Libraries
For creating this chart, we will need to load the following libraries:
- pandas for data manipulation
- matplotlib for styling the chart
- seaborn for creating the chart
numpyfor creating the data
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as snsDataset
Since our goal is to create a simple density chart, we only need one numerical variable:
x = np.random.normal(10, 3, 1000)
df = pd.DataFrame({'x': x})Default plot
Let's start by creating a figure with a simple density plot
fig, ax = plt.subplots(figsize=(8, 6))
sns.kdeplot(df['x'], color='grey', ax=ax, shade=True)
plt.show()
Add median line
fig, ax = plt.subplots(figsize=(8, 6))
sns.kdeplot(df['x'], color='grey', ax=ax, shade=True)
# add vertical line at median
median = df['x'].median()
ax.axvline(median, color='black', linestyle='--')
plt.show()
Add quantiles
It can be interesting to add quantiles to a distribution chart in order to have a better understanding of the data distribution.
First we have to compute the quantiles using the np.percentile() function from numpy. Then we can add them to the chart using the axvline() function.
fig, ax = plt.subplots(figsize=(8, 6))
sns.kdeplot(df['x'], color='grey', ax=ax, shade=True)
# add vertical line at median
median = df['x'].median()
plt.axvline(median, color='black', linestyle='--')
# compute quantiles
quantiles_to_compute = [5, 25, 75, 95]
quantiles = np.percentile(
df['x'],
quantiles_to_compute
)
quantiles = quantiles.tolist()
# add small vertical lines at the quartiles
for quantile in quantiles:
ax.axvline(
quantile, # position on the x-axis
color='black', # color of the line
ymax=0.1 # 10% of the plot height
)
plt.show()
Fill between quantiles
It is also possible to fill the area between quantiles in order to highlight a specific area of the distribution.
This can be done using the fill_between() function from matplotlib that needs the following arguments:
- the x values (quantiles)
- the y values (height of the rectangle)
- the color of the rectangle
fig, ax = plt.subplots(figsize=(8, 6))
sns.kdeplot(df['x'], color='grey', ax=ax, shade=True)
# compute quantiles
quantiles_to_compute = [5, 25, 40, 60, 75, 95]
quantiles = np.percentile(
df['x'],
quantiles_to_compute
)
quantiles = quantiles.tolist()
darkgreen = '#9BC184'
midgreen = '#C2D6A4'
lightgreen = '#E7E5CB'
colors = [lightgreen, midgreen, darkgreen, midgreen, lightgreen]
for i in range(len(quantiles) - 1):
ax.fill_between(
[quantiles[i], # lower bound
quantiles[i+1]], # upper bound
0, # start from 0 on the y-axis
0.01, # height of the colored area
color=colors[i]
)
plt.show()
Annotations
When adding quantiles to a distribution chart, it's not necessary obvious to know what they represent. It can be useful to add annotations to the chart in order to explicitly show the value of each quantile.
For this, we use the text() function from matplotlib that needs the following arguments:
- the x position of the annotation
- the y position of the annotation
- the text to display
- the horizontal alignment of the text
- the font size of the text
fig, ax = plt.subplots(figsize=(6, 6))
sns.kdeplot(df['x'], color='grey', ax=ax, shade=True)
# compute quantiles
quantiles_to_compute = [5, 25, 50, 75, 95]
quantiles = np.percentile(
df['x'],
quantiles_to_compute
)
quantiles = quantiles.tolist()
# plot regions between quantiles
darkgreen = '#9BC184'
midgreen = '#C2D6A4'
lightgreen = '#E7E5CB'
colors = [lightgreen, midgreen, darkgreen, 'green']
for i in range(len(quantiles) - 1):
ax.fill_between(
[quantiles[i], # lower bound
quantiles[i+1]], # upper bound
0, # start from 0 on the y-axis
0.01, # height of the colored area
color=colors[i]
)
# annotate the quantiles
for i, quantile in enumerate(quantiles):
ax.text(
quantile, # x-coordinate
0.015, # y-coordinate
f'{quantiles_to_compute[i]}%', # text
horizontalalignment='center', # centered
fontsize=8, # small font size
)
plt.show()
Going further
This article explains how to create a density chart with quantiles using the seaborn library.
You might be interested in this beautiful density plot with quantiles and how to highlight as specific point
