Pricting the payback of a project, improving traffic flow and calculating demand for goods under certain conditions – all this is possible with Data Scince. And Python is us as the main language in the field of data analysis and machine learning. For convenience, libraries are connect to it – files with ready-made code templates. In the article, we share a selection of such libraries.
Pandas – for primary analysis
Pandas is almost like Excel, but much cooler. It can be us to process large amounts of data. The library is an open source project, meaning its source code is publicly available , and all users can make comments and additions. The official website has detail instructions for installing the package.
The peculiarity of pandas is that it is suitable for working with already structur tabular, or, as they are also call, panel data. Hence the name of the library – PANel DAta. Pandas allows you to prepare data for their further use in machine learning: process large volumes of information, create graphs and diagrams, conduct statistical analysis of data.
With pandas you can:
Create tables: pandas allows you to work with formats such as CSV, JSON, or XML. In addition, the library helps you convert different data formats into formats suitable for analysis in Python.
Process data: filter, sort, aggregate (convert a set of data into a single report) and transform data.
Visualize data: create line graphs, bar charts, pie charts, and summary reports.
Work with databases: pandas can be us, for example, to read and write data in MySQL, PostgreSQL or MongoDB.
Example calculations with pandas to create a table with two columns ‘column1’ and ‘column2’:
NumPy – for working with arrays of numbers
A data array is an order set of elements of the same type that allows you to efficiently store and process large amounts of data. An array could be, for example, a shopping list with information about the buyer, price, and product name. A two-dimensional array looks like an Excel table – two axes and cells.
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NumPy is another widely us Python package, a library for working with multidimensional arrays of numbers. The full name of the library is Numerical Python extensions. Like pandas, it is open source and available to users. The library contains powerful tools for numerical calculations, image processing, and other tasks involving complex mathematical operations on data arrays.
What is the NumPy library useful for:
Processing and analyzing large amounts of data, for example for scientific research or stores. For example, if you ne to ruce shipping costs and the percentage of return goods, you can mathematically calculate popular product categories and optimal shipping methods.
Machine learning: NumPy can be us to create ML models. This is useful, for example, for analyzing demand in stores or studying traffic on the roads.
Image analysis: for example, if you ne to detect certain objects in photographs or determine specific characteristics.
Financial Analysis: NumPy makes it easier to analyze income statements, balance sheets, and create financial forecasts. This makes it easier for companies to make inform investment decisions and assess risks.
Video processing: NumPy can be us for video processing, facial recognition, or motion capture. This allows companies to analyze video data and identify potential safety hazards or violations of regulations — for example, to determine whether employees are wearing masks in the workplace.
Matplotlib – for creating graphs and data visualizations
Graphs are always easier to perceive than endless tables. Especially when it comes to data analysis and metrics comparison. With Matplotlib, you can create different types of graphs and integrate them into applications via API.
Line, scatter, circle, histogram, spectrogram, contour graphs – the library provides visualizations of any complexity.
Example of a histogram with multiple data sets plott using Matplotlib
SciPy – for mathematical problems
SciPy is a library for complex engineering and russia telegram marketing scientific calculations bas on NumPy. While NumPy is design for basic calculations, SciPy is for deeper analysis, it has more methods and functions.
Examples of problems that can be solv with the SciPy library:
Calculating probabilities: For example, if you ne to determine the probability that a user will choose a particular product, or the probability that a particular event will occur at a given point in time.
Calculation of mathematical functions: raising to a power, extracting roots, taking logarithms, solving differential equations.
Working with genetic algorithms, for example, to optimize hyperparameters of a machine learning model.
Image processing: you can filter images by phone number lt any feature and find specific objects.
An example of using SciPy to output statistical characteristics of each element in a sample:
Plotly – for 3D data visualization
Another open-source library and powerful tool for data visualization. Unlike Matplotlib, Plotly allows you to create not just different types of charts, but also make them 3D and interactive — for example, if you want users to be able to interact with them.
Plotly has a lot of additional features: customizing the appearance of the chart, adding animations, timers and other controls. This makes it convenient for use in various fields, from scientific research to marketing.
TensorFlow — for creating neural networks of any complexity
TensorFlow is an open, comprehensive platform for machine learning. It allows you to create neural networks with high accuracy and spe. The library was develop at Google and is now us in many projects, including the creation of chatbots, voice assistants, and speech recognition systems. At the same time, it supports not only Python, but also Java and C++.
TensorFlow features:
creation of deep neural networks (Deep Learning) that can process large amounts of data and perform complex tasks;
optimization of neural networks and acceleration of the learning process;
using different types of data to train the model: for example, you can use images or text data to create more complex models; creation of chatbots, voice assistants.
