Plotly & Cufflinks | A Data Visualisation Library with Modern Features | Python | Data Science | Data Visualisation

Plotly

Plotly gives you lots of interactive and dynamic data visualisation & UI tools for data science, Machine Learning & Engineering. It has some great features which we will discuss in this post. So, be ready for the tutorial.

Installation Guide on your Machine:

For Plotly put this on your terminal and run.

pip install plotly
or
conda install -c plotly plotly

Cufflinks

For using Plotly library we have to configure it with the pandas, so the cufflinks library giver us a privilege to do it. Because the Plotly is built on the top of d3.js.

Installation Guide on your Machine:

For cufflinks put this on your terminal and run.

pip install cufflinks
or
conda install -c conda-forge cufflinks-py

Let's begin...

Import all the Libraries needed.

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

Line 1: Importing Pandas for making data frame.

Line 2: Importing Numpy, will help us to do some maths.

Line 3: Importing cufflinks for connecting pandas and Plotly.

Line 4: From Plotly's offline version we're importing four classes which we will use to make some interactive visualisations.

init_notebook_mode(connected = True)

Line 5: It will connect your notebook to your javascript, it will allow your notebook to access visualisations.

cf.go_offline()

Line 6: It will allow your cufflinks, to go offline. 

df1=pd.DataFrame(np.random.randn(50,5),columns='A B C D E'.split())
print(df1.head())

Line 7: Making a data frame, with random numbers, of size 100x5, naming columns as A, B, C, D, E. Let's have a look at the data.

Line 8: Printing that Data frame.

OUTPUT: df1.head()

df2=pd.DataFrame({'Category':['A','B','C'],'Values':[43,21,34]})
print(df2)

Line 9: Creating a data frame with the help of a dictionary. Let's have a look at the data.

Line 10: Printing that data frame.

OUTPUT: df2

print(df1.plot())

Line Graph

Line 11: Plotting the line graph with the help of pandas' inbuilt method.

OUTPUT: df1.plot()

This plot shows you the line plot of ABCDE, and you have a mess all around your screen. It is very hard to read. Here the concept of Plotly comes which will give a number of useful options to do with your graph.

adv_plot=df.iplot()
print(adv_plot)

Line 12: *Important* By using plot() method we can get an advance control per the graph like you can zoom in a selected area to see the insight of this mess. You can hide/unhide any line from the legend window. You can hover your cursor over this graph, you can compare any real-time values at any point and whatnot. You can even download the graph. You can easily observe from the plot below.

OUTPUT: adv_plot

Scatter Plot

scatter_plot = df.iplot(kind='scatter',x='A',y='C',mode='markers',theme='space')
print(scatter_plot)

Line 13: Printing a scatter plot, by Plotly. You can change the type of kind to print different types of plot. The available values of 'kind' are { 'scatter', 'bar',  'box',  'spread',  'ratio',  'heatmap', 'surface',  'histogram',  'bubble',  'bubble3d',  'scatter3d',   'scattergeo',  'ohlc',  'candle',  'pie', 'choroplet' }. You can also change the themes as per your requirement. Available themes are: [ 'ggplot', 'pearl', 'solar', 'space', 'white', 'polar', 'henanigans' ].

Line 14: Printing the plot.

OUTPUT: scatter_plot

box_plot =  df.iplot(kind='box',theme='pearl')
print(box_plot)

Line 15: Making a box plot.

Line 16: Printing the box plot.

OUTPUT: box_plot

Surface 3D Plot

df3=pd.DataFrame({'x':[1,2,3,4,5],'y':[10,50,30,20,30],'z':[500,100,600,300,200]})
3D_plot = df3.iplot(kind='surface', colorscale='paired' )
print(3D_plot)

Line 17: Making a data frame from dictionary of three columns for the 3D plot.

Line 18: With the help of 'iplot', a surface 3d plot is assigned to 3D_plot.

Line 19: Printing 3D plot.

OUTPUT: 3D_plot

Spread plot

spread_plot = df[['B','C','D']].iplot(kind='spread')
print(spread_plot)

Line 20: A spread plot is a plot which shows you the spread between multiple values, at every single point in your data set, here is the code for spread plot.

Line 21: Printing a spread plot.

OUTPUT: spread_plot

Bubble Plot

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

Line 22: Bubble plot is same as the scatter plot but in this case, the size of bubbles varies with respect to the other column. Let's say for example, here size='C'

Line 23: Printing the bubble plot.

OUTOUT: bubble+plot

Conclusion:

For this tutorial, we are stopping here, but the potty itself has lots of features and it can make a lot more plots. They all are can't be covered in a single tutorial, so go and check out the documentation for more. The aim of this tutorial was to make you enough aware of this type of library.

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