Plotly - plotting with Pandas¶

Get cufflinks¶

Cufflinks integrates plotly with pandas to allow plotting right from pandas dataframes. Install using pip

pip install cufflinks

In [1]:

import numpy as np
import pandas as pd
import cufflinks as cf
from plotly.offline import download_plotlyjs, init_notebook_mode
from plotly.offline import plot, iplot

#set notebook mode
init_notebook_mode(connected=True)
cf.go_offline()

import numpy as np import pandas as pd import cufflinks as cf from plotly.offline import download_plotlyjs, init_notebook_mode from plotly.offline import plot, iplot #set notebook mode init_notebook_mode(connected=True) cf.go_offline()

IOPub data rate exceeded.
The notebook server will temporarily stop sending output
to the client in order to avoid crashing it.
To change this limit, set the config variable
`--NotebookApp.iopub_data_rate_limit`.

In [2]:

df = pd.DataFrame(np.random.randn(100,4), 
                  columns='A B C D'.split(' '))
df.head()

df = pd.DataFrame(np.random.randn(100,4), columns='A B C D'.split(' ')) df.head()

Out[2]:

	A	B	C	D
0	-1.539048	-0.876097	-0.236866	-0.591233
1	-0.347391	0.584317	1.223430	0.269432
2	0.342396	-1.198989	0.692799	0.451392
3	-2.244973	0.979081	-0.896566	-0.114954
4	-1.358436	1.246352	1.182089	-1.072197

In [3]:

df2 = pd.DataFrame({'category':['A','B','C'], 'values':[33,56,67]})
df2

df2 = pd.DataFrame({'category':['A','B','C'], 'values':[33,56,67]}) df2

Out[3]:

	category	values
0	A	33
1	B	56
2	C	67

interactive plotting¶

line plots¶

With Plotly, you can turn on and off data values by clicking on the legend

In [4]:

df.iplot()

df.iplot()

bar plot¶

In [5]:

df2.iplot(kind='bar')

df2.iplot(kind='bar')

box plot¶

In [6]:

df.iplot(kind='box')

df.iplot(kind='box')

surface plot¶

In [7]:

df3 = pd.DataFrame({'x':[1,2,3,4,5],
                   'y':[11,22,33,44,55],
                    'z':[5,4,3,2,1]})
df3

df3 = pd.DataFrame({'x':[1,2,3,4,5], 'y':[11,22,33,44,55], 'z':[5,4,3,2,1]}) df3

Out[7]:

	x	y	z
0	1	11	5
1	2	22	4
2	3	33	3
3	4	44	2
4	5	55	1

In [8]:

df3.iplot(kind='surface')

df3.iplot(kind='surface')

histograms¶

In [9]:

df.iplot(kind='hist',bins=50)

df.iplot(kind='hist',bins=50)

spread plots¶

Used to show the spread in data value between two columns / variables.

In [12]:

df[['A','B']].iplot(kind='spread')

df[['A','B']].iplot(kind='spread')

bubble scatter plots¶

same as scatter, but you can easily size the dots by another column

In [16]:

df.iplot(kind='bubble',x='A', y='B', size='C')

df.iplot(kind='bubble',x='A', y='B', size='C')

scatter matrix¶

This is similar to seaborn's pairplot

In [17]:

df.scatter_matrix()

df.scatter_matrix()