Intro to Plotly Express¶

In this notebook I’ll try to explore gapminder dataset using interactive data visualization library called Plotly. About the Dataset: Data Source

Import Libraries¶

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px # for visualization 
import plotly.offline as py 
import plotly.graph_objs as go 
from plotly.figure_factory import create_table # for creating nice table 

Loading the Dataset¶

# load built-in gapminder dataset from plotly 
gapminder = px.data.gapminder() 

# examine first few rows 
gapminder.head() 

	country	continent	year	lifeExp	pop	gdpPercap	iso_alpha	iso_num
0	Afghanistan	Asia	1952	28.801	8425333	779.445314	AFG	4
1	Afghanistan	Asia	1957	30.332	9240934	820.853030	AFG	4
2	Afghanistan	Asia	1962	31.997	10267083	853.100710	AFG	4
3	Afghanistan	Asia	1967	34.020	11537966	836.197138	AFG	4
4	Afghanistan	Asia	1972	36.088	13079460	739.981106	AFG	4

Creating a Table¶

# create a publication quality table 
table = create_table(gapminder.head(10))
py.iplot(table)

Quick Visualizations with Bar Charts¶

# filtering data for Canada and store into a variable called canada_data 
canada_data = px.data.gapminder().query("country == 'Canada' ")

# create bar charts 
fig = px.bar(canada_data, x='year', y='pop', height=400)
fig.show() 

# let's add color by lifeExp and other parameters 
fig = px.bar(canada_data, x='year', y='pop', color='lifeExp', labels={'pop': 'Population of Canada'}, height=400)
fig.show() 

Plot Life Expectency vs GDP Per Capita¶

# filter 2007 data only from dataset 
gapminder2007 = gapminder.query('year == 2007')

# create scatter plot 
fig = px.scatter(gapminder2007, x='gdpPercap', y='lifeExp')
fig.show() 

# color by continent 
fig = px.scatter(gapminder2007, x='gdpPercap', y='lifeExp', color='continent')
fig.show() 

Create Interactive Bubble Charts¶

# create a bubble chart 
fig = px.scatter(gapminder2007, x='gdpPercap', y='lifeExp', color='continent', size='pop', size_max=60)
fig.show() 

# hover name 
fig = px.scatter(gapminder2007, x='gdpPercap', y='lifeExp', color='continent', size='pop', size_max=60, 
                 hover_name='country')
fig.show() 

Represent Geographic Data as Animated Maps¶

# create a map using line_geo()
fig = px.line_geo(gapminder.query('year == 2007'), locations='iso_alpha', color='continent', projection='orthographic')
fig.show() 

# create a map using choropleth
fig = px.choropleth(gapminder, locations='iso_alpha', color='lifeExp', hover_name='country', 
                    animation_frame='year', color_continuous_scale=px.colors.sequential.Plasma, projection='natural earth')
fig.show() 

10: Using Plotly Template in Any Graphs¶

# print available themes or template 
import plotly.io as pio
pio.templates

Templates configuration
-----------------------
    Default template: 'plotly'
    Available templates:
        ['ggplot2', 'seaborn', 'simple_white', 'plotly',
         'plotly_white', 'plotly_dark', 'presentation', 'xgridoff',
         'ygridoff', 'gridon', 'none']

# let's use plotly_dark in our previous bar chart 
fig = px.bar(canada_data, x='year', y='pop', color='lifeExp', labels={'pop': 'Population of Canada'},
             height=400, template='plotly_dark')
fig.show()

# seaborn
fig = px.bar(canada_data, x='year', y='pop', color='lifeExp', labels={'pop': 'Population of Canada'},
             height=400, template='seaborn')
fig.show()

# ggplot2 
fig = px.bar(canada_data, x='year', y='pop', color='lifeExp', labels={'pop': 'Population of Canada'},
             height=400, template='ggplot2')
fig.show()

Overview of Plotly Introduction to Cufflinks