## Yahoo-Fin: Fetching Historical Stock Data with Python’s Yahoo Finance API

This guide provides an easy-to-understand foundation for beginners and intermediate users to leverage the Yahoo Finance API with Python for financial data analysis. Yahoo Finance API offers a wealth of financial data, from stock prices and market trends to currency exchange rates. This guide will introduce you to using the Yahoo Finance API yahoo_fin with … Read more

## Matplotlib Colors: A Comprehensive Guide for Effective Visualization

Here’s a minimal example of using colors in Matplotlib. This example creates a simple line plot with a specified color: In this minimal code example, plt.plot(x, y, color=’red’) creates a line plot of x versus y, with the line color set to red. You can replace ‘red’ with other color names like ‘blue’, ‘green’, etc., … Read more

## DALL·E 3 Trick: Using Seeds to Recreate the Same Image

Problem Formulation You may have encountered the following: When issuing the same prompt twice to ChatGPT with DALL·E 3, you’ll get different images even though the prompt is identical. How can I recreate the same image in a new Chat, either for myself or for somebody else to reproduce it? Here’s an example in a … Read more

## Python Code for Getting Historical Weather Data

To get historical weather data in Python, install the Meteostat library using pip install meteostat or run !pip install meteostat with the exclamation mark prefix ! in a Jupyter Notebook. If you haven’t already, also install the Matplotlib library using pip install matplotlib. Then, copy the following code into your programming environment and change the … Read more

## Want Exploding Bitcoin Prices North of \$500,000 per BTC? “Grow N” Says Metcalfe’s Law

Metcalfe’s law states that the value of a network (V) is proportional to the square of the number of connected users of the system (N²). In this article, I’ll develop a Bitcoin price prediction (V) in the year 2030 based on the average growth rate of the number of Bitcoin nodes (N). General Intuition Metcalfe’s … Read more

## How I Created Charts Using Charts.js in Django Web Application

If you want to display charts in your Django web application, whether it is a bar chart, a pie chart, or other charts, you have so many options at your disposal — from using Plotly in Python language to using HighCharts.js or Charts.js in JavaScript. The beauty of using the later over the former is … Read more

## How I Solved a Real-World Problem Using Monte Carlo Simulation

In this project, you are taking the role of a data analyst. Your client, a large retail outlet has been running an affiliate marketing program in an effort to increase its sales. You are tasked to come up with a predictive model to predict how much should be budgeted for sales commission for the following … Read more

## Towards Reverse Engineering Matplotlib Code From Images

I tried a few helpful applications of Google Bards Image Recognition capabilities for coders. I don’t know about you but I often see beautiful plots (e.g., in research papers or data science reports) and wonder how I could recreate them. Well, Google Bard to the rescue! ✅ Reverse Engineer Exponential Plot in 2D First, let’s … Read more

## How to Plot a 3D Normal Distribution in Python?

To create a 3D surface plot of a bivariate normal distribution define two normally distributed random variables x and y, each with its own mean (mu_x, mu_y) and variance (variance_x, variance_y). The random variables are independent,the covariance between x and y is 0. Use the grid of (x, y) pairs to calculate the probability density … Read more

## (Fixed) TypeError: FigureBase.gca() got an unexpected keyword argument ‘projection’

When trying to plot a 3D normal distribution recently, I encountered the following error: TypeError Traceback (most recent call last) <ipython-input-10-ee1b0cd4b744> in <cell line: 32>() 30 fig = plt.figure() 31 —> 32 ax = fig.gca(projection=’3d’) 33 34 # create a 3D surface plot of the multivariate normal distribution 👉 TypeError: FigureBase.gca() got an unexpected keyword … Read more

## How Do I Make a 3D Waterfall Plot with Colored Heights in Python?

To generate a 3D waterfall plot with colored heights create a 2D sine wave using the NumPy meshgrid() function, then apply a colormap to the heights using Matplotlib’s Normalize function. The plot_surface() function generates the 3D plot, while the color gradient is added using a ScalarMappable object. Here’s a code example for copy and paste: … Read more

## GPT-4 Code Interpreter – How to Run Python & Plot Data in ChatGPT

GPT-4 now provides a friendly little tool that has the potential to completely change the coding industry. Again. You can activate the code interpreter in your ChatGPT Settings: Toggle the “Code Interpreter” option (currently in “Beta features” but not for long): Now you can run the code interpreter using a simple natural language prompt such … Read more