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Best python libraries you must use.

best python libraries

Best Python Libraries : Python is one of the top programming language of the world and widely used programming language of this planet. There are lots of reason, developers across the globe, prefer Python as a programming language. Python has huge collection of libraries.

Python library is a chunk of code that you may want to include in your projects. These libraries will help you in data science, machine learning and deep learning. These are helpers. You don’t want to create your own library  for your project, unless you are a giant company like google or oracle.

You just have to import these library to your project and use all the features of that library.


Numpy :

NumPy (Numerical Python) is an open source Python library that’s used in almost every field of science and engineering. It’s the universal standard for working with numerical data in Python, and it’s at the core of the scientific Python and PyData ecosystems.

It is extremely faster than normal handling of an array. When you have 2 matrix and you perform all the computation work, it is tedious and slow. Numpy helps to compute faster.

For example – When you multiply two matrix, it takes so many lines of code and also nested for loops. but in Numpy just one line of code and you will get the result.

NumPy users include everyone from beginning coders to experienced researchers doing state-of-the-art scientific and industrial research and development.

The NumPy API is used extensively in Pandas, SciPy, Matplotlib, scikit-learn, scikit-image and most other data science and scientific Python packages.

The NumPy library contains multidimensional array and matrix data structures. It is one of the most used python library and most useful library.


Pandas :

Pandas is python library for data analysis and data manipulation. It’s an open source library. It is fast, flexible and easy to use.

It is fundamental high level building block for doing real word data analysis in python.

Real world data is full with errors and also missing data. Pandas helps you to deal with all the ambiguity. For example, you have a excel spreadsheet of student data  with thousands of column. In many fields, age of the student is written as bcf, abs instead of number, or some times age is written as 899, 8233, 83201. These are invalid data, Pandas helps to find out these errors and also helps to fix errors.

Pandas is suitable for different kind of data’s :

Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet

Ordered and unordered (not necessarily fixed-frequency) time series data.

Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels

Any other form of observational / statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure


Seaborn :

Seaborn is python data visualisation library based on matplotlib. It provides high level interface for drawing attractive and informative statistical graphics.

It is closely integrated with pandas data structure.

Here is some of the functionality that seaborn offers:

1. A dataset-oriented API for examining relationships between multiple variables

2. Specialized support for using categorical variables to show observations or aggregate statistics

3. Options for visualizing univariate or bivariate distributions and for comparing them between subsets of data

4. Automatic estimation and plotting of linear regression models for different kinds dependent variables

5. Convenient views onto the overall structure of complex datasets

6. High-level abstractions for structuring multi-plot grids that let you easily build complex visualizations

7. Concise control over matplotlib figure styling with several built-in themes

8. Tools for choosing color palettes that faithfully reveal patterns in your data. [ source ]


Matplotlib :

Matplotlib is python library for python programming language and numpy. It  is used to create high quality graphs, charts and figures. These figures provide each and every detail.  It was introduced by John Hunter in the year 2002.

One of the best benefits of visualisation is that it allows us to visualise a huge amount of data easily.

Matplotlib consists of several plots like line, bar, scatter, histogram etc.

Plots helps us to understand data, trends, patterns so that we can make correlation.

Also read : Kaggle : Where Machine Learning Noobs Becomes Noob Master

Also read: Python facts you must know | Python Programming language.

Scikit learn :

Scikit learn is python library for machine learning. If you want to learn machine learning than scikit learn is perfect choice for yourself.

It is an open source machine learning library that supports supervised and unsupervised learning. It also provides various tools for model fitting, data pre processing, model selection and evaluation, and many other utilities.

Scikit learn helps in training small data set.

Top features include : 

– Simple and efficient tools for predictive data analysis

– Accessible to everybody, and reusable in various contexts

– Built on NumPy, SciPy, and matplotlib

– Open source, commercially usable – BSD license

– built by google


Tensorflow :

TensorFlow is an end-to-end open source platform for machine learning.

It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications. [ source : https://www.tensorflow.org/ ]

It was developed by Google brain team for internal use. It was released on November 9, 2015.

Tensorflow makes machine learning and deep learning much more easier. It helps to acquire data, creating models and training them, serving prediction and helps to refine future results.

It helps in large scale machine learning and deep learning(aka neural networking).

Tensorflow can make models that helps to recognise Hand written digits or alphabets, image recognition, word embedding etc.

You don’t need a high performance computer to run tensorflow, google provides google colab, where you can train your model without any difficulties, you just need a high speed internet service.


keras :

Keras is an open source neural network library written in python. It is capable of running on the top of TensorFlow.

It is designed to be modular, fast and easy to use. It was developed by François Chollet, a Google engineer.

pytorch :

It is open source machine learning library. It is used for computer vision, natural language processing. It was developed by facebook’s AI research lab.

Many  deep learning software are built on pytorch such as Tesla Autopilot, Uber’s pyro, pytorch lightning, catalyst.

PyTorch provides two high-level features:

– Tensor computing (like NumPy) with strong acceleration via graphics processing units (GPU)

– Deep neural networks built on a tape-based automatic differentiation system. [source : wikipedia]

Conclusion : Best python libraries

This is my list of best python libraries. If you like it, please share . If you find any mistake, please comment down, so that i can correct my mistake.

Thank you for reading.








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