Top 15 Python Libraries for Data Science

Data science involves critical use of analytics and data processing; that is why having your hands at some extreme programming, and coding essentials like Python is a must-have. Python provides various types of convenience. Knowing simply a few lines of code can be immensely helpful in shaping your career and making day-to-day data analysis a simple project. Data Scientists can Accelerate career growth by using the right Python libraries as these can help them to take on a particular action with success and transform the way they use Python and related technologies.

So, if you are a data scientist, analyst or a python enthusiast then you must know about the most popular python libraries shared as follows;

1. Scrapy

It is one of the most popular libraries in Python. Scrapy helps the user build the crawling programs known as spider bots that can help the user retrieve structured data from the website. It can be the URL of the site or other contact info that can be gauged out of it. However, the ultimate goal of this library is to scan the data, and its concepts are widely used in Python machine learning models.

2. BeautifulSoup

BeautifulSoup is another helpful library of Python that can be used for data crawling and scraping. If you are interested in collecting the data that is available on some random website, then BeautifulSoup can help you do that, and after you have scrapped it, you can use the data and rearrange it in whatever format you desire.

3. NumPy

NumPy can be regarded as a library in Python that is specifically used for solving math problems, scientific computing, and performing basic and advanced array operations. The library itself can help the user to solve various arrays along with the data that they store onto them. Math problems and data-intensive algorithms can be processed and solved much faster with NumPy.

4. SciPy

SciPy can be regarded as an extension of the NumPy library and is used to solve various math problems, scientific exploration, solving algebra, integration and optimization, and statistics. The extensive documentation available for this particular library makes it easier to work with and understand its logic.

5. Pandas

Pandas library has a main function, and that is to help the user stay organized and structured with the data they are working on. It is based on two different data structures, such as a series that is one-dimensional and the data frames that hold data in a two-dimensional array such as numbers along with the columns they are placed in. Data manipulation, visualization as well as finding missing data vectors become easier with Pandas library.

6. Keras

It is a great library if you want to build neural networks and then funnel them into modeling. It is extremely convenient to use and provide the developers with a great sense of extensibility, too in the long run. You don’t have to bother designing digital constructs with this one and is extremely recommended if you want to experiment quickly using the compact systems.

7. SciKit-Learn

SciKit-Learn, in reality, is an industry-standard for the data science projects that are based on Python. It intervenes greatly with the SciPy stack and performs various actions such as image processing, for instance. SciKit-Learn can also be used for the sake of conducting machine learning and data mining related tasks. It also comes with detailed documentation and offers high performance.

8. TensorFlow

TensorFlow is developed by Google, and since its debut, it is being used to perform a variety of deep learning and machine learning-oriented tasks such as object identification, speech recognition, and many others. It can improve the overall credibility of machine learning processes that you use and rely heavily on upon.

9. XG Boost

XG Boost is a python-oriented library that can implement the machine learning algorithms in almost any digital interface you want to. It is something that is heavily portable, extremely efficient, and flexible to use. Another lingering advantage includes that the developers can run the same code on a variety of different distributed environments.

• Matplotlib

Matplotlib is among those python libraries that are extremely useful in the data science-oriented projects. You can generate data visualizations as well as two-dimensional setups such as diagrams and graphs.

• PyTorch

This is an extremely efficient tool for data scientists who want to work with deep learning technologies. It also provides access for performing Tensor computations with the GPU acceleration.

• Seaborn

Relying heavily on the prospects of Matplotlib Seaborn can be used to devise various machine learning tools regarding the visualization of the statistical models.

• Bokeh

Bokeh can be used to create visualizations within the browsers with the help of JavaScript widgets.

• Plotly

This library offers the visualization of the data systems and two-dimensional prospects with the use of out of the box graphics too. The library is known to work well with interactive web applications.

• Pydot

You can use this library for the sake of generating the oriented and non-oriented graphs. You can easily unveil the current structure of the graph with the help of this library.

Now you can acquire python certification and advance your career to the next possible level. You have to train yourself with Python essentials and pass the examination to unlock the doors of ultimate success in your career.