Top 10 Python Packages Every Developer Should Learn

There are more than 200,000 Python packages in the world (and that’s just counting those hosted on PyPI, the official Python Package Index). That begs the question: with so many packages out there, which are the most important ones that every Python programmer needs to learn?

To help answer that question, here’s a list of the top ten most important, useful and ubiquitous Python packages that you should familiarize yourself with since they will come up more often than not as you take on various projects. To that end, this list focuses on Python packages that cover a wide range of programming scenarios and goals, as opposed to just focusing on a specific niche, like data science or Web development.

#1 NumPy

You can do basic mathematical operations without any special Python packages. However, if you’re going to do any kind of complex math, the NumPy package will make your coding life much easier.

NumPy provides tools to help build multi-dimensional arrays and perform calculations on the data stored in them. You can solve algebraic formulas, perform common statistical operations, and much more.

While NumPy is a valuable Python package for a variety of general-purpose programming tasks, it’s particularly important if you want to do machine learning, since it provides part of the foundation for libraries like TensorFlow.

#2 Pendulum

If you have at least a little Python programming experience, you probably know that you can use the datetime module to manage dates and times within an application.

While datetime is great for basic work along these lines, the Pendulum Python package makes it easier to do more complex coding involving dates and times. It’s more intuitive to work with, and it manages time zones automatically.

Best of all, Pendulum is designed to be a drop-in replacement for datetime. That means you can use it with code you’ve already written based on datetime. With only a few exceptions, Pendulum will work just as well, without the need to modify the code, while providing extra features not present in plain-old datetime.

#3 Python Imaging Library

If your Python application interacts with images in any way, the Python imaging library, also known as PIL or Pillow, is a Python must-have. It makes it easy to write code that opens, modifies, and saves images in a variety of formats.

If you’re doing more advanced work with images (like image recognition, in which case OpenCV would be a good package to consider), Pillow won’t cut it on its own. But for basic image importing, manipulation, and exporting, Pillow is your go-to solution.

#4 MoviePy

MoviePy is to videos what Pillow is to images. It provides a range of functionality for common tasks associated with importing, modifying, and exporting video files. It also lets you do things like insert titles into videos or rotate videos 90 degrees (if for some reason you decide you want to do that).

Like Pillow, MoviePy is not intended as a tool for advanced data manipulation. If you’re writing a video editing app, you’ll probably also need to rely on OpenCV (which can work with videos as well as images) to provide the advanced functionality that MoviePy lacks. But for most standard tasks involving videos in Python code, MoviePy gets the job done quite well.

#5 Requests

Writing code that sends HTTP requests can be tricky, due in no small part to the fact that HTTP does not exactly format data in a way that is easy for humans to read.

The Requests Python package (motto: “HTTP for Humans”) tackles this problem by automating many of the tedious tasks that you would otherwise need to perform in order to send HTTP requests from your application. It removes the need to add query strings, or do POST form encoding. It also keeps connections with HTTP servers alive automatically, eliminating the need to write a bunch of code for doing that.

In short, if your application sends any data over HTTP, Requests is a must-have package.

#6 Tkinter

Want to develop a Python app with a Graphical User Interface (GUI)? There are a variety of packages designed to help you do that (indeed, we could make a top ten list of just Python GUI packages). But I think most Python developers would agree that Tkinter is the most important — and most commonly used — framework for creating GUIs. It binds Python to the TK GUI toolkit, which works on virtually every modern operating system.

Unless you have a strong preference for a different GUI toolkit, Tkinter is probably the best place to start when creating a Python GUI.

#7 PyQt

The preceding sentence notwithstanding, PyQT, another Python package for building GUIs, is also a strong contender. It provides bindings to (you guessed it) the Qt toolkit, which is also cross-platform. It’s intended for heavier-duty GUI programming than Tkinter. That means that PyQT may be overkill if you’re building an app that has a pretty simple interface — say, just a window with some buttons and text fields — but it is a good tool if you want to build a complex, multi-dimensional GUI.

#8 Pandas

There is a long list of Python packages designed for working with complex data sets. But arguably, Pandas is the most important. Pandas helps you manipulate and analyze large sets of data without having to learn a specialized data-processing language like R.

Pandas has its limits in that it’s not intended for advanced statistical modelling (in that case, you would want to learn R, or use a Python package like statsmodels). But if you need to do things like process time-series data or perform statistical analysis on a data set, Pandas has you covered.

#9 Pywin32

For Windows Python programming in particular, Pywin32 is a must-have package. It provides access to many of the native Windows API functions, allowing you to do things like interact with the Windows registry, use the Windows clipboard, and much more.

Pywin32 won’t do you much good if you’re building a cross-platform Python app, but Windows developers might find that they like it so much that they use it instead of native Windows tooling.

#10 Pytest

If you have a Python development project of any complexity, being able to perform testing on new code is essential. The Pytest package provides a variety of modules to help you do this. Whether it’s a simple unit test or a more complex functional test, Pytest can help you write it.

Top 10 Python Modules in 2020

Top 10 Python Libraries:

Python is an ocean of libraries that serve various purposes and as a Python developer, you must have sound knowledge of the best ones. To help you in this, here is an article that brings to you the Top 10 Python Libraries for machine learning which are:

• TensorFlow
• Scikit-Learn
• Numpy
• Keras
• PyTorch
• LightGBM
• Eli5
• SciPy
• Theano
• Pandas

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Introduction

Python is one of the most popular and widely used programming languages and has replaced many programming languages in the industry.

There are a lot of reasons why Python is popular among developers and one of them is that it has an amazingly large collection of libraries that users can work with. 

Here are a few important reasons as to why Python is popular:

• Python has a huge collection of libraries.
• Python is known as the beginner’s level programming language because of it simplicity and easiness.
• From developing to deploying and maintaining Python wants their developers to be more productive.
• Portability is another reason for huge popularity of Python.
• Python’s programming syntax is simple to learn and is of high level compared to C, Java, and C++.

Hence, new applications can be developed by writing fewer lines of codes.

The simplicity of Python has attracted many developers to create new libraries for machine learning. Because of the huge collection of libraries Python is becoming hugely popular among machine learning experts.

So the first library in our Top 10 Python libraries blog is TensorFlow.

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TensorFlow

What Is TensorFlow?

If you are currently working on a machine learning project in Python, then you may have heard about this popular open source library known as TensorFlow.

This library was developed by Google in collaboration with Brain Team. TensorFlow is used in almost every Google application for machine learning.

TensorFlow works like a computational library for writing new algorithms that involve a large number of tensor operations, since neural networks can be easily expressed as computational graphs they can be implemented using TensorFlow as a series of operations on Tensors. Plus, tensors are N-dimensional matrices which represent your data.

Features of TensorFlow

TensorFlow is optimized for speed, it makes use of techniques like XLA for quick linear algebra operations.

1. Responsive Construct

With TensorFlow, we can easily visualize each and every part of the graph which is not an option while using Numpy or SciKit.

2. Flexible

One of the very important Tensorflow Features is that it is flexible in its operability, meaning it has modularity and the parts of it which you want to make standalone, it offers you that option.

3. Easily Trainable

It is easily trainable on CPU as well as GPU for distributed computing.

4. Parallel Neural Network Training

TensorFlow offers pipelining in the sense that you can train multiple neural networksand multiple GPUs which makes the models very efficient on large-scale systems.

5. Large Community

Needless to say, if it has been developed by Google, there already is a large team of software engineers who work on stability improvements continuously.

6. Open Source

The best thing about this machine learning library is that it is open source so anyone can use it as long as they have internet connectivity.

Where Is TensorFlow Used?

You are using TensorFlow daily but indirectly with applications like Google Voice Search or Google Photos. These applications are developed using this library.

All the libraries created in TensorFlow are written in C and C++. However, it has a complicated front-end for Python. Your Python code will get compiled and then executed on TensorFlow distributed execution engine built using C and C++.

The number of applications of TensorFlow is literally unlimited and that is the beauty of TensorFlow.

So, next up on this ‘Top 10 Python Libraries’ blog we have Scikit-Learn!

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Scikit-Learn

What Is Scikit-learn?

It is a Python library is associated with NumPy and SciPy. It is considered as one of the best libraries for working with complex data.

There are a lot of changes being made in this library. One modification is the cross-validation feature, providing the ability to use more than one metric. Lots of training methods like logistics regression and nearest neighbors have received some little improvements.

Features Of Scikit-Learn

1. Cross-validation: There are various methods to check the accuracy of supervised models on unseen data.

2. Unsupervised learning algorithms: Again there is a large spread of algorithms in the offering – starting from clustering, factor analysis, principal component analysis to unsupervised neural networks.

3. Feature extraction: Useful for extracting features from images and text (e.g. Bag of words)

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Where Is Scikit-Learn Used?

It contains a numerous number of algorithms for implementing standard machine learning and data mining tasks like reducing dimensionality, classification, regression, clustering, and model selection.

So, next up on this ‘Top 10 Python Libraries’ blog, we have Numpy!

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Numpy

What Is Numpy?

Numpy is considered as one of the most popular machine learning library in Python.

TensorFlow and other libraries uses Numpy internally for performing multiple operations on Tensors. Array interface is the best and the most important feature of Numpy.

Features Of Numpy

1. Interactive: Numpy is very interactive and easy to use.
2. Mathematics: Makes complex mathematical implementations very simple.
3. Intuitive: Makes coding real easy and grasping the concepts is easy.
4. Lot of Interaction: Widely used, hence a lot of open source contribution.

Where Is Numpy Used?

This interface can be utilized for expressing images, sound waves, and other binary raw streams as an array of real numbers in N-dimensional.

For implementing this library for machine learning having knowledge of Numpy is important for full stack developers.

So next up on this ‘Top 10 Python Libraries’ blog, we have Keras!

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Keras

What Is Keras?

Keras is considered as one of the coolest machine learning libraries in Python. It provides an easier mechanism to express neural networks. Keras also provides some of the best utilities for compiling models, processing data-sets, visualization of graphs, and much more.

In the backend, Keras uses either Theano or TensorFlow internally. Some of the most popular neural networks like CNTK can also be used. Keras is comparatively slow when we compare it with other machine learning libraries. Because it creates a computational graph by using back-end infrastructure and then makes use of it to perform operations. All the models in Keras are portable.

Features Of Keras

• It runs smoothly on both CPU and GPU.
• Keras supports almost all the models of a neural network – fully connected, convolutional, pooling, recurrent, embedding, etc. Furthermore, these models can be combined to build more complex models.
• Keras, being modular in nature,  is incredibly expressive, flexible, and apt for innovative research.
• Keras is a completely Python-based framework, which makes it easy to debug and explore.

Where Is Keras Used?

You are already constantly interacting with features built with Keras — it is in use at Netflix, Uber, Yelp, Instacart, Zocdoc, Square, and many others. It is especially popular among startups that place deep learning at the core of their products.

Keras contains numerous implementations of commonly used neural network building blocks such as layers, objectives, activation functions, optimizers and a host of tools to make working with image and text data easier. 

Plus, it provides many pre-processed data-sets and pre-trained models like MNIST, VGG, Inception, SqueezeNet, ResNet etc.

Keras is also a favorite among deep learning researchers, coming in at #2. Keras has also been adopted by researchers at large scientific organizations, in partic,ular CERN and NASA.

So, next up on this ‘Top 10 Python Libraries’ blog, we have PyTorch!

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PyTorch

What Is PyTorch?

PyTorch is the largest machine learning library that allow developers to perform tensor computations wan ith acceleration of GPU, creates dynamic computational graphs, and calculate gradients automatically. Other than this, PyTorch offers rich APIs for solving application issues related to neural networks.

This machine learning library is based on Torch, which is an open source machine library implemented in C with a wrapper in Lua.

This machine library in Python was introduced in 2017, and since its inception, the library is gaining popularity and attracting an increasing number of machine learning developers.

Features Of PyTorch

Hybrid Front-End

A new hybrid front-end provides ease-of-use and flexibility in eager mode, while seamlessly transitioning to graph mode for speed, optimization, and functionality in C++ runtime environments.

Distributed Training

Optimize performance in both research and production by taking advantage of native support for asynchronous execution of collective operations and peer-to-peer communication that is accessible from Python and C++.

Python First

PyTorch is not a Python binding into a monolithic C++ framework. It’s built to be deeply integrated into Python so it can be used with popular libraries and packages such as Cython and Numba.

Libraries And Tools

An active community of researchers and developers have built a rich ecosystem of tools and libraries for extending PyTorch and supporting development in areas from computer vision to reinforcement learning.

Where Is PyTorch Used?

PyTorch is primarily used for applications such as natural language processing.

It is primarily developed by Facebook’s artificial-intelligence research group and Uber’s “Pyro” software for probabilistic programming is built on it.

PyTorch is outperforming TensorFlow in multiple ways and it is gaining a lot of attention in the recent days.

You can check out this PyTorch or TensorFlow blog to find out which is better for you.

So, next up on this ‘Top 10 Python Libraries’ blog, we have LightGBM!

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LightGBM

What Is LightGBM?

Gradient Boosting is one of the best and most popular machine learning library, which helps developers in building new algorithms by using redefined elementary models and namely decision trees. Therefore, there are special libraries which are designed for fast and efficient implementation of this method.

These libraries are LightGBM, XGBoost, and CatBoost. All these libraries are competitors that helps in solving a common problem and can be utilized in almost the similar manner.

Features of LightGBM

Very fast computation ensures high production efficiency.

Intuitive, hence makes it user friendly.

Faster training than many other deep learning libraries.

Will not produce errors when you consider NaN values and other canonical values.

Where Is LightGBM Used?

These library provides provide highly scalable, optimized, and fast implementations of gradient boosting, which makes it popular among machine learning developers. Because most of the machine learning full stack developers won machine learning competitions by using these algorithms.

So, next up on this ‘Top 10 Python Libraries’ blog, we have Eli5!

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Eli5

What Is Eli5?

Most often the results of machine learning model predictions are not accurate, and Eli5 machine learning library built in Python helps in overcoming this challenge. It is a combination of visualization and debug all the machine learning models and track all working steps of an algorithm.

Features of Eli5

Moreover, Eli5 supports wother libraries XGBoost, lightning, scikit-learn, and sklearn-crfsuite libraries. All the above-mentioned libraries can be used to perform different tasks using each one of them.

Where Is Eli5 Used?

Mathematical applications which requires a lot of computation in a short time.

Eli5 plays a vital role where there are dependencies with other Python packages.

Legacy applications and implementing newer methodologies in various fields.

So, next up on this ‘Top 10 Python Libraries’ blog, we have SciPy!

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SciPy

What Is SciPy?

SciPy is a machine learning library for application developers and engineers. However, you still need to know the difference between SciPy library and SciPy stack. SciPy library contains modules for optimization, linear algebra, integration, and statistics.

Features Of SciPy

The main feature of SciPy library is that it is developed using NumPy, and its array makes the most use of NumPy.

In addition, SciPy provides all the efficient numerical routines like optimization, numerical integration, and many others using its specific submodules.

All the functions in all submodules of SciPy are well documented.

Where Is SciPy Used?

SciPy is a library that uses NumPy for the purpose of solving mathematical functions. SciPy uses NumPy arrays as the basic data structure, and comes with modules for various commonly used tasks in scientific programming.

Tasks including linear algebra, integration (calculus), ordinary differential equation solving and signal processing are handled easily by SciPy.

So, next up on this ‘Top 10 Python Libraries’ blog, we have Theano!

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Theano

What Is Theano?

Theano is a computational framework machine learning library in Python for computing multidimensional arrays. Theano works similar to TensorFlow, but it not as efficient as TensorFlow. Because of its inability to fit into production environments.

Moreover, Theano can also be used on a distributed or parallel environments just similar to TensorFlow.

Features Of Theano

• Tight integration with NumPy – Ability to use completely NumPy arrays in Theano-compiled functions.
• Transparent use of a GPU – Perform data-intensive computations much faster than on a CPU.
• Efficient symbolic differentiation – Theano does your derivatives for functions with one or many inputs.
• Speed and stability optimizations – Get the right answer for log(1+x) even when x is very tiny. This is just one of the examples to show the stability of Theano.
• Dynamic C code generation – Evaluate expressions faster than ever before, thereby, increasing efficiency by a lot.
• Extensive unit-testing and self-verification – Detect and diagnose multiple types of errors and ambiguities in the model.

Where Is Theano Used?

The actual syntax of Theano expressions is symbolic, which can be off putting to beginners used to normal software development. Specifically, expression are defined in the abstract sense, compiled and later actually used to make calculations.

It was specifically designed to handle the types of computation required for large neural network algorithms used in Deep Learning. It was one of the first libraries of its kind (development started in 2007) and is considered an industry standard for Deep Learning research and development.

Theano is being used in multiple neural network projects today and the popularity of Theano is only growing with time.

And, lastly, on this ‘Top 10 Python Libraries’ blog, we have Pandas!

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Pandas

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What Is Pandas?

Pandas is a machine learning library in Python that provides data structures of high-level and a wide variety of tools for analysis. One of the great feature of this library is the ability to translate complex operations with data using one or two commands. Pandas have so many inbuilt methods for grouping, combining data, and filtering, as well as time-series functionality.

All these are followed by outstanding speed indicators.

Features Of Pandas

Pandas make sure that the entire process of manipulating data will be easier. Support for operations such as Re-indexing, Iteration, Sorting, Aggregations, Concatenations and Visualizations are among the feature highlights of Pandas.

Where Is Pandas Used?

Currently, there are fewer releases of pandas library which includes hundred of new features, bug fixes, enhancements, and changes in API. The improvements in pandas regards its ability to group and sort data, select best suited output for the apply method, and provides support for performing custom types operations.

Data Analysis among everything else takes the highlight when it comes to usage of Pandas. But, Pandas when used with other libraries and tools ensure high functionality and good amount of flexibility.