Data Science using Python Tutorials

About Data Science Using Python

Data science is a significant and in-demand field currently. At the same time, it is one of the best long-term investments considering the benefits it comes with. Python is one of the best programming languages to extract values from data because of its great ability for data modeling, easy readability, and statistical analysis.

Another reason for the considerable popularity of data science using Python is that it has extensive library support for data science and analytics. Here we go with the 10 important things You Didn’t Know About Data Science Using Python.

Python Library for Data Processing

1. Pandas

It is the free python library used for data analysis and data handling. Pandas provide a lot of high-performance and easy-to-use data structures and operations that manipulate data in numerical tables and time series. It also offers multiple tools for reading and writing data between in-memory data structures and different file formats.

2. NumPy

NumPy is another free software library for numerical computing of data that is in the form of large arrays and multidimensional matrices. These multi-dimensional matrices are the main objects of NumPy, where their dimensions are known as axes and the number of axes as rank. It also offers various tools to work with these arrays and high-level mathematics to manipulate the data using linear algebra.

3. SciPy

This free software is used for scientific and technical computing on the data. Its library is built on the NumPy array object, and it is part of the NumPy stack. It also includes other scientific computing tools like – Matplotlib, SymPy, Pandas, etc.

4. Scikit-learn

It is a free software library used for machine learning coding, basically in python programming. Scikit- learn to provide full interoperability as it is built on top of the other python libraries. Many supervised and unsupervised models can be implemented in Scikit-learn.

5. TensorFlow

This is another free end-to-end open-source platform with various tools, libraries, and artificial intelligence resources. The machine learning model with high-level APIs can be quickly built using TensorFlow. It also offers to deploy machine learning models anywhere, such as cloud, browser, or your own device. 

6. Keras

It is a free open source neural library written in Python. It was created to be user-friendly, extensible, and modular while providing support for deep neural network experiments. That is why it can be run on top of the other libraries like – Toolkit, R, Theano, etc.

Python Library for Data Visualization

7. Matplotlib

It is the widely used and popular plotting library in the python community. It has got an interactive environment for various platforms. It can be used in the python scripts, IPython shells &  the Python, Jupyter notebook, and web application server. It is also used to embed plots into applications using various GUI toolkits like Tkinter, GTK+, Qt, wxPython, etc.

8. Seaborn

The python data visualization library is based on Matplotlib and integrated with the NumPy and panda data structures. Seaborn has various dataset-oriented plotting functions that operate on data frames and arrays with whole datasets within them.  

9. Plotly

It is an open-source free library for graphing, which is used to form data visualizations. It is built on the top of JavaScript library and used to create web-based data visualizations that are displayed on web applications or Jupyter notebook or can also be used to save as individual HTML files.

10. GGplot

This is to gain a python data visualization library based on the implementation of ggplot2 created for R programming. It is used to create data visualizations like – charts, pie diagrams, histograms, error charts, etc. with the help of high-level API.

There are a few other things that you should know when you will start learning python. In this article, we tried to cover the 10 most important ones.

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