Python is the most popular programming language nowadays. Python never fails to amaze its users when it comes to managing tasks and data science-related issues. The majority of data scientists are using Python on a regular basis. It is object-oriented, available open-source. These features make it simple to learn and easy to debug. Python was created with outstanding libraries that programmers use to solve daily problems. Data science is an incredibly influential field in current times. Data Science is in high demand because of the enormous value of data. Python language is extremely used to pull valuable insights from this data. Since python has the capability for data modelling, statistical analysis. Great Python libraries for data science are another factor in its massive success.
A variety of tools, and techniques are available in python libraries for analysing data. These libraries have a specific suitable area. Some suitable for managing neural networks, data visualisation and some for data mining, and other types of data. Some libraries focus on textual data and images.
Here you can understand the top 5 Python libraries for Data Science:
Pandas available as a free Python library used for handling and analysing data. It was first made available in about 2008 as a community library effort. Pandas deliver a variety of high-performance and simple-to-use data structures. It supports operations for working with data as time series and numerical tables. Pandas offer a variety of tools for reading and writing data between various file formats and in-memory data structures. You can say, it is ideal for quick and simple data reading, writing, data visualisation, and data aggregation. Moreover, Pandas can generate data frames from received data. These data were received from different file formats like SQL database or CSV and excel. This data frame is known as a Python object.
Keras is also a free and open-source library used for neural networks. It was initially made available on March 27, 2015, by Google engineer François Chollet. Keras was designed to enable deep neural network experimentation in a modular, user-friendly, and extendable way. It can thus be used in confluence with various programming languages. It also worked with different frameworks like R, TensorFlow, Microsoft Cognitive and Toolkit. For coding in deep neural networks, Keras contains a variety of tools. These tools make it simpler to work with various textual inputs and picture formats. Also, it delivers many modes of implementing neural network-building pieces. It provides different ways like objectives, optimizers, activation functions, and layers. With Keras, you can create custom function layers and write functions with repeated code blocks that are many layers deep.
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Python's Matplotlib library uses to do plotting in 2-D and data visualisation. It was first made available in 2003. The python community often used a plotting module. It has an interactive environment that functions across different platforms. Matplotlib is suitable for IPython shells, web application servers, and Python scripts. Plots can be incorporated into programs that use a variety of GUI toolkits, such as wxPython, Tkinter, and Qt, GTK+. Plot Visualisation charts like Power spectra, Scatterplots, Plots, histograms, and bar charts. You can even make error charts, and pie charts using Matplotlib. The Pyplot package also presents a MATLAB-like user interface that is completely free.
Scikit-learn was developed primarily in the Python programming language. It is a free software library for ML first introduced in June 2007. Scikit-learn delivers complete interaction with various libraries like Matplotlib, Keras, Pandas, and NumPy. Some of the basic algorithms of Scikit-learn have been developed in Cython also. This increases the Scikit-learn's efficiency. From Scikit-learn, you may employ a variety of supervised and unsupervised machine learning models. You can use models like Support Vector Machines, Classification, Clustering. Scikit-learn also supports Naive Bayes, Regression, Random forests, Decision trees, and Nearest Neighbours.
TensorFlow consists of various ranges of libraries, resources, and tools used for AI. It is available as an open-source module. On November 9, 2015, TensorFlow was released. With this, you can quickly create and train machine learning models using high-level APIs similar to Keras. Moreover, it offers various abstraction levels, permitting you to select the best suitable for your model. You may also use TensorFlow to organise Machine Learning models even in the browser, in the cloud, or on your device. To get the full experience, you can use TensorFlow Extended (TFX). For mobile devices, TensorFlow Lite is the best choice. TensorFlow.js is the best option for training and deploying the models in JavaScript environments. You can use TensorFlow for C API and Python. Java, Go, C++, JavaScript, and Swift are also used with TensorFlow but these are not available with API backward compatibility.
Apart from the above, there are many other Python libraries also available like SciPy, NumPy, GGPlot, etc. SciPy is used for mainly scientific and technical computation work done on the available data. It is developed on the NumPy array and also works with other modules like pandas, Matplotlib, etc. SciPy permits you to do data modification, data integration, and data optimisation. All these operations are performed using mathematical computations like Fourier transforms, special functions, random number generation, linear algebra, etc.
NumPy works very well on data forms in large arrays and multidimensional matrices. It gives you a wide range of tools to handle these arrays. It consists of tools that can be implemented to perform high-level mathematical operations like Fourier transforms, linear algebra, etc. GGPlot can form data visualisations such as error charts, histograms, bar charts, pie charts, scatterplots, etc. employing high-level API. It also permits you to add layers in a single visualisation or different types of data visualisation features.
Python environment is a wide ocean consisting of a vast range of libraries that are available for Data science professionals. The best method to ensure that you are prepared to work as a data science professional is to familiarise yourself with the numerous Python libraries and tools. This post intends to expose you to the most extensively used Python libraries for data science. By learning and leveraging these libraries as beginners you can head start in the fields of AI, data science, and Machine Learning. However, as explained there are a large number of libraries available in Python.
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