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

#programming #python #tutorial #beginners

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()