Matplotlib on 春江暮客

Using Matplotlib's fill_between for Area Filling in Python

Wed, 29 Dec 2021 08:47:31 +0800

When plotting with matplotlib in Python, sometimes to enhance the aesthetics of the graph, it’s necessary to fill with color above or below the plot. The ‘fill_between’ function is used for this, where the ‘y2’ parameter determines the second line, indicating whether to fill upwards or downwards.

Calculating Confidence Intervals Using Bootstrapping

Tue, 28 Dec 2021 10:19:45 +0800

Confidence interval (CI) is the range within which the population parameter lies with a certain confidence level. It is estimated based on the original observed sample and is usually defined as 95%, commonly referred to as the 95% confidence interval.

K-Means Clustering and Implementation with sklearn

Mon, 20 Dec 2021 08:19:34 +0800

Cluster analysis allows us to find groups of similar samples or features, with stronger correlations among these objects. Common uses include grouping samples by different gene expression profiles or grouping genes by classifications of different samples.

Four Plotting Tips in Python

Mon, 13 Dec 2021 18:25:17 +0800

When visualizing data, it’s common to plot multiple charts in a single figure. For example, visualizing the same variable from different perspectives like side-by-side histograms and boxplots for numerical variables is useful.

Calculating the Gini Coefficient and Plotting the Lorenz Curve with matplotlib

Sun, 31 May 2020 19:22:50 +0800

The Gini coefficient and Lorenz curve are widely used to represent data inequality, especially wealth inequality. However, currently in Python, there isn’t a very good function to directly plot the Lorenz curve. Since the current project requires it, this article records how to use numpy, pandas, matplotlib, and other packages to calculate the Gini coefficient and plot the Lorenz curve for practical use.

Drawing the Butterfly Curve with Python

Wed, 07 Nov 2018 08:03:32 +0000

The butterfly curve, discovered by Temple H. Fay, is a beautiful curve that can be expressed using a polar coordinate function. Because of its elegance, I wanted to use it as my blog’s favicon.ico. Here, I’ll use Python’s matplotlib.pyplot package to draw the desired butterfly curve. First, let’s admire the beautiful butterfly curve.

1. First, We Need to Define the Mathematical Expression of the Butterfly Curve

It can also be expressed using spherical coordinates:

2. Choosing `matplotlib.pyplot` as the Plotting Tool in Python

1. First, import the necessary Python packages

import numpy as np
import matplotlib.pyplot as plt

2. Set the parameter values

t = np.arange(0.0, 12*np.pi, 0.01)
x = np.sin(t)*(np.e**np.cos(t) - 2*np.cos(4*t)-np.sin(t/12)**5)
y = np.cos(t)*(np.e**np.cos(t) - 2*np.cos(4*t)-np.sin(t/12)**5)

3. According to the formula, use `numpy` functions with `plt` to draw the required image

plt.figure(figsize=(8,6))
plt.axis('off')
plt.plot(x,y,color='blue',linewidth = '2')
#plt.show()
plt.savefig("butter.jpg",dpi=400)

4. Use `Pillow` to resize the image to an appropriate size for a favicon

from PIL import Image
im = Image.open("butter.jpg")
favicon = im.resize((50,50))
favicon.save("favicon.ico")