Top 12 Python Packages with its Pros and Cons


Lovers of basic movies would have positively heard of the Monty Python sequence. The programming language that it impressed continues to remain among the many many hottest languages. Guess why Python has consistently topped the charts of probably the most well-liked programming languages? Due to its rich environment of libraries and devices, its simple code readability and the reality that it’s so simple to decide on up.  You title the realm, and also you’re going to get Python libraries on the market, that may help you out in fixing points. Proper from Synthetic Intelligence, Knowledge Science, Machine Studying, Picture Processing, Speech Recognition, Laptop Imaginative and prescient and further, Python has fairly just a few makes use of. These libraries and frameworks are open provide and could also be merely built-in with the occasion environment that one has.

These software program program frameworks, the platforms which provides essential libraries and code elements, are backbones for devloping capabilities. Learn on to see which are the very best ML frameworks and libraries in Python.

1. Numpy 

Because the title implies, that’s the library which helps numerical calculations and duties. It helps array operations and first mathematical capabilities on the array and completely different information types of Python. The basic information sort of this library is ndArray object.   

Numpy has many advantages

  • The backside information building is N –Dimensional array. 
  • Wealthy capabilities to take care of the N-dimensional array efficiently. 
  • Helps integration of C, C++ and completely different language code fragments. 
  • Helps many capabilities related to linear algebra, random numbers, transforms, statistics and lots of others. 


  • No GPU and TPU assist. 
  • Can not robotically calculate the derivatives which is required in all ML algorithms. 
  • Numpy effectivity goes down when extreme difficult calculations are required. 

2. PandasPandas

That is basically probably the most useful library for information preprocessing and getting ready the knowledge for the Machine Studying algorithms. The data from quite a few recordsdata like CSV, Excel, Knowledge and lots of others. could also be merely be taught using PandasThe data is available on the market in a spreadsheet like house, which makes processing simple. There are three main information buildings on the core of Pandas library: 

  • Sequence – One-dimensional array like object containing information and label (or index). 
  • Dataframe – Spreadsheet-like information building containing an order assortment of columns. It has every a row and column index. 
  • Panel – Assortment of dataframes nevertheless not usually used information building. 


  • Structured information could also be be taught merely. 
  • Useful gizmo for coping with of knowledge. 
  • Robust capabilities for manipulation and preprocessing of information. 
  • Knowledge Exploration capabilities help in greater understanding information. 
  • Knowledge preprocessing capabilities assist make information ready for the software program of ML algorithms. 
  • Primary Plotting capabilities are supplied for visualization of knowledge.  
  • Datasets could also be merely joined or merged. 
  • The capabilities of Pandas are optimized for large datasets. 


  • Attending to know the Pandas functionalities is time consuming. 
  • The syntax is difficult when a variety of operations are required. 
  • Help for 3D metrics is poor. 
  • Correct documentation isn’t on the market for analysis. 

three. Matplotlib

Matplotlib is an important Python library which helps in information visualization. Understanding the knowledge is important for an data scientist sooner than devising any machine learning primarily based model. This library helps in understanding the knowledge in a visual method. Knowledge could also be visualized using quite a few graphical methods like line graph, bar graph, pie chart and lots of others. This is usually a 2D visualization library with fairly just a few strategies of visualizing information. Matplotlib

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  • Easy and easy to review for newbies. 
  • Built-in with Pandas for visualization of knowledge in environment friendly method. 
  • Varied plots are supplied for greater understanding of knowledge like Bar Chart, Stacked Bar chart, Pie chart, Scatter Plot and lots of others. 
  • Types a base for lots of superior plotting libraries. 
  • Helps storing of the various graphs as photos so that they’re usually built-in with completely different capabilities. 
  • Can plot timeseries information (with date) very merely. 


  • Complicated Syntax for plotting simple graphs. 
  • The code turns into extended and complex for visualizations. 
  • Help for plotting of categorial information isn’t supplied. 
  • It’s a 2D visualization library. 
  • When a variety of fields are required to be plotted and visualized efficiently, matplotlib code can develop to be extended. 
  • Managing a variety of decides is troublesome. 

4. Seaborn 

Visualizations are made simpler and further superior with the help of Seaborn library. The backside for Seaborn is Matplotlib. It’s a boon for programmers as statistical visualizations are simplified. 


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  • Greatest high-level interface for drawing statistical graphics. 
  • Offers assist for plotting of categorial information efficiently. 
  • The library provides default themes and loads of visualization patterns. 
  • Quite a few figures are robotically created. 
  • The syntax might be very simple and compact. 
  • There are many methods to mix with Pandas dataframe, making this library most useful for visualization. 


  • Reminiscence factors due to creation of a variety of figures. 
  • Much much less customizable and versatile as compared with Matplotlib. 
  • Scalability factors. 

5. Scipy   

Scipy is a Scientific Python library primarily based totally on Numpy. It has capabilities which are most interesting acceptable for Arithmetic, Science and Engineering. Many libraries are supplied for Picture and Sign Processing, Fourier Remodel, Linear Algebra, Integration and Optimization. The capabilities are useful for ML algorithms and packages. 


  • The backside library is Numpy. 
  • Many ML related capabilities are currentd liokaye Linear Algebra, Optimization, Compressed Sparce Knowledge Construction and lots of others. 
  • Helpful Linear Algebra capabilities might be discovered which are required for implementation of ML related algorithms. 
  • The capabilities could also be utilized with Pandas Dataframe straight. 


  • Complicated capabilities might be discovered and space information is required to know and implement these capabilities. 
  • There are performance factors when information measurement will improve. 
  • Many completely different environment friendly numerous libraries might be discovered with the wished efficiency. 

6. Scikit-Study 

Scikit-Learn is a useful open entry library for use to Python builders. It’s an in depth and well-liked library with many Machine Studying Supervised and Unsupervised algorithms utilized. These algorithms could also be fine-tuned with the help of hyperparameters. This library incorporates many beneficial capabilities for preprocessing of knowledge, useful metrics to measure effectivity of algorithms and optimization strategies.  


  • It’s a traditional Machine Studying library constructed on prime of Numpy, Pandas and Matplotlib. 
  • Easy to know and use even for novice programmers. 
  • Helpful Machine Studying Algorithms, every Supervised and Unsupervised, are utilized. 
  • Common library for doing Machine Studying related duties. 
  • Wealthy in Knowledge Preprocessing and Knowledge Sampling capabilities and strategies. 
  • Plethora of research measures utilized to hint the effectivity of algorithms. 
  • Very environment friendly for quick coding and establishing Machine Studying Fashions. 


  • Scikit research, as is based on Numpy, requires additional assist to run on GTP and TPU 
  • Efficiency is a matter with measurement of knowledge. 
  • Greatest acceptable for main Machine Studying capabilities. 
  • This library may be useful if one wishes to write simple code, nevertheless it’s not your best choice for further detailed learning. 

7. NLTK 

Natural Language processing is an necessary topic of analysis for builders preferring to evaluation and downside themselvesThis library provides a base for Pure Language processing by providing simple functionalities to work with and understand languages.


  • Quite easy to utilize for processing pure language information. 
  • Many main functionalities like tokenizing the phrases, eradicating of stop phrases, conversion to phrase vectors and lots of others. are supplied which varieties the thought to start with pure language processing fashions. 
  • It’s an incredible library to play with pure language using Python. 
  • It has larger than 50 educated fashions and lexical sources like wordnet on the market for use. 
  • Wealthy dialogue boards and many examples are on the market to debate simple strategies to make use of this library efficiently. 


  • It is primarily based totally on string processing, which itself has many limitations. 
  • Slower as compared with completely different Pure Language processing libraries like Spacy.

eight. Keras 

Keras is a library written in Python for Neural Community programming. It supplies fairly easy interface to code the neural neighborhood and related algorithms. It’s a particularly well-liked library for Deep Studying algorithms, fashions and capabilities and would possibly moreover be blended with quite a few deep learning frameworks. It provides assist for GPU and TPU computation of algorithms. 

  • The API supplied is simple, an identical as Scikit-learn. 
  • Keras is totally primarily based totally on Fashions and Graphs. A model has Enter, output and intermediate layers to hold out the various duties as per requirement. 
  • Efficient functionalities and fashions supplied to code deep learning algorithms like Neural Community, Recurrent Neural Community, Lengthy Quick-Time interval memory, Autoencoders and lots of others. 
  • Permits to create merchandise merely supporting a variety of backends 
  • Helps multi-platform use. 
  • Can be utilized with TensorFlow, might be utilized in browser using web primarily based keras and provides native ML assist for iPhone app enchancment. 

9. TensorFlow 

TensorFlow is the talk about of the town as a result of its capabilities acceptable for Machine Studying and Deep Studying fashions. It is among the many most interesting, and most well-liked frameworks, adopted by corporations world huge for Machine Studying and Deep StudyingIts assist for Internet along with Cellular software program coupled with Deep Studying fashions has made it well-liked amongst engineers and researchers. Many giants like IBM, Dropbox, Nvidia and lots of others. use TensorFlow for creating and deploying Machine Studying Fashions. 

This library has many capabilities like image recognition, video analysis, speech recognitionPure Language Processing, Suggestion System and lots of others. TensorFlow lite and TensorFlow JS has made it further well-liked for web capabilities and Cellular Functions. 


  • Developed by Google, it’s among the many most interesting deep learning frameworks. 
  • Easy Machine Studying duties are moreover supported iTensorFlow. 
  • Helps many well-known libraries like scikit research, Okeras and lots of others. which are part of TensorFlow. 
  • The basic unit is Tensor which is an n-dimensional array. 
  • The basic derivatives are inherently computed which helps in rising many Machine learning Fashions merely. 
  • The fashions developed are supported on CPT, TPU and GPU. 
  • Tensorboard is the environment friendly instrument for information visualization. 
  • Many completely different supported devices might be discovered to facilitate Internet Growth, App Growth and IoT Functions using Machine Studying. 


  • Understanding Tensor and computational graphs is tedious. 
  • Computational graphs make the code difficult and usually face effectivity points. 

10. Pytorch 

A popular Python frameworkPytorch helps machine learning and deep learning algorithms and is a scientific computing framework. This is usually a framework which is also used by Twitter, Google and Fb. The library helps difficult Tensor computations and is used to assemble deep neural networks. 


  • The facility of Pytorch lies in constructing of Deep Neural Networks. 
  • Wealthy capabilities and utilities are supplied to assemble and use Neural Networks. 
  • Highly efficient in relation to creation of producing ready fashions. 
  • It helps GPU operations with rich math-based library capabilities. 
  • In distinction to Numpyit provides the capabilities which calculates gradient of the function, useful for the constructing of the neural neighborhood. 
  • Offers assist for Gradient primarily based optimization which helps in scaling up the fashions merely to massive information. 


  • It’s a complex framework, so learning is troublesome. 
  • Documentation assist for learning isn’t readily on the market. 
  • Scalability may be an issue as compared with TensorFlow. 

11. Theano 

Theano is a library for evaluating and optimizing the mathematical computations. It’s primarily based totally on NumPy nevertheless provides assist for every the GPU and CPU. 


  • It’s a fast computation library in Python. 
  • Makes use of native libraries like BIAS to indicate the code in faster computation. 
  • Greatest suited to take care of computations in Deep Studying algorithms. 
  • Industry customary for Deep Studying evaluation and enchancment. 


  • It’s not very well-liked amongst researchers as a result of it’s among the many earlierer frameworks. 
  • It’s not as simple to utilize as TensorFlow.

12. CNTK 

CNTK is Microsoft’s Cognitive Toolkit for the occasion of Deep Studying primarily based fashions. It’s a industrial distributed deep learning instrument. 


  • It’s a distributed open-source deep learning framework. 
  • Common fashions like Deep Neural Network, Convolutional Neural Network fashions could also be blended merely to kind new fashions. 
  • Offers interface with C, C++ and Java to include Machine Studying fashions. 
  • Can be used to assemble reinforcement learning fashions as huge capabilities might be discovered. 
  • Can be utilized to develop GAN (Generative Adversarial Networks). 
  • Offers quite a few strategies to measure the effectivity of the fashions constructed. 
  • Excessive accuracy parallel computation on Quite a few GPU is obtainable. 


  • Correct documentation is not on the market. 
  • There’s inadequate neighborhood assist. 


Python, being in all probability probably the most well-liked languages for the occasion of Machine Studying fashions, has a plethora of devices and frameworks on the market for use. The choice of instrument relies upon the developers experience along with the sort of software program to be developed. Every instrument has some sturdy elements and some weaknesses, so one has to rigorously choose the instrument or framework for the enchancment of Machine Lincomes primarily based capabilities. The documentation and assist on the market are moreover important requirements to be saved in ideas whereas choosing the most relevant instrument. 


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