Python Libraries for Data science

What is Python?

Python is often a choice for development that needs to be applied for census and data analysis to work or data scientists whose work should be integrated into web applications or the production environment. In particular, python actually looks at the learning point of the machine. The combination of python's teaching and library libraries makes it particularly suited to develop modern lenses and predecessors forecasts directly connected to the production process.

One of the biggest assets in python is the large library. Libraries are the types and timetables of a particular language. Hard libraries can make it easier for developers to perform complex tasks and not rewrite many code lines.

BASIC LIBRARIES FOR DATA SCIENCE

These are basic python libraries that change the language of the general program-intensive and powerful tool for analyzing and retrieving data. Sometimes called "scipy stack", is the basis of the most suitable tool.

1.numpy is a basic library of computer science labs and many of the libraries in this list using the array numpy as the key and their basic production. in short, it presents the arrangement of arrows and array elements and the power of development to perform mathematical functions and the matrices of the matrices with the least number of possible codes.

2. Scipy is based on numpy to add a collection of highly algorithms to judge and provide the information presented. this package includes complex multitasking operations to solve differential equations, simplification and more.

3. Pandas integrate data structures and tools for effective financial analysis, statistics, social studies, and engineering. pandas work perfectly with incomplete information, unified and unlabeled (ie, what type of data is most likely to meet in the real world) and provides reserved, deleted, distributed, distributed and stereo formatting.

4.ipython increases the functionality of python interpreter with a shell that combines a well-developed image, rich media, sconces, complete tabs and repetition of the order history. it also works as a reliable translator for your programs that can actually be used for errors. if you have ever used Matematica or Matlab, you should feel comfortable with python.

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5. matplotlib is the standard python library to create 2d graphics and graphs. it is a very low level, which means more require additional instructions to some of the libraries with more advanced photographs and better numbers. However, the loose left side. With sufficient commands, you can make any type of graph you want a mattotlib.

LIBRARIES FOR MACHINE LEARNING

Computer engineering is at the crossroads of the craft science and cyber statistics. When training a genre of real-world computer information, we can create algorithms that make many accurate and modern predictions, if we are talking about driving directions better or building computers that can refer to events just watching images. The following python libraries provide the ability to handle some of the learning functions of the machine, performing basic regressions on neural networks.

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1. scikit-based is based on numpy and scipy by adding a set of algorithms for typical workmanship and mining information, which includes integrating, allocating and expanding. Library, scikit-baran is more likely to offer. Their tools are well-documented, and collaborators have a lot of experts on learning the machine. in addition, it is very curated libraries, which means that the development will not have to be different between the different versions of the combination. its strong and easy to use to popularize with many new heavyweight companies such as Evernote, OkCupid Spotify, and birchbox.

2. Theano uses similar numbers to numpy to optimize and evaluate mathematical expressions. What should be done to strengthen the GPU computer computationally faster than 100 times faster than the processor? The nano speed specially evaluates deeper learning and other complex mathematical activities.

3. Tensor flow is another participant in the high profile of the machine, developed by Google as an alternative source of disbelief, for the first model, for the training of neural networks. Tensor flow uses a multi-layer layer that lets you reserve rights, train quickly and efficiently on the artificial neural network with a major data type. it is possible for Google to identify pictures of objects or to understand the words to speak on the coding application.

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LIBRARIES FOR DATA MINING AND NATURAL LANGUAGE PROCESSING

What if the company does not have the luxury to access a great brand? For many companies, the information they need is not something that can be downloaded, but they are removed from paper or web pages. The following tools are for different types of activities, removing valuable information from websites to convert natural resources to your use.

1. Scrapping is a suitable name library to generate ax axle to systematically track web and create customized values such as value, contact information and URL data. Basically, for web browsing, scrappy can also produce data from the api.

2. Nltk is a set of libraries for natural processing (nlp). nltk provides basic functions that allow you to find textual data, identify institutional reviews and present the names of trees, such as the diagram of the diagrams that show the elements of speech and dependence. from there, you can do something more complicated than automatic focusing and automatic summation. it also comes with all the more valuable, because of a book on nltk writing reviews.

3. The collaboration of the scrapy and nltk library is designed to serve as a ready to use internet mining solutions, nlp, computer education and networking. tools such as a web crawler; api for google, twitter and Wikipedia, and algorithms analysis writing such as spell buttons and sensors can be done with only a few lines of code.

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LIBRARIES FOR PLOTTING AND VISUALIZATIONS

The best and most modern analysis is simply nothing if you can pass it on to others. The libraries are based on the matplotlib that allows you to easily create scaffolds, tables, and maps with exciting and modern looking, regardless of the type of analysis you are trying to make.

1. Seaborn is a librarian of popular marketing based on matplotlib. The first thing you will know about seaborn is that the default systems are more mattotlib than conventional. Beyond, Seaborn is a high-librarian library, which means it's easier to generate some types of board, including charts, heat strings, and violin.

2. Bokeh makes interactive and zooms in modern web browsers using javascript widgets. Bokeh another good feature is to come with three levels of interface, from high abstractions to allow you to quickly generate solid layers, even the low level offers a maximum flexibility in developing your application.map incorporates simple matplotlib maps to take on the matplotlib and requesting more than 25 different predictions. The library is based on the folyon.

3. Basemap that allows the creation of interactive web maps, similar widgets created by bokeh javascript.

4. Networkx lets you create and analyze graphs and networks. it is designed to provide standard formats and unusual data, making them work particularly scalable and scalable. Networkx all makes it particularly suited to complex social networks.

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