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Python is one of the most used programming languages in the programming industry. It is gaining popularity as it has a huge collection of libraries. Python helps its developers to be a bit more creative and productive. Additionally, it is quite simple and easy to learn. This is the reason why a lot of developers are attracted to Python to create new libraries.
Also, it should be known that Python’s programming syntax is of high level compared to C, Java, and C++, but it is simple to learn. While you are looking to hire Python Developers, make sure they have a good knowledge of these Libraries for Python Development.
Also, for machine learning, we have curated a list of Top 10 Python Libraries that are crucial to be known in 2020. Let us dive into the analysis of each library right away:
Developed by Google in collaboration with Brain Team, TensorFlow has been used in most of the Google applications for machine learning like Google Photos and Google Voice Search. All the libraries are written in C and C++. TensorFlow is an open-source library so, anyone can use it with an internet connection. It runs on mobile devices, single CPU systems, and GPU as well.
TensorFlow is one of the well-known Python Development Libraries for its speed and flexibility. Moreover, it uses techniques such as XLA for quick linear algebra operations. It offers pipelining that allows you to train multiple neural networks and multiple GPUs. Therefore, models become very efficient and effective.
One of the best Python libraries associated with NumPy and SciPy is Scikit-Learn. It is well-known for working with complex data. Scikit-Learn come equipped with a ton of amazing features. It offers a cross-validation feature to check the accuracy of the supervised models.
Scikit-Learn is very useful for extracting features from images and text. Other than that, Scikit-Learn possesses numerous algorithms for implementing machine learning and reduce regression, cluster, classification, and model selection.
The next up on this list is NumPy, well-known for its array interface, in Python development libraries. TensorFlow and many other libraries are associated with NumPy to perform multiple operations on Tensors. NumPy is very interactive and easy to use library.
Coding in NumPy is quite easy along with an intuitive feature that grasps the concepts easily. It makes complex mathematical implementations simple. As it is widely used so, it has a lot of open-source contributions. NumPy is used for expressing images, sound waves, and other raw streams as an array of real numbers in N-dimension.
With an easy mechanism and amazing features, Keras is another one in our best Python libraries’ list. It comes equipped with features like compiling models, visualization of graphs, and processing data-sets. And hence, it is loved by learning researchers. It has a complete Python-based framework that works to debug and explore.
Keras runs on both CPU and GPU with almost zero interruptions. It supports almost all the models of neural networks such as pooling, recurrent, embedding, etc. These models can be further combined to build more complex models. All of its models are portable. As it is modular in nature, it is extremely expressive, flexible and best suited for innovative research.
Another machine learning library in our list is LightGBM which helps in building new algorithms with the help of elementary models and decision trees. Its main features are as follows:
Being intuitive, LightGBM is a user-friendly library.
It ensures high production with very fast computation.
LightGBM does not produce errors if you consider NaN and other canonical values.
Its fast implementations of gradient boosting made it popular among the developers.
Introduced in 2017, PyTorch has gained much popularity and became the largest machine learning library. It allows you to perform tensor computations, create dynamic computational graphs and calculate gradients automatically. Also, PyTorch is an open-source machine library written in C.
It has rich APIs to solve application issues related to neural networks. PyTorch has a new hybrid front-end, which provides flexible and seamless transitions to graph mode for speed and optimization. Moreover, PyTorch is not bound into a monolithic C++ framework rather it is deeply integrated into Python to use it with popular libraries like Cython. Other than that, it can be used for applications such as natural language processing.
When it comes to the accurate results of machine learning model predictions, Eli5 needs no further introduction. Being a combination of visualization, it debugs the entire machine learning models and tracks all the working steps of an algorithm. Eli5, one of the popular libraries for Python Development has the following features:
It is used in mathematical applications that need a lot of computations in a short time span.
Eli5 further supports other libraries like XGBoost, Lightning and Scikit-Learn libraries, etc. All these libraries perform different jobs. Moreover, it helps in implementing newer methodologies in various other fields.
SciPy is one of the most popular libraries for Python Development among application developers and engineers. It comprises of modules for linear algebra, integration, statistics, and optimization, SciPy is known for doing all the mentioned tasks. As it is developed using NumPy, hence its array makes the most use of it.
Furthermore, SciPy makes proper use of its sub-modules and provides efficient numerical gateways for integration, ordinary differential equations, and many other tasks. All the functions in its sub-modules are properly documented.
It started in 2007 and designed to handle large computations, Theano, and its popularity is still growing with time. It is a computational, deep learning library used to optimize, evaluate mathematical numerical equations and multi-dimensional array. Theano, moreover, acts as a compiler and provides tight integration with NumPy arrays. With the use of a GPU, it performs data-intensive computations much faster than on a CPU.
Theano is quite efficient in symbolic differentiation. It differentiates the functions with one or many inputs and provides you their derivatives. Its speed and stability are well optimized as it can get you the right answer for log (1+x), even for very small values of x. Theano, moreover, detects various errors in the model. These days it is used in multiple neural projects.
The last number in our list is reserved for Pandas is a machine learning library with a variety of tools for data analysis. It has many in-built methods for grouping, filtering, and combining data. Along with these, it has amazing speed indicators. The process of data manipulation is made easier by Pandas.
Pandas also support many operations such as sorting, visualizations, concatenations, and aggregations. Its newly added features include bug fixes, API modifications, and other enhancements. Other than that, Pandas has the ability to group and sort data. Data Analysis is one of its most attractive features.
So, this was our list of top 10 Python libraries, which are attracting every Python developer. These libraries for Python development will surely help you to kick start. Moreover, you will also be able to develop something with high functionality and sufficient flexibility.