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Four Types of Array Data-Based Bar Charts in Pythonby@luca1iu
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Four Types of Array Data-Based Bar Charts in Python

by Luca LiuMarch 13th, 2024
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import matplotlib.pyplot as plt, numpy as numpy, data as data. Data.Data (men, women, men, women) = 5. Data (men) = (20, 35, 30, 35), women_means = (25, 32, 34, 20, 25) x-axis position = 0.35, y-axisposition = 1, width of each bar at 0.5, height of each axis at 1.5. Y-axis height = 1.
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Simple bar chart based on an array in Python

import matplotlib.pyplot as plt
import numpy as np

x = np.array(['A', 'B', 'C', 'D', 'E'])
y = np.array([50, 30, 70, 80, 60])

plt.bar(x, 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 based on arrays in Python

import matplotlib.pyplot as plt
import numpy as np
x = np.array(['A', 'B', 'C', 'D', 'E'])
y1 = np.array([50, 30, 70, 80, 60])
y2 = np.array([20, 40, 10, 50, 30])

plt.bar(x, y1, align='center', width=0.5, color='b', label='Series 1')
plt.bar(x, y2, bottom=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 arrays in Python

import matplotlib.pyplot as plt
import numpy as np

# Prepare the data
N = 5
men_means = (20, 35, 30, 35, 27)
women_means = (25, 32, 34, 20, 25)
ind = np.arange(N)  # x-axis position
width = 0.35  # width of each bar

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

# Add labels, legend, and axis labels
ax.set_xticks(ind + width / 2)
ax.set_xticklabels(('G1', 'G2', 'G3', 'G4', 'G5'))
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 arrays in Python

import matplotlib.pyplot as plt
import numpy as np

# Prepare the data
x = ['Group 1', 'Group 2', 'Group 3', 'Group 4', 'Group 5']
y = np.array([[10, 20, 30],
              [20, 25, 30],
              [15, 30, 25],
              [25, 15, 20],
              [30, 20, 10]])

# calculate percentage
y_percent = y / np.sum(y, axis=1, keepdims=True) * 100

# Plot the chart
fig, ax = plt.subplots()
ax.bar(x, y_percent[:, 0], label='Series 1', color='r')
ax.bar(x, y_percent[:, 1], bottom=y_percent[:, 0], label='Series 2', color='g')
ax.bar(x, y_percent[:, 2], bottom=y_percent[:, 0] + y_percent[:, 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!👋


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