Python is one of the most popular programming languages today. It’s used by millions of developers and software professionals around the world. Python has become the go-to language for web development, artificial intelligence research, scientific computing, and many other fields. So it’s not surprising that there are many libraries available for Python—libraries that help you do things faster with less effort! Here are a few great Python libraries you might not be aware of yet:
SciPy
SciPy is a Python library for scientific computing in Python. It’s built on NumPy, the Python programming language, and provides access to high level mathematical fun high-level mathematical functions, numerical algorithms,
SciPy offers support for many different areas of science and engineering such as signal processing, linear algebra, and even statistics. Some of its more popular features include:
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A large collection of modules for mathematics, science, and engineering (with more being added daily). These include:
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Scipy. optimize() – Optimization routines including unconstrained minimization problems; Gaussian Quadrature methods; PDE solvers; Fourier transforms; Statistical distributions like Gaussian or Student t-distribution; Neural networks models (including multilayer perceptrons); Random forests classification trees etc…
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Matplotlib plotting system can be used with any kind of graph you want!
Pandas
Pandas is a Python library for data analysis and manipulation, built on the NumPy and Scikit-Learn libraries. This project aimed to provide high-performance, easy-to-use tools for working with tabular data.
Pandas provide many useful methods for working with tabular (or “indexed”) datasets and performing common statistical operations such as summarizing values or calculating correlations between variables.
Using Pandas’ DataFrame object you can easily access all columns in your dataset by specifying an index name or column name; e.g., if your input dataset is named my data, then you could use:
“`python
print(mydata[0]) # returns the first column value without brackets around it
“`
NumPy
NumPy is a Python library that provides high-performance array processing, data type conversion, and other numerical methods to Python programs. It can be used to add, subtract, multiply and divide arrays of numbers or scalars. You can also use it to calculate the mean and standard deviation of a set of numbers in your program; generate random numbers; perform linear algebra operations on matrices; transform data sets into new ones by applying functions on their elements (such as element-wise operations); perform scientific computing tasks such as integration where you need multiple integrals at once instead of having one huge function call for each integral; find maxima/minima points in 2D space with functions such as sigmoid() which takes 2D points along an axis into consideration when calculating its output value from values given at two different points along that same axis so there’s no need for 3D scaling down here!
Keras and TensorFlow
Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It offers an easy-to-use interface for defining and training deep learning models.
TensorFlow is an open-source software library for numerical computation using data flow graphs. Google developed it as an alternative to its internal TensorFlow project that was originally based on its own internal tensor processing system (TENSOR). TensorFlow allows users to specify computations through declarative mathematical expressions, making it more flexible than other libraries with similar functionality such as MATLAB’s MEX toolbox
Requests
The request is a library that allows you to send HTTP requests. It’s one of the most popular Python libraries and has excellent documentation. It’s also one of the most popular Python libraries on Github—and for good reason: it’s easy to use!
Requests are designed so that any code written in Python can use it as little or as much as needed, depending on what you need from it. If you’re just trying to get some data from somewhere (like your Facebook profile), then Requests won’t be necessary at all. Still, if you want more control over how exactly this data gets sent back out again—or even if you want complete control over every step along the way—then Requests will come in handy!
Flask
Flask is a microframework for Python, written in the Python language. It’s built on Werkzeug, Jinja2, and good intentions.
Flask is BSD licensed and as simple as it gets.
Learn these Python libraries to make your life easier as a programmer.
Python libraries are the tools you can use to make your life easier as a programmer. These libraries are available for free, so you don’t have to worry about paying for them! They’re also easy to install on most computers and don’t take up much storage space.
To get started learning about these Python libraries, let’s first look at some of the most popular ones:
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[Pillow](https://pypi.python- Pillow) – This library lets you access images from Python programs without having to worry about loading them separately from another program or file manager (like Windows Explorer). You just need this one simple command: `import pillow`.
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[Mako](https://makoproject/mako/) – This library allows users within their projects’ directory structure to access MongoDB databases as SQLite databases do through Mongoose by providing an interface similar to any other database management system such as MySQL or Postgresql 9+, etcetera…
Conclusion
And that’s it! We hope you enjoyed learning about these Python libraries, and that we’ve given you some ideas for how to use them in your projects.
If none of the libraries listed above are right for your project, remember that there are many other great ones out there—and if we left one off this list, make sure to check out our GitHub page where we have a complete list of Python libraries.
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