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imagewidth (px) 800
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stringlengths 14
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| values
stringlengths 6
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stringclasses 4
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stringclasses 5
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stringlengths 490
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stringlengths 12
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['essay', 'soil'] | [1, 2] | Title | Values |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['essay', 'soil']
values = [1, 2]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Title') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Values') # Label for the y-axis
# Display the chart
plt.show()
| figure_0.png |
|
['race', 'cheek', 'land', 'wage', 'cotton', 'move'] | [20, 30, 70, 60, 10, 60] | Most preferred objects | Percent of People |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['race', 'cheek', 'land', 'wage', 'cotton', 'move']
values = [20, 30, 70, 60, 10, 60]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Most preferred objects') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Percent of People') # Label for the y-axis
# Display the chart
plt.show()
| figure_1.png |
|
['world', 'battle', 'drift', 'defeat', 'knife'] | [7, 8, 1, 5, 7] | Most preferred objects | Number of People |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['world', 'battle', 'drift', 'defeat', 'knife']
values = [7, 8, 1, 5, 7]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Most preferred objects') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Number of People') # Label for the y-axis
# Display the chart
plt.show()
| figure_10.png |
|
['god', 'living', 'back', 'gain', 'drop', 'key'] | [40, 40, 20, 30, 60, 30] | Title | Values |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['god', 'living', 'back', 'gain', 'drop', 'key']
values = [40, 40, 20, 30, 60, 30]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Title') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Values') # Label for the y-axis
# Display the chart
plt.show()
| figure_100.png |
|
['figure', 'couple', 'year', 'crop'] | [40, 50, 40, 40] | Title | Values |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['figure', 'couple', 'year', 'crop']
values = [40, 50, 40, 40]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Title') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Values') # Label for the y-axis
# Display the chart
plt.show()
| figure_1000.png |
|
['west', 'faith', 'cancer', 'fan'] | [1, 3, 2, 6] | Most preferred objects | Number of People |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['west', 'faith', 'cancer', 'fan']
values = [1, 3, 2, 6]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Most preferred objects') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Number of People') # Label for the y-axis
# Display the chart
plt.show()
| figure_1001.png |
|
['dinner', 'reply'] | [3, 4] | Most preferred objects | Number of People |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['dinner', 'reply']
values = [3, 4]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Most preferred objects') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Number of People') # Label for the y-axis
# Display the chart
plt.show()
| figure_1002.png |
|
['plane', 'source', 'effort', 'march', 'coast', 'actor'] | [80, 50, 60, 10, 20, 40] | Accuracy of different algorithms | Accuracy |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['plane', 'source', 'effort', 'march', 'coast', 'actor']
values = [80, 50, 60, 10, 20, 40]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Accuracy of different algorithms') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Accuracy') # Label for the y-axis
# Display the chart
plt.show()
| figure_1003.png |
|
['bit', 'joke'] | [8, 5] | Sales statistics for different items | Units sold |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['bit', 'joke']
values = [8, 5]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Sales statistics for different items') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Units sold') # Label for the y-axis
# Display the chart
plt.show()
| figure_1004.png |
|
['merit', 'trail', 'cast'] | [3, 7, 8] | Accuracy of different algorithms | Accuracy |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['merit', 'trail', 'cast']
values = [3, 7, 8]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Accuracy of different algorithms') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Accuracy') # Label for the y-axis
# Display the chart
plt.show()
| figure_1005.png |
|
['sphere', 'gang', 'store', 'piano', 'party', 'foil', 'attack', 'tray'] | [80, 90, 80, 50, 50, 90, 50, 50] | Title | Values |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['sphere', 'gang', 'store', 'piano', 'party', 'foil', 'attack', 'tray']
values = [80, 90, 80, 50, 50, 90, 50, 50]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Title') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Values') # Label for the y-axis
# Display the chart
plt.show()
| figure_1006.png |
|
['rage', 'bond'] | [60, 50] | Title | Values |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['rage', 'bond']
values = [60, 50]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Title') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Values') # Label for the y-axis
# Display the chart
plt.show()
| figure_1007.png |
|
['side', 'belt', 'course', 'mile', 'dust', 'shop', 'supply'] | [70, 30, 20, 10, 40, 60, 80] | Most preferred objects | Percent of People |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['side', 'belt', 'course', 'mile', 'dust', 'shop', 'supply']
values = [70, 30, 20, 10, 40, 60, 80]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Most preferred objects') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Percent of People') # Label for the y-axis
# Display the chart
plt.show()
| figure_1008.png |
|
['entry', 'bottom', 'gate', 'text', 'stress', 'bread', 'denial', 'flight'] | [4, 9, 6, 4, 4, 8, 2, 5] | Most preferred objects | Number of People |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['entry', 'bottom', 'gate', 'text', 'stress', 'bread', 'denial', 'flight']
values = [4, 9, 6, 4, 4, 8, 2, 5]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Most preferred objects') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Number of People') # Label for the y-axis
# Display the chart
plt.show()
| figure_1009.png |
|
['prison', 'fault', 'mirror', 'cellar', 'tree'] | [1, 4, 8, 3, 6] | Most preferred objects | Number of People |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['prison', 'fault', 'mirror', 'cellar', 'tree']
values = [1, 4, 8, 3, 6]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Most preferred objects') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Number of People') # Label for the y-axis
# Display the chart
plt.show()
| figure_101.png |
|
['tongue', 'income', 'drunk', 'sky'] | [4, 3, 2, 6] | Accuracy of different algorithms | Accuracy |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['tongue', 'income', 'drunk', 'sky']
values = [4, 3, 2, 6]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Accuracy of different algorithms') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Accuracy') # Label for the y-axis
# Display the chart
plt.show()
| figure_1010.png |
|
['atom', 'arc', 'match', 'meat', 'head'] | [9, 1, 7, 6, 7] | Sales statistics for different items | Units sold |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['atom', 'arc', 'match', 'meat', 'head']
values = [9, 1, 7, 6, 7]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Sales statistics for different items') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Units sold') # Label for the y-axis
# Display the chart
plt.show()
| figure_1011.png |
|
['notion', 'edge', 'range'] | [8, 5, 6] | Accuracy of different algorithms | Accuracy |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['notion', 'edge', 'range']
values = [8, 5, 6]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Accuracy of different algorithms') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Accuracy') # Label for the y-axis
# Display the chart
plt.show()
| figure_1012.png |
|
['aid', 'adult', 'right', 'mirror', 'light', 'mean'] | [10, 20, 80, 20, 50, 10] | Title | Values |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['aid', 'adult', 'right', 'mirror', 'light', 'mean']
values = [10, 20, 80, 20, 50, 10]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Title') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Values') # Label for the y-axis
# Display the chart
plt.show()
| figure_1013.png |
|
['myth', 'issue'] | [1, 2] | Sales statistics for different items | Units sold |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['myth', 'issue']
values = [1, 2]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Sales statistics for different items') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Units sold') # Label for the y-axis
# Display the chart
plt.show()
| figure_1014.png |
|
['herd', 'moment', 'top', 'height', 'copy', 'flood', 'judge', 'threat'] | [8, 8, 6, 1, -7, 4, 3, 1] | Title | Values |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['herd', 'moment', 'top', 'height', 'copy', 'flood', 'judge', 'threat']
values = [8, 8, 6, 1, -7, 4, 3, 1]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Title') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Values') # Label for the y-axis
# Display the chart
plt.show()
| figure_1015.png |
|
['ice', 'screen', 'piece'] | [9, 4, 9] | Sales statistics for different items | Units sold |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['ice', 'screen', 'piece']
values = [9, 4, 9]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Sales statistics for different items') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Units sold') # Label for the y-axis
# Display the chart
plt.show()
| figure_1016.png |
|
['truth', 'award', 'foot'] | [7, 6, 3] | Title | Values |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['truth', 'award', 'foot']
values = [7, 6, 3]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Title') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Values') # Label for the y-axis
# Display the chart
plt.show()
| figure_1017.png |
|
['pink', 'cabin', 'autumn', 'smoke', 'gun', 'stake', 'fog', 'court', 'board'] | [70, 60, 70, 40, 70, 10, 70, 60, 80] | Accuracy of different algorithms | Accuracy |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['pink', 'cabin', 'autumn', 'smoke', 'gun', 'stake', 'fog', 'court', 'board']
values = [70, 60, 70, 40, 70, 10, 70, 60, 80]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Accuracy of different algorithms') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Accuracy') # Label for the y-axis
# Display the chart
plt.show()
| figure_1018.png |
|
['blow', 'bone', 'camp'] | [20, 50, 30] | Most preferred objects | Percent of People |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['blow', 'bone', 'camp']
values = [20, 50, 30]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Most preferred objects') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Percent of People') # Label for the y-axis
# Display the chart
plt.show()
| figure_1019.png |
|
['yield', 'patent', 'pastor'] | [70, 50, 30] | Title | Values |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['yield', 'patent', 'pastor']
values = [70, 50, 30]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Title') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Values') # Label for the y-axis
# Display the chart
plt.show()
| figure_102.png |
|
['hand', 'craft', 'male', 'lay', 'team', 'string', 'mercy'] | [50, 30, 40, 20, 90, 30, 50] | Accuracy of different algorithms | Accuracy |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['hand', 'craft', 'male', 'lay', 'team', 'string', 'mercy']
values = [50, 30, 40, 20, 90, 30, 50]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Accuracy of different algorithms') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Accuracy') # Label for the y-axis
# Display the chart
plt.show()
| figure_1020.png |
|
['crisis', 'belief', 'drink', 'night'] | [4, 4, 5, 9] | Accuracy of different algorithms | Accuracy |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['crisis', 'belief', 'drink', 'night']
values = [4, 4, 5, 9]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Accuracy of different algorithms') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Accuracy') # Label for the y-axis
# Display the chart
plt.show()
| figure_1021.png |
|
['vision', 'way', 'girl', 'maid'] | [1, 4, 9, 3] | Accuracy of different algorithms | Accuracy |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['vision', 'way', 'girl', 'maid']
values = [1, 4, 9, 3]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Accuracy of different algorithms') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Accuracy') # Label for the y-axis
# Display the chart
plt.show()
| figure_1022.png |
|
['day', 'drive'] | [4, 6] | Title | Values |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['day', 'drive']
values = [4, 6]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Title') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Values') # Label for the y-axis
# Display the chart
plt.show()
| figure_1023.png |
|
['vector', 'diet', 'meat', 'artery', 'atom'] | [9, 5, 9, 1, 9] | Accuracy of different algorithms | Accuracy |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['vector', 'diet', 'meat', 'artery', 'atom']
values = [9, 5, 9, 1, 9]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Accuracy of different algorithms') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Accuracy') # Label for the y-axis
# Display the chart
plt.show()
| figure_1024.png |
|
['graph', 'smell'] | [6, 6] | Title | Values |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['graph', 'smell']
values = [6, 6]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Title') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Values') # Label for the y-axis
# Display the chart
plt.show()
| figure_1025.png |
|
['engine', 'soil'] | [2, 7] | Sales statistics for different items | Units sold |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['engine', 'soil']
values = [2, 7]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Sales statistics for different items') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Units sold') # Label for the y-axis
# Display the chart
plt.show()
| figure_1026.png |
|
['right', 'trade'] | [4, 2] | Most preferred objects | Number of People |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['right', 'trade']
values = [4, 2]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Most preferred objects') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Number of People') # Label for the y-axis
# Display the chart
plt.show()
| figure_1027.png |
|
['blind', 'knife', 'trail', 'ring', 'cut', 'virtue'] | [6, 6, 4, 9, 9, 5] | Title | Values |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['blind', 'knife', 'trail', 'ring', 'cut', 'virtue']
values = [6, 6, 4, 9, 9, 5]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Title') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Values') # Label for the y-axis
# Display the chart
plt.show()
| figure_1028.png |
|
['light', 'hell', 'result', 'driver'] | [4, 8, 7, 9] | Accuracy of different algorithms | Accuracy |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['light', 'hell', 'result', 'driver']
values = [4, 8, 7, 9]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Accuracy of different algorithms') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Accuracy') # Label for the y-axis
# Display the chart
plt.show()
| figure_1029.png |
|
['evil', 'movie', 'trip', 'horror'] | [7, 5, 4, 4] | Title | Values |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['evil', 'movie', 'trip', 'horror']
values = [7, 5, 4, 4]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Title') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Values') # Label for the y-axis
# Display the chart
plt.show()
| figure_103.png |
|
['fluid', 'sort'] | [8, 2] | Accuracy of different algorithms | Accuracy |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['fluid', 'sort']
values = [8, 2]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Accuracy of different algorithms') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Accuracy') # Label for the y-axis
# Display the chart
plt.show()
| figure_1030.png |
|
['savage', 'dinner', 'barrel', 'pause', 'pipe', 'food', 'wit'] | [90, 30, 60, 50, 10, 50, 40] | Accuracy of different algorithms | Accuracy |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['savage', 'dinner', 'barrel', 'pause', 'pipe', 'food', 'wit']
values = [90, 30, 60, 50, 10, 50, 40]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Accuracy of different algorithms') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Accuracy') # Label for the y-axis
# Display the chart
plt.show()
| figure_1031.png |
|
['cut', 'sauce', 'cotton', 'drunk', 'mark'] | [6, 9, 1, 8, 7] | Sales statistics for different items | Units sold |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['cut', 'sauce', 'cotton', 'drunk', 'mark']
values = [6, 9, 1, 8, 7]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Sales statistics for different items') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Units sold') # Label for the y-axis
# Display the chart
plt.show()
| figure_1032.png |
|
['pure', 'walk', 'ground', 'close'] | [7, 5, 4, 9] | Title | Values |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['pure', 'walk', 'ground', 'close']
values = [7, 5, 4, 9]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Title') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Values') # Label for the y-axis
# Display the chart
plt.show()
| figure_1033.png |
|
['uncle', 'iodine'] | [7, 8] | Accuracy of different algorithms | Accuracy |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['uncle', 'iodine']
values = [7, 8]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Accuracy of different algorithms') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Accuracy') # Label for the y-axis
# Display the chart
plt.show()
| figure_1034.png |
|
['skirt', 'salary', 'doubt', 'fuel', 'realm'] | [90, 80, 80, 80, 0] | Title | Values |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['skirt', 'salary', 'doubt', 'fuel', 'realm']
values = [90, 80, 80, 80, 0]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Title') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Values') # Label for the y-axis
# Display the chart
plt.show()
| figure_1035.png |
|
['crew', 'rain', 'lay'] | [1, 5, 3] | Accuracy of different algorithms | Accuracy |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['crew', 'rain', 'lay']
values = [1, 5, 3]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Accuracy of different algorithms') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Accuracy') # Label for the y-axis
# Display the chart
plt.show()
| figure_1036.png |
|
['length', 'artery', 'wound'] | [2, 8, 5] | Title | Values |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['length', 'artery', 'wound']
values = [2, 8, 5]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Title') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Values') # Label for the y-axis
# Display the chart
plt.show()
| figure_1037.png |
|
['help', 'heard', 'verse', 'victim', 'pass', 'floor'] | [30, 90, 60, 10, 30, 40] | Accuracy of different algorithms | Accuracy |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['help', 'heard', 'verse', 'victim', 'pass', 'floor']
values = [30, 90, 60, 10, 30, 40]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Accuracy of different algorithms') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Accuracy') # Label for the y-axis
# Display the chart
plt.show()
| figure_1038.png |
|
['head', 'site', 'combat', 'lock', 'cure'] | [100, 100000, 100000000, 10000000, 10] | Title | Values |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['head', 'site', 'combat', 'lock', 'cure']
values = [100, 100000, 100000000, 10000000, 10]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Title') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Values') # Label for the y-axis
# Display the chart
plt.show()
| figure_1039.png |
|
['stress', 'break', 'trap', 'cause', 'load', 'bulk'] | [3, 8, 3, 0, 1, 4] | Title | Values |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['stress', 'break', 'trap', 'cause', 'load', 'bulk']
values = [3, 8, 3, 0, 1, 4]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Title') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Values') # Label for the y-axis
# Display the chart
plt.show()
| figure_104.png |
|
['parlor', 'bottle', 'talk', 'well', 'laugh'] | [90, 20, 20, 20, 80] | Title | Values |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['parlor', 'bottle', 'talk', 'well', 'laugh']
values = [90, 20, 20, 20, 80]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Title') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Values') # Label for the y-axis
# Display the chart
plt.show()
| figure_1040.png |
|
['mine', 'race', 'speed', 'change', 'mold'] | [20, 90, 50, 90, 80] | Title | Values |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['mine', 'race', 'speed', 'change', 'mold']
values = [20, 90, 50, 90, 80]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Title') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Values') # Label for the y-axis
# Display the chart
plt.show()
| figure_1041.png |
|
['town', 'kid', 'snake', 'eye', 'person', 'error', 'status', 'season'] | [8, 7, 2, 9, 8, 6, 3, 5] | Most preferred objects | Number of People |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['town', 'kid', 'snake', 'eye', 'person', 'error', 'status', 'season']
values = [8, 7, 2, 9, 8, 6, 3, 5]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Most preferred objects') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Number of People') # Label for the y-axis
# Display the chart
plt.show()
| figure_1042.png |
|
['form', 'water', 'action', 'figure', 'run'] | [4, 4, 1, 4, 4] | Title | Values |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['form', 'water', 'action', 'figure', 'run']
values = [4, 4, 1, 4, 4]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Title') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Values') # Label for the y-axis
# Display the chart
plt.show()
| figure_1043.png |
|
['beef', 'snake', 'draft', 'close'] | [1, 8, 5, 9] | Title | Values |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['beef', 'snake', 'draft', 'close']
values = [1, 8, 5, 9]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Title') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Values') # Label for the y-axis
# Display the chart
plt.show()
| figure_1044.png |
|
['chair', 'barn'] | [4, 4] | Sales statistics for different items | Units sold |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['chair', 'barn']
values = [4, 4]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Sales statistics for different items') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Units sold') # Label for the y-axis
# Display the chart
plt.show()
| figure_1045.png |
|
['sake', 'light', 'award', 'bottle', 'slave', 'lumber', 'farm', 'death'] | [3, 4, 5, 9, 8, 7, 6, 4] | Title | Values |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['sake', 'light', 'award', 'bottle', 'slave', 'lumber', 'farm', 'death']
values = [3, 4, 5, 9, 8, 7, 6, 4]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Title') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Values') # Label for the y-axis
# Display the chart
plt.show()
| figure_1046.png |
|
['drive', 'random', 'luck', 'battle'] | [4, 9, 8, 6] | Most preferred objects | Number of People |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['drive', 'random', 'luck', 'battle']
values = [4, 9, 8, 6]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Most preferred objects') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Number of People') # Label for the y-axis
# Display the chart
plt.show()
| figure_1047.png |
|
['bar', 'valley', 'chain', 'south', 'muscle'] | [0, 5, 1, 5, 2] | Title | Values |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['bar', 'valley', 'chain', 'south', 'muscle']
values = [0, 5, 1, 5, 2]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Title') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Values') # Label for the y-axis
# Display the chart
plt.show()
| figure_1048.png |
|
['bag', 'role', 'volume', 'effect', 'reply'] | [10, 40, 60, 30, 20] | Most preferred objects | Percent of People |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['bag', 'role', 'volume', 'effect', 'reply']
values = [10, 40, 60, 30, 20]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Most preferred objects') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Percent of People') # Label for the y-axis
# Display the chart
plt.show()
| figure_1049.png |
|
['file', 'camp', 'law', 'street'] | [9, 5, 2, 6] | Title | Values |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['file', 'camp', 'law', 'street']
values = [9, 5, 2, 6]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Title') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Values') # Label for the y-axis
# Display the chart
plt.show()
| figure_105.png |
|
['shift', 'march', 'union', 'victim', 'good', 'week'] | [90, 90, 80, 40, 10, 30] | Most preferred objects | Percent of People |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['shift', 'march', 'union', 'victim', 'good', 'week']
values = [90, 90, 80, 40, 10, 30]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Most preferred objects') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Percent of People') # Label for the y-axis
# Display the chart
plt.show()
| figure_1050.png |
|
['loan', 'factor', 'truck', 'hair', 'sin', 'moon', 'farm'] | [40, 10, 50, 10, 70, 40, 10] | Most preferred objects | Percent of People |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['loan', 'factor', 'truck', 'hair', 'sin', 'moon', 'farm']
values = [40, 10, 50, 10, 70, 40, 10]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Most preferred objects') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Percent of People') # Label for the y-axis
# Display the chart
plt.show()
| figure_1051.png |
|
['index', 'favor', 'bench'] | [40, 50, 10] | Accuracy of different algorithms | Accuracy |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['index', 'favor', 'bench']
values = [40, 50, 10]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Accuracy of different algorithms') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Accuracy') # Label for the y-axis
# Display the chart
plt.show()
| figure_1052.png |
|
['cut', 'wheel', 'nation'] | [4, 2, 9] | Sales statistics for different items | Units sold |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['cut', 'wheel', 'nation']
values = [4, 2, 9]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Sales statistics for different items') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Units sold') # Label for the y-axis
# Display the chart
plt.show()
| figure_1053.png |
|
['item', 'age', 'tax', 'holder'] | [3, 2, 5, 4] | Most preferred objects | Number of People |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['item', 'age', 'tax', 'holder']
values = [3, 2, 5, 4]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Most preferred objects') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Number of People') # Label for the y-axis
# Display the chart
plt.show()
| figure_1054.png |
|
['floor', 'neck', 'plot', 'wind', 'bit', 'wonder', 'farm', 'cause'] | [9, 9, 8, 8, 3, 2, 3, 9] | Accuracy of different algorithms | Accuracy |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['floor', 'neck', 'plot', 'wind', 'bit', 'wonder', 'farm', 'cause']
values = [9, 9, 8, 8, 3, 2, 3, 9]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Accuracy of different algorithms') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Accuracy') # Label for the y-axis
# Display the chart
plt.show()
| figure_1055.png |
|
['rush', 'credit', 'code', 'savage'] | [6, 7, 5, 1] | Title | Values |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['rush', 'credit', 'code', 'savage']
values = [6, 7, 5, 1]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Title') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Values') # Label for the y-axis
# Display the chart
plt.show()
| figure_1056.png |
|
['force', 'pocket', 'impact', 'tube', 'term'] | [50, 40, 10, 80, 80] | Title | Values |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['force', 'pocket', 'impact', 'tube', 'term']
values = [50, 40, 10, 80, 80]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Title') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Values') # Label for the y-axis
# Display the chart
plt.show()
| figure_1057.png |
|
['twenty', 'engine', 'income', 'aspect', 'image', 'wave', 'steel'] | [90, 40, 70, 20, 80, 90, 80] | Most preferred objects | Percent of People |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['twenty', 'engine', 'income', 'aspect', 'image', 'wave', 'steel']
values = [90, 40, 70, 20, 80, 90, 80]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Most preferred objects') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Percent of People') # Label for the y-axis
# Display the chart
plt.show()
| figure_1058.png |
|
['stick', 'hair', 'growth'] | [6, 1, 8] | Sales statistics for different items | Units sold |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['stick', 'hair', 'growth']
values = [6, 1, 8]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Sales statistics for different items') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Units sold') # Label for the y-axis
# Display the chart
plt.show()
| figure_1059.png |
|
['genius', 'bag', 'sale', 'glass'] | [0, 2, 9, 1] | Title | Values |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['genius', 'bag', 'sale', 'glass']
values = [0, 2, 9, 1]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Title') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Values') # Label for the y-axis
# Display the chart
plt.show()
| figure_106.png |
|
['pistol', 'light', 'gun', 'dance', 'coat', 'mad', 'member'] | [3, 5, 8, 5, 2, 2, 3] | Accuracy of different algorithms | Accuracy |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['pistol', 'light', 'gun', 'dance', 'coat', 'mad', 'member']
values = [3, 5, 8, 5, 2, 2, 3]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Accuracy of different algorithms') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Accuracy') # Label for the y-axis
# Display the chart
plt.show()
| figure_1060.png |
|
['vein', 'foot', 'mad'] | [6, 4, 6] | Accuracy of different algorithms | Accuracy |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['vein', 'foot', 'mad']
values = [6, 4, 6]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Accuracy of different algorithms') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Accuracy') # Label for the y-axis
# Display the chart
plt.show()
| figure_1061.png |
|
['group', 'bridge', 'mud', 'fund'] | [4, 8, 5, 6] | Sales statistics for different items | Units sold |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['group', 'bridge', 'mud', 'fund']
values = [4, 8, 5, 6]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Sales statistics for different items') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Units sold') # Label for the y-axis
# Display the chart
plt.show()
| figure_1062.png |
|
['valley', 'foam', 'lip'] | [2, 9, 5] | Most preferred objects | Number of People |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['valley', 'foam', 'lip']
values = [2, 9, 5]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Most preferred objects') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Number of People') # Label for the y-axis
# Display the chart
plt.show()
| figure_1063.png |
|
['garden', 'writer', 'shop', 'spot', 'today', 'driver', 'beat', 'idea'] | [10000000, 1000, 100000, 10000000, 10000, 1000, 100, 1000] | Title | Values |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['garden', 'writer', 'shop', 'spot', 'today', 'driver', 'beat', 'idea']
values = [10000000, 1000, 100000, 10000000, 10000, 1000, 100, 1000]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Title') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Values') # Label for the y-axis
# Display the chart
plt.show()
| figure_1064.png |
|
['bar', 'combat', 'gas', 'corn', 'hero'] | [70, 20, 60, 20, 10] | Accuracy of different algorithms | Accuracy |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['bar', 'combat', 'gas', 'corn', 'hero']
values = [70, 20, 60, 20, 10]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Accuracy of different algorithms') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Accuracy') # Label for the y-axis
# Display the chart
plt.show()
| figure_1065.png |
|
['date', 'whole', 'impact'] | [70, 10, 60] | Accuracy of different algorithms | Accuracy |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['date', 'whole', 'impact']
values = [70, 10, 60]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Accuracy of different algorithms') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Accuracy') # Label for the y-axis
# Display the chart
plt.show()
| figure_1066.png |
|
['face', 'autumn', 'rain', 'jungle', 'cover'] | [40, 90, 60, 80, 10] | Accuracy of different algorithms | Accuracy |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['face', 'autumn', 'rain', 'jungle', 'cover']
values = [40, 90, 60, 80, 10]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Accuracy of different algorithms') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Accuracy') # Label for the y-axis
# Display the chart
plt.show()
| figure_1067.png |
|
['get', 'ward', 'excuse', 'woman', 'type'] | [5, 7, 3, 8, 9] | Title | Values |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['get', 'ward', 'excuse', 'woman', 'type']
values = [5, 7, 3, 8, 9]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Title') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Values') # Label for the y-axis
# Display the chart
plt.show()
| figure_1068.png |
|
['round', 'silver', 'plate', 'end', 'pencil'] | [3, 8, 7, 3, 9] | Sales statistics for different items | Units sold |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['round', 'silver', 'plate', 'end', 'pencil']
values = [3, 8, 7, 3, 9]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Sales statistics for different items') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Units sold') # Label for the y-axis
# Display the chart
plt.show()
| figure_1069.png |
|
['middle', 'sphere', 'wealth', 'laugh', 'smell', 'campus', 'grip'] | [1, 8, 9, 2, 7, 9, 4] | Most preferred objects | Number of People |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['middle', 'sphere', 'wealth', 'laugh', 'smell', 'campus', 'grip']
values = [1, 8, 9, 2, 7, 9, 4]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Most preferred objects') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Number of People') # Label for the y-axis
# Display the chart
plt.show()
| figure_107.png |
|
['danger', 'motel', 'dawn', 'ring', 'risk', 'bone', 'couple', 'detail'] | [1, 3, -5, 8, -4, 9, -7, 1] | Title | Values |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['danger', 'motel', 'dawn', 'ring', 'risk', 'bone', 'couple', 'detail']
values = [1, 3, -5, 8, -4, 9, -7, 1]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Title') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Values') # Label for the y-axis
# Display the chart
plt.show()
| figure_1070.png |
|
['wisdom', 'essay', 'dollar'] | [80, 50, 20] | Most preferred objects | Percent of People |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['wisdom', 'essay', 'dollar']
values = [80, 50, 20]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Most preferred objects') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Percent of People') # Label for the y-axis
# Display the chart
plt.show()
| figure_1071.png |
|
['lady', 'feed', 'item', 'brief', 'savage', 'doubt'] | [7, 6, 7, 2, 6, 7] | Most preferred objects | Number of People |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['lady', 'feed', 'item', 'brief', 'savage', 'doubt']
values = [7, 6, 7, 2, 6, 7]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Most preferred objects') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Number of People') # Label for the y-axis
# Display the chart
plt.show()
| figure_1072.png |
|
['flight', 'grace', 'shop', 'marine', 'suite', 'paint', 'fuel'] | [6, 5, 2, 7, 3, 5, 7] | Accuracy of different algorithms | Accuracy |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['flight', 'grace', 'shop', 'marine', 'suite', 'paint', 'fuel']
values = [6, 5, 2, 7, 3, 5, 7]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Accuracy of different algorithms') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Accuracy') # Label for the y-axis
# Display the chart
plt.show()
| figure_1073.png |
|
['skill', 'hat', 'trap'] | [6, 2, 4] | Title | Values |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['skill', 'hat', 'trap']
values = [6, 2, 4]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Title') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Values') # Label for the y-axis
# Display the chart
plt.show()
| figure_1074.png |
|
['profit', 'past', 'driver'] | [1, 7, 6] | Title | Values |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['profit', 'past', 'driver']
values = [1, 7, 6]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Title') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Values') # Label for the y-axis
# Display the chart
plt.show()
| figure_1075.png |
|
['vein', 'myth', 'holder', 'cheek', 'lap'] | [40, 20, 50, 90, 70] | Title | Values |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['vein', 'myth', 'holder', 'cheek', 'lap']
values = [40, 20, 50, 90, 70]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Title') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Values') # Label for the y-axis
# Display the chart
plt.show()
| figure_1076.png |
|
['hill', 'belt', 'disk', 'garage'] | [1, 1, 2, 2] | Accuracy of different algorithms | Accuracy |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['hill', 'belt', 'disk', 'garage']
values = [1, 1, 2, 2]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Accuracy of different algorithms') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Accuracy') # Label for the y-axis
# Display the chart
plt.show()
| figure_1077.png |
|
['list', 'couple', 'pistol', 'hole', 'lobby', 'jet'] | [2, 8, 4, 1, 8, 2] | Title | Values |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['list', 'couple', 'pistol', 'hole', 'lobby', 'jet']
values = [2, 8, 4, 1, 8, 2]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Title') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Values') # Label for the y-axis
# Display the chart
plt.show()
| figure_1078.png |
|
['coffee', 'middle', 'joy'] | [5, 1, 3] | Most preferred objects | Number of People |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['coffee', 'middle', 'joy']
values = [5, 1, 3]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Most preferred objects') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Number of People') # Label for the y-axis
# Display the chart
plt.show()
| figure_1079.png |
|
['price', 'skirt', 'bank', 'rise'] | [4, 2, 4, 2] | Accuracy of different algorithms | Accuracy |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['price', 'skirt', 'bank', 'rise']
values = [4, 2, 4, 2]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Accuracy of different algorithms') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Accuracy') # Label for the y-axis
# Display the chart
plt.show()
| figure_108.png |
|
['bride', 'battle', 'union', 'barrel', 'crowd', 'car', 'touch'] | [30, 90, 10, 90, 30, 50, 10] | Most preferred objects | Percent of People |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['bride', 'battle', 'union', 'barrel', 'crowd', 'car', 'touch']
values = [30, 90, 10, 90, 30, 50, 10]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Most preferred objects') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Percent of People') # Label for the y-axis
# Display the chart
plt.show()
| figure_1080.png |
|
['flood', 'barn'] | [10000000, 10] | Title | Values |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['flood', 'barn']
values = [10000000, 10]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Title') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Values') # Label for the y-axis
# Display the chart
plt.show()
| figure_1081.png |
|
['widow', 'bay', 'ladder', 'cloud', 'light', 'tax', 'shock', 'safety', 'opera'] | [8, 4, 5, 8, 3, 3, 6, 7, 2] | Accuracy of different algorithms | Accuracy |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['widow', 'bay', 'ladder', 'cloud', 'light', 'tax', 'shock', 'safety', 'opera']
values = [8, 4, 5, 8, 3, 3, 6, 7, 2]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Accuracy of different algorithms') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Accuracy') # Label for the y-axis
# Display the chart
plt.show()
| figure_1082.png |
|
['vice', 'none', 'savage', 'gate', 'walk'] | [4, 6, 9, 2, 7] | Sales statistics for different items | Units sold |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['vice', 'none', 'savage', 'gate', 'walk']
values = [4, 6, 9, 2, 7]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Sales statistics for different items') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Units sold') # Label for the y-axis
# Display the chart
plt.show()
| figure_1083.png |
|
['strip', 'bare', 'valley', 'iron'] | [3, 3, 9, 6] | Title | Values |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['strip', 'bare', 'valley', 'iron']
values = [3, 3, 9, 6]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Title') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Values') # Label for the y-axis
# Display the chart
plt.show()
| figure_1084.png |
|
['mother', 'plan', 'reply', 'horror'] | [40, 70, 20, 30] | Accuracy of different algorithms | Accuracy |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['mother', 'plan', 'reply', 'horror']
values = [40, 70, 20, 30]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Accuracy of different algorithms') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Accuracy') # Label for the y-axis
# Display the chart
plt.show()
| figure_1085.png |
|
['lay', 'clouds', 'poem', 'record', 'island', 'pink'] | [10, 60, 20, 20, 90, 80] | Title | Values |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['lay', 'clouds', 'poem', 'record', 'island', 'pink']
values = [10, 60, 20, 20, 90, 80]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Title') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Values') # Label for the y-axis
# Display the chart
plt.show()
| figure_1086.png |
|
['animal', 'notice', 'space', 'tube', 'belt', 'list'] | [3, 2, 1, 8, 3, 9] | Accuracy of different algorithms | Accuracy |
import matplotlib.pyplot as plt
# Categories and their corresponding values
categories = ['animal', 'notice', 'space', 'tube', 'belt', 'list']
values = [3, 2, 1, 8, 3, 9]
# Creating the bar chart
plt.figure(figsize=(8, 5)) # Set the figure size (optional)
plt.bar(categories, values, color='skyblue') # Plot the bars with skyblue color
# Adding title and labels
plt.title('Accuracy of different algorithms') # Add a title to the chart
plt.xlabel('Categories') # Label for the x-axis
plt.ylabel('Accuracy') # Label for the y-axis
# Display the chart
plt.show()
| figure_1087.png |