Top 15 Python Libraries for Data Science in 2019

Data science is a field which has been growing immensely since the boom in technology in the 21st century. The advent of data science over time has led to many programming languages being used in data science. One of such languages, Python, has gained colossal popularity among the programming population, due to its proficiency and easy readability.

Python is popularly known for its wide array of libraries in the field of data science. In layman terms, a library in the programming world is a set or sets of functions and routines which are written in a particular language, which in this case, is Python. Libraries can help developers in implementing sophisticated and complex tasks with ease.

Following are the top 15 Python libraries for Data science in 2019!

  • NumPy:

Sounds weird, doesn’t it? Well, NumPy is a library which is very popular in the Python Programming language. It is the foundational library in Python, and has a powerful N-dimensional array object, along with refined functions and tools for scientific computing.

  • SciPy:

SciPy, or Scientific Python, is a Python Programming library, which builds upon on NumPy by adding a set of algorithms and high-level, intricate commands for handling and envisaging data in a simple and modest manner. A developer can use SciPy to enhance functions such as regression, optimization, solving differential equations, minimization, etc.

  • Pandas:

Pandas is a Python Programming library, which is mostly used for data analysis. It is designed in such a way to make it easier for developers to manipulate data quickly and present it visually.

  • Seaborn:

Seaborn is a visualization library. It concentrates on the visualization and visual depiction of statistical models, which will help in summarizing data in a concrete manner.

  • Matplotlib:

If you want to create and develop 2d models and graphs, matplotlib is the Python Programming library to use. It is a bit complex to use, but it develops brilliant and nice-looking models and graphs than most of the other libraries in use.

  • IPython:

IPython is a command shell developed originally for the Python Programming language, which promotes interactive computing in many programming languages. It offers rich media, introspection, tab completion, and tools for parallel computing!

  • Theano:

One of the top most rated Python Programming libraries for data science, Theano is used for optimizing and evaluating mathematical expressions in an efficient way. Theano is a bit tough to learn and adapt to for regular Python users, as its functions and framework for utility vary from the essential Python functions.

  • Bokeh:

Bokeh is an interactive Python Programming library, which makes interactive plots in web browsers. It comes with three levels of interface.

  • NTLK:

NTLK helps programmers to operate and work with human language data. IT helps in tagging texts, and displaying sentence diagrams and also in classification, stemming, etc.

  • Scrapy:

Fittingly named, Scrapy is a Python Programming library which helps in extracting data such as URLs and contact information.

  • TensorFlow:

It is an open source software library, which supports high-performance numerical computation.

  • Scikit-learn:

An extension to NumPy and SciPy, Scikit-Learn is used for clustering, regression and it’s an incredibly curated Python programming library.

  • Keras:

It is an open-source library written exclusively for Python. It is very user-friendly and helps I the creation of sustainable data science models.

  • PyBrain:

PyBrain is a Python Programming library used extensively for neural networks and unofficial education.

  • Statsmodels:

It is a Python Programming library module which helps in exploring and mining data and make statistical models and forecasts.