Create README.md

Author: madhurimarawat

README.md

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+# Python-for-Datascience
+This repository contains basic programs in the python programming language.<br><br>
+<img src="https://media3.giphy.com/media/coxQHKASG60HrHtvkt/giphy.gif?cid=790b7611dkgau1ujakt3igpplm9r0nkfvams42q5y263yifr&ep=v1_gifs_search&rid=giphy.gif&ct=g" title="Python Gif" alt="Python">
+
+<br>
+<centre><h1>About Python Programming</h1></centre>
+--> Python is a high-level, general-purpose, and very popular programming language.<br><br>
+--> Python programming language (latest Python 3) is being used in web development, Machine Learning applications, along with all cutting-edge technology in Software Industry.
+.<br><br>
+--> Python is available across widely used platforms like Windows, Linux, and macOS.<br><br>
+--> The biggest strength of Python is huge collection of standard library .<br>
+
+<centre><h1>Modes of Executions</h1></centre>
+Python programming language can be executed in the following two modes:
+<h2>1. Interactive mode</h2>
+<h3>a) Python Shell</h3>
+Python Shell is a command line tool that starts up the python interpreter to read a Python statement, 
+evaluate the result of that statement and then prints the result on the screen.<br>
+<h3>b) IDLE</h3>
+In Windows search Type IDLE. It is an acronym of "Integrated DeveLopment Environment".<br>
+<h3>c) Anaconda</h3>
+Installing Anaconda Software and using Jupyter Notebook.<br>
+<h3>d) Google Colab</h3>
+Colaboratory, or “Colab” for short, is a product from Google Research which allows anybody to write and execute python code in Jupyter notebook through the browser.<br>
+
+<h2>2. Script mode</h2>
+Python programs are written in editors and saved as the file with the .py extension which can be executed further. <br>
+<br>
+
+
+![20638Python-Data-Types](https://github.com/madhurimarawat/Python-for-Datascience/assets/105432776/62c5b8d2-21f1-4515-9b8a-8acf12ea1a58)
+
+<br>
+<centre><h1>Basic Datatypes</h1></centre>
+<h2> Numbers</h2>
+✓ Number data type stores numerical values only.<br><br>
+--> It is further classified into three different types: <br>
+&nbsp   &nbsp &nbsp   a) Int  b) Float  c) Complex
+<h2>String</h2>
+✓ A string is a group of characters and can include alphabets, digits or special characters including 
+spaces.<br><br>
+--> We can use single, double, or triple quotes to define a string.
+<h2>List</h2>
+✓ Lists are used when we need a simple iterable collection of data that may go for frequent modifications.<br><br>
+-->For example, if we store the names of students of a class in a list, then it is easy to update the list when 
+some new students join or some leave the course.
+<h2>Tuple</h2>
+✓ Tuples are used when we do not need any change in the data.<br><br>
+-->For example, names of months in a year.
+<h2>Sets</h2>
+✓ Sets are used when we need uniqueness of elements and to avoid duplicacy it is preferable to use sets.<br><br>
+-->For example, list of items in a museum.
+<h2>Dictionary</h2>
+✓ Dictionaries are used if our data is being constantly modified or we need a fast lookup based on a custom 
+key or we need a association between the key : value pair.<br><br>
+-->For Example, A mobile phone book is a good application of dictionary.
+
+<br>
+<h1>Libraries Used</h1>
+<p>Short Description about all libraries used.</p>
+<ul>
+<li>NumPy (Numerical Python) – Enables with collection of mathematical functions
+to operate on array and matrices. </li>
+  <li>Pandas (Panel Data/ Python Data Analysis) - This library is mostly used for analyzing,
+cleaning, exploring, and manipulating data.</li>
+  
+</ul>