Grouped Bar Chart with Multiple Markers
In this article, we will explore how to create a grouped bar chart with multiple markers using Python and the popular data visualization library, Matplotlib. We will also discuss how to align these markers with the bars and customize their appearance.
Introduction
A grouped bar chart is a type of bar chart that displays multiple groups or categories on the x-axis, with each group represented by a different color or marker. In this article, we will focus on creating a grouped bar chart with two sets of markers: one for the upper limit and another for the lower limit.
Problem Statement
The original code uses Matplotlib’s plot function to create a grouped bar chart with two sets of markers: one for each data point in df2 and df3. However, the markers are not aligned with the bars, which makes it difficult to read. We will explore alternative approaches to create a grouped bar chart with aligned markers.
Alternative Approach using Seaborn
One approach is to use Seaborn’s pointplot function, which allows us to customize the appearance of the points and align them with the bars. In this approach, we need to melt our data into long format using melt, create an explicit column for the original index, and adjust the dodge width to spread the markers.
Merging Data
To use Seaborn’s pointplot function, we need to merge our data into long format using melt. This will allow us to specify a column for the variable (x-axis) and another column for the value (y-axis).
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# Create sample data
df_data = {'A': np.random.randint(20, 50, 5),
'B': np.random.randint(10, 50, 5),
'C': np.random.randint(30, 50, 5),
'D': np.random.randint(30, 100, 5)}
df2_data = {'A': np.random.randint(20, 50, 5),
'B': np.random.randint(10, 50, 5),
'C': np.random.randint(30, 50, 5),
'D': np.random.randint(30, 100, 5)}
df3_data = {'A': np.random.randint(20, 50, 5),
'B': np.random.randint(10, 50, 5),
'C': np.random.randint(30, 50, 5),
'D': np.random.randint(30, 100, 5)}
# Create dataframes
df = pd.DataFrame(data=df_data)
df2 = pd.DataFrame(data=df2_data)
df3 = pd.DataFrame(data=df3_data)
# Melt the data into long format
df_melted = df.melt(ignore_index=False).reset_index()
df2_melted = df2.melt(ignore_index=False).reset_index()
df3_melted = df3.melt(ignore_index=False).reset_index()
print(df_melted)
print(df2_melted)
print(df3_melted)
Creating the Plot
Now that we have our data in long format, we can create the plot using Seaborn’s pointplot function.
# Create a figure and axis object
fig = plt.figure()
ax = sns.pointplot(data=df2_melted.reset_index(),
x='index', y='value', hue='variable', linestyles='', markers='^',
palette=['red', 'blue', 'green', 'goldenrod'],
dodge=.8 / len(['A', 'B', 'C', 'D']))
sns.pointplot(data=df3_melted.reset_index(),
x='index', y='value', hue='variable', linestyles='', markers='o',
palette=['red', 'blue', 'green', 'goldenrod'],
dodge=.8 / len(['A', 'B', 'C', 'D']), ax=ax)
sns.barplot(data=df_melted.reset_index(),
x='index', y='value', hue='variable',
palette=['red', 'blue', 'green', 'goldenrod'], dodge=True, ax=ax)
# Customize the plot
handles, labels = ax.get_legend_handles_labels()
leg_num = len(['A', 'B', 'C', 'D'])
ax.legend(handles[-leg_num:], labels[-leg_num:]) # use only four last elements for the legend
for markers in ax.collections:
markers.set_facecolor('white')
markers.set_linewidth(1)
plt.show()
Conclusion
In this article, we explored how to create a grouped bar chart with multiple markers using Python and Seaborn. We discussed the challenges of aligning these markers with the bars and presented an alternative approach using pointplot function from Seaborn. By merging our data into long format and adjusting the dodge width, we can create a grouped bar chart with aligned markers.
Last modified on 2023-09-09