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() 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() 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() 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() 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() Thank you for taking the time to explore data-related insights with me. I appreciate your engagement. If you find this information helpful, I invite you to follow me or connect with me on LinkedIn or X (@Luca_DataTeam). You can also catch glimpses of my personal life on Instagram, Happy exploring!👋 LinkedIn X Instagram

The code in this story is for educational purposes. The readers are solely responsible for whatever they build with it.

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

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

Untitled Story

A Quick Guide to Listing All Measures in Power BI via DAX Studio

Canvas And React: A Simple Guide To Data Charts

Creating a Grouped Bar Graph using Jetpack Compose

Creating Stunning 3D Charts With Highcharts And React.

How to Make Beautiful, Easy-to-read Charts with React Native Gifted Charts

A Quick Guide to Listing All Measures in Power BI via DAX Studio

Canvas And React: A Simple Guide To Data Charts

Creating a Grouped Bar Graph using Jetpack Compose

Creating Stunning 3D Charts With Highcharts And React.

How to Make Beautiful, Easy-to-read Charts with React Native Gifted Charts

Light-Mode

Classic

Newspaper

Dark-Mode

Neon Noir

Minty

HN StartUps