Learn Python Libraries for Data Science

 

Introduction 

Python is the go-to language for individuals in the data science space. As a newcomer, you might find it challenging to locate fundamental data science tools. In this guide, we will help you learn the basics of python through the most popular Python libraries for data science. 

Why Learn Python Libraries for Data Science?

Python is a popular programming language used by data scientists and software developers. Today, python for data analysis is used for data science tasks. Python libraries are helpful as they can foretell outcomes, computerized tasks, modernize processes, and offer business intelligence insights.

Python libraries for data science make working with data hassle-free. There are several open-source libraries in Python adapted for Data Science. Some of the popular python libraries list include Pandas, NumPy, SciPy, Matplotlib, sci-kit-learn, TensorFlow, and Seaborn. All of these python libraries make working in data science easy.

Python Libraries List: 10 Best Python Libraries for Data Science 

NumPy

NumPy stands for Numerical Python and it is an ideal tool for scientific computing, data processing, and performing basic and advanced array operations. The Python library offers several easy-to-use features performing operations on n-arrays and matrices in Python.

Keras

Keras is an ideal library for building neural networks and modeling. This Python tool is open to use and provides developers with 100% extensibility. The library offers value to other packages like Theano or TensorFlow.

TensorFlow

TensorFlow is an approved Python framework for machine learning and deep learning. Data scientists use this tool for tasks such as object identification and speech recognition. This tool works with artificial neural networks and it helps to handle multiple data sets. The library incorporates layer-helpers (tflearn, tf-slim, skflow) and it is highly functional.

Bokeh

Bokeh is a significant tool that is used to build interactive visualizations within browsers. Data analytics use this tool to build convoluted plots using simple commands. The primary focus of Bokeh is interactivity and other complex visualizations.

Pandas

Data scientists have to work with tabular data in Python. The tool pandas are the preferred choice for data analysis and manipulation. The top-most feature of pandas is to execute a data frame and a dedicated data structure for two-dimensional data.

What are some useful features of the Python Libraries?

• Python libraries for data science use classic syntax, hence the programs are exploitable to read.

• Python is a programming language that is simple to use. Hence, you can learn python libraries easily for achieving the program working.

• Python library comes with standard support.

• The collaborative model of the Python library makes it simple to test codes.

• Python libraries support developing and running the code anywhere including Windows, Mac OS X, UNIX, and Linux.

How to Learn Python for Data Analysis?

You can learn Python for data analysis very easily. Follow the steps mentioned below:

Work on Basics

Python is a readable and easy language; it has a simple syntax. Hence, this is a good choice for students to program. the first step to learning python libraries is to read, write, and understand its syntax. You must learn the basics including types, expressions, variables, and string operations.

Deal with Data Structures

Once you are done with the basics, try to understand various data structures in Python. They include tuples, sets, and dictionaries. You can use Python libraries to write code in Python.

Data Visualization Concept

Python comes with vast and diverse options for choosing a library to perform visualization. You have to be an expert with Matplotlib, Seaborn, ggplot, plotly, and Bokeh.

Use Python with Data

In the third step, you have to learn the process of using Python with data. Python for data analysis includes reading and writing files with Python libraries.

Analyze Data

Learning data analysis will help you generate insights. You have to use various Python libraries such as ndarray from NumPy, multiple functions and methods from SciPy, and various machine learning methods from scikit-learn.

Also Read- Learn Python for Data Science

Conclusion

The Python ecosystem and its libraries offer a wide scope of help in completing machine learning tasks and building algorithms. Data scientists involved in data science projects prefer using Python libraries for building high-performing ML models in Python.

All in all, we can say that Python has ample libraries that fulfill the requirements of data scientists in every field. These python libraries for a data scientist are useful for decision making.

The Python libraries are used for innumerable functions like managing and analyzing data. Every library in Python provides a particular focus on managing image and textual data, data mining, neural networks, data visualization, etc.