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Four Types of Bar Charts in Python - Based on Tabular Data
source link: https://hackernoon.com/four-types-of-bar-charts-in-python-based-on-tabular-data
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Luca LiuHello there! 👋 I'm Luca, a BI Developer with a passion for all things data, Proficient in Python, SQL and Power BI
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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()
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