Top 10 python libraries for data science

A few years back, the Python language was not so famous. We do know about Java, C++, C language. But when AI(Artificial Intelligence) and data science were introduced in the market, Python(because of libraries) came into play. Harvard Business Review said, “Data science is the sexiest job in the 21st century“.

Today, Python has become the most demanded language. It is also one of the three official languages of Google. It doesn’t mean Java,c++ had lost their identity. They are still in the market. Python has a vast library of AI that professionals use.

Here I will show you the top 10 Python libraries for AI, data science. Please note that the list is not in order:-

1. Numpy

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Numpy stands for numerical python. It is used for numerical computing for data that may be stored as array or matrices. It is used in a broad range of domains like machine learning, data science, data visualization, data analysis, etc. Numpy uses many operations such as adding, slicing, multiplying, flattening and reshaping, and indexing. It helps to increase the performance of the execution time.

2. Pandas

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Pandas is used for data analysis and data handling. It was introduced in 2008. This library makes the task easy to use and fast. It works in labelled and relatively data. Pandas is used along with NumPy and Matplotlib. It has amazing support in running CSV files into data frame format and helps in to deal with missing data.

3. SciPy

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SciPy stands for scientific python. It was initially released in 2001. It works on all scientific programming projects which include science, mathematics, and engineering. The core components that SciPy works with are Matplotlib, NumPy, SymPy, Pandas, IPython, etc.

4. Scikit-learn

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Scikit is a group of packages that includes Pandas, NumPy, SciPy, Matplotlib, etc. It was initially released in 2007. This is used in python, Cython, c, and c++. It can be used for various problems which include regression, classification, support vector machines, random forest, nearest neighbours, naive Bayes, decision trees, clustering, dimensionality reduction, and many more.

5. TensorFlow

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Tensorflow is the python library to implement deep networks. It was developed by Google Brain in 2015. It is used to develop and implement machine learning models with the help of high-level APIs like Keras using Tensorflow.

This library can handle a variety of tasks such as object identification, speech recognition, etc. With the help of TensorFlow, you can implement CNN(Convolutional neural network), RNN(Recurrent neural nets). With the help of (ANN)Artificial neural networks, it handles the multiple datasets.

6. Keras

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Keras is a great library for building neural networks by providing optimized solutions. It was created in 2015. It allows for faster iteration and deployment of models.

7. Theano

Theano is a python library that lets you optimize, define, and evaluate mathematical expression involving multidimensional array. Initially, it was released in 2007 at the University of Montreal. It works faster on GPU(Graphics processing unit) than CPU. It is a popular library in Deep learning because it handles the computations required for the neural network algorithm.

8. Matplotlib

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Matplotlib is a data visualization library and 2D plotting library in python. It was released in 2003. It contains plots like histograms, scatterplots, bar charts, pie charts, error charts, etc. Matplotlib is very useful in data science projects. Along with Numpy, it is used to solve a mathematical task.

9. Seaborn

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Seaborn is a data visualization library used based on matplotlib and delivers statistical visualization. It is integrated with NumPy and pandas. Seaborn has plotting functions that operate on data frames and arrays. It contains a vast collection of visualizations and tools which help to provide complex visualization. For visualization, it includes pie charts, histograms, scatters plots, bar charts, etc.

10. Pytorch

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Pytorch is a python library which helps to develop deep learning models. It is developed by the Facebook research lab. This library is used in the task like computer vision, natural language processing. It supports tensor computing with GPU(Graphics processing unit) acceleration. It helps in building an optimized neural network.

Besides the above, there are many other python libraries such as Pyspark, OpenCV, Selenium, NLTK, etc. which helps to build data science or AI projects. The significance of libraries depends on which type of project you are working on. For example, OpenCV used for computer vision, NLTK used for voice recognition, etc.

I hope you like the article.

Thank you!

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