Topic- Top 11 Python Libraries for Data Science
Python is undoubtedly the best programming language for Data Science, due to its awesome libraries designed to perform data-driven tasks!
In this article, we will discuss the top 11 Python libraries for data science tasks.
We will give a brief description of the tasks, that each of the libraries is awesome in doing.
Before starting with the libraries, let us get a brief intuition on two of the most frequent questions, people working in the data science domain ask!
Which Python version is best for Data Science?
It is suggested to have the latest version of programming languages in your system, to work with the latest techs.
Installing the latest version of any language comes with better readability of that language and a lot of tasks, in-built and out built can be performed more efficiently and with greater ease.
So, it is always good to have Python >=3x, installed in your system for doing data science and its related sub-domains.
Which Python Library is used mostly for Data Science?
Every library that Python has, in-built or external, got some specific task specialization which makes it precise from the rest.
But, to work with Data Science, there are some libraries, which are building blocks of your data. You can’t carry out any other task later, without using such libraries.
So, the python libraries, which are a mandatory requirement for data science are Pandas, NumPy and Matplotlib!
We use these libraries for data loading, cleaning, preprocessing, and visualizing. Hence, YOU MUST HAVE THEM!
Python Libraries for Data Science
Here is the list of Top 11 Python Libraries for Data Science:
1. NumPy — A must library for Data Preprocessing
NumPy, or Numerical Python, is a library of multidimensional array objects. NumPy offers many handy features performing operations on arrays and matrices in Python.
It helps to process arrays that store values of the same data type and makes performing math operations on arrays (and their vectorization) easier.
The vectorization of mathematical operations on the NumPy array type increases performance and accelerates the execution time.
2. Pandas– a must-have for data wrangling, manipulation and processing
Pandas is an easy-to-use, open-source Python library providing high-performance data analysis tools for the Python programming language
Pandas is the library created to help developers and data analysts work with “labeled” and “relational” data intuitively.
Pandas allow converting data structures to-
- DataFrame object
- Handling missing data
- Adding/deleting columns from DataFrame
- Plotting data
3. SciPy – A must for scientific computing
SciPy is a free and open-source Python library used for scientific computing, as the name suggests.
It contains modules for optimization, linear algebra, uncertainty and statistics, integration, interpolation, special functions, FFT, signal, and image processing.
Also, it works great for all kinds of scientific programming projects (science, mathematics, and engineering).
4. D-Tale– a great library for analyzing Pandas data structures
The recently launched user-interactive D-Tale shows us the data in the same way as pandas do.
The only difference is the menu in the top left corner that allows us to do many things with the data- that too automatically without any coding!
Clicking on any of the column headings opens a drop-down menu that gives us options to sort the data and display it exactly as we want it,providing user-controllable interface.
It integrates greatly with Jupyter notebooks & Python terminals and is indeed a great tool for quick statistical analysis.
5. Matplotlib– a must for data-visualization
Matplotlib is a plotting Python library, used extensively for data visualization.
This is a standard data science library that helps to generate data visualizations such as two-dimensional diagrams and graphs (histograms, scatter plots, box-plots,etc).
Matplotlib is one of those plotting libraries that are useful in data science projects for its object-oriented API for embedding plots into applications.
6. Seaborn– a high-level interface library for data analysis
Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.
As a superset of the Matplotlib library, it helps in visualizing univariate and bivariate data. It uses beautiful themes for decorating Matplotlib graphics.
Seaborn is more comfortable in handling Pandas data frames. It uses basic sets of methods to provide beautiful graphics in python, hence widely used by data-scientists and developers.
7. Plotly — the best web-based data visualization tool
The interactive graphing library for Python (includes Plotly Express), that develops online data analytics and visualization tools.
It is easy to build, test, and deploy beautiful interactive web apps, charts and graphs—in a quick timestamp!
Something more great about it?
Version 4 of plotly includes no code or functionality for uploading figures or data to any cloud service.
8. Scrapy – The best data science tool for web-scraping
Scrapy is one of the most popular Python data science libraries. It helps to build crawling programs (spider bots) that can retrieve structured data from the web — for example, URLs or contact info.
It’s a great tool for scraping data used in, for example, Python machine learning models.
Developers use it for gathering data from APIs. It is a Python framework for large-scale web scraping.
9. SciKit-Learn — the best library for predictive modelling
Scikit-learn is a free software machine learning library for the Python programming language. Scikit–learn provides a range of supervised and unsupervised learning algorithms via a consistent interface in Python.
Scikit-learn (Sklearn) is the most useful and robust library for machine learning in Python.
It provides a selection of efficient tools for machine learning and statistical modeling including classification, regression, clustering, and dimensionality reduction via a consistent interface in Python.
10. Bokeh — great library for interactive and scalable visualizations
Don’t like to code with JavaScript but like JavaScript applications?
Yes, Bokeh is for you!
Bokeh is a Python library for creating interactive visualizations for modern web browsers.
It helps you build beautiful graphics, ranging from simple plots to complex dashboards with streaming datasets.
With Bokeh, you can create JavaScript-powered visualizations without writing any JavaScript yourself.
11. Sweetviz — best tool for automated data analysis
Sweetviz is an open-source Python library that generates beautiful, high-density visualizations to kickstart EDA (Exploratory Data Analysis) with a single line of code!!
Output is a fully self-contained HTML application. The system is built around quickly visualizing target values and comparing datasets.
Its goal is to help quick analysis of target characteristics, training vs testing data, and other such data characterization tasks.
Conclusion-
In addition to these top 11 Python libraries for data science, there are many other cool python libraries that deserve to be looked at.
Although the libraries we talked about, in this article will be your long-term companion in becoming a professional data scientist. You also need to read the documentation carefully before getting started with each of them!
I hope you all had fun reading all this information about top data science libraries in Python!
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