7 Best Python Libraries You Shouldn't Miss in 2021

With more than 137,000 python libraries available today, choosing the one relevant for your project can be challenging.

Python libraries are critical if you’re looking to start a data science career. However, we will walk you through some of the best libraries that are worth learning this year.

That being said, let us start talking about Python libraries.

1. NumPy

NumPy is used for the support it offers for N-dimensional arrays. The feature of its array is multidimensional and are nearly 50 times sturdy as compared to the Python list, thus making this library one of the most loved amongst data scientist.

Other libraries like TensorFlow also used NumPy to detect internal computation on tensors. The Python library is renowned to extend quick precompiled functions for a numerical routine that is tough to solve manually.

2. Pandas

Most data scientists spend their time cleaning data, data munging, and data exploration. Therefore, Pandas is being extensively used for data analysis and is one of the most popular Python libraries. Pandas come with a bundle of great tools that can be used to gather data, clean data, and analyze the data. This Python library (Pandas) can even load, prepare data of all sorts — be it structured or unstructured. 

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3. Gradio

Building and deploying web apps for machine learning models get better with Gradio. You can now complete the process with just three lines of code. Although it serves the same purpose as Flask and Streamlight, it is much easier and quicker to get your ML model deployed.

Some of the added advantages of Gradio — ideal way of conducting demos, easier to distribute and implement since the web app can be directly accessible by the public by just sharing a link, and it also allows further modeling to happen, if needed.

4. SciPy

SciPy is perfect for scientific functions and mathematical functions obtained from NumPy.

Major features include signal processing functions, stats functions, and optimization functions. SciPy is great at optimizing and solving differential equations.

The best features of SciPy are — they have multi-dimensional image processing, they can perform efficient linear algebra computation, and solve Fourier transforms.

5. Plotly

Plotly is a must-have tool that can be used for visualizations. It is powerful and easy to use. Perhaps it is one of the major advantages of being ideal for building visualizations.

Dash is another tool that works great with Plotly is building dynamic dashboards. Being a web-based Python interface, Dash minimizes the need to use JavaScript for analytical web applications. Therefore, these plots can be run online and offline as well. Having skills in Python libraries can be crucial for a successful data science career.

6. Seaborn

Seaborn is built on top of Matplotlib and is an ideal library used for building different visualization. 

The interfaces are high-level are the themes can be customized. Because of such features, Seaborn can provide attractive data visualizations.

Seaborn’s best feature includes amplified data visuals.

7. Keras

Keras is highly suitable for data scientists looking to create deep learning models like neural networks. Build on top of Theano and TensorFlow, Keras easily help to build a neural network. However, this library is comparatively slow to the other libraries since it tends to generate a computational graph using back-end infrastructure.

Conclusion

Python is known to have a huge collection of libraries and is popular amongst aspiring data scientists and machine learning experts. These libraries are perfect when working with bigger projects. Whatever the case, learning Python and its libraries is a great way to kick start a career in data science.