86 rugplot and kdeplot

https://vlrtraining.com/courses/python-data-science-beginner-tutorial

In [1]:

import seaborn as sns
%matplotlib inline
tips = sns.load_dataset('tips')
tips.head(1)

Out[1]:

	total_bill	tip	sex	smoker	day	time	size
0	16.99	1.01	Female	No	Sun	Dinner	2

rugplot¶

rugplots are actually a very simple concept, they just draw a dash mark for every point on a univariate distribution. They are the building block of a KDE plot:

In [2]:

sns.rugplot(tips['total_bill'])

Out[2]:

<matplotlib.axes._subplots.AxesSubplot at 0xb262128>

In [4]:

sns.distplot(tips['total_bill'])

Out[4]:

<matplotlib.axes._subplots.AxesSubplot at 0xba83438>

kdeplot¶

kdeplots are Kernel Density Estimation plots. These KDE plots replace every single observation with a Gaussian (Normal) distribution centered around that value. For example:

In [4]:

# Don't worry about understanding this code!
# It's just for the diagram below
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats

#Create dataset
dataset = np.random.randn(25)

# Create another rugplot
sns.rugplot(dataset);

# Set up the x-axis for the plot
x_min = dataset.min() - 2
x_max = dataset.max() + 2

# 100 equally spaced points from x_min to x_max
x_axis = np.linspace(x_min,x_max,100)

# Set up the bandwidth, for info on this:
url = 'http://en.wikipedia.org/wiki/Kernel_density_estimation#Practical_estimation_of_the_bandwidth'

bandwidth = ((4*dataset.std()**5)/(3*len(dataset)))**.2


# Create an empty kernel list
kernel_list = []

# Plot each basis function
for data_point in dataset:
    
    # Create a kernel for each point and append to list
    kernel = stats.norm(data_point,bandwidth).pdf(x_axis)
    kernel_list.append(kernel)
    
    #Scale for plotting
    kernel = kernel / kernel.max()
    kernel = kernel * .4
    plt.plot(x_axis,kernel,color = 'grey',alpha=0.5)

plt.ylim(0,1)

Out[4]:

(0, 1)

In [5]:

# To get the kde plot we can sum these basis functions.

# Plot the sum of the basis function
sum_of_kde = np.sum(kernel_list,axis=0)

# Plot figure
fig = plt.plot(x_axis,sum_of_kde,color='indianred')

# Add the initial rugplot
sns.rugplot(dataset,c = 'indianred')

# Get rid of y-tick marks
plt.yticks([])

# Set title
plt.suptitle("Sum of the Basis Functions")

Out[5]:

Text(0.5,0.98,'Sum of the Basis Functions')

In [6]:

sns.kdeplot(tips['tip'])
sns.rugplot(tips['tip'])

Out[6]:

<matplotlib.axes._subplots.AxesSubplot at 0x7d323c8>

In [6]:

tips.head(1)

Out[6]:

	total_bill	tip	sex	smoker	day	time	size
0	16.99	1.01	Female	No	Sun	Dinner	2

In [7]:

sns.kdeplot(tips['total_bill'])
sns.rugplot(tips['total_bill'])

Out[7]:

<matplotlib.axes._subplots.AxesSubplot at 0xbdd15c0>

In [ ]:

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