NumPy

Get to know the basics of NumPy ArraysPhoto by Pierre Bamin on UnsplashA NumPy array is a data object from the Python library NumPy, which is used to store objects of a data type. Since it is programmed much closer to memory than comparable Python data objects, it can store data sets more efficiently and thus also be processed faster.Python offers a variety of data structures that can be used to store data without additional libraries. However, these structures, such as Python lists, are only very poorly suited for…

The world under the coversImage by Elias from PixabayThis is a short article on how NumPy handles arrays internally. It is considered an advanced topic and deep understanding is not strictly required for many NumPy casual users. However, my belief is that knowing fundamental NumPy concepts is useful regardless if you are using NumPy directly or indirectly, e.g. through pandas. It is not only about satisfying personal curiosity, but more about fine tuning performance when arrays are filling up memory. In such cases…

OptimisationThe speed of C with the simplicity of PythonPhoto by cottonbro studioPython, along with the numpy/pandas libraries, has essentially become the language of choice for the data science profession (…I’ll add a quick nod to R here).However, it is well known that Python, although fast and easy to implement, is a slow language. Hence the need for excellent libraries like numpy to increase efficiency…but what if there was a better alternative?Julia claims to be at least as easy and intuitive to use as Python, whilst…

Some basic Knowledge of the NumPy library and ufuncsPhoto by Erik Mclean on UnsplashNumPy stands for Numerical Python and is a Python library for working with arrays. With the help of these arrays, elements from linear algebra, such as vectors and matrices, can be represented in Python. Since a large part of the library is written in C, it can perform particularly efficient and fast calculations even with large matrices.Python offers a variety of data structures that can be used to store data without additional libraries.…

As Computer Vision applications are becoming omnipresent in our lives, understanding the functioning principles of Convolutional Neural Networks is essential for every Data Science practitionerSource: canva.comIn my previous article, I built a Deep Neural Network without using popular modern deep learning libraries such as Tensorflow, Pytorch, and Keras. I later used that network to classify handwritten digits. The obtained results were not state-of-the-art level, but they were nevertheless satisfactory. Now I want to…

PythonData cleaning is one of the boring yet crucial step in data analysisPhoto by PixabayData cleaning is one of the most time-consuming tasks!I must admit, the real-world data is always messy and rarely in the clean form. It contains incorrect or abbreviated column names, missing data, incorrect data types, too much information in a single column and so on.It is important to fix these issues before processing the data. Ultimately, clean data always boosts the productivity and enables you to create best, accurate…

When speed matters, lists aren’t the best.Photo by Alireza Kaviani from Pexels.Speed is always a concern for developers — especially for data-savvy work.The ability to iterate is the basis of all automation and scaling. The first and foremost choice for all of us is a for-loop. It’s excellent, simple, and flexible. Yet, they are not built for scaling up to massive datasets.This is where vectorization comes in. When you do extensive data processing in for-loops, consider vectorization. And Numpy comes in handy there.This…

Modern Deep Learning libraries are powerful tools but they may lead practitioners to take for granted neural networks’ functioning principlesSource: unsplash.comIn this project, I build a deep neural network without the aid of any deep learning library (Tensorflow, Keras, Pytorch). The reason for imposing myself on this task is that, nowadays, it is effortless to build deep and complex neural networks using the high-level tools provided by multiple python libraries. Undoubtedly, this is a great advantage for Machine…

A hands-on tutorial and framework to use any scikit-learn model for time series forecasting in PythonPhoto by Yu Wang on UnsplashThere are many so-called traditional models for time series forecasting, such as the SARIMAX family of models, exponential smoothing, or BATS and TBATS.However, very few times do we mention the most common machine learning models for regression, such as decision trees, random forests, gradient boosting, or even a support vector regressor. We see these models applied extensively in typical…

A comprehensive guide to creating NumPy arraysPhoto by Vardan Papikyan on UnsplashThe NumPy library in Python forms the fundamental building blocks of almost every Data Science and Machine Learning project today.Due to its immense potential in supporting vectorized operations and processing large amounts of numerical data efficiently, it has become one of the most important libraries ever built in Python.Moreover, the whole data-driven ecosystem is in some way or the other dependent upon NumPy and its core…