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Parallel Plot for Cateogrical and Continuous variable by Plotly Express

Comments

Goal

This post aims to introduce how to draw Parallel Plot for categorical and continuous variable by Plotly Express image

Reference

Libraries

In [19]:
import pandas as pd
import plotly.express as px
import plotly.io as pio
pio.renderers.default = "png"

Create continuous data

In [4]:
df = px.data.election()
df.head()
Out[4]:
district Coderre Bergeron Joly total winner result
0 101-Bois-de-Liesse 2481 1829 3024 7334 Joly plurality
1 102-Cap-Saint-Jacques 2525 1163 2675 6363 Joly plurality
2 11-Sault-au-Récollet 3348 2770 2532 8650 Coderre plurality
3 111-Mile-End 1734 4782 2514 9030 Bergeron majority
4 112-DeLorimier 1770 5933 3044 10747 Bergeron majority

Visualize Parallel Plot for continuous data

In [20]:
fig = px.parallel_coordinates(df,color='total', color_continuous_scale=px.colors.sequential.Inferno)
fig

Create categorical data

In [9]:
df = px.data.election()
df.head()
Out[9]:
district Coderre Bergeron Joly total winner result
0 101-Bois-de-Liesse 2481 1829 3024 7334 Joly plurality
1 102-Cap-Saint-Jacques 2525 1163 2675 6363 Joly plurality
2 11-Sault-au-Récollet 3348 2770 2532 8650 Coderre plurality
3 111-Mile-End 1734 4782 2514 9030 Bergeron majority
4 112-DeLorimier 1770 5933 3044 10747 Bergeron majority

Visualize Parallel for continuous data

In [21]:
fig = px.parallel_categories(df, color="total", color_continuous_scale=px.colors.sequential.Inferno)
fig

Visualization Samples by Plotly Express

Comments

Goal

This post aims to introduce examples of visualization by Plotly Express.

The followings are introduced:

  • Prepared example data
  • Scatter plot
    • basic
    • basic + size
    • basic + size + color
    • basic + size + color + time
    • heatmap + histogram

2019-07-19 21-54-58 2019-07-19 21_56_00_gapminder_plotly_express

Reference

Libraries

In [8]:
import pandas as pd
import numpy as np
import plotly_express as px
import plotly.io as pio
pio.renderers.default = "png"

Load a prepared data

Car Share

In [2]:
df = px.data.carshare()
df.head()
Out[2]:
centroid_lat centroid_lon car_hours peak_hour
0 45.471549 -73.588684 1772.750000 2
1 45.543865 -73.562456 986.333333 23
2 45.487640 -73.642767 354.750000 20
3 45.522870 -73.595677 560.166667 23
4 45.453971 -73.738946 2836.666667 19

Tips

In [3]:
df = px.data.tips()
df.head()
Out[3]:
total_bill tip sex smoker day time size
0 16.99 1.01 Female No Sun Dinner 2
1 10.34 1.66 Male No Sun Dinner 3
2 21.01 3.50 Male No Sun Dinner 3
3 23.68 3.31 Male No Sun Dinner 2
4 24.59 3.61 Female No Sun Dinner 4

Election

In [4]:
df = px.data.election()
df.head()
Out[4]:
district Coderre Bergeron Joly total winner result
0 101-Bois-de-Liesse 2481 1829 3024 7334 Joly plurality
1 102-Cap-Saint-Jacques 2525 1163 2675 6363 Joly plurality
2 11-Sault-au-Récollet 3348 2770 2532 8650 Coderre plurality
3 111-Mile-End 1734 4782 2514 9030 Bergeron majority
4 112-DeLorimier 1770 5933 3044 10747 Bergeron majority

Wind

In [5]:
df = px.data.wind()
df.head()
Out[5]:
direction strength frequency
0 N 0-1 0.5
1 NNE 0-1 0.6
2 NE 0-1 0.5
3 ENE 0-1 0.4
4 E 0-1 0.4

Gap Minder

In [6]:
df = px.data.gapminder()
df.head()
Out[6]:
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

Scatter Plot

Basic Scatter plot

In [10]:
px.scatter(df, x='gdpPercap', y='lifeExp', width=900, height=400)

Scatter plot + size

In [11]:
px.scatter(df, x='gdpPercap', y='lifeExp', size='pop', width=900, height=400)

Scatter plot + size + color

In [12]:
px.scatter(df, x='gdpPercap', y='lifeExp', size='pop', color='country', width=900, height=400)

Scatter plot + size + color + time

In [13]:
px.scatter(df, x='gdpPercap', y='lifeExp', size='pop', color='country', animation_frame='year', width=900, height=400)
In [14]:
px.density_heatmap(df, x="gdpPercap", y="lifeExp", marginal_y="histogram", marginal_x="histogram")