84 jointplot

jointplot¶

jointplot() allows you to basically match up two distplots for bivariate data. With your choice of what kind parameter to compare with:

• “scatter”
• “reg”
• “resid”
• “kde”
• “hex”

In [10]:

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

Out[10]:

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

In [11]:

sns.jointplot(x='total_bill',y='tip',data=tips)

Out[11]:

<seaborn.axisgrid.JointGrid at 0x8576c18>

In [12]:

sns.jointplot(x='total_bill',y='tip',data=tips,kind='scatter')

Out[12]:

<seaborn.axisgrid.JointGrid at 0x86d1470>

In [13]:

sns.jointplot(x='total_bill',y='tip',data=tips,kind='hex')

Out[13]:

<seaborn.axisgrid.JointGrid at 0xd94ec50>

In [14]:

sns.jointplot(x='total_bill',y='tip',data=tips,kind='reg')

Out[14]:

<seaborn.axisgrid.JointGrid at 0xdae20f0>

In [15]:

sns.jointplot(x='total_bill',y='tip',data=tips,kind='kde')

Out[15]:

<seaborn.axisgrid.JointGrid at 0xdc2e978>

In [16]:

sns.jointplot(x='total_bill',y='tip',data=tips,kind='resid')

Out[16]:

<seaborn.axisgrid.JointGrid at 0xdcb8b00>

In [ ]:

sns.jointplot(x='total_bill',y='tip',data=tips)

In [17]:

tips.head(1)

Out[17]:

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

In [21]:

sns.jointplot(x='size',y='total_bill',data=tips,kind="reg")

Out[21]:

<seaborn.axisgrid.JointGrid at 0xf1b7b38>