Top 10 Python Libraries You mus know in 2024

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The Python libraries offer great tools for data crunching and preparation, as well as for complex scientific data analysis and modeling. Here I am going to discuss the list of top Python frameworks that allow you to carry out complex mathematical computations and create sophisticated models that make sense of your data.


  1. As Python is already a proven language in the data science industry and is widely accepted by most of the industry, it is now taken the lead as the toolkit for scientific data analysis and modeling. 

  2. Here I would like to highlight some of the most popular and go-to Python libraries for data science. 

  3. These are open-sourced libraries, offering alternate ways of deriving the same output. 

  4. As technology nowadays gets more and more competitive, data scientists and engineers are continually striving for ways to process information, extract insights, and model, by processing massive datasets. 

  5. 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.

Here I would like to discuss some important libraries which is mostly required by Python developers.


  1. It is the best and the Ultimate Machine Learning and Deep Learning Framework, which consists of many libraries that use a system of multi-layered nodes to enable the setting up, training, and deployment of artificial neural networks when working with large datasets. 

  2. It was set up by Google Brain and is written in C++ but can be called in Python. 

  3. The most prolific applications of TensorFlow are object identification, speech recognition, word embedding, and recurrent neural networks.

  4. It is also used for sequence-to-sequence models for machine translation, natural language processing, and PDE (partial differential equation) based simulations. 

  5. It also supports production prediction at scale, using the same models used for training.

  6. It has many features such as a 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.


  1. It is the Library for Neural Networks.

  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. Keras is user-friendly, with simple APIs and easy fast experimentation, making it possible to work on more complex models. 

  4. Its modular and extendable nature allows you to use a variety of modules from neural layers, to optimizers, and activation functions to develop a new model. 

  5. This makes Keras a good option for data scientists when they want to add a new module as classes and functions.

NumPy :

  1. It is being considered as the Core Numeric and Scientific Computation Library.

  2. The NumPy is also refer as Numerical Python and it is the core library that forms the mainstay of the ecosystem of data science tools in Python. 

  3. It supports scientific computing with high-quality mathematical functions and logical operations on built-in multi-dimensional arrays and matrices. 

  4. Besides n-dimensional array objects, NumPy provides functionality in basic algebraic functions, random numbers, basic Fourier transforms, sophisticated random number capabilities, and tools for integrating Fortran code and C/C++ code. 

  5. The Array interface of NumPy also allows multiple options to reshape large datasets.

  6. It is one of the best data science toolkits and is being used by most other data science or machine learning Python packages (SciPy, MatplotLib, ScikitLearn, etc.) are built on it.


  1. As we have already discussed above regarding the NumPy, the SciPy is the Numeric and Scientific Computation Library.

  2. SciPy is an important Python library for researchers, developers, and data scientists

  3. SciPy is also referred to as Scientific Python which is considered as another core library for scientific computing with algorithms and complex mathematical tools for Python.

  4. It contains tools for numerical integration, interpolation, optimization, etc., and 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 SciPy key data structure is also a multidimensional array, implemented by NumPy.

  6. It basically gets set up after the NumPy installation is done on the environment.

  7. It offers an edge to NumPy by improving useful functions for regression, minimization, Fourier transformation, and more.


  1. It is considered the Data Analysis Library and is a dedicated library for data analysis, data cleaning, data handling, data discovery, and steps executed prior to machine learning projects.

  2. It is basically used to provide tools for shaping, merging, reshaping, and slicing datasets.

  3. Here we have three types of data structures such as “series” (single-dimensional, homogenous array), “data frames” (two-dimensional, heterogeneous columns), and “panel” (three-dimensional, size mutable array). 

  4. These are used to enable merging, grouping, filtering, slicing, and combining data, besides providing a built-in time-series functionality. Data in multiple formats such as CSV, SQL, HDFS, or Excel can also be processed easily.

  5. The Panda is the go-to library for data analysis in domains like finance, statistics, social sciences, and engineering. 

  6. Its easy adaptability, and ability to work well with incomplete, unstructured, and uncategorized data, make it popular among data scientists.


  1. It is basically used for the Data Analysis and Machine Learning Library to solve complex machine learning problems.

  2. It basically used to provide algorithms for common machine learning and data mining tasks such as clustering, regression, classification, dimensionality reduction, feature extraction, image processing, model selection, and pre-processing. 

  3. It is built on the top of SciPy, Numpy, and Matplotlib.

  4. SciKit-Learn has great supporting documentation that makes it user-friendly.

  5. The various functionalities of SciKit-Learn help data scientists in use cases like spam filters, image recognition, drug response, stock pricing, and customer segmentation.


  1. It is the other Largest Machine Learning Framework used to solve the more complex problems.

  2. The PyTorch library has several features that make it the ultimate choice for data science. 

  3. It is the largest machine learning library supporting complex tasks like dynamic computational graphs design and fast tensor computations with GPU acceleration. 

  4. For applications calling for neural network algorithms, PyTorch offers a rich API. It supports a cloud-based ecosystem for scaling of resources used in deployment and testing.

  5. PyTorch allows you to define your computational graph dynamically and transition in graph mode for optimization. 

  6. It is a great library for your deep learning research projects as it provides great flexibility and native support for establishing P2P communication.


  1. It is another important concept that is being used in Python.

  2. Using the Light Gradient Boosting Machine model to find important features in a dataset with many features.

  3. If you look in the lightgbm docs for the feature_importance function, you will see that it has a parameter importance_type. 

  4. The two valid values for these parameters are split(default one) and gain. 

  5. It is not necessarily important that both split and gain produce the same feature importance. There is a new library for feature importance shape

  6. Here you should use verbose_eval and early_stopping_rounds to track the actual performance of the model upon training. 


  1. For sklearn-compatible estimators, eli5 provides PermutationImportance wrapper.

  2. This method can be useful not only for introspection but also for feature selection - one can compute feature importances using PermutationImportance, then drop unimportant features using e.g. sklearn’s SelectFromModel or RFE.

  3.  Here the permutation importance should be used for feature selection with care (like many other feature importance measures). 

  4. For example, if several features are correlated, and the estimator uses them all equally, permutation importance can be low for all of these features: 

  5. Dropping one of the features 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.

  7. The eli5 provides a way to compute feature importances for any black-box estimator by measuring how the score decreases when a feature is not available; the method is also known as “permutation importance” or “Mean Decrease Accuracy (MDA)”.


  1. Theano is a Python library that allows us to evaluate mathematical operations including multi-dimensional arrays so efficiently. 

  2. It is mostly used in building Deep Learning Projects. 

  3. It works away faster on a Graphics Processing Unit (GPU) rather than on the CPU. 

  4. Theano attains high speeds that give tough competition to C implementations for problems involving large amounts of data. 

  5. It can take advantage of GPUs which makes it perform better than C on a CPU by considerable orders of magnitude under certain circumstances.

  6. It is mainly designed to handle the types of computation required for large neural network algorithms used in Deep Learning.

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