Introduction

A violin plot is used to display the distribution and probability density of multiple data groups. Similar to a box plot, it offers a better representation of data density. Violin plots are particularly useful when dealing with very large datasets that are difficult to display individually. Python’s Seaborn package makes it very convenient to create violin plots.

Parameters

The parameters corresponding to each position in a violin plot are shown above. The middle line represents the box plot data, specifically the 25th, 50th (median), and 75th percentiles. The thin lines indicate the 95% confidence interval.

Drawing Violin Plots with Seaborn

Single Variable Data

While a box plot would suffice for a single variable, a violin plot can certainly be used as well:

    import seaborn as sns
    sns.set(color_codes=True)
    sns.set_style("white")
    df = sns.load_dataset('iris')
    sns.violinplot( y=df["sepal_length"] )

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In python, it is common to need to copy specific objects, and you might encounter various bugs because understanding the difference between these three operations is key: assignment, shallow copy, and deep copy.

Assignment (=), shallow copy (copy), and deep copy (deepcopy) are relatively easy to distinguish regarding assignment vs. copying, but shallow copy and deep copy are harder to differentiate.

The assignment statement does not copy the object; it simply binds the variable to the object. Any change to one object will affect the other. Copying allows you to change one object without affecting the other.

The difference between shallow and deep copy is that shallow copy does not affect the other object when values change, but adding or deleting elements can affect it. Deep copy creates a completely independent object, and changes to one will not affect the other.

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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")

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