“Sexiest Job of the 21st century” when nobody expected geeky jobs to ever be sexy! But Data Science is sexy now and that is because of the immense value of data. And Python is one of the best programming languages to extract value from this data because of its capacity for statistical analysis, data modeling, and easy readability.
Python< in Data Science< is its extensive library support for data science and analytics. There are many Python libraries that contain a host of functions, tools, and methods to manage and analyze data. Each of these libraries has a particular focus with some libraries managing image and textual data, data mining, neural networks, data visualization, and so on. Here we have divided the top 10 Python libraries for Data Science into those focusing on data processing and data visualization respectively. So let’s check out these libraries now!
1. Pandas
2. NumPy
3. SciPy
SciPy This NumPy stack has users which also use comparable applications such as GNU Octave, MATLAB, GNU Octave, Scilab, etc. Fourier transforms, random number generation, special functions<, etc. Just like NumPy, the multidimensional matrices are the main objects in SciPy, which are provided by the NumPy module itself.
4. Scikit-learn
5. TensorFlow
TensorFlow< is a free end-to-end open-source platform that has a wide variety of tools, libraries, and resources for Artificial Intelligence. It was developed by the Google Brain team and initially released on November 9, 2015. You can easily build and train Machine Learning models with high-level API’s such as Keras using TensorFlow. It also provides multiple levels of abstraction so you can choose the option you need for your model. TensorFlow also allows you to deploy Machine Learning models anywhere such as the cloud, browser, or your own device. You should use TensorFlow Extended (TFX) if you want the full experience, TensorFlow Lite if you want usage on mobile devices, and TensorFlow.js if you want to train and deploy models in JavaScript environments. TensorFlow is available for Python and C APIs and also for C++, Java, JavaScript, Go, Swift, etc. but without an API backward compatibility guarantee. Third-party packages are also available for MATLAB<, C#, Julia, Scala, R, Rust, etc.
6. Keras
Python Libraries for Data Visualization
1. Matplotlib
Matplotlib GUI toolkits like Tkinter, GTK+<, wxPython, Qt, etc. So you can use Matplotlib to< create plots, bar charts, pie charts, histograms, scatterplots, error charts, power spectra, stemplots, and whatever other visualization charts you want! The Pyplot module also provides a MATLAB-like interface that is just as versatile and useful as MATLAB while being totally free and open source.
