id stringlengths 7 11 | category stringclasses 7
values | difficulty stringclasses 3
values | prompt stringlengths 82 282 | solution stringlengths 96 321 | checks stringlengths 14 71 | input_data stringlengths 16 425 | expected_output stringlengths 500 3.55k |
|---|---|---|---|---|---|---|---|
line_001 | line_plots | easy | Using the provided x and y arrays, create a line plot with title 'Linear Growth', x-label 'X Values', and y-label 'Y Values'. | fig, ax = plt.subplots()
ax.plot(x, y)
ax.set_title('Linear Growth')
ax.set_xlabel('X Values')
ax.set_ylabel('Y Values') | ["line_data", "title", "xlabel", "ylabel"] | {"type": "arrays", "x": [1, 2, 3, 4, 5], "y": [2, 4, 6, 8, 10]} | {"axes_count": 1, "figure_size": [6.4, 4.8], "axes": [{"index": 0, "title": "Linear Growth", "xlabel": "X Values", "ylabel": "Y Values", "xlim": [0.8, 5.2], "ylim": [1.6, 10.4], "xscale": "linear", "yscale": "linear", "grid_on": false, "lines": [{"xdata": [1, 2, 3, 4, 5], "ydata": [2, 4, 6, 8, 10], "color": "#1f77b4", ... |
line_002 | line_plots | easy | Using the provided x and y arrays, create a line plot with title 'Sales Over Time', x-label 'Quarter', and y-label 'Revenue ($)'. | fig, ax = plt.subplots()
ax.plot(x, y)
ax.set_title('Sales Over Time')
ax.set_xlabel('Quarter')
ax.set_ylabel('Revenue ($)') | ["line_data", "title", "xlabel", "ylabel"] | {"type": "arrays", "x": [1, 2, 3, 4], "y": [100, 150, 130, 180]} | {"axes_count": 1, "figure_size": [6.4, 4.8], "axes": [{"index": 0, "title": "Sales Over Time", "xlabel": "Quarter", "ylabel": "Revenue ($)", "xlim": [0.85, 4.15], "ylim": [96.0, 184.0], "xscale": "linear", "yscale": "linear", "grid_on": false, "lines": [{"xdata": [1, 2, 3, 4], "ydata": [100, 150, 130, 180], "color": "#... |
line_003 | line_plots | easy | Using the provided x and y arrays, create a red line plot with title 'Quadratic Function', x-label 'Input', and y-label 'Output'. | fig, ax = plt.subplots()
ax.plot(x, y, color='red')
ax.set_title('Quadratic Function')
ax.set_xlabel('Input')
ax.set_ylabel('Output') | ["line_data", "line_color", "title", "xlabel", "ylabel"] | {"type": "arrays", "x": [0, 1, 2, 3], "y": [0, 1, 4, 9]} | {"axes_count": 1, "figure_size": [6.4, 4.8], "axes": [{"index": 0, "title": "Quadratic Function", "xlabel": "Input", "ylabel": "Output", "xlim": [-0.15000000000000002, 3.15], "ylim": [-0.45, 9.45], "xscale": "linear", "yscale": "linear", "grid_on": false, "lines": [{"xdata": [0, 1, 2, 3], "ydata": [0, 1, 4, 9], "color"... |
line_004 | line_plots | medium | Using the provided x, y1, and y2 arrays, create a plot with two lines. Plot y1 in blue and y2 in red. Add title 'Comparison', x-label 'X', and y-label 'Y'. | fig, ax = plt.subplots()
ax.plot(x, y1, color='blue')
ax.plot(x, y2, color='red')
ax.set_title('Comparison')
ax.set_xlabel('X')
ax.set_ylabel('Y') | ["line_count", "line_data", "line_color", "title", "xlabel", "ylabel"] | {"type": "multi_arrays", "x": [0, 1, 2, 3, 4], "y1": [0, 1, 4, 9, 16], "y2": [0, 2, 4, 6, 8]} | {"axes_count": 1, "figure_size": [6.4, 4.8], "axes": [{"index": 0, "title": "Comparison", "xlabel": "X", "ylabel": "Y", "xlim": [-0.2, 4.2], "ylim": [-0.8, 16.8], "xscale": "linear", "yscale": "linear", "grid_on": false, "lines": [{"xdata": [0, 1, 2, 3, 4], "ydata": [0, 1, 4, 9, 16], "color": "#0000ff", "linestyle": "-... |
line_005 | line_plots | medium | Using the provided x, y1, and y2 arrays, create a plot with two lines. Label y1 as 'quadratic' and y2 as 'linear'. Add a legend, title 'Function Comparison', x-label 'X', and y-label 'Y'. | fig, ax = plt.subplots()
ax.plot(x, y1, label='quadratic')
ax.plot(x, y2, label='linear')
ax.legend()
ax.set_title('Function Comparison')
ax.set_xlabel('X')
ax.set_ylabel('Y') | ["line_count", "line_data", "legend", "title", "xlabel", "ylabel"] | {"type": "multi_arrays", "x": [0, 1, 2], "y1": [0, 1, 4], "y2": [0, 1, 2]} | {"axes_count": 1, "figure_size": [6.4, 4.8], "axes": [{"index": 0, "title": "Function Comparison", "xlabel": "X", "ylabel": "Y", "xlim": [-0.1, 2.1], "ylim": [-0.2, 4.2], "xscale": "linear", "yscale": "linear", "grid_on": false, "lines": [{"xdata": [0, 1, 2], "ydata": [0, 1, 4], "color": "#1f77b4", "linestyle": "-", "m... |
line_006 | line_plots | medium | Using the provided x and y arrays, create a line plot with circle markers ('o') and a dashed line style ('--'). Add title 'Data Points', x-label 'Index', and y-label 'Value'. | fig, ax = plt.subplots()
ax.plot(x, y, marker='o', linestyle='--')
ax.set_title('Data Points')
ax.set_xlabel('Index')
ax.set_ylabel('Value') | ["line_data", "line_style", "title", "xlabel", "ylabel"] | {"type": "arrays", "x": [1, 2, 3, 4, 5], "y": [1, 3, 2, 4, 3]} | {"axes_count": 1, "figure_size": [6.4, 4.8], "axes": [{"index": 0, "title": "Data Points", "xlabel": "Index", "ylabel": "Value", "xlim": [0.8, 5.2], "ylim": [0.85, 4.15], "xscale": "linear", "yscale": "linear", "grid_on": false, "lines": [{"xdata": [1, 2, 3, 4, 5], "ydata": [1, 3, 2, 4, 3], "color": "#1f77b4", "linesty... |
line_007 | line_plots | hard | Using the provided x, y1, and y2 arrays, create a 2x1 subplot. Plot y1 in the top subplot with title 'Quadratic' and y2 in the bottom subplot with title 'Exponential'. Add grid to both. | fig, (ax1, ax2) = plt.subplots(2, 1)
ax1.plot(x, y1)
ax1.set_title('Quadratic')
ax1.grid(True)
ax2.plot(x, y2)
ax2.set_title('Exponential')
ax2.grid(True) | ["axes_count", "line_data", "grid"] | {"type": "multi_arrays", "x": [0, 1, 2, 3, 4], "y1": [0, 1, 4, 9, 16], "y2": [1, 2, 4, 8, 16]} | {"axes_count": 2, "figure_size": [6.4, 4.8], "axes": [{"index": 0, "title": "Quadratic", "xlabel": "", "ylabel": "", "xlim": [-0.2, 4.2], "ylim": [-0.8, 16.8], "xscale": "linear", "yscale": "linear", "grid_on": true, "lines": [{"xdata": [0, 1, 2, 3, 4], "ydata": [0, 1, 4, 9, 16], "color": "#1f77b4", "linestyle": "-", "... |
line_008 | line_plots | hard | Using the provided x and y arrays, create a line plot with logarithmic scale on the y-axis. Add title 'Exponential Growth', x-label 'Time', and y-label 'Value (log scale)'. | fig, ax = plt.subplots()
ax.plot(x, y)
ax.set_yscale('log')
ax.set_title('Exponential Growth')
ax.set_xlabel('Time')
ax.set_ylabel('Value (log scale)') | ["line_data", "yscale", "title", "xlabel", "ylabel"] | {"type": "arrays", "x": [1, 2, 3, 4, 5], "y": [10, 100, 1000, 10000, 100000]} | {"axes_count": 1, "figure_size": [6.4, 4.8], "axes": [{"index": 0, "title": "Exponential Growth", "xlabel": "Time", "ylabel": "Value (log scale)", "xlim": [0.8, 5.2], "ylim": [6.309573444801933, 158489.3192461114], "xscale": "linear", "yscale": "log", "grid_on": false, "lines": [{"xdata": [1, 2, 3, 4, 5], "ydata": [10,... |
scatter_001 | scatter_plots | easy | Using the provided x and y arrays, create a scatter plot with title 'Squared Values', x-label 'X', and y-label 'X Squared'. | fig, ax = plt.subplots()
ax.scatter(x, y)
ax.set_title('Squared Values')
ax.set_xlabel('X')
ax.set_ylabel('X Squared') | ["scatter_data", "title", "xlabel", "ylabel"] | {"type": "arrays", "x": [1, 2, 3, 4], "y": [1, 4, 9, 16]} | {"axes_count": 1, "figure_size": [6.4, 4.8], "axes": [{"index": 0, "title": "Squared Values", "xlabel": "X", "ylabel": "X Squared", "xlim": [0.85, 4.15], "ylim": [0.25, 16.75], "xscale": "linear", "yscale": "linear", "grid_on": false, "lines": [], "line_count": 0, "collections": [{"offsets": [[1.0, 1.0], [2.0, 4.0], [3... |
scatter_002 | scatter_plots | easy | Using the provided x and y arrays, create a scatter plot with green markers. Add title 'Inverse Relationship', x-label 'X', and y-label 'Y'. | fig, ax = plt.subplots()
ax.scatter(x, y, c='green')
ax.set_title('Inverse Relationship')
ax.set_xlabel('X')
ax.set_ylabel('Y') | ["scatter_data", "scatter_color", "title", "xlabel", "ylabel"] | {"type": "arrays", "x": [1, 2, 3, 4, 5], "y": [5, 4, 3, 2, 1]} | {"axes_count": 1, "figure_size": [6.4, 4.8], "axes": [{"index": 0, "title": "Inverse Relationship", "xlabel": "X", "ylabel": "Y", "xlim": [0.8, 5.2], "ylim": [0.8, 5.2], "xscale": "linear", "yscale": "linear", "grid_on": false, "lines": [], "line_count": 0, "collections": [{"offsets": [[1.0, 5.0], [2.0, 4.0], [3.0, 3.0... |
scatter_003 | scatter_plots | easy | Using the provided x and y arrays, create a scatter plot with marker size 100. Add title 'Large Markers', x-label 'X', and y-label 'Y'. | fig, ax = plt.subplots()
ax.scatter(x, y, s=100)
ax.set_title('Large Markers')
ax.set_xlabel('X')
ax.set_ylabel('Y') | ["scatter_data", "scatter_size", "title", "xlabel", "ylabel"] | {"type": "arrays", "x": [1, 2, 3], "y": [1, 2, 3]} | {"axes_count": 1, "figure_size": [6.4, 4.8], "axes": [{"index": 0, "title": "Large Markers", "xlabel": "X", "ylabel": "Y", "xlim": [0.9, 3.1], "ylim": [0.9, 3.1], "xscale": "linear", "yscale": "linear", "grid_on": false, "lines": [], "line_count": 0, "collections": [{"offsets": [[1.0, 1.0], [2.0, 2.0], [3.0, 3.0]], "fa... |
scatter_004 | scatter_plots | medium | Using the provided x, y_a, and y_b arrays, create two scatter series. Plot y_a in red and y_b in blue. Add title 'Two Groups', x-label 'X', and y-label 'Y'. | fig, ax = plt.subplots()
ax.scatter(x, y_a, c='red')
ax.scatter(x, y_b, c='blue')
ax.set_title('Two Groups')
ax.set_xlabel('X')
ax.set_ylabel('Y') | ["scatter_data", "scatter_color", "title", "xlabel", "ylabel"] | {"type": "multi_arrays", "x": [1, 2, 3], "y_a": [2, 3, 4], "y_b": [4, 5, 6]} | {"axes_count": 1, "figure_size": [6.4, 4.8], "axes": [{"index": 0, "title": "Two Groups", "xlabel": "X", "ylabel": "Y", "xlim": [0.9, 3.1], "ylim": [1.8, 6.2], "xscale": "linear", "yscale": "linear", "grid_on": false, "lines": [], "line_count": 0, "collections": [{"offsets": [[1.0, 2.0], [2.0, 3.0], [3.0, 4.0]], "facec... |
scatter_005 | scatter_plots | medium | Using the provided x, y_a, and y_b arrays, create two scatter series with alpha=0.5 for y_a and alpha=0.7 for y_b. Add a legend with labels 'A' and 'B', title 'Transparency Demo', x-label 'X', and y-label 'Y'. | fig, ax = plt.subplots()
ax.scatter(x, y_a, alpha=0.5, label='A')
ax.scatter(x, y_b, alpha=0.7, label='B')
ax.legend()
ax.set_title('Transparency Demo')
ax.set_xlabel('X')
ax.set_ylabel('Y') | ["scatter_data", "legend", "title", "xlabel", "ylabel"] | {"type": "multi_arrays", "x": [1, 2, 3], "y_a": [2, 3, 4], "y_b": [4, 5, 6]} | {"axes_count": 1, "figure_size": [6.4, 4.8], "axes": [{"index": 0, "title": "Transparency Demo", "xlabel": "X", "ylabel": "Y", "xlim": [0.9, 3.1], "ylim": [1.8, 6.2], "xscale": "linear", "yscale": "linear", "grid_on": false, "lines": [], "line_count": 0, "collections": [{"offsets": [[1.0, 2.0], [2.0, 3.0], [3.0, 4.0]],... |
scatter_006 | scatter_plots | hard | Using the provided x, y, and sizes arrays, create a scatter plot where each point has a different size based on the sizes array. Add title 'Variable Size Points', x-label 'X', and y-label 'Y'. | fig, ax = plt.subplots()
ax.scatter(x, y, s=sizes)
ax.set_title('Variable Size Points')
ax.set_xlabel('X')
ax.set_ylabel('Y') | ["scatter_data", "scatter_size", "title", "xlabel", "ylabel"] | {"type": "multi_arrays", "x": [1, 2, 3, 4, 5], "y": [1, 4, 9, 16, 25], "sizes": [20, 50, 100, 200, 400]} | {"axes_count": 1, "figure_size": [6.4, 4.8], "axes": [{"index": 0, "title": "Variable Size Points", "xlabel": "X", "ylabel": "Y", "xlim": [0.8, 5.2], "ylim": [-0.20000000000000018, 26.2], "xscale": "linear", "yscale": "linear", "grid_on": false, "lines": [], "line_count": 0, "collections": [{"offsets": [[1.0, 1.0], [2.... |
scatter_007 | scatter_plots | hard | Using the provided x, y, and colors arrays, create a scatter plot where point colors are determined by the colors array using the 'viridis' colormap. Add a colorbar, title 'Color Mapped Data', x-label 'X', and y-label 'Y'. | fig, ax = plt.subplots()
sc = ax.scatter(x, y, c=colors, cmap='viridis')
plt.colorbar(sc)
ax.set_title('Color Mapped Data')
ax.set_xlabel('X')
ax.set_ylabel('Y') | ["scatter_data", "title", "xlabel", "ylabel"] | {"type": "multi_arrays", "x": [1, 2, 3, 4, 5], "y": [1, 2, 3, 4, 5], "colors": [0, 25, 50, 75, 100]} | {"axes_count": 2, "figure_size": [6.4, 4.8], "axes": [{"index": 0, "title": "Color Mapped Data", "xlabel": "X", "ylabel": "Y", "xlim": [0.8, 5.2], "ylim": [0.8, 5.2], "xscale": "linear", "yscale": "linear", "grid_on": false, "lines": [], "line_count": 0, "collections": [{"offsets": [[1.0, 1.0], [2.0, 2.0], [3.0, 3.0], ... |
bar_001 | bar_charts | easy | Using the provided categories and heights arrays, create a bar chart with title 'Category Counts', x-label 'Category', and y-label 'Count'. | fig, ax = plt.subplots()
ax.bar(categories, heights)
ax.set_title('Category Counts')
ax.set_xlabel('Category')
ax.set_ylabel('Count') | ["bar_data", "title", "xlabel", "ylabel"] | {"type": "bar_data", "categories": ["A", "B", "C"], "heights": [10, 20, 15]} | {"axes_count": 1, "figure_size": [6.4, 4.8], "axes": [{"index": 0, "title": "Category Counts", "xlabel": "Category", "ylabel": "Count", "xlim": [-0.54, 2.5400000000000005], "ylim": [0.0, 21.0], "xscale": "linear", "yscale": "linear", "grid_on": false, "lines": [], "line_count": 0, "collections": [], "collection_count":... |
bar_002 | bar_charts | easy | Using the provided categories and heights arrays, create a bar chart with orange bars. Add title 'Sales by Region', x-label 'Region', and y-label 'Sales'. | fig, ax = plt.subplots()
ax.bar(categories, heights, color='orange')
ax.set_title('Sales by Region')
ax.set_xlabel('Region')
ax.set_ylabel('Sales') | ["bar_data", "bar_color", "title", "xlabel", "ylabel"] | {"type": "bar_data", "categories": ["X", "Y", "Z"], "heights": [5, 15, 10]} | {"axes_count": 1, "figure_size": [6.4, 4.8], "axes": [{"index": 0, "title": "Sales by Region", "xlabel": "Region", "ylabel": "Sales", "xlim": [-0.54, 2.5400000000000005], "ylim": [0.0, 15.75], "xscale": "linear", "yscale": "linear", "grid_on": false, "lines": [], "line_count": 0, "collections": [], "collection_count": ... |
bar_003 | bar_charts | medium | Using the provided categories and widths arrays, create a horizontal bar chart with title 'Product Revenue', x-label 'Revenue ($)', and y-label 'Product'. | fig, ax = plt.subplots()
ax.barh(categories, widths)
ax.set_title('Product Revenue')
ax.set_xlabel('Revenue ($)')
ax.set_ylabel('Product') | ["bar_data", "title", "xlabel", "ylabel"] | {"type": "bar_data", "categories": ["Product A", "Product B", "Product C"], "widths": [100, 150, 80]} | {"axes_count": 1, "figure_size": [6.4, 4.8], "axes": [{"index": 0, "title": "Product Revenue", "xlabel": "Revenue ($)", "ylabel": "Product", "xlim": [0.0, 157.5], "ylim": [-0.54, 2.5400000000000005], "xscale": "linear", "yscale": "linear", "grid_on": false, "lines": [], "line_count": 0, "collections": [], "collection_c... |
bar_004 | bar_charts | medium | Using the provided categories and heights arrays, create a bar chart with blue fill and black edge color with edge width of 2. Add title 'Monthly Sales', x-label 'Month', and y-label 'Sales'. | fig, ax = plt.subplots()
ax.bar(categories, heights, color='blue', edgecolor='black', linewidth=2)
ax.set_title('Monthly Sales')
ax.set_xlabel('Month')
ax.set_ylabel('Sales') | ["bar_data", "bar_color", "title", "xlabel", "ylabel"] | {"type": "bar_data", "categories": ["Jan", "Feb", "Mar", "Apr"], "heights": [30, 45, 38, 50]} | {"axes_count": 1, "figure_size": [6.4, 4.8], "axes": [{"index": 0, "title": "Monthly Sales", "xlabel": "Month", "ylabel": "Sales", "xlim": [-0.5900000000000001, 3.5900000000000003], "ylim": [0.0, 52.5], "xscale": "linear", "yscale": "linear", "grid_on": false, "lines": [], "line_count": 0, "collections": [], "collectio... |
bar_005 | bar_charts | medium | Using the provided categories, values_a, and values_b arrays, create a grouped bar chart. Place group A bars and group B bars side by side. Use width=0.35 and label them 'Group A' and 'Group B'. Add a legend, title 'Quarterly Comparison', x-label 'Quarter', and y-label 'Value'. | fig, ax = plt.subplots()
x = np.arange(len(categories))
width = 0.35
ax.bar(x - width/2, values_a, width, label='Group A')
ax.bar(x + width/2, values_b, width, label='Group B')
ax.set_xticks(x)
ax.set_xticklabels(categories)
ax.legend()
ax.set_title('Quarterly Comparison')
ax.set_xlabel('Quarter')
ax.set_ylabel('Value'... | ["bar_data", "legend", "xticklabels", "title", "xlabel", "ylabel"] | {"type": "grouped_bar_data", "categories": ["Q1", "Q2", "Q3"], "values_a": [20, 35, 30], "values_b": [25, 32, 34]} | {"axes_count": 1, "figure_size": [6.4, 4.8], "axes": [{"index": 0, "title": "Quarterly Comparison", "xlabel": "Quarter", "ylabel": "Value", "xlim": [-0.485, 2.4849999999999994], "ylim": [0.0, 36.75], "xscale": "linear", "yscale": "linear", "grid_on": false, "lines": [], "line_count": 0, "collections": [], "collection_c... |
bar_006 | bar_charts | hard | Using the provided categories, heights_a, and heights_b arrays, create a stacked bar chart. Plot heights_a in blue with label 'Product A', then stack heights_b on top in orange with label 'Product B'. Add a legend, title 'Stacked Revenue', x-label 'Quarter', and y-label 'Revenue'. | fig, ax = plt.subplots()
ax.bar(categories, heights_a, label='Product A', color='blue')
ax.bar(categories, heights_b, bottom=heights_a, label='Product B', color='orange')
ax.legend()
ax.set_title('Stacked Revenue')
ax.set_xlabel('Quarter')
ax.set_ylabel('Revenue') | ["bar_data", "bar_color", "legend", "title", "xlabel", "ylabel"] | {"type": "stacked_bar_data", "categories": ["Q1", "Q2", "Q3"], "heights_a": [20, 25, 30], "heights_b": [15, 20, 25]} | {"axes_count": 1, "figure_size": [6.4, 4.8], "axes": [{"index": 0, "title": "Stacked Revenue", "xlabel": "Quarter", "ylabel": "Revenue", "xlim": [-0.54, 2.5400000000000005], "ylim": [0.0, 57.75], "xscale": "linear", "yscale": "linear", "grid_on": false, "lines": [], "line_count": 0, "collections": [], "collection_count... |
bar_007 | bar_charts | hard | Using the provided categories, heights, and errors arrays, create a bar chart with error bars (capsize=5). Add title 'Measurements with Error', x-label 'Sample', and y-label 'Value'. | fig, ax = plt.subplots()
ax.bar(categories, heights, yerr=errors, capsize=5)
ax.set_title('Measurements with Error')
ax.set_xlabel('Sample')
ax.set_ylabel('Value') | ["bar_data", "title", "xlabel", "ylabel"] | {"type": "bar_error_data", "categories": ["A", "B", "C", "D"], "heights": [25, 40, 30, 55], "errors": [2, 3, 2.5, 4]} | {"axes_count": 1, "figure_size": [6.4, 4.8], "axes": [{"index": 0, "title": "Measurements with Error", "xlabel": "Sample", "ylabel": "Value", "xlim": [-0.5900000000000001, 3.5900000000000003], "ylim": [0.0, 61.95], "xscale": "linear", "yscale": "linear", "grid_on": false, "lines": [{"xdata": [0.0, 1.0, 2.0, 3.0], "ydat... |
bar_008 | bar_charts | hard | Using the provided categories and heights arrays, create a bar chart and add the height value as a text label on top of each bar. Add title 'Values with Labels', x-label 'Category', and y-label 'Value'. | fig, ax = plt.subplots()
bars = ax.bar(categories, heights)
for bar in bars:
height = bar.get_height()
ax.text(bar.get_x() + bar.get_width()/2., height, f'{height}', ha='center', va='bottom')
ax.set_title('Values with Labels')
ax.set_xlabel('Category')
ax.set_ylabel('Value') | ["bar_data", "texts", "title", "xlabel", "ylabel"] | {"type": "bar_data", "categories": ["A", "B", "C"], "heights": [10, 25, 15]} | {"axes_count": 1, "figure_size": [6.4, 4.8], "axes": [{"index": 0, "title": "Values with Labels", "xlabel": "Category", "ylabel": "Value", "xlim": [-0.54, 2.5400000000000005], "ylim": [0.0, 26.25], "xscale": "linear", "yscale": "linear", "grid_on": false, "lines": [], "line_count": 0, "collections": [], "collection_cou... |
hist_001 | histograms | easy | Using the provided values array, create a histogram with 5 bins and black edges (edgecolor='black'). Add title 'Value Distribution', x-label 'Value', and y-label 'Frequency'. | fig, ax = plt.subplots()
ax.hist(values, bins=5, edgecolor='black')
ax.set_title('Value Distribution')
ax.set_xlabel('Value')
ax.set_ylabel('Frequency') | ["patch_count", "title", "xlabel", "ylabel"] | {"type": "hist_data", "values": [1, 2, 2, 3, 3, 3, 4, 4, 5, 5, 5, 5]} | {"axes_count": 1, "figure_size": [6.4, 4.8], "axes": [{"index": 0, "title": "Value Distribution", "xlabel": "Value", "ylabel": "Frequency", "xlim": [0.7999999999999998, 5.199999999999999], "ylim": [0.0, 4.2], "xscale": "linear", "yscale": "linear", "grid_on": false, "lines": [], "line_count": 0, "collections": [], "col... |
hist_002 | histograms | easy | Using the provided values array, create a histogram with green bars, black edges (edgecolor='black'), and 10 bins. Add title 'Data Histogram', x-label 'Value', and y-label 'Count'. | fig, ax = plt.subplots()
ax.hist(values, bins=10, color='green', edgecolor='black')
ax.set_title('Data Histogram')
ax.set_xlabel('Value')
ax.set_ylabel('Count') | ["patch_count", "bar_color", "title", "xlabel", "ylabel"] | {"type": "hist_data", "values": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50]} | {"axes_count": 1, "figure_size": [6.4, 4.8], "axes": [{"index": 0, "title": "Data Histogram", "xlabel": "Value", "ylabel": "Count", "xlim": [-1.4500000000000006, 52.45], "ylim": [0.0, 5.25], "xscale": "linear", "yscale": "linear", "grid_on": false, "lines": [], "line_count": 0, "collections": [], "collection_count": 0,... |
hist_003 | histograms | medium | Using the provided values array, create a histogram with blue bars, black edges (edgecolor='black'), and 8 bins. Add title 'Score Distribution', x-label 'Score', and y-label 'Frequency'. | fig, ax = plt.subplots()
ax.hist(values, bins=8, color='blue', edgecolor='black')
ax.set_title('Score Distribution')
ax.set_xlabel('Score')
ax.set_ylabel('Frequency') | ["patch_count", "bar_color", "title", "xlabel", "ylabel"] | {"type": "hist_data", "values": [10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80]} | {"axes_count": 1, "figure_size": [6.4, 4.8], "axes": [{"index": 0, "title": "Score Distribution", "xlabel": "Score", "ylabel": "Frequency", "xlim": [6.5, 83.5], "ylim": [0.0, 2.1], "xscale": "linear", "yscale": "linear", "grid_on": false, "lines": [], "line_count": 0, "collections": [], "collection_count": 0, "patches"... |
hist_004 | histograms | medium | Using the provided values array, create a histogram with density=True (normalized so area sums to 1), black edges (edgecolor='black'). Use the default number of bins. Add title 'Probability Density', x-label 'Value', and y-label 'Density'. | fig, ax = plt.subplots()
ax.hist(values, density=True, edgecolor='black')
ax.set_title('Probability Density')
ax.set_xlabel('Value')
ax.set_ylabel('Density') | ["patch_count", "title", "xlabel", "ylabel"] | {"type": "hist_data", "values": [1, 1, 2, 2, 2, 3, 3, 4, 5, 5, 5, 5]} | {"axes_count": 1, "figure_size": [6.4, 4.8], "axes": [{"index": 0, "title": "Probability Density", "xlabel": "Value", "ylabel": "Density", "xlim": [0.8, 5.2], "ylim": [0.0, 0.8749999999999992], "xscale": "linear", "yscale": "linear", "grid_on": false, "lines": [], "line_count": 0, "collections": [], "collection_count":... |
hist_005 | histograms | hard | Using the provided values_a and values_b arrays, create two overlapping histograms with alpha=0.5 and black edges (edgecolor='black'). Use 10 bins, label them 'Group A' and 'Group B'. Add a legend, title 'Group Comparison', x-label 'Value', and y-label 'Frequency'. | fig, ax = plt.subplots()
ax.hist(values_a, bins=10, alpha=0.5, edgecolor='black', label='Group A')
ax.hist(values_b, bins=10, alpha=0.5, edgecolor='black', label='Group B')
ax.legend()
ax.set_title('Group Comparison')
ax.set_xlabel('Value')
ax.set_ylabel('Frequency') | ["patch_count", "legend", "title", "xlabel", "ylabel"] | {"type": "multi_hist_data", "values_a": [20, 25, 30, 35, 40, 45, 50, 55, 60], "values_b": [30, 35, 40, 45, 50, 55, 60, 65, 70]} | {"axes_count": 1, "figure_size": [6.4, 4.8], "axes": [{"index": 0, "title": "Group Comparison", "xlabel": "Value", "ylabel": "Frequency", "xlim": [17.5, 72.5], "ylim": [0.0, 1.05], "xscale": "linear", "yscale": "linear", "grid_on": false, "lines": [], "line_count": 0, "collections": [], "collection_count": 0, "patches"... |
hist_006 | histograms | hard | Using the provided values array, create a cumulative histogram (cumulative=True) with 20 bins and black edges (edgecolor='black'). Add title 'Cumulative Distribution', x-label 'Value', and y-label 'Cumulative Count'. | fig, ax = plt.subplots()
ax.hist(values, bins=20, cumulative=True, edgecolor='black')
ax.set_title('Cumulative Distribution')
ax.set_xlabel('Value')
ax.set_ylabel('Cumulative Count') | ["patch_count", "title", "xlabel", "ylabel"] | {"type": "hist_data", "values": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, ... | {"axes_count": 1, "figure_size": [6.4, 4.8], "axes": [{"index": 0, "title": "Cumulative Distribution", "xlabel": "Value", "ylabel": "Cumulative Count", "xlim": [-3.9500000000000015, 104.95000000000002], "ylim": [0.0, 105.0], "xscale": "linear", "yscale": "linear", "grid_on": false, "lines": [], "line_count": 0, "collec... |
box_001 | boxplots | medium | Using the provided data array, create a basic boxplot with default styling. Add title 'Data Distribution', x-label 'Sample', and y-label 'Value'. | fig, ax = plt.subplots()
ax.boxplot(data)
ax.set_title('Data Distribution')
ax.set_xlabel('Sample')
ax.set_ylabel('Value') | ["box_count", "title", "xlabel", "ylabel"] | {"type": "box_data", "data": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]} | {"axes_count": 1, "figure_size": [6.4, 4.8], "axes": [{"index": 0, "title": "Data Distribution", "xlabel": "Sample", "ylabel": "Value", "xlim": [0.5, 1.5], "ylim": [0.55, 10.45], "xscale": "linear", "yscale": "linear", "grid_on": false, "lines": [{"xdata": [0.925, 1.075, 1.075, 0.925, 0.925], "ydata": [3.25, 3.25, 7.75... |
box_002 | boxplots | medium | Using the provided data_a, data_b, and data_c arrays, create three boxplots on a single axes with default styling. Add title 'Group Comparison', x-label 'Group', and y-label 'Value'. | fig, ax = plt.subplots()
ax.boxplot([data_a, data_b, data_c])
ax.set_title('Group Comparison')
ax.set_xlabel('Group')
ax.set_ylabel('Value') | ["box_count", "title", "xlabel", "ylabel"] | {"type": "multi_box_data", "data_a": [1, 2, 3, 4, 5], "data_b": [3, 4, 5, 6, 7], "data_c": [5, 6, 7, 8, 9]} | {"axes_count": 1, "figure_size": [6.4, 4.8], "axes": [{"index": 0, "title": "Group Comparison", "xlabel": "Group", "ylabel": "Value", "xlim": [0.5, 3.5], "ylim": [0.6, 9.4], "xscale": "linear", "yscale": "linear", "grid_on": false, "lines": [{"xdata": [0.85, 1.15, 1.15, 0.85, 0.85], "ydata": [2.0, 2.0, 4.0, 4.0, 2.0], ... |
box_003 | boxplots | medium | Using the provided data_a and data_b arrays, create two boxplots with labels 'Control' and 'Treatment' using default styling. Add title 'Experiment Results', x-label 'Group', and y-label 'Measurement'. | fig, ax = plt.subplots()
ax.boxplot([data_a, data_b], labels=['Control', 'Treatment'])
ax.set_title('Experiment Results')
ax.set_xlabel('Group')
ax.set_ylabel('Measurement') | ["box_count", "xticklabels", "title", "xlabel", "ylabel"] | {"type": "multi_box_data", "data_a": [10, 12, 14, 16, 18], "data_b": [20, 22, 24, 26, 28]} | {"axes_count": 1, "figure_size": [6.4, 4.8], "axes": [{"index": 0, "title": "Experiment Results", "xlabel": "Group", "ylabel": "Measurement", "xlim": [0.5, 2.5], "ylim": [9.1, 28.9], "xscale": "linear", "yscale": "linear", "grid_on": false, "lines": [{"xdata": [0.925, 1.075, 1.075, 0.925, 0.925], "ydata": [12.0, 12.0, ... |
box_004 | boxplots | hard | Using the provided data_a and data_b arrays, create two horizontal boxplots (vert=False) with default styling. Add title 'Horizontal Boxplots', x-label 'Value', and y-label 'Dataset'. | fig, ax = plt.subplots()
ax.boxplot([data_a, data_b], vert=False)
ax.set_title('Horizontal Boxplots')
ax.set_xlabel('Value')
ax.set_ylabel('Dataset') | ["box_count", "title", "xlabel", "ylabel"] | {"type": "multi_box_data", "data_a": [15, 18, 20, 22, 25], "data_b": [30, 32, 35, 38, 40]} | {"axes_count": 1, "figure_size": [6.4, 4.8], "axes": [{"index": 0, "title": "Horizontal Boxplots", "xlabel": "Value", "ylabel": "Dataset", "xlim": [13.75, 41.25], "ylim": [0.5, 2.5], "xscale": "linear", "yscale": "linear", "grid_on": false, "lines": [{"xdata": [18.0, 18.0, 22.0, 22.0, 18.0], "ydata": [0.925, 1.075, 1.0... |
box_005 | boxplots | hard | Using the provided DataFrame df (with columns 'value' and 'group'), create a seaborn boxplot comparing the groups. Add title 'Group Analysis', x-label 'Group', and y-label 'Value'. | fig, ax = plt.subplots()
sns.boxplot(data=df, x='group', y='value', ax=ax)
ax.set_title('Group Analysis')
ax.set_xlabel('Group')
ax.set_ylabel('Value') | ["patch_count", "title", "xlabel", "ylabel"] | {"type": "dataframe", "df": {"value": [10, 12, 14, 16, 18, 20, 22, 24, 26, 28], "group": ["A", "A", "A", "A", "A", "B", "B", "B", "B", "B"]}} | {"axes_count": 1, "figure_size": [6.4, 4.8], "axes": [{"index": 0, "title": "Group Analysis", "xlabel": "Group", "ylabel": "Value", "xlim": [-0.5, 1.5], "ylim": [9.1, 28.9], "xscale": "linear", "yscale": "linear", "grid_on": false, "lines": [{"xdata": [0.0, 0.0], "ydata": [12.0, 10.0], "color": "#3f3f3f", "linestyle": ... |
box_006 | boxplots | hard | Using the provided data_a and data_b arrays, create a violin plot showing both distributions with default styling. Add title 'Distribution Comparison', x-label 'Dataset', and y-label 'Value'. | fig, ax = plt.subplots()
ax.violinplot([data_a, data_b])
ax.set_title('Distribution Comparison')
ax.set_xlabel('Dataset')
ax.set_ylabel('Value') | ["violin_count", "title", "xlabel", "ylabel"] | {"type": "multi_box_data", "data_a": [45, 48, 50, 52, 55, 47, 49, 51, 53, 46], "data_b": [55, 58, 60, 62, 65, 57, 59, 61, 63, 56]} | {"axes_count": 1, "figure_size": [6.4, 4.8], "axes": [{"index": 0, "title": "Distribution Comparison", "xlabel": "Dataset", "ylabel": "Value", "xlim": [0.675, 2.325], "ylim": [44.0, 66.0], "xscale": "linear", "yscale": "linear", "grid_on": false, "lines": [], "line_count": 0, "collections": [{"offsets": [[0.0, 0.0]], "... |
text_001 | annotations | easy | Create an empty plot with title 'My Plot', x-label 'X Axis', and y-label 'Y Axis'. | fig, ax = plt.subplots()
ax.set_title('My Plot')
ax.set_xlabel('X Axis')
ax.set_ylabel('Y Axis') | ["title", "xlabel", "ylabel"] | {"type": "none"} | {"axes_count": 1, "figure_size": [6.4, 4.8], "axes": [{"index": 0, "title": "My Plot", "xlabel": "X Axis", "ylabel": "Y Axis", "xlim": [0.0, 1.0], "ylim": [0.0, 1.0], "xscale": "linear", "yscale": "linear", "grid_on": false, "lines": [], "line_count": 0, "collections": [], "collection_count": 0, "patches": [], "patch_c... |
text_002 | annotations | easy | Using the provided x and y arrays, create a line plot with title 'Temperature Over Time', x-label 'Day', and y-label 'Temperature (C)'. | fig, ax = plt.subplots()
ax.plot(x, y)
ax.set_title('Temperature Over Time')
ax.set_xlabel('Day')
ax.set_ylabel('Temperature (C)') | ["line_data", "title", "xlabel", "ylabel"] | {"type": "arrays", "x": [1, 2, 3, 4, 5], "y": [20, 22, 21, 23, 25]} | {"axes_count": 1, "figure_size": [6.4, 4.8], "axes": [{"index": 0, "title": "Temperature Over Time", "xlabel": "Day", "ylabel": "Temperature (C)", "xlim": [0.8, 5.2], "ylim": [19.75, 25.25], "xscale": "linear", "yscale": "linear", "grid_on": false, "lines": [{"xdata": [1, 2, 3, 4, 5], "ydata": [20, 22, 21, 23, 25], "co... |
text_003 | annotations | medium | Using the provided x and y arrays, create a line plot. Add a text annotation 'Peak' at position (3, 9). Add title 'Peak Detection', x-label 'X', and y-label 'Y'. | fig, ax = plt.subplots()
ax.plot(x, y)
ax.text(3, 9, 'Peak')
ax.set_title('Peak Detection')
ax.set_xlabel('X')
ax.set_ylabel('Y') | ["line_data", "texts", "title", "xlabel", "ylabel"] | {"type": "arrays", "x": [1, 2, 3, 4, 5], "y": [1, 4, 9, 4, 1]} | {"axes_count": 1, "figure_size": [6.4, 4.8], "axes": [{"index": 0, "title": "Peak Detection", "xlabel": "X", "ylabel": "Y", "xlim": [0.8, 5.2], "ylim": [0.6, 9.4], "xscale": "linear", "yscale": "linear", "grid_on": false, "lines": [{"xdata": [1, 2, 3, 4, 5], "ydata": [1, 4, 9, 4, 1], "color": "#1f77b4", "linestyle": "-... |
text_004 | annotations | medium | Using the provided x and y arrays, create a line plot. Add an annotation 'Maximum' pointing to the point (4, 16) with an arrow from position (2, 12). Add title 'Quadratic Function', x-label 'X', and y-label 'X Squared'. | fig, ax = plt.subplots()
ax.plot(x, y)
ax.annotate('Maximum', xy=(4, 16), xytext=(2, 12), arrowprops=dict(arrowstyle='->'))
ax.set_title('Quadratic Function')
ax.set_xlabel('X')
ax.set_ylabel('X Squared') | ["line_data", "texts", "title", "xlabel", "ylabel"] | {"type": "arrays", "x": [0, 1, 2, 3, 4], "y": [0, 1, 4, 9, 16]} | {"axes_count": 1, "figure_size": [6.4, 4.8], "axes": [{"index": 0, "title": "Quadratic Function", "xlabel": "X", "ylabel": "X Squared", "xlim": [-0.2, 4.2], "ylim": [-0.8, 16.8], "xscale": "linear", "yscale": "linear", "grid_on": false, "lines": [{"xdata": [0, 1, 2, 3, 4], "ydata": [0, 1, 4, 9, 16], "color": "#1f77b4",... |
text_005 | annotations | hard | Using the provided x and y arrays, create a line plot. Add annotations 'Start' at the first point and 'End' at the last point, both with arrows. Add title 'Journey Progress', x-label 'Time', and y-label 'Progress'. | fig, ax = plt.subplots()
ax.plot(x, y)
ax.annotate('Start', xy=(0, 10), xytext=(0.5, 8), arrowprops=dict(arrowstyle='->'))
ax.annotate('End', xy=(4, 20), xytext=(3.5, 22), arrowprops=dict(arrowstyle='->'))
ax.set_title('Journey Progress')
ax.set_xlabel('Time')
ax.set_ylabel('Progress') | ["line_data", "texts", "title", "xlabel", "ylabel"] | {"type": "arrays", "x": [0, 1, 2, 3, 4], "y": [10, 15, 12, 18, 20]} | {"axes_count": 1, "figure_size": [6.4, 4.8], "axes": [{"index": 0, "title": "Journey Progress", "xlabel": "Time", "ylabel": "Progress", "xlim": [-0.2, 4.2], "ylim": [9.5, 20.5], "xscale": "linear", "yscale": "linear", "grid_on": false, "lines": [{"xdata": [0, 1, 2, 3, 4], "ydata": [10, 15, 12, 18, 20], "color": "#1f77b... |
text_006 | annotations | medium | Using the provided x and y arrays, create a line plot. Set x-axis limits to (0, 10) and y-axis limits to (0, 50). Add title 'Linear Trend', x-label 'X', and y-label 'Y'. | fig, ax = plt.subplots()
ax.plot(x, y)
ax.set_xlim(0, 10)
ax.set_ylim(0, 50)
ax.set_title('Linear Trend')
ax.set_xlabel('X')
ax.set_ylabel('Y') | ["line_data", "xlim", "ylim", "title", "xlabel", "ylabel"] | {"type": "arrays", "x": [1, 2, 3, 4, 5], "y": [10, 20, 30, 40, 50]} | {"axes_count": 1, "figure_size": [6.4, 4.8], "axes": [{"index": 0, "title": "Linear Trend", "xlabel": "X", "ylabel": "Y", "xlim": [0.0, 10.0], "ylim": [0.0, 50.0], "xscale": "linear", "yscale": "linear", "grid_on": false, "lines": [{"xdata": [1, 2, 3, 4, 5], "ydata": [10, 20, 30, 40, 50], "color": "#1f77b4", "linestyle... |
text_007 | annotations | hard | Using the provided categories and heights arrays, create a bar chart. Set the x-tick labels to ['January', 'February', 'March'] with 45-degree rotation. Add title 'Monthly Revenue', x-label 'Month', and y-label 'Revenue ($)'. | fig, ax = plt.subplots()
ax.bar(categories, heights)
ax.set_xticks(categories)
ax.set_xticklabels(['January', 'February', 'March'], rotation=45)
ax.set_title('Monthly Revenue')
ax.set_xlabel('Month')
ax.set_ylabel('Revenue ($)') | ["bar_data", "xticklabels", "title", "xlabel", "ylabel"] | {"type": "bar_data", "categories": [0, 1, 2], "heights": [100, 150, 120]} | {"axes_count": 1, "figure_size": [6.4, 4.8], "axes": [{"index": 0, "title": "Monthly Revenue", "xlabel": "Month", "ylabel": "Revenue ($)", "xlim": [-0.54, 2.5400000000000005], "ylim": [0.0, 157.5], "xscale": "linear", "yscale": "linear", "grid_on": false, "lines": [], "line_count": 0, "collections": [], "collection_cou... |
layout_001 | layouts | medium | Using the provided line_data and scatter_x/scatter_y arrays, create a 1x2 subplot. Left: line plot of line_data with title 'Line Plot'. Right: scatter plot using scatter_x and scatter_y with title 'Scatter Plot'. | fig, (ax1, ax2) = plt.subplots(1, 2)
ax1.plot(line_data)
ax1.set_title('Line Plot')
ax2.scatter(scatter_x, scatter_y)
ax2.set_title('Scatter Plot') | ["axes_count", "line_count", "collection_count"] | {"type": "layout_data", "line_data": [1, 2, 3, 4], "scatter_x": [1, 2, 3], "scatter_y": [1, 2, 3]} | {"axes_count": 2, "figure_size": [6.4, 4.8], "axes": [{"index": 0, "title": "Line Plot", "xlabel": "", "ylabel": "", "xlim": [-0.15000000000000002, 3.15], "ylim": [0.85, 4.15], "xscale": "linear", "yscale": "linear", "grid_on": false, "lines": [{"xdata": [0.0, 1.0, 2.0, 3.0], "ydata": [1, 2, 3, 4], "color": "#1f77b4", ... |
layout_002 | layouts | medium | Using the provided y1 and y2 arrays, create a 2x1 subplot (2 rows, 1 column). Top: line plot of y1 with title 'Quadratic'. Bottom: line plot of y2 with title 'Linear'. | fig, (ax1, ax2) = plt.subplots(2, 1)
ax1.plot(y1)
ax1.set_title('Quadratic')
ax2.plot(y2)
ax2.set_title('Linear') | ["axes_count", "line_data"] | {"type": "multi_arrays", "y1": [1, 4, 9, 16], "y2": [1, 2, 3, 4]} | {"axes_count": 2, "figure_size": [6.4, 4.8], "axes": [{"index": 0, "title": "Quadratic", "xlabel": "", "ylabel": "", "xlim": [-0.15000000000000002, 3.15], "ylim": [0.25, 16.75], "xscale": "linear", "yscale": "linear", "grid_on": false, "lines": [{"xdata": [0.0, 1.0, 2.0, 3.0], "ydata": [1, 4, 9, 16], "color": "#1f77b4"... |
layout_003 | layouts | medium | Using the provided x and y arrays, create a line plot with figure size (10, 6). Add title 'Squares', x-label 'X', and y-label 'X Squared'. | fig, ax = plt.subplots(figsize=(10, 6))
ax.plot(x, y)
ax.set_title('Squares')
ax.set_xlabel('X')
ax.set_ylabel('X Squared') | ["figure_size", "line_data", "title", "xlabel", "ylabel"] | {"type": "arrays", "x": [1, 2, 3, 4, 5], "y": [1, 4, 9, 16, 25]} | {"axes_count": 1, "figure_size": [10.0, 6.0], "axes": [{"index": 0, "title": "Squares", "xlabel": "X", "ylabel": "X Squared", "xlim": [0.8, 5.2], "ylim": [-0.20000000000000018, 26.2], "xscale": "linear", "yscale": "linear", "grid_on": false, "lines": [{"xdata": [1, 2, 3, 4, 5], "ydata": [1, 4, 9, 16, 25], "color": "#1f... |
layout_004 | layouts | hard | Create a 2x2 grid of subplots. In position (0,0) plot a line [1,2,3,4] with title 'Line'. In (0,1) scatter points (1,1),(2,2),(3,3) with title 'Scatter'. In (1,0) create bars with heights [3,2,1] with title 'Bar'. In (1,1) create a histogram of [1,1,2,2,2,3] with title 'Histogram'. | fig, axs = plt.subplots(2, 2)
axs[0, 0].plot(line_y)
axs[0, 0].set_title('Line')
axs[0, 1].scatter(scatter_x, scatter_y)
axs[0, 1].set_title('Scatter')
axs[1, 0].bar([0, 1, 2], bar_heights)
axs[1, 0].set_title('Bar')
axs[1, 1].hist(hist_values)
axs[1, 1].set_title('Histogram') | ["axes_count"] | {"type": "complex_layout", "line_y": [1, 2, 3, 4], "scatter_x": [1, 2, 3], "scatter_y": [1, 2, 3], "bar_heights": [3, 2, 1], "hist_values": [1, 1, 2, 2, 2, 3]} | {"axes_count": 4, "figure_size": [6.4, 4.8], "axes": [{"index": 0, "title": "Line", "xlabel": "", "ylabel": "", "xlim": [-0.15000000000000002, 3.15], "ylim": [0.85, 4.15], "xscale": "linear", "yscale": "linear", "grid_on": false, "lines": [{"xdata": [0.0, 1.0, 2.0, 3.0], "ydata": [1, 2, 3, 4], "color": "#1f77b4", "line... |
layout_005 | layouts | hard | Using the provided y1 and y2 arrays, create a 2x1 subplot with shared x-axis (sharex=True). Plot y1 in top with title 'Ascending' and y2 in bottom with title 'Descending'. | fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True)
ax1.plot(y1)
ax1.set_title('Ascending')
ax2.plot(y2)
ax2.set_title('Descending') | ["axes_count", "line_data"] | {"type": "multi_arrays", "y1": [1, 4, 9, 16, 25], "y2": [25, 16, 9, 4, 1]} | {"axes_count": 2, "figure_size": [6.4, 4.8], "axes": [{"index": 0, "title": "Ascending", "xlabel": "", "ylabel": "", "xlim": [-0.2, 4.2], "ylim": [-0.20000000000000018, 26.2], "xscale": "linear", "yscale": "linear", "grid_on": false, "lines": [{"xdata": [0.0, 1.0, 2.0, 3.0, 4.0], "ydata": [1, 4, 9, 16, 25], "color": "#... |
layout_006 | layouts | hard | Using the provided x, y1, and y2 arrays, create a plot with two y-axes. Plot y1 on the left axis in blue with y-label 'Temperature (C)' and y2 on the right axis (using ax.twinx()) in red with y-label 'Revenue ($)'. Add title 'Dual Axis Plot' and x-label 'Month'. | fig, ax1 = plt.subplots()
ax1.plot(x, y1, color='blue')
ax1.set_ylabel('Temperature (C)')
ax1.set_xlabel('Month')
ax1.set_title('Dual Axis Plot')
ax2 = ax1.twinx()
ax2.plot(x, y2, color='red')
ax2.set_ylabel('Revenue ($)') | ["axes_count", "line_count", "line_color", "title", "xlabel", "ylabel"] | {"type": "multi_arrays", "x": [1, 2, 3, 4, 5], "y1": [10, 20, 30, 40, 50], "y2": [1000, 2000, 1500, 2500, 3000]} | {"axes_count": 2, "figure_size": [6.4, 4.8], "axes": [{"index": 0, "title": "Dual Axis Plot", "xlabel": "Month", "ylabel": "Temperature (C)", "xlim": [0.8, 5.2], "ylim": [8.0, 52.0], "xscale": "linear", "yscale": "linear", "grid_on": false, "lines": [{"xdata": [1, 2, 3, 4, 5], "ydata": [10, 20, 30, 40, 50], "color": "#... |
layout_007 | layouts | hard | Create a 2x2 subplot. Add a unique title to each subplot: 'Plot 1', 'Plot 2', 'Plot 3', 'Plot 4'. Use plt.tight_layout() to prevent overlapping. | fig, axs = plt.subplots(2, 2)
axs[0, 0].set_title('Plot 1')
axs[0, 1].set_title('Plot 2')
axs[1, 0].set_title('Plot 3')
axs[1, 1].set_title('Plot 4')
plt.tight_layout() | ["axes_count", "title"] | {"type": "none"} | {"axes_count": 4, "figure_size": [6.4, 4.8], "axes": [{"index": 0, "title": "Plot 1", "xlabel": "", "ylabel": "", "xlim": [0.0, 1.0], "ylim": [0.0, 1.0], "xscale": "linear", "yscale": "linear", "grid_on": false, "lines": [], "line_count": 0, "collections": [], "collection_count": 0, "patches": [], "patch_count": 0, "le... |
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