python 101: Introduction to python for Data science
Published: 2023-02-17 14:28:56

**Why learn Python for Data Science?**

Python is usually everyone’s choice for a programming language in the Data Science industry, and it has gradually gained popularity in recent years. In 2017, Python replaced R in terms of popularity, as per a poll conducted by KDNuggets, and a similar was the case for the year 2018. And quite recently, Python has emerged as the most popular programming language as per the TIOBE index of 2021.


**B**eginners in Data Science often ask, Is Python required for data science? The answer is No, Python is not necessary for learning Data Science, but if you learn it, that would be helpful. We now present you with a few reasons behind the popularity of Python among the masses.

- Python makes writing codes easy.
- Python is an easy-to-read language.
- It is free and open source.
- It is an object-oriented programming language.
- It is one of the best languages for completing data 
  visualization and data cleaning tasks.
- It contains libraries like NumPy, Pandas, etc., that easily 
  handle big data.
- It is supported by a large community of Developers from across 
  the globe.

The list of why python is a good choice for Data Science is never-ending. But, we’d like to pause here and move ahead with the next section highlighting the perks of learning Python for Data Science.



**Python Fundamentals for Data Science**

Before exploring libraries that assist in implementing data science algorithms, it is crucial to learn python fundamentals. So, learn how to write simple statements in Python and implement different loops in Python. Focus on the list of keywords reserved by Python and explore the language’s different data types (array, list, tuple, dictionary, sets, etc.). After learning the basics, try to create sample programs for the following problems: 

- Check whether an input number is prime or not.
- Print the HCF and LCM of two input numbers.
- Print the following pattern using for loop:

**Python Libraries for Data Science**
Python has many libraries that support the implementation of various algorithms in Data Science. Below are a few of those libraries and respective project ideas for learning about them.

**Scikit-learn**

This library contains all the codes for implementing machine learning algorithms like linear regression, logistic regression, etc. Data Scientists use this library to implement ML algorithms and evaluate the quality of the model fit. One can also use Scikit-learn to implement cross-validation technique over the given dataset. It also contains a few sample datasets that one can use for understanding the implementation of different algorithms.

**Pandas**

This library contains various functions that can be used to read, write, and analyze .csv file. It also offers the series data structure that data scientists can use to handle one-dimensional data. The exciting part of using this library is that it allows the conversion of data types like list, tuple, or dictionary to Series structure. With Pandas, you can transform your data into a DataFrame and use various predefined methods to get an overview of that data.

**NumPy**

This library contains various methods for the scientific computation of n-dimensional arrays. One can create, manipulate, and index arrays with the help of NumPy, and additionally, it provides necessary methods for broadcasting arrays.

[](https://numpy.org/doc/)

**Seaborn**

Seaborn is another data visualization library that allows users to plot different types of graphs. Seaborn offers better visually-appealing graphs than matplotlib.


**Python Frameworks for Data Science**

Python Frameworks support the development process of a web application. Data scientists use these frameworks to automate the deployment of standard solutions. They help data scientists increase productivity as the Python frameworks take care of routine elements.

**Flask**

According to Wikipedia, “Flask is a micro web framework written in Python. It is classified as a microframework because it does not require particular tools or libraries. It has no database abstraction layer, form validation, or other components where pre-existing third-party libraries provide common functions.“ Flask is used to create web applications with the help of Python programming language. Specifically, Data Scientists use Flask to deploy machine learning projects on the web.

**TensorFlow**

It provides the framework for implementing deep learning (DL) and machine learning (ML) algorithms. Data Scientists use TensorFlow to build scalable machine learning and deep learning projects. TensorFlow offers various APIs for building ML and DL models, and it is highly portable as it can run on various platforms.

**PyTorch**
PyTorch is a framework based on Python’s torch library, used for Machine Learning and Natural Language Processing (NLP) applications. Data Scientists prefer using PyTorch for implementing deep learning models. and PyTorch is used to escalate the process between research prototyping and deployment.