In this blog series, powerful Python libraries are leveraged to help uncover some hidden statistical truths in basketball. The first step in any data-driven approach is to identify and collect the data needed. Luckily for us, Basketball-Reference.com hosts pages of basketball data that can be easily scraped. The processes of this walkthrough can be easily … Read more
In this blog post, we’ll discuss how to normalize a matrix using the popular Python library NumPy. But first things first: 👇 What is Normalization? In mathematics, normalizing refers to making something standardized or regular. Normalization of a matrix is a process of scaling the matrix so that the elements of the matrix have a … Read more
Pandas is a great library for data analysis in Python. With Pandas, you can create visualizations, filter rows or columns, add new columns, and save the data in a wide range of formats. The workhorse of Pandas is the DataFrame. 👉 Recommended: 10 Minutes to Pandas (in 5 Minutes) So the first step working with … Read more
This article will review some of the critical techniques used in Exploratory Data Analysis, specifically for Bivariate Analysis. We will review some of the essential concepts, understand some of the math behind correlation coefficients and provide sufficient examples in Python for a well-rounded, comprehensive understanding. What is Bivariate Analysis? Exploratory Data Analysis, or EDA, is … Read more
NumPy vs Lists NumPy is an important Python library used for numerical operations and data science. Its mathematical operations on arrays are faster and more efficient than on lists. Since NumPy is primarily written in C and C++, it can perform faster operations. NumPy arrays are homogeneous arrays that can store only one type of … Read more
Welcome to the bonus content of “The Book of Dash”. 🤗 Here you will find additional examples of Plotly Dash components, layouts and style. To learn more about making dashboards with Plotly Dash, and how to buy your copy of “The Book of Dash”, please see the reference section at the bottom of this article. … Read more
Welcome to the bonus content of “The Book of Dash”. 🤗 Here you’ll find additional examples of Plotly Dash components, layouts and style. To learn more about making dashboards with Plotly Dash, and how to buy your copy of “The Book of Dash”, please see the reference section at the bottom of this article. This … Read more
💬 Question: How to convert a hexadecimal string such as “00-ff-ef-01-0f-0a-1a” to a NumPy array of integers [ 0 255 239 1 15 10 26]? Method 1: Hex String to List to NumPy Array You can solve this problem easily by breaking it into two easier ones: Here’s the code: Method 2: Use NumPy fromiter() … Read more
💬 Question: How can I return a Matplotlib axis, plot, or figure object in Python so I can set some defaults and plot a figure quickly based on those defaults? Basic Solution To return a Matplotlib object from a function, follow these steps: 👇 Create a function my_plot(x, y, …) with arguments to customize your … Read more
💬 Programming Challenge: Given a Pandas DataFrame or a Pandas Series object. How to convert them to a NumPy array? In this short tutorial, you’ll learn (1) how to convert a 1D pandas Series to a NumPy array, and (2) how to convert a 2D pandas DataFrame to an array. Let’s get started with the … Read more
Quick Answer: To print a logarithmic x-axis or y-axis (base 10) without a Matplotlib axis object use plt.xscale(‘log’) or plt.yscale(‘log’). To set different bases or switch back to a linear scale, use {“linear”, “log”, “symlog”, “logit”} or set the basex and basey arguments (e.g., plt.yscale(‘log’, basey=2) for log base 2). Next, we’ll have a look … Read more
In this tutorial, you will learn – How to create a basic linear regression model How to save and load an ML model using Pickle module How to save and load an ML model using Joblib module Background and Motivation Over the past years, Machine Learning (ML) has grown in importance with easy access to … Read more