. Pandas
You’ve probably heard that 70 to 80 percent of a data scientist’s job is researching and preparing data.
Pandas is primarily used for data analysis, it is one of the most popular libraries. It provides many useful tools for collecting, cleaning, and modeling data. With Pandas, you can load, prepare, parse and manipulate any indexed data. The machine learning libraries also use Pandas data frames as input.
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2. NumPy
The main advantage of NumPy is its support for ndimensional arrays. These multidimensional arrays are 50 times more robust than Python lists. Thanks to them, NumPy is loved by data scientists.
NumPy is often used by other libraries like TensorFlow for internal tensor calculations. The library offers fast universal functions for routine calculations that are difficult to do manually. NumPy uses functions optimized for multidimensional arrays with performance comparable to MATLAB.
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3. Scikitlearn
Scikitlearn is arguably the most important machine learning library in Python. After cleaning and manipulating data in Pandas or NumPy, Scikitlearn is used to create machine learning models. The library provides many tools for predictive modeling and analysis.
There are many reasons to use Scikitlearn. For example, to create multiple types of machine learning models, with and without reinforcement, crossvalidate the accuracy of the models, and select important features.
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4. Gradio
Gradio lets you build and deploy machine learning web applications with just a few lines of code. It serves the same purpose as Streamlit or Flask, but makes it faster and easier to deploy models.
The main advantages of Gradio:

 Allows further model validation. It allows interactive tests of various model inputs.
 This is a good way to do demos.
 Easy to launch and distribute because web applications are available to anyone via a link.
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5. TensorFlow
TensorFlow is one of the most popular Python neural network libraries. It uses multidimensional arrays, also known as tensors, which allow you to perform multiple operations on the same input.
Due to its multithreaded nature, it can train multiple neural networks at the same time and create highly efficient and scalable models.
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6. Hard
Keras is mainly used for building deep learning models and neural networks . It uses TensorFlow and Theano and makes it easy to build neural networks. Due to the fact that Keras generates a computational graph on the server, it is slightly slower than other libraries.
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7. SciPy
A distinctive feature of this library is functions that are useful in mathematics and other sciences. For example statistical functions, optimization functions, signal processing. For solving differential equations and optimization, it includes functions for finding the numerical solution of integrals. Important areas of its application:
 multidimensional image processing;
 solutions of Fourier transforms and diff. equations;
 thanks to optimized algorithms, it can perform linear algebra calculations very efficiently and with high reliability.
8. State models
Statsmodels is a great library for hardcore statistics. It incorporates the graphics capabilities and functions from Matplotlib, uses Pandas for data processing, it uses Pasty for R similar formulas, and it also uses Numpy and SciPy.
The library is used to create statistical models such as linear regression, and conduct statistical tests.
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9. Plotly
Plotly is a powerful, easytouse visualization tool that lets you interact with them.
Along with Plotly, there is Dash, which allows you to create dynamic dashboards using Plotly visualizations. Dash is a web interface for Python that eliminates the need to use Js in analytical web applications and allows them to run online and offline.
Learn more about visualizing data with Plotly.
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10. Seaborn
Seaborn is an efficient Python library for creating various visualizations in Data Science using Matplotlib.
One of its main features is data visualization, which allows you to see the correlation where it was not obvious. This allows data scientists to better understand the data.
Thanks to customizable themes and highlevel interfaces, you can get such highquality and representative visualizations that you can later show them to clients.