Top 10 Python Libraries You Must Need to Know In 2022

As we know that the Python is a most versatile programming language which is used to offer great tools and libraries for data crunching and preparation, as well as for complex scientific data analysis and modelling. Hence, in the coming time to be a good Python developer you need to be very much through in every aspect. At least You need to be familiar with some important library which you are going to use very frequently in coming 2024. Here I am going to discuss the list of top Python frameworks and library that allows you to carry out complex mathematical computations and create sophisticated models that make sense of your data.

Introduction:

1. Python is already being considered as a most versatile programming language in the field of AI, ML and Data Science industry and proven itself as a most powerful environment which is widely accepted by most of the industry.
2. In this current trend of technology, as it provides many possible ways of computing the scenario, so it is now taken the lead as the toolkit for scientific data analysis and modelling. 
3. All these libraries are completely open-sourced and used to offering many alternate ways of deriving the same output. 
4. As we all know that the technology now a days gets more and more competitive and hence most of the corporates are looking for the better platform to execute such a massive complex data computation. 
5. Most of the Data scientists and engineers are continually striving for ways to process information, extract insights and model, by processing massive datasets. 
6. Python is the only platform where we can be able to explore the various, so you need to be well versed in the various Python libraries that support your data science tasks and the benefits they offer to make your outputs more robust and speedier.
7. Here in the blog, I would like to focus on some major aspects of Python library and trying to highlight some of the most popular and go-to Python libraries for this modern cut edge of technology era like AI, ML, and Data science. 

TensorFlow:

1. Tensor flow is basically get considered as the best and the Ultimate Machine Learning and Deep Learning Framework at the present era of technology.
2. In its bare minimum, to deal with and when working with large datasets the training and deployment of ANN (Artificial Neural Networks) it is get used. 
3. It was set up by Google Brain, and is written in C++ but can be called in Python. 
4. Among the utility function prospect, the most prolific applications of TensorFlow are identification of runtime object, speech recognition and word embedding in recurrent neural networks.
5. It is also used for sequence-to-sequence models for machine translation, natural language processing, and PDE (partial differential equation) based simulations. 
6. It also supports production prediction at scale, using the same models used for training.
7. It has many features such as high level of performance, flexible architecture, and the ability to run on any target like a local machine, a cluster in the cloud, iOS and Android devices, CPUs or GPUs.

Keras:

1. Keras is a library which is basically used for Neural Networks based application development.
2. Keras is a high performing library for working with neural networks, running on top of TensorFlow, Theano, and CNTK (Microsoft’s Cognitive Toolkit). 
3. It is very user-friendly and have simple APIs which let’s enable the developer to develop the application in easy and fast experimentation. 
4. In its architecture wise it is modular and extendable nature.
5. Due to its extendable nature, it allows you to use and develop the varieties of modules from neural layers, optimizers, and activation functions to develop a new model. 
6. This makes Keras a good option for data scientists when they want to add a new module as classes and functions.

NumPy:

1. The NumPy is Numerical Python which is being considered as the Core Numeric and Scientific Computation Library.
2. It is the core library that forms the mainstay of the ecosystem of data science tools in Python. 
3. It supports many built-in multi-dimensional arrays and matrices, scientific computing technique with high-quality mathematical functions and logical operations. 
4. It is also used for performing the operations on n-dimensional array objects.
5. The NumPy provides inbuilt functionality in basic algebraic functions, random numbers, basic Fourier transforms, sophisticated random number capabilities, tools for integrating Fortran code and C/C++ code. 

SciPy:

1. Just like similar to NumPy, the SciPy is also being used for the Numeric and Scientific Computation Library.
2. It is being widely used for research work, developers as it has a core library for scientific computing with much efficient algorithms to solve complex mathematical expression. 
3. It contains important tools for solving the mathematical expression like numerical integration, interpolation, optimization, etc., 
4. It also helps to solve problems in linear algebra, probability theory, integral calculus, fast Fourier transform, signal processing, and other such tasks of data science. 
5. The important feature of SciPy library is that it used to have the key data structure which is used to solve multidimensional array-based applications.
6. It usually works along with the NumPy library. So, it is recommended to install after installation of NumPy is get done on the environment.
7. It offers an edge to NumPy by improving useful functions for regression, minimization, Fourier-transformation, and more. 

Why Python is So Popular with Developers

Pandas:

1. The Pandas is a most widely used library for solving the Data Analysis based application.
2. The pandas library is basically used to support three types of data structures for solving the complex problems such as “series” (single-dimensional, homogenous array), “data frames” (two-dimensional, heterogeneous columns) and “panel” (three-dimensional, size mutable array). 
3. It is used to support for data cleaning, data handling, and data discovery, for most of the machine learning based projects.
4. It is also used to enable merging, grouping, filtering, slicing and combining data, besides providing a built-in time-series functionality. 
5. For processing the data in most precise manner it is used to support many formats such as CSV, SQL, HDFS or excel can also be processed easily.
6. The Panda is the go-to library for data analysis in domains like finance, statistics, social sciences, and engineering. 
7. Its easy adaptability, ability to work well with incomplete, unstructured, and uncategorized data, makes it popular among data scientists.

SciKit-Learn:

1. It is basically used for Machine Learning based application development and used to solve the complex machine learning problems.
2. It is being widely used when we are going for the most complex feature in ML like clustering, regression, classification, dimensionality reduction, feature extraction, image processing, model selection and pre-processing etc. 
3. Like other important library it is built on the top of SciPy, Numpy, and Matplotlib library as because it provides better algorithms for machine learning and data mining tasks. 
4. The also provides various other functionalities like spam filters, image recognition, drug response, stock pricing, and customer segmentation.

PyTorch:

1. It is the Machine Learning Framework used to solve the more complex problems and provides several features that make it the ultimate choice for data scientist. 
2. PyTorch allows you to define your computational graph dynamically and transitioning in graph mode for optimization. 
3. It is supports to solve complex tasks like dynamic computational graphs design and fast tensor computations with GPU acceleration. 
4. For applications calling for neural network algorithms, the PyTorch offers a rich API. It supports a cloud-based ecosystem for scaling of resources used in deployment and testing.
5. It is a great library for your deep learning research projects as it provides great flexibility and native support for establishing P2P communication.

LightGBM:

1. It is most efficient concept to find important features in a dataset when we are going to deals with the model analysis in ML.
2. It enables the model analysis using two valid values such as split (default one) and gain. But in generally not necessary that both split and gain produce same feature. To predict the feature in more accurate way we are having a new enhanced library called shap. 
3. If you look in the lightgbm docs for feature_importance function, you will see that it has a parameter importance_type. 
4. Here you should use verbose_eval and early_stopping_rounds to track the actual performance of the model upon training. 

Eli5:

1. The eli5 provides a way to compute feature importances for any black-box estimator by measuring how score decreases when a feature is not available; the method is also known as “permutation importance” or “Mean Decrease Accuracy (MDA)”.
2. For sklearn-compatible estimators eli5 provides PermutationImportance wrapper.
3. This method can be useful not only for introspection, but also for feature selection. 
4.  Here the permutation importance should be used for feature selection with care (like many other feature importance measures). 
5. Here when we are dropping one of the features then it may not affect the result, as estimator still has access to the same information from other features. 
6. So, if features are dropped based on importance threshold, such correlated features could be dropped all at the same time, regardless of their usefulness.  

Theano:

1. It is basically used to evaluate mathematical operations including multi-dimensional arrays so efficiently like similar to NumPy and SciPy. But mostly get used for Deep Learning based Projects. 
2. It is basically used to works on Graphics Processing Unit (GPU) rather than on CPU. 
3. When the amount of data size is more and complex then it is recommended to use Theano as a tool as it attains high speeds during data processing. 
4. Since, it is also used to work on GPUs based approach so, it performs better than others by considerable orders of magnitude under some certain circumstances.
5. When we are trying to work on neural network-based algorithms and Deep Learning concept in fused manner then it is highly recommended. 

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