Four Types of Bar Charts in Python - Based on Tabular Data

Simple Bar Charts in Python Based on Tabular Data

import matplotlib.pyplot as plt
import pandas as pd

df = pd.DataFrame({'x': ['A', 'B', 'C', 'D', 'E'],
                   'y': [50, 30, 70, 80, 60]})

plt.bar(df['x'], df['y'], align='center', width=0.5, color='b', label='data')
plt.xlabel('X axis')
plt.ylabel('Y axis')
plt.title('Bar chart')
plt.legend()
plt.show()

Stacked bar chart in Python Based on Tabular Data

import matplotlib.pyplot as plt
import pandas as pd

df = pd.DataFrame({'x': ['A', 'B', 'C', 'D', 'E'],
                   'y1': [50, 30, 70, 80, 60],
                   'y2': [20, 40, 10, 50, 30]})

plt.bar(df['x'], df['y1'], align='center', width=0.5, color='b', label='Series 1')
plt.bar(df['x'], df['y2'], bottom=df['y1'], align='center', width=0.5, color='g', label='Series 2')
plt.xlabel('X axis')
plt.ylabel('Y axis')
plt.title('Stacked Bar Chart')
plt.legend()
plt.show()

Grouped bar chart based on Tabular Data in Python

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

# Prepare the data
df = pd.DataFrame({
    'group': ['G1', 'G2', 'G3', 'G4', 'G5'],
    'men_means': [20, 35, 30, 35, 27],
    'women_means': [25, 32, 34, 20, 25]
})
ind = np.arange(len(df))  # x-axis position
width = 0.35  # width of each bar

# Plot the bar chart
fig, ax = plt.subplots()
rects1 = ax.bar(ind, df['men_means'], width, color='r')
rects2 = ax.bar(ind + width, df['women_means'], width, color='y')

# Add labels, legend, and axis labels
ax.set_xticks(ind + width / 2)
ax.set_xticklabels(df['group'])
ax.legend((rects1[0], rects2[0]), ('Men', 'Women'))
ax.set_xlabel('Groups')
ax.set_ylabel('Scores')

# Display the plot
plt.show()

Percent stacked bar chart based on Tabular Data in Python

import matplotlib.pyplot as plt
import pandas as pd

# Prepare the data
df = pd.DataFrame({
    'x': ['Group 1', 'Group 2', 'Group 3', 'Group 4', 'Group 5'],
    'y1': [10, 20, 30, 25, 30],
    'y2': [20, 25, 30, 15, 20],
    'y3': [30, 30, 25, 20, 10]
})

# calculate percentage
y_percent = df.iloc[:, 1:].div(df.iloc[:, 1:].sum(axis=1), axis=0) * 100

# plot the chart
fig, ax = plt.subplots()
ax.bar(df['x'], y_percent.iloc[:, 0], label='Series 1', color='r')
ax.bar(df['x'], y_percent.iloc[:, 1], bottom=y_percent.iloc[:, 0], label='Series 2', color='g')
ax.bar(df['x'], y_percent.iloc[:, 2], bottom=y_percent.iloc[:, :2].sum(axis=1), label='Series 3', color='b')

# Display the plot
plt.show()

Explore more

Luca LiuFollow

Hello there! 👋 I'm Luca, a Business Intelligence Developer with passion for all things data. Proficient in Python, SQL, Power BI, Tableau, SAP Business Objects.

Thank you for taking the time to explore data-related insights with me. I appreciate your engagement.

🚀 Connect with me on LinkedIn

🎃 Connect with me on X

🌍 Connect with me on Instagram