When it comes to programming, i usually find myself connecting it with Python. The reasons are obvious as it is one of the most popular programming language at present.
Python is an easy to learn programming language with strong data handling ability. Having so many libraries for every single task it is easy to create robust functions. The main thing that makes Python an amazing language is its use in the field of Data Science.
Python language is used to handle data, clean data, visualize data and for a lot more data operations. The language does have libraries for handling tons of data and bringing out useful information from it.
In this post we will be looking at the most useful Python Libraries for Data Scientists:
1. Numpy Package
While learning Python , the first library you will come across is Numpy. It is used for handling large and multidimensional arrays. It also helps in performing various mathematical functions on the arrays as well as matrices.
The most basic and effective package for scientific and mathematical computation on data. Even a large dataset could be easily handles with the help of Numpy library as it allows creating n dimensional array of data elements.
Mostly referred as Python Data Analysis Library and is very much useful in framing data. The library allows you in defining data structures while importing the dataset. You can also analyse data with the help of Pandas library.
The main data structure with this library is DataFrame with which you can easily store data in the form of tables. These tables can be further managed over rows and columns. It has multiple functions like read and write associated with files including CSV, SQL and other tabular data formats.
Based on the Numpy library and is used for extended features of the same. Scientific computation can be easily performed by SciPy library. It has multidimensional array as its basic structure and all the functions are carried out on it.
The library has got all the tools for handling the tasks about probability, linear algebra, calculas and many other problems. Further routines help in solving integration and optimization problems.
Python provides all the necessary tools needed for a Data Scientist. Matplotlib is one among the popular tools used by Data Scientists around the globe.
Matplotlib allows the user to plot a 2D graph based on the data and functions. It helps in visualizing the data in the form of Bar graphs, Histograms, Scatter plots and a lot more type of graphs. Visual form data is much more appealing and helps define certain categorical pattern of given data.
Tensorflow is very popular library in the field of Machine Learning. The library is developed by Google Brain Developers and provides a way to train neural networks with the help of multiple data sets.
Tensorflow can be used to build applications like Object Identification, Recommendations Systems, Classification, etc. As the library is under Google development thus it gets all the new updates of language as well as platform faster.
If you are working on a Machine learning project with large data sets you might need this library. It is one of the most helpful library in building machine learning and deep learning algorithms. Users can avail an API for computations related to Neural networks as well.
The library helps in performing faster tensor computation with GPU acceleration. You can also create dynamic computational graphs with the help of PyTorch. The deployment of every structure is based on a cloud based environment which makes it portable as well as scalable.
Theano is another library provided by Python for helping Data Scientists. It is mainly used in optimizing, Evaluating and Expressing general mathematical operations. These mainly include operations based on multi dimensional arrays and matrices.
The computations are based on Numpy and thus they can be processed on GPU as well as CPU efficiently. This way Data Scientists can manage both distributed and parallel computations.
Keras is highly used Python library built to be working with Deep Neural Networks. The library could run on top of other base libraries including Tensorflow, Keras and PlaidML.
The library provides easy and efficient environment for API integrations. New models can be easily developed with the help of Keras’ already built functions. The execution suits best for both GPU and CPU listing. This way the base load for each API could be divided onto multiple modules.
Scikit-learn is a Python library used to develop Machine Learning algorithms. The library is built over other base libraries such as Numpy, Matplotlib and SciPy. It provides various classification, clustering and regression algorithms to be used.
Major applications that could be built with Scikit-learn are Image recognition, Client segmentation, Spam detection, etc.
Based on Matplotlib library and is used to design interactive graphs from the data. The library focuses more on the latest web browser and techniques they use to represent data.
Anyone can easily build plots, graphs, dashboards and visual elements from large datasets. In addition with the normal looks that Matplotlib provides there are multiple other layout and ways to represent data.
These were some of the Python libraries popular among Data Scientist. There are hundreds of other libraries as well and we have selected according to my use and demand.
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