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

We can pass various parameters to

Lets explore Distribution Plot by generating 150 random numbers.

import pandas as pd

import seaborn as sns

import matplotlib.pyplot as plt

%matplotlib inline

num = np.random.randn(150)

num

sns.distplot(num)

sns.distplot(num,

sns.distplot(num,

sns.distplot(num,

sns.distplot(num,

label_dist = pd.Series(num,

sns.distplot(label_dist)

sns.distplot(label_dist,

sns.distplot(label_dist,

You can download my Jupyter notebook from here. I recommend to also try above code with Tips and Iris dataset.

**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**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.

## No comments:

## Post a Comment