In Python, the numpy.gradient() function approximates the gradient of an N-dimensional array. It uses the second-order accurate central differences in the interior points and either first or second-order accurate one-sided differences at the boundaries for gradient approximation. The returned gradient hence has the same shape as the input array. Here is the argument table of … Read more
In this article, we’ll learn how to generate the Matplotlib color palette and then we will use it to select a specific color for our plot. Problem and Solution Overview Problem: When presenting data, the color that you assign to a plot is very important; a bad color choice can make your data difficult to … Read more
The z-scores can be used to compare data with different measurements and for normalization of data for machine learning algorithms and comparisons. 💡 Note: There are different methods to calculate the z-score. The quickest and easiest one is: scipy.stats.zscore(). What is the z-score? The z-score is used for normalization or standardization to make differently scaled … Read more
Before we learn about the money, let’s get this question out of the way: What is a Quant Developer? A quantitative developer (i.e., Quant) is a financial programmer focused on financial modeling and quantitative finance and trading. Quants use their profound knowledge of statistics and math, finance, data structures, algorithms, machine learning, scientific computing, data … Read more
In Python, the numpy.zeros() function returns a new array of given shape and type, filled with zeros. Here is the parameter table of numpy.zeros(). If it sounds great to you, please continue reading, and you will fully understand the numpy.zeros() function through Python code snippets and vivid visualization. Concretely, I will introduce its syntax and … Read more
Problem Formulation 💬 Given a text query/keyword such as “History of Chess”. How to scrape the top Google results for that search query (=keyword) in Python? Disclaimer: Have a look at the important question: Is Web Scraping Legal? Method Summary You can get the top Google search results given a certain keyword string by installing … Read more
Before we learn about the money, let’s get this question out of the way: What is Tableau? Let’s have a look at the definition inspired by the official Tableau website: Tableau is a visual data analytics platform focused on the business analytics use case that helps you use data to solve problems. It is great … Read more
What is Data Cleaning? Data cleaning describes the process of turning messy data into clean datasets that can be used for research and data science purposes. For example, tidy data will be in a wide format: every column contains a variable, and every row contains one case. Also, data cleaning means getting rid of corrupt … Read more
Problem Formulation and Solution Overview This article will show you how to count duplicates in a Pandas DataFrame in Python. To make it more fun, we have the following running scenario: Rivers Clothing has a CSV containing all its employees. However, their CSV file has more rows than employees. This is a definite problem! They … Read more
Before we learn about the money, let’s get this question out of the way: What Is a Chatbot Developer? A chatbot developer creates software to automate communication with customers and users. An example chatbot application is in customer service for an eCommerce website. Chatbot developers use machine learning and artificial intelligence techniques to communicate with … Read more
Preparation Before any data manipulation can occur, four (4) new libraries will require installation. The Pandas library enables access to/from a DataFrame. The NumPy library supports multi-dimensional arrays and matrices in addition to a collection of mathematical functions. The pandas_gbq allows access to Google Big Query (GBQ) The google.auth authentication. To install these libraries, navigate … Read more
This article focuses on the serialization and conversion methods of a Python DataFrame: to_gbq(), to_coo(). Let’s get started! Preparation Before any data manipulation can occur, four (4) new libraries will require installation. The Pandas library enables access to/from a DataFrame. The NumPy library supports multi-dimensional arrays and matrices in addition to a collection of mathematical … Read more