With the increasing digitization all around the world, the business space is critically revolutionizing while data analytics is becoming more important for business growth.
If we see today’s web development scenarios, then Python proved itself as a boon for web developers. It is considered as the best programming language that offers the best choice of open-source libraries, especially in data science.
Thus, turn your programming to this self computing language and model the best web development application with Python. Python has 1.37,000 libraries that significantly helps in data analysis to make the critical decision making.
We have prepared this guidepost on the best Python libraries 2019 that can help you to grow your business. Whether you are planning to start your Python web development company or investing to make a career out of Data Science; the below mentioned best Python libraries would help you.
Let’s kick-off this article on Python web application development with the first set of the library;
Python Libraries For Statistical Analysis
Statistics are the fundamentals of Data Science and Machine Learning. Moreover, Python web application development offers us four libraries which by statistical analysis helps you in your business affairs.
NumPy or Numerical Python
Numpy Python library allows data scientists to turn computing language into a powerful scientific analytic and modeling tool. One of the best Python library known as TensorFlow uses Numpy to get better performance while doing multiple operations.
Being a versatile library, NumPy performs multi-dimensional arrays effortlessly on various generic data indexing, sorting, reshaping and conveying images multidimensionally.
If you want to do scientific and technical computation related work, SciPy can work very well with NumPy arrays. It is the one particular library that offers many user-friendly and fast N-dimensional array manipulation to make customer data analytics easier.
SciPy library has sub-packages in it which solve the most fundamental problems in statistical analysis. Also, it is widely used in processing the array elements that are defined by the NumPy library. NumPy and SciPy combination compute those mathematical equations which aren’t single-handily done by NumPy.
For working on data manipulation and data analysis project, Pandas is the most efficient choice for you! Call it another essential statistical library in these wide ranges of fields! It is truly magnificent and works best for python web application development projects in statistics, banking, finance, economics, and many more.
The library entirely relies on the NumPy array to process pandas data objects. All these 3 libraries, NumPy, Pandas, and SciPy heavily depend on each other when performing data computations and manipulations.
The StatsModels Python library is the best to create statistical models over NumPy & SciPy. Moreover, it also efficiently manages data handling.
If you are looking to make data handling more effective in Python web development, then use NumPy arrays and SciPy library modes, and integrate it with Pandas. This combination of best python libraries is famously known for statistical testing and data exploration.
Python Libraries For Machine Learning
Machine Learning models accurately predict the outcome by solving data science-related problems and filters better data for ease of your business.
Scikit-learn Python Library is widely used for data modeling and comes with tons of distinct functions and algorithms to Ensemble and Boosting Machine Learning.
This particular library also implements standard machine learning, data mining tasks, while featuring Data Cross-validation, and Data extractions.
Eli5 is an ML Python library which always helps the python developers to overcome challenges like debugging and predictions. It is mainly designed to track down the complete working process while offering build-in support.
In addition, it also helps in implementing algorithms so that inspecting black-box models and other supportive libraries like Scikit-learn, LightGBM, etc. becomes easy.
XGBoost abbreviated as Extreme Gradient Boosting is the best Python library package to boost machine learning. Also, it supports libraries like LightGBM and CatBoost, which makes them fully equipped with well-defined functions, algorithms, and methods. In short, it just improves the performance & accuracy of other libraries and data models.
Python Libraries For Deep Learning
If we go deeper into artificial intelligence, data analytics, then we learn about Deep Learning. In Python web development services, Deep Learning builds complex models and processes the huge data sets.
Tensorflow Python library is closely associated with defining and running those computations which involve tensors. You can also call this library as a computational library that repeatedly writes new algorithms while being used in applications for Artificial Intelligence & Machine Learning.
In addition to it, TensorFlow uses techniques like XLA for linear algebraic operations, optimizes the computation speed, makes it accessible to visualize every part of a graph. This option is not present in the NumPy or SciKit Python libraries.
Therefore, if you are working or planning to work on a Machine learning project in Python, then we would suggest you choose TensorFlow.
Pytorch is the most scientific computing package that offers open-source libraries and functions to implement Deep Learning techniques and Neural Networks on large datasets.
Such kind of heavy library is used by the Facebook face recognition app and data tracking system. Moreover, it can also be used with other best python libraries of 2019, such as Cython, Numba, NLP, and many more.
Theono is a very similar Python library to Tensorflow, which purely helps the data scientists in performing many multi-dimensional arrays that are quite relevant to the computing operations.
With Theono Python library, you can express, optimize, and array all the mathematical enabled operations amongst data scientists who love to use C code generator.
These are the most enriched collection of web development industry based Python libraries that are mainly focused on mathematical computation, data frames, and dynamic models for artificial learning, data modeling, machine learning, and deep learning.