is Data Science?

Data science is the field of computer science that uses computing algorithms to deal with the bulk amount of data. The techniques and algorithms tend to find helpful information by finding hidden patterns and making essential business decisions. As the source data can be of any type and format, data science incorporates complex machine learning tools and algorithms to build predictive models.

## Python for Data Science

It is a user-friendly high-level programming language and contains powerful libraries to store, manipulate, visualize, and extract information, and it is the first choice for many programmers worldwide.

Data Science is a broad term with a lifecycle based on data capture, maintenance, processing, analysis, and communication in readable forms. Therefore, this tutorial covers the lifecycle basics in the below steps:

### 1. Data Capture and Analysis

The first and most crucial step in Data Science is capturing and exploring data to understand the underlying patterns. The data heterogeneity requires the algorithm to transform the data into computing data types such as arrays. For instance, the images are only two-dimensional arrays of numbers representing the brightness of each image pixel, which makes the image data analyzable, making it easier for the data scientists to understand and manipulate the data. Python has some powerful packages such as panda and NumPy to store and handle such data. The users can use any of these to load their data and use the available methods to explore it.

The users can visualize the most commonly uses Numpy array as python “Lists”, but these are much more efficient. Following is the example of importing the NumPy package and using its methods.

import numpy as np np.random.seed(0) x1= np.random.randint(10, size=5) print(x1[0])

The seed method in the above example ensures that the array contains the exact numbers whenever the user runs the program. Then the code declares and initializes the variable “x” with the array of random numbers of size “5” and the range “0 to 10.” The users can access an array index by directly specifying the index number. In this way, the users can manipulate the array and use the built-in python methods to manipulate it.

### 2. Data Visualization

The term “data visualization” refers to taking the raw data in any form, such as numbers, and converting it into something colorful like graphs and images. Python has powerful packages such as matplotlib, seaborn, and datashader to visualize the data. Among these, “matplotlib” is the most commonly used package for data visualization, a multi-platform library built on Numpy arrays.

Following is the code sample to demonstrate the working of “matplotlib” with the NumPy arrays:

import matplotlib.pyplot as plott import numpy as np temp= np.linspace(0, 10, 100) plott.plot(temp, np.sin(temp)) plott.show()

The above code imports the “matplotlib” and “numpy” packages as “plott” and “np”, respectively. The “linspace” method of NumPy generates an array containing evenly spaced 100 numbers starting from “0” and ending at “10.” The “sin” function then calculates the trigonometric sine of all numbers in the array, and the “plot” method plots the sine array. The method’s first parameter represents the “x-axis”, and the second parameter represents the “y-axis.” The “show” method takes the values and opens a window to display the figure in graphical form.

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