Lets visualize our data with Distribution Plot which is present in Seaborn library. By default, Distribution Plot uses Histogram and KDE (Kernel Density Estimate). We can specify number of bins to the histogram as per our requirement. Please note that Distribution Plot is a univariate plot.
We can pass various parameters to distplot like bins, hist, kde, rug, vertical, color etc.
Lets explore Distribution Plot by generating 150 random numbers.
Step 1: Import required libraries
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
Step 2: Generate 150 random numbers
num = np.random.randn(150)
num
Step 3: Explore data using Distribution Plot
sns.distplot(num)
Specify number of bins
sns.distplot(num, bins=20)
Remove histogram from distribution plot
sns.distplot(num, hist=False)
Remove KDE from distribution plot
sns.distplot(num, kde=False)
Add rug parameter to distribution plot
sns.distplot(num, hist=False, rug=True)
Add label to distribution plot
label_dist = pd.Series(num, name=”variable x”)
sns.distplot(label_dist)
Change orientation of distribution plot
sns.distplot(label_dist, vertical=True)
Add cosmetic parameter: color
We can pass various parameters to distplot like bins, hist, kde, rug, vertical, color etc.
Lets explore Distribution Plot by generating 150 random numbers.
Step 1: Import required libraries
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
Step 2: Generate 150 random numbers
num = np.random.randn(150)
num
Step 3: Explore data using Distribution Plot
sns.distplot(num)
Specify number of bins
sns.distplot(num, bins=20)
Remove histogram from distribution plot
sns.distplot(num, hist=False)
Remove KDE from distribution plot
sns.distplot(num, kde=False)
Add rug parameter to distribution plot
sns.distplot(num, hist=False, rug=True)
Add label to distribution plot
label_dist = pd.Series(num, name=”variable x”)
sns.distplot(label_dist)
Change orientation of distribution plot
sns.distplot(label_dist, vertical=True)
Add cosmetic parameter: color
sns.distplot(label_dist, color='red')
You can download my Jupyter notebook from here. I recommend to also try above code with Tips and Iris dataset.
