As with Plotly, no Seaborn function enables us to directly plot a ridgeline. In order to do so, we inspired from this example displayed in Seaborn documentation, that makes use of a Seaborn FacetGrid object with kdeplots to generate a ridgeline graph. We adapted the data as well as some lines of code. Feel free to investigate by yourself how to customize this graph further!

# getting necessary libraries
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_theme(style="white", rc={"axes.facecolor": (0, 0, 0, 0)})

# getting the data
temp = pd.read_csv('') # we retrieve the data from plotly's GitHub repository
temp['month'] = pd.to_datetime(temp['Date']).dt.month # we store the month in a separate column

# we define a dictionnary with months that we'll use later
month_dict = {1: 'january',
              2: 'february',
              3: 'march',
              4: 'april',
              5: 'may',
              6: 'june',
              7: 'july',
              8: 'august',
              9: 'september',
              10: 'october',
              11: 'november',
              12: 'december'}

# we create a 'month' column
temp['month'] = temp['month'].map(month_dict)

# we generate a pd.Serie with the mean temperature for each month (used later for colors in the FacetGrid plot), and we create a new column in temp dataframe
month_mean_serie = temp.groupby('month')['Mean_TemperatureC'].mean()
temp['mean_month'] = temp['month'].map(month_mean_serie)

At this point, you can have a look at what the dataframe looks like

Date Max_TemperatureC Mean_TemperatureC Min_TemperatureC month mean_month
0 1/1/1948 10 8.0 7.0 january 4.493982
1 1/2/1948 6 4.0 3.0 january 4.493982
2 1/3/1948 7 4.0 2.0 january 4.493982
3 1/4/1948 7 4.0 2.0 january 4.493982
4 1/5/1948 7 3.0 0.0 january 4.493982

Eventually, we generate the ridgeline plot by first instantiating a Seaborn FacetGrid object.

# we generate a color palette with Seaborn.color_palette()
pal = sns.color_palette(palette='coolwarm', n_colors=12)

# in the sns.FacetGrid class, the 'hue' argument is the one that is the one that will be represented by colors with 'palette'
g = sns.FacetGrid(temp, row='month', hue='mean_month', aspect=15, height=0.75, palette=pal)

# then we add the densities kdeplots for each month, 'Mean_TemperatureC',
      bw_adjust=1, clip_on=False,
      fill=True, alpha=1, linewidth=1.5)

# here we add a white line that represents the contour of each kdeplot, 'Mean_TemperatureC', 
      bw_adjust=1, clip_on=False, 
      color="w", lw=2)

# here we add a horizontal line for each plot, y=0,
      lw=2, clip_on=False)

# we loop over the FacetGrid figure axes (g.axes.flat) and add the month as text with the right color
# notice how ax.lines[-1].get_color() enables you to access the last line's color in each matplotlib.Axes
for i, ax in enumerate(g.axes.flat):
    ax.text(-15, 0.02, month_dict[i+1],
            fontweight='bold', fontsize=15,
# we use matplotlib.Figure.subplots_adjust() function to get the subplots to overlap

# eventually we remove axes titles, yticks and spines
g.despine(bottom=True, left=True)

plt.setp(ax.get_xticklabels(), fontsize=15, fontweight='bold')
plt.xlabel('Temperature in degree Celsius', fontweight='bold', fontsize=15)
g.fig.suptitle('Daily average temperature in Seattle per month',

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