Plotting graphs using Python's plotly and cufflinks module

plotly is a Python library which is used to design graphs, especially interactive graphs. It can plot various graphs and charts like histogram, barplot, boxplot, spreadplot and many more. It is mainly used in data analysis as well as financial analysis. plotly is an interactive visualization library.

cufflinks connects plotly with pandas to create graphs and charts of dataframes directly.

choropleth is used to describe geographical plotting of USA. choropleth is used in the plotting of world maps and many more. Let's plot different types of plots like boxplot, spreadplot, etc. using plotly and cufflinks.

Command to install plotly:

pip install cufflinks plotly                                                                                                                                            

Command to install cufflinks:

pip install plotly                                       

Code #1:

Show dataframe

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

%matplotlib inline
from plotly import __version__
import cufflinks as cf

from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot

# to get the connection
init_notebook_mode(connected=True)

# plotly also serves online,
# but we are using just a sample
cf.go_offline()

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

df2 = pd.DataFrame({'Category': ['A', 'B', 'C'], 'Values': [32, 43, 50]})
df2.head()

Output:

Code #2:

Normal Plot

# plotly function
df.iplot()

Output:

Code #3:

Scatter Plot

# markers are made to point in the graph
df.iplot(kind ='scatter', x ='A', y ='B', mode ='markers')

Output:

Code #4:

Box Plot

# boxplot
df.iplot(kind ='box')

Output:

Code #5:

Plot dataframes

# creating dataframe with three axes
df3 = pd.DataFrame({'x':[1, 2, 3, 4, 5],
                    'y':[10, 20, 30, 20, 10],
                    'z':[5, 4, 3, 2, 1]})

Output:

Code #6:

Surface plot

# surface plot
# colorscale:red(rd), yellow(yl), blue(bu)
df3.iplot(kind ='surface', colorscale ='rdylbu')

Output: