Top 12 Python Libraries for Data Science in 2020

Python Programming Language has become one of the most leading programming languages which are used to solve the problems, challenges and tasks of Data Science. The Python Libraries have proved to become the most beneficial libraries for developers to encode data Science algorithms.  Let us have a look at the twelve most popular Python Libraries

Most Important Python Libraries

1. NumPy 

NumPy is a critical library package in the area of scientific applications. It can help a developer to process large matrices and multidimensional arrays. It also has an extensive collection of implemented methods and mathematical functions of high-level, which creates the possibility for a developer to execute several operations using these objects.

This library has got a considerable number of upgrades and improvements in the past, including fixation of compatibility issues and bug fixing. Handling of files is also possible in any encoding using some functions that are available in Python too.

2. SciPy 

SciPy is another handy Python library for computing scientific calculations. This library is based on the NumPy library and increases the capabilities of NumPy. The Data structure of SciPy is implemented by NumPy and is a multidimensional array. This package contains various tools that can help a developer in solving many tasks like integral calculus, probability theory, linear algebra, etc.

SciPy has also received significant build improvement, which allowed for continuous integration into various operating systems, new methods, and functions. Its latest updated optimizers are also very important along with LAPACK and BLAS functions.

3. Pandas

Pandas Python Library has a wide variety of analysis tools and also provides data structures of high-level. It has an excellent capability to translate operations of compound nature with data in one or two commands only. This is one of the main features of the Pandas library.

There are several built-in methods in Pandas that can be used for time-series functionality, combining data, filtering and grouping along with speed indicators. New releases of pandas library have got several significant improvements in pandas library in areas such as support in performing custom types operations, more appropriate output to apply method, sorting, and grouping of data.

4. StatsModels 

Statsmodels is one of the main Python modules in which a developer can find many opportunities to perform the statistical test, statistical models estimation, statistical data analysis and many more. A developer can explore many different possibilities in plotting and implement a lot of methods in machine learning. The StatsModels library is enriching and evolving continuously with new opportunities over time.

In the most recent releases of Pandas, one can find new multivariate methods such as repeated measures within ANOVA, MANOVA and factor analysis. In the new release, a machine learning developer can also find new count models such as NegativeBinomialP, zero-inflated models and GeneralizedPoisson along with time series improvements.

5. Matplotlib

Matplotlib Python Library can help a developer to build various graphs and diagrams such as Graphs of Non-Cartesian coordinates, scatterplots, histograms, two-dimensional diagrams and many more. Many plotting libraries are created to work in coordination with the matplotlib library.

In the latest release update for improvement, one can find new changes to legends, fonts, sizes, colours, style, etc. There is also an improvement in the colour cycle by creating a colourblind-friendly colour cycle along with an appearance improvement such as alignment of axes legends automatically.

6. Seaborn

Seaborn is an API of higher-level that is based on the library of matplotlib which contains very appropriate default settings to process charts. A developer can also use the rich visualization gallery of Seaborn, which also includes complex types such as violin diagrams, joint plots, violin diagrams and many more.

In the new updates of the seaborn library, it was mostly about bug fixing. Also, in the new release of Seaborn, options and parameters are added to visualization and compatibility has been improved between improved backends of interactive matplotlib and PairGrid or FacetGrid.

7. Plotly

Plotly is a Python Library package which a developer can use to build refined graphics quickly. It is also designed to work and adapt to interactive web apps. Plotly has amazing visualization galleries such as 3D charts, ternary plots, contour graphics and many more. There are new features in Plotly python library now which have brought support for crosstalk integration, animation and “multiple-linked views” due to the continuous enhancements in new features and graphics.

8. Bokeh 

Bokeh library is a Python library that uses JavaScript widgets to create scalable and interactive visualizations in the browser. There are many useful features in the Bokeh library of Python such as defining callbacks, adding widgets, interaction capabilities in the form of plots linking, styling possibilities along with many versatile collections of graphs. Bokeh has many enhanced interactive abilities such as customized tooltip field enhancements, small zoom tool as well as rotation of labels of a categorical tick.

9. Pydot

Pydot library is a python library that is used to generate complex non-oriented and oriented diagrams. It is written purely in Python language and is an interface to Graphviz. Pydot becomes very helpful in building decision trees based algorithms and neural networks by making it possible to display the structure of graphs.

10. Scikit-learn 

If a Data Science developer wants to work with data, then Scikit-learn is one of the best libraries for it. This library can also provide algorithms for data mining such as model selection, dimensionality reduction, classification, regression, clustering, as well as many algorithms for standard machine learning. A lot of enhancements have been made to this library, including improvements in cross-validation.  Scikit-learn now provides the ability to use more than one metric.

11. TensorFlow 

TensorFlow is one of the most popular frameworks for machine learning and deep learning which was developed by Google in Google Brain. One can use multiple data sets to create artificial neural networks using this framework. There are many useful applications of TensorFlow such as speech recognition, object identification and many more. A machine learning developer can also find many useful layer helpers such as skflow, tf-slim, tflearn, etc. on top of regular TensorFlow.

12. Keras

Keras is one of the best python libraries, which is very user-friendly and has an excellent ability to work with enormous data and deep neural networks. One can use MxNet and CNTK also as the backends and run on top of Theano and TensorFlow. Lots of functional improvements have been made on API improvements, documentation, usability, and performance of Keras in new update release with new features like self-normalizing networks, new MobileNet application, Conv3DTranspose layer, etc.

Conclusion

Data science is the fastest-growing field of computer science. Data science is a blend of mathematics, statistics and computational algorithms. These are the Python libraries that are commonly used for data science implementations.

If you are curious about learning data science to be in the front of fast-paced technological advancements, check out upGrad & IIIT-B’s PG Diploma in Data Science and upskill yourself for the future.

Rohit Sharma

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