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Grouped Bar Charts from Pandas Dataframe


1. Grouped Bar Charts: 100% Distribution Across Groups

Code: [Select all] [Expand/Collapse]

1. import pandas as pd
2. import numpy as np
3. import matplotlib.pyplot as plt
4.  
5. import textwrap
6. from textwrap import wrap
7. textstr = 'Created at \nwww.tssfl.com'
8.  
9. #Create a random dataframe
10. group_names = np.random.choice(['Group A', 'Group B', 'Group C', 'Group D', 'Group E', 'Group F', 'Group G'], size=100)
11. responses = np.random.choice(['agree', 'disagree', 'strongly agree', 'strongly disagree'], size=100)
12. df = pd.DataFrame({'Column1': group_names, 'Column2': responses})
13.  
14.  
15. #Convert dataframe to preserve the original order of responses.
16. df = pd.crosstab(df['Column1'], df['Column2'])
17.  
18. #Convert pandas dataframe to numpy array
19. data = df.to_numpy()
20.  
21. #Calculate the total percentage for each group
22. group_totals = np.sum(data, axis=1)
23. total = np.sum(group_totals)
24.  
25. #Convert the list to a pandas Series
26. group_series = pd.Series(group_names)
27. #Extract unique values only - NumPy array
28. group_names = group_series.unique()
29.  
30. response_series = pd.Series(responses)
31. responses = response_series.unique()
32.  
33. #Plot grouped bar charts
34. fig, ax = plt.subplots(figsize=(14,7))
35. bar_width = 0.1
36. opacity = 0.8
37.  
38. colors = ['green', 'crimson', '#00FF00', "#FFD700", 'blue', '#4286f4', "#FF4500"]
39.  
40. for i, group_data in enumerate(data):
41.     x = np.arange(len(responses))
42.     bars = ax.bar(x + i * bar_width, group_data, bar_width,
43.                   alpha=opacity, color=colors[i], label=group_names[i], align='edge')
44.  
45.     #Add percentage annotations for each bar
46.     for j, bar in enumerate(bars):
47.         percentage = '{:.2f}%'.format(100 * bar.get_height() / total)
48.         frequency = str(int(bar.get_height()))
49.         x_pos = bar.get_x() + bar.get_width() / 2
50.         y_pos = bar.get_height()
51.         ax.annotate(percentage, (x_pos, y_pos), rotation=90, xytext=(0, 15),
52.                     textcoords="offset points", ha="center", va="bottom", color='red')
53.         if y_pos >= 0:
54.             ax.annotate(frequency, (x_pos, y_pos), rotation=0, xytext=(0, 7),
55.                         textcoords="offset points", ha="center", va="center", color='green')
56.  
57. ax.spines['top'].set_visible(False)
58. #Set axis labels and title
59. ax.set_xlabel('Responses')
60. ax.set_ylabel('Frequency')
61. ax.set_title('Grouped Bar Charts: Frequency and 100% Distribution Across Groups', size=12, pad=40)
62.  
63. #Set x-axis tick labels
64. ax.set_xticks(x + (len(group_names) - 1) * bar_width / 2)
65. ax.set_xticklabels(responses)
66.  
67. #Wrap column titles for legend, break long column titles after 20 characters for legend titles
68. #legend_title = "\n".join(textwrap.wrap(df.columns[2], 20))
69. #legend_title = "\n".join(textwrap.wrap(column2, 30))
70.  
71. #Create a legend box outside the plot area
72. legend = plt.legend(title='Legend', bbox_to_anchor=(1.05, 1), loc='upper left')
73. plt.subplots_adjust(right=0.9)  #Adjust the right margin to accommodate the legend
74.  
75. #Add the text annotation outside the plot area
76. #plt.figtext(0.1, 0.5, textstr, fontsize=10, verticalalignment='center')
77. plt.gcf().text(0.02, 0.94, textstr, fontsize=14, color='green')
78.  
79. plt.tight_layout()
80. plt.show()
81.  
82. """
83. #Calculate the total number for each group in Column1
84. group_counts = df['Column1'].value_counts()
85.  
86. #Convert the Series to a NumPy array
87. group_totals = group_counts.values
88. """

Horizontal Bar Charts

Code: [Select all] [Expand/Collapse]

1. import pandas as pd
2. import numpy as np
3. import matplotlib.pyplot as plt
4. textstr = 'Created at \nwww.tssfl.com'
5.  
6. # Create a random dataframe
7. group_names = np.random.choice(['Group A', 'Group B', 'Group C', 'Group D', 'Group E', 'Group F', 'Group G'], size=100)
8. responses = np.random.choice(['agree', 'disagree', 'strongly agree', 'strongly disagree'], size=100)
9. df = pd.DataFrame({'Column1': group_names, 'Column2': responses})
10.  
11. #Convert dataframe to preserve the original order of responses.
12. df = pd.crosstab(df['Column1'], df['Column2'])
13.  
14. #Convert pandas dataframe to numpy array
15. data = df.to_numpy()
16.  
17. # Calculate the total percentage for each group
18. group_totals = np.sum(data, axis=1)
19. total = np.sum(group_totals)
20.  
21. #Convert the list to a pandas Series
22. group_series = pd.Series(group_names)
23. #Extract unique values only - NumPy array
24. group_names = group_series.unique()
25. group_names = group_names[::-1]  #Reverse the order of group labels
26.  
27. response_series = pd.Series(responses)
28. responses = response_series.unique()
29.  
30. # Plot grouped bar charts
31. fig, ax = plt.subplots(figsize=(10, 10))
32. bar_width = 0.1
33. opacity = 0.8
34.  
35. colors = ['green', 'crimson', '#00FF00', "#FFD700", 'blue', '#4286f4', "#FF4500"]
36. colors = colors[:len(group_names)][::-1]  # Reverse the order of colors and limit to the number of groups
37.  
38. for i, group_data in enumerate(data):
39.     x = np.arange(len(responses))
40.     bars = ax.barh(x + i * bar_width, group_data, bar_width,
41.                    alpha=opacity, color=colors[i], label=group_names[i])
42.  
43.     # Add percentage annotations for each bar
44.     for j, bar in enumerate(bars):
45.         percentage = '{:.2f}%'.format(100 * bar.get_width() / total)
46.         frequency = str(int(bar.get_width()))
47.         y_pos = bar.get_y() + bar.get_height() / 2
48.         x_pos = bar.get_width()
49.         ax.annotate(percentage, (x_pos, y_pos), xytext=(12, 0),
50.                     textcoords="offset points", ha="left", va="center", color='red')
51.        
52.         if x_pos >= 0:
53.             ax.annotate(frequency, (x_pos, y_pos), rotation=0, xytext=(6, 0),
54.                         textcoords="offset points", ha="center", va="center", color='green')
55.        
56.  
57. ax.spines['right'].set_visible(False)
58. # Set axis labels and title
59. ax.set_xlabel('Frequency')
60. ax.set_ylabel('Responses')
61. ax.set_title('Grouped Bar Charts: Frequency and 100% Distribution for Each Group', size=12, pad=20)
62.  
63. # Set y-axis tick labels
64. ax.set_yticks(x + (len(group_names) - 1) * bar_width / 2)
65. ax.set_yticklabels(responses)
66.  
67. #Set legend with correct ordering of colors and labels
68. handles, labels = ax.get_legend_handles_labels()
69. legend_bbox = bbox_to_anchor=(1.05, 1)
70.  
71. #Place the legend outside the plot area
72. plt.gca().legend(handles[::-1], labels[::-1], title="Groups", loc='upper left', bbox_to_anchor=legend_bbox)
73. plt.subplots_adjust(right=0.9)  #Adjust the right margin to accommodate the legend
74.  
75. #Add the text annotation outside the plot area
76. plt.gcf().text(0.02, 0.95, textstr, fontsize=14, color='green')
77.  
78. #Show the plot
79. plt.tight_layout()
80. plt.show()

2. Grouped Bar Charts - 100% Distribution for Each Group

Code: [Select all] [Expand/Collapse]

1. import pandas as pd
2. import numpy as np
3. import matplotlib.pyplot as plt
4.  
5. import textwrap
6. from textwrap import wrap
7. textstr = 'Created at \nwww.tssfl.com'
8.  
9. #Create a random dataframe
10. group_names = np.random.choice(['Group A', 'Group B', 'Group C', 'Group D', 'Group E', 'Group F', 'Group G'], size=100)
11. responses = np.random.choice(['agree', 'disagree', 'strongly agree', 'strongly disagree'], size=100)
12. df = pd.DataFrame({'Column1': group_names, 'Column2': responses})
13.  
14.  
15. #Convert dataframe to preserve the original order of responses.
16. df = pd.crosstab(df['Column1'], df['Column2'])
17.  
18. #Convert pandas dataframe to numpy array
19. data = df.to_numpy()
20.  
21. #Calculate the total percentage for each group
22. group_totals = np.sum(data, axis=1)
23.  
24. #Convert the list to a pandas Series
25. group_series = pd.Series(group_names)
26. #Extract unique values only - NumPy array
27. group_names = group_series.unique()
28.  
29. response_series = pd.Series(responses)
30. responses = response_series.unique()
31.  
32. #Plot grouped bar charts
33. fig, ax = plt.subplots(figsize=(14,7))
34. bar_width = 0.1
35. opacity = 0.8
36.  
37. colors = ['green', 'crimson', '#00FF00', "#FFD700", 'blue', '#4286f4', "#FF4500"]
38.  
39. for i, group_data in enumerate(data):
40.     x = np.arange(len(responses))
41.     bars = ax.bar(x + i * bar_width, group_data, bar_width,
42.                   alpha=opacity, color=colors[i], label=group_names[i], align='edge')
43.  
44.     #Add percentage annotations for each bar
45.     for j, bar in enumerate(bars):
46.         percentage = '{:.2f}%'.format(100 * bar.get_height() / group_totals[i])
47.         frequency = str(int(bar.get_height()))
48.         x_pos = bar.get_x() + bar.get_width() / 2
49.         y_pos = bar.get_height()
50.         ax.annotate(percentage, (x_pos, y_pos), rotation=90, xytext=(0, 15),
51.                     textcoords="offset points", ha="center", va="bottom", color='red')
52.         if y_pos >= 0:
53.             ax.annotate(frequency, (x_pos, y_pos), rotation=0, xytext=(0, 7),
54.                         textcoords="offset points", ha="center", va="center", color='green')
55.  
56. ax.spines['top'].set_visible(False)
57. #Set axis labels and title
58. ax.set_xlabel('Responses')
59. ax.set_ylabel('Frequency')
60. ax.set_title('Grouped Bar Charts: Frequency and 100% Distribution for Each Group', size=12, pad=40)
61.  
62. #Set x-axis tick labels
63. ax.set_xticks(x + (len(group_names) - 1) * bar_width / 2)
64. ax.set_xticklabels(responses)
65.  
66. #Wrap column titles for legend, break long column titles after 20 characters for legend titles
67. #legend_title = "\n".join(textwrap.wrap(df.columns[2], 20))
68. #legend_title = "\n".join(textwrap.wrap(column2, 30))
69.  
70. #Create a legend box outside the plot area
71. legend = plt.legend(title='Legend', bbox_to_anchor=(1.05, 1), loc='upper left')
72. plt.subplots_adjust(right=0.9)  #Adjust the right margin to accommodate the legend
73.  
74. #Add the text annotation outside the plot area
75. #plt.figtext(0.1, 0.5, textstr, fontsize=10, verticalalignment='center')
76. plt.gcf().text(0.02, 0.94, textstr, fontsize=14, color='green')
77.  
78. plt.tight_layout()
79. plt.show()

Horizontal Bar Charts

Code: [Select all] [Expand/Collapse]

1. import pandas as pd
2. import numpy as np
3. import matplotlib.pyplot as plt
4. textstr = 'Created at \nwww.tssfl.com'
5.  
6. # Create a random dataframe
7. group_names = np.random.choice(['Group A', 'Group B', 'Group C', 'Group D', 'Group E', 'Group F', 'Group G'], size=100)
8. responses = np.random.choice(['agree', 'disagree', 'strongly agree', 'strongly disagree'], size=100)
9. df = pd.DataFrame({'Column1': group_names, 'Column2': responses})
10.  
11. #Convert dataframe to preserve the original order of responses.
12. df = pd.crosstab(df['Column1'], df['Column2'])
13.  
14. #Convert pandas dataframe to numpy array
15. data = df.to_numpy()
16.  
17. # Calculate the total percentage for each group
18. group_totals = np.sum(data, axis=1)
19.  
20. #Convert the list to a pandas Series
21. group_series = pd.Series(group_names)
22. #Extract unique values only - NumPy array
23. group_names = group_series.unique()
24. group_names = group_names[::-1]  #Reverse the order of group labels
25.  
26. response_series = pd.Series(responses)
27. responses = response_series.unique()
28.  
29. # Plot grouped bar charts
30. fig, ax = plt.subplots(figsize=(10, 10))
31. bar_width = 0.1
32. opacity = 0.8
33.  
34. colors = ['green', 'crimson', '#00FF00', "#FFD700", 'blue', '#4286f4', "#FF4500"]
35. colors = colors[:len(group_names)][::-1]  # Reverse the order of colors and limit to the number of groups
36.  
37. for i, group_data in enumerate(data):
38.     x = np.arange(len(responses))
39.     bars = ax.barh(x + i * bar_width, group_data, bar_width,
40.                    alpha=opacity, color=colors[i], label=group_names[i])
41.  
42.     # Add percentage annotations for each bar
43.     for j, bar in enumerate(bars):
44.         percentage = '{:.2f}%'.format(100 * bar.get_width() / group_totals[i])
45.         frequency = str(int(bar.get_width()))
46.         y_pos = bar.get_y() + bar.get_height() / 2
47.         x_pos = bar.get_width()
48.         ax.annotate(percentage, (x_pos, y_pos), xytext=(12, 0),
49.                     textcoords="offset points", ha="left", va="center", color='red')
50.        
51.         if x_pos >= 0:
52.             ax.annotate(frequency, (x_pos, y_pos), rotation=0, xytext=(6, 0),
53.                         textcoords="offset points", ha="center", va="center", color='green')
54.        
55.  
56. ax.spines['right'].set_visible(False)
57. # Set axis labels and title
58. ax.set_xlabel('Frequency')
59. ax.set_ylabel('Responses')
60. ax.set_title('Grouped Bar Charts: Frequency and 100% Distribution for Each Group', size=12, pad=20)
61.  
62. # Set y-axis tick labels
63. ax.set_yticks(x + (len(group_names) - 1) * bar_width / 2)
64. ax.set_yticklabels(responses)
65.  
66. #Set legend with correct ordering of colors and labels
67. handles, labels = ax.get_legend_handles_labels()
68. legend_bbox = bbox_to_anchor=(1.05, 1)
69.  
70. #Place the legend outside the plot area
71. plt.gca().legend(handles[::-1], labels[::-1], title="Groups", loc='upper left', bbox_to_anchor=legend_bbox)
72. plt.subplots_adjust(right=0.9)  #Adjust the right margin to accommodate the legend
73.  
74. #Add the text annotation outside the plot area
75. plt.gcf().text(0.02, 0.95, textstr, fontsize=14, color='green')
76.  
77. #Show the plot
78. plt.tight_layout()
79. plt.show()

3. Grouped Barcharts - 100% Distribution for Each Response

Code: [Select all] [Expand/Collapse]

1. import pandas as pd
2. import numpy as np
3. import matplotlib.pyplot as plt
4. textstr = 'Created at \nwww.tssfl.com'
5.  
6. #Create a random dataframe
7. group_names = np.random.choice(['Group A', 'Group B', 'Group C', 'Group D', 'Group E', 'Group F', 'Group G'], size=100)
8. responses = np.random.choice(['agree', 'disagree', 'strongly agree', 'strongly disagree'], size=100)
9. df = pd.DataFrame({'Column1': group_names, 'Column2': responses})
10.  
11.  
12. #Convert dataframe to preserve the original order of responses.
13. df = pd.crosstab(df['Column1'], df['Column2'])
14.  
15. #Convert pandas dataframe to numpy array
16. data = df.to_numpy()
17.  
18. #Calculate the total number for each group
19. response_totals = np.sum(data, axis=0)
20. normalized_data = data / response_totals[np.newaxis, :]
21.  
22. #Convert the list to a pandas Series
23. group_series = pd.Series(group_names)
24. #Extract unique values only - NumPy array
25. group_names = group_series.unique()
26.  
27. response_series = pd.Series(responses)
28. responses = response_series.unique()
29.  
30. #Plot grouped bar charts
31. fig, ax = plt.subplots(figsize=(14,7))
32. bar_width = 0.1
33. opacity = 0.8
34.  
35. #colors = ['#4286f4', '#f44174', '#f4d641', '#41f45e']
36. colors = ['green', 'crimson', '#00FF00', "#FFD700", 'blue', '#4286f4', "#FF4500"]
37.  
38. for i, group_data in enumerate(normalized_data):
39.     x = np.arange(len(responses))
40.     bars = ax.bar(x + i * bar_width, group_data, bar_width,
41.                   alpha=opacity, color=colors[i], label=group_names[i], align='edge')
42.  
43.     #Add percentage annotations for each bar
44.     for j, bar in enumerate(bars):
45.         percentage = '{:.2f}%'.format(100 * bar.get_height())
46.         frequency = str(int(response_totals[j]*bar.get_height()))
47.         x_pos = bar.get_x() + bar.get_width() / 2
48.         y_pos = bar.get_height()
49.         ax.annotate(percentage, (x_pos, y_pos), rotation=90, xytext=(0, 15),
50.                     textcoords="offset points", ha="center", va="bottom", color='red')
51.        
52.         if y_pos >= 0:
53.             ax.annotate(frequency, (x_pos, y_pos), rotation=0, xytext=(0, 7),
54.                         textcoords="offset points", ha="center", va="center", color='blue')
55.  
56. ax.spines['top'].set_visible(False)
57. #Set axis labels and title
58. ax.set_xlabel('responses')
59. ax.set_ylabel('Fraction')
60. ax.set_title('Grouped Bar Charts: 100% Distribution for Each Response', size=12, pad=40)
61.  
62. #Set x-axis tick labels
63. ax.set_xticks(x + (len(group_names) - 1) * bar_width / 2)
64. ax.set_xticklabels(responses)
65.  
66. #Create a legend box outside the plot area
67. legend = plt.legend(title='Groups', bbox_to_anchor=(1.05, 1), loc='upper left')
68. plt.subplots_adjust(right=0.9)  #Adjust the right margin to accommodate the legend
69.  
70. #Add the text annotation outside the plot area
71. plt.gcf().text(0.02, 0.94, textstr, fontsize=14, color='green')
72.  
73. #Show the plot
74. plt.tight_layout()
75. plt.show()

Horizontal Bar Charts Version

Code: [Select all] [Expand/Collapse]

1. import pandas as pd
2. import numpy as np
3. import matplotlib.pyplot as plt
4. textstr = 'Created at \nwww.tssfl.com'
5.  
6. #Create a random dataframe
7. group_names = np.random.choice(['Group A', 'Group B', 'Group C', 'Group D', 'Group E', 'Group F', 'Group G'], size=100)
8. responses = np.random.choice(['agree', 'disagree', 'strongly agree', 'strongly disagree'], size=100)
9. df = pd.DataFrame({'Column1': group_names, 'Column2': responses})
10.  
11. #Convert dataframe to preserve the original order of responses.
12. df = pd.crosstab(df['Column1'], df['Column2'])
13.  
14. #Convert pandas dataframe to numpy array
15. data = df.to_numpy()
16.  
17. #Calculate the total number for each response
18. response_totals = np.sum(data, axis=0)
19. normalized_data = data / response_totals[np.newaxis, :]
20.  
21. #Convert the list to a pandas Series
22. group_series = pd.Series(group_names)
23. #Extract unique values only - NumPy array
24. group_names = group_series.unique()
25.  
26. response_series = pd.Series(responses)
27. responses = response_series.unique()
28.  
29. #Plot grouped bar charts
30. fig, ax = plt.subplots(figsize=(10, 10))
31. bar_width = 0.1
32. opacity = 0.8
33.  
34. colors = ['green', 'crimson', '#00FF00', "#FFD700", 'blue', '#4286f4', "#FF4500"]
35. colors = colors[:len(group_names)][::-1]  # Reverse the order of colors and limit to the number of groups
36.  
37. for i, group_data in enumerate(normalized_data):
38.     x = np.arange(len(responses))
39.     bars = ax.barh(x + i * bar_width, group_data, bar_width,
40.                    alpha=opacity, color=colors[i], label=group_names[i])
41.  
42.     # Add percentage annotations for each bar
43.     for j, bar in enumerate(bars):
44.         percentage = '{:.2f}%'.format(100 * bar.get_width())
45.         frequency = str(int(response_totals[j]*bar.get_width()))
46.         y_pos = bar.get_y() + bar.get_height() / 2
47.         x_pos = bar.get_width()
48.         ax.annotate(percentage, (x_pos, y_pos), xytext=(12, 0),
49.                     textcoords="offset points", ha="left", va="center", color='red')
50.        
51.         if y_pos >= 0:
52.             ax.annotate(frequency, (x_pos, y_pos), rotation=0, xytext=(6, 0),
53.                         textcoords="offset points", ha="center", va="center", color='blue')
54.            
55. ax.spines['right'].set_visible(False)
56. #Set axis labels and title
57. ax.set_xlabel('Fraction (Out of 1)')
58. ax.set_ylabel('Responses')
59. ax.set_title('Grouped Bar Charts: 100% Distribution for Each Response', size=12, pad=30)
60.  
61. # Set y-axis tick labels
62. ax.set_yticks(x + (len(group_names) - 1) * bar_width / 2)
63. ax.set_yticklabels(responses)
64.  
65. #Set legend with correct ordering of colors and labels
66. handles, labels = ax.get_legend_handles_labels()
67. legend_bbox = bbox_to_anchor=(1.05, 1)
68.  
69. #Place the legend outside the plot area
70. plt.gca().legend(reversed(handles), reversed(labels), title="Groups", loc='upper left', bbox_to_anchor=legend_bbox)
71. plt.subplots_adjust(right=0.9)  #Adjust the right margin to accommodate the legend
72.  
73. #Add the text annotation outside the plot area
74. plt.gcf().text(0.02, 0.95, textstr, fontsize=14, color='green')
75.  
76. #Show the plot
77. plt.tight_layout()
78. plt.show()

[Eli]

Stacked Bar Charts

Chart 1

Code: [Select all] [Expand/Collapse]

1. import pandas as pd
2. import numpy as np
3. import matplotlib.pyplot as plt
4.  
5. #Randomized string values for column1
6. column1_values = np.random.choice(['agree', 'disagree', 'strongly agree', 'strongly disagree'], size=100)
7.  
8. #Randomized string values for column2
9. column2_values = np.random.choice(['Group A', 'Group B', 'Group C', 'Group D', 'Group E', 'Group F', 'Group G'], size=100)
10.  
11. #Create the DataFrame
12. df = pd.DataFrame({'Column1': column1_values, 'Column2': column2_values})
13.  
14. #Calculate the percentage distribution for each group
15. grouped_counts = df.groupby(['Column2', 'Column1']).size()
16. grouped_percentages = grouped_counts.groupby(level=0).apply(lambda x: 100 * x / x.sum())
17.  
18. #Reshape the DataFrame for plotting
19. grouped_percentages = grouped_percentages.unstack()
20.  
21.  
22. #Plotting the grouped bar charts
23. fig, ax = plt.subplots(figsize=(10, 6))
24. ax = grouped_percentages.plot(kind='bar', stacked=True, ax=ax)
25.  
26. #Set the plot labels and title
27. plt.xlabel('Column2')
28. plt.ylabel('Percentage')
29. plt.title('Grouped Bar Charts')
30.  
31. #Add percentage labels on top of each bar
32. for p in ax.patches:
33.     width = p.get_width()
34.     height = p.get_height()
35.     x, y = p.get_xy()
36.     ax.text(x + width/2, y + height/2, '{:.1f}%'.format(height), ha='center')
37.  
38. #Modify x-axis labels
39. x_labels = [label.get_text().split(',')[0].replace('(', '').strip() for label in ax.get_xticklabels()]
40. ax.set_xticklabels(x_labels)
41.  
42. #Move the legend outside the plot
43. lgd = ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
44.  
45. #Display the plot
46. plt.tight_layout()
47. plt.show()

Chart 2

Code: [Select all] [Expand/Collapse]

1. import matplotlib.pyplot as plt
2. import numpy as np
3.  
4. #Create data
5. data = np.array([[35, 25, 25, 15],
6.               [20, 30, 30, 20],
7.               [30, 20, 25, 25],
8.                 [40, 50, 60, 70]])
9.  
10. #Create the stacked percentage bar chart
11. fig, ax = plt.subplots()
12. ax.stackplot(np.arange(4), data, labels=['A', 'B', 'C', 'D'])
13.  
14. #Increase the width and size of the plot
15. fig.set_size_inches(10, 6)
16. ax.set_xlabel('Type', fontsize=14)
17. ax.set_ylabel('Count', fontsize=14)
18. ax.set_title('Stacked Bar Chart', fontsize=16)
19. ax.legend(fontsize=12)
20.  
21. #Move the legend outside the plot
22. lgd = ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))
23. #Show the plot
24. plt.tight_layout()
25. plt.show()