Visualising univariate data using seaborn

In [1]:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from mpl_toolkits.basemap import Basemap
%matplotlib inline
import warnings
warnings.filterwarnings('ignore')
%config InlineBackend.figure_format = 'retina'

In [4]:

cars = pd.read_csv("http://web.pdx.edu/~gerbing/data/cars.csv")

In [9]:

cars.columns = ["model","mpg","cyl","engine","hp","weight","accelerate","year","origin"]

In [10]:

cars.head()

Out[10]:

	model	mpg	cyl	engine	hp	weight	accelerate	year	origin
0	amc ambassador dpl	15.0	8	390.0	190	3850	8.5	70	American
1	amc gremlin	21.0	6	199.0	90	2648	15.0	70	American
2	amc hornet	18.0	6	199.0	97	2774	15.5	70	American
3	amc rebel sst	16.0	8	304.0	150	3433	12.0	70	American
4	buick estate wagon (sw)	14.0	8	455.0	225	3086	10.0	70	American

Strip plots¶

(Single continuous data vs categorical data)

In [16]:

plt.figure(figsize=(10,8))
plt.subplot(2,1,1)
sns.stripplot(x='cyl', y='hp', data=cars)

Out[16]:

<matplotlib.axes._subplots.AxesSubplot at 0x123de9828>

In [17]:

plt.figure(figsize=(10,8))
plt.subplot(2,1,2)
sns.stripplot(x='cyl', y='hp', data=cars, jitter=True, size=5)

Out[17]:

<matplotlib.axes._subplots.AxesSubplot at 0x1240b2e10>

Swarm plots¶

• Single continuous data vs categorical data
• It spreads out datapoint for better visualization

In [19]:

plt.figure(figsize=(10,8))
plt.subplot(2,1,1)
sns.swarmplot(x='cyl', y='hp', data=cars)

Out[19]:

<matplotlib.axes._subplots.AxesSubplot at 0x124395cc0>

In [22]:

plt.figure(figsize=(10,10))
plt.subplot(2,1,2)
sns.swarmplot(x='hp', y='cyl', data=cars, hue='origin', orient='h')

Out[22]:

<matplotlib.axes._subplots.AxesSubplot at 0x124aced30>

Both Strip plots and Swarm plots are meaningless(overplotting) for visualizing a large dataset.

Violin plots¶

• Similar to box plots

In [26]:

plt.figure(figsize=(10,10))
plt.subplot(2,1,1)
sns.violinplot(x='cyl', y='hp', data=cars)

Out[26]:

<matplotlib.axes._subplots.AxesSubplot at 0x125ae4208>

Combining strip plot and violin plot¶

In [27]:

plt.figure(figsize=(10,10))
plt.subplot(2,1,2)
sns.violinplot(x='cyl', y='hp', data=cars, inner=None, color='lightgray')
# Overlay a strip plot on the violin plot
sns.stripplot(x='cyl', y='hp', data=cars, jitter=True, size=1.5)

Out[27]:

<matplotlib.axes._subplots.AxesSubplot at 0x126752278>