Drawing a ridgeline graph with Plotly

logo of a chart:Joyplot

A Ridgelineplot (formerly called a Joyplot) allows to study the distribution of a numeric variable for several groups. Throughout the following example, we will consider average temperatures in Seattle between 1950 and 2010 and we will show how to visualize their distribution.

To do so, we will use Plotly's amazing Python graphing library. As no function enables us to directly plot a ridgeline, we will make extensive use of plotly.graph_objects library that enables us to add traces to a figure.

# getting necessary libraries
import plotly.graph_objects as go
import numpy as np
import pandas as pd

# getting the data
temp = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2016-weather-data-seattle.csv') # we retrieve the data from plotly's GitHub repository
temp['year'] = pd.to_datetime(temp['Date']).dt.year # we store the year in a separate column

# Since we do not want to plot 50+ lines, we only select some years to plot
year_list = [1950, 1960, 1970, 1980, 1990, 2000, 2010]
temp = temp[temp['year'].isin(year_list)]

# as we expect to plot histograms-like plots for each year, we group by year and mean temperature and aggregate with 'count' function
temp = temp.groupby(['year', 'Mean_TemperatureC']).agg({'Mean_TemperatureC': 'count'}).rename(columns={'Mean_TemperatureC': 'count'}).reset_index()
# you can have a look at what the data looks like at this point
year Mean_TemperatureC count
0 1950 -12.0 1
1 1950 -11.0 1
2 1950 -10.0 2
3 1950 -9.0 6
4 1950 -8.0 4
... ... ... ...
199 2010 22.0 4
200 2010 23.0 3
201 2010 24.0 2
202 2010 26.0 2
203 2010 27.0 2

204 rows × 3 columns

# the idea behind this ridgeline plot with Plotly is to add traces manually, each trace corresponding to a particular year's temperature distribution
# thus, we are to store each year's data (temperatures and their respective count) in seperate arrays or pd.series that we store in a dictionnary to retrieve them easily
array_dict = {} # instantiating an empty dictionnary
for year in year_list:
    array_dict[f'x_{year}'] = temp[temp['year']==year]['Mean_TemperatureC'] # storing the temperature data for each year
    array_dict[f'y_{year}'] = temp[temp['year']==year]['count'] # storing the temperature count for each year
    array_dict[f'y_{year}'] = (array_dict[f'y_{year}'] - array_dict[f'y_{year}'].min()) \
                                / (array_dict[f'y_{year}'].max() - array_dict[f'y_{year}'].min()) # we normalize the array (min max normalization)

# once all of this is done, we can create a plotly.graph_objects.Figure and add traces with fig.add_trace() method
# since we have stored the temperatures and their respective count for each year, we can plot scatterplots (go.Scatter)
# we thus iterate over year_list and create a 'blank line' that is placed at y = index, then the corresponding temperature count line
fig = go.Figure()
for index, year in enumerate(year_list):
                            x=[-20, 40], y=np.full(2, len(year_list)-index),
                            y=array_dict[f'y_{year}'] + (len(year_list)-index) + 0.4,
    # plotly.graph_objects' way of adding text to a figure

# here you can modify the figure and the legend titles
                title='Average temperature from 1950 until 2010 in Seattle',
                xaxis=dict(title='Temperature in degree Celsius'),
                yaxis=dict(showticklabels=False) # that way you hide the y axis ticks labels


Note that you can save this chart to a standalone file thanks to the write.html() function. It is then possible to render it in any html document using an <iframe>

# If you need to save this file as a standalong html file:
<iframe src="../../interactiveCharts/ridgeline-graph-plotly.html" width="800" height="600" title="ridgeline chart with plotly" style="border:none"></iframe>

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👋 This document is a work by Yan Holtz. You can contribute on github, send me a feedback on twitter or subscribe to the newsletter to know when new examples are published! 🔥

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