| {"id": "DV_1", "question": "Create a line chart showing the trends of Separation Efficiency (%) and Unit Energy Consumption (unit: kWh/m) over time. Requirements: the horizontal axis represents time (aggregated by month, format YYYY-MM); the left vertical axis represents Separation Efficiency (%); the right vertical axis represents Unit Energy Consumption; the two lines are distinguished by different colors (Separation Efficiency in blue, Unit Energy Consumption in orange); the chart title is \"Monthly Trend of Separation Efficiency and Unit Energy Consumption\"; display data labels (retain 4 decimal places); the legend is located in the upper right corner; time is arranged in ascending order; there are no missing values; the time column granularity is daily and has been aggregated by month using the mean.", "input_file": "process data.xlsx", "output_type": "Data Visualization", "output_file": "monthly trend of separation efficiency and unit energy consumption.png", "rubrics": "1. Separation Efficiency reached its highest point in January 2025 (86.62%), while it was lowest in November 2024 (81.95%) \n2. Unit Energy Consumption reached its lowest point in January 2025 (0.0748 kWh/L), while it was highest in February 2025 (0.0791 kWh/L) \n3. Overall, there appears to be a certain negative correlation between Separation Efficiency and Unit Energy Consumption, i.e., when Separation Efficiency is relatively high, Unit Energy Consumption tends to be relatively low."} | |
| {"id": "DV_2", "question": "Based on \"process_data2.xlsx\", create a scatter plot where the horizontal axis represents Operating Temperature (in the original units from the dataset) and the vertical axis represents Separation Efficiency (displayed as a percentage, rounded to 2 decimal places). The scatter point colors are mapped to energy consumption levels using a continuous color scale (cool colors such as blue for low energy consumption, warm colors such as red for high energy consumption). A rectangular box in the chart marks the \"high efficiency and low energy consumption\" optimal operating region, defined as the temperature and efficiency range covered by data points where: Separation Efficiency ≥ the 75th percentile of Separation Efficiency in the dataset AND energy consumption ≤ the 25th percentile of energy consumption in the dataset. The chart title is \"Operating Temperature vs Separation Efficiency (Color = Energy Consumption Level)\", the horizontal axis label is \"Operating Temperature\", the vertical axis label is \"Separation Efficiency (%)\", a color legend labeled \"Energy Consumption Level\" is displayed, and a text annotation is added to the chart indicating the boundary values of the optimal region.", "input_file": "process_data2.xlsx", "output_type": "Data Visualization", "output_file": "operating_temperature_vs_separation_efficiency.png", "rubrics": "1. Data points with low energy consumption (blue tones) tend to cluster in the high Separation Efficiency region, visually demonstrating the superior process characteristic of \"low energy consumption, high efficiency\";\n2. Points with high Separation Efficiency (above 90%) are mainly concentrated in the Operating Temperature_°C range of 76–91°C, which is the core interval for achieving efficient separation;\n3. The chart title is \"Operating Temperature vs Separation Efficiency (Color = Energy Consumption Level)\", the horizontal axis label is \"Operating Temperature\", the vertical axis label is \"Separation Efficiency (%)\", and the color legend is labeled \"Energy Consumption Level\".\n4. Blue points are more prevalent at lower operating temperatures, while red points are more prevalent at higher operating temperatures."} | |
| {"id": "DV_3", "question": "Use the first row of data as the initial coordinates (reference point), and calculate the deformation of each subsequent row relative to the initial coordinates (current coordinate value − initial coordinate value) to generate a deformation curve line chart. The horizontal axis represents the data row index (starting from row 2, labeled as Point 1, Point 2, ...), and the vertical axis represents the deformation amount (unit: mm, rounded to 2 decimal places). If the data contains multiple coordinate columns (e.g., X, Y, Z or multiple measurement points), each coordinate column generates one independent line series, with the legend labeled by the respective coordinate column name. The chart title is \"Data Deformation Curve\", the vertical axis label is \"Deformation (mm)\", and the horizontal axis label is \"Measurement Point Index\". (Points with empty values are skipped and not counted in the index.)", "input_file": "deformation.xlsx", "output_type": "Data Visualization", "output_file": "data_deformation_curve.png", "rubrics": "1. The deformation in the Z direction is much greater than in the X and Y directions, with a maximum exceeding 40000 mm.\n2. The deformations in the X and Y directions are relatively small, varying within a range of 3000 mm.\n3. Due to the excessively large deformation in the Z direction, the deformation curves for the X and Y directions appear nearly as straight lines in the chart, because the Y-axis scale is dominated by the Z-direction data.\n4. The Y direction exhibits predominantly negative deformation, while X and Z exhibit positive deformation."} | |
| {"id": "DV_4", "question": "Perform PCA dimensionality reduction on all numeric fields in the table to 2 dimensions, then generate a scatter plot for visualization. Specific requirements: (1) Automatically identify all numeric columns in the table as input features for dimensionality reduction; (2) Use PCA to reduce the data to 2 principal components; (3) Generate a scatter plot with the first principal component (PC1) on the horizontal axis and the second principal component (PC2) on the vertical axis; (4) If the data contains a clear categorical label column (non-numeric or a column with fewer than 10 unique values), use different colors to distinguish categories and display a legend; if no categorical column exists, use a single color for all points; (5) The chart title should be \"PCA Dimensionality Reduction Visualization\", the horizontal axis label should be \"First Principal Component\", and the vertical axis label should be \"Second Principal Component\"; (6) Use the default size for all points; (7) Do not display data labels.", "input_file": "5._Data_Dimensionality_Reduction.xlsx", "output_type": "Data Visualization", "output_file": "PCA Dimensionality Reduction Visualization Results.png", "rubrics": "1. The distribution of samples along the first principal component (PC1) axis spans a wider range compared to PC2.\n2. There is a relatively isolated point in the upper left corner.\n3. The point with the lowest second principal component value is located at a lower position along the first principal component.\n3. All samples are uniformly dispersed in the two-dimensional principal component space with no obvious clustering or stratification, and no clearly separable clusters are formed."} | |
| {"id": "DV_5", "question": "Using the \"Identity\" group field in \"participants.xlsx\", count the number of people in each identity group and generate a bar chart for comparison. The horizontal axis represents identity group categories, and the vertical axis represents the number of people. The bar chart is sorted in descending order by number of people, with the specific value labeled above each bar. The chart title is \"Comparison of Number of People by Identity Group\", the vertical axis label is \"Number of people\", and the horizontal axis label is \"Identity Group\". If the number of people in an identity group differs from the overall mean by more than 20%, mark that bar with \"*\" to indicate a significant difference.", "input_file": "participants.xlsx\n1-4 question format.xlsx", "output_type": "Data Visualization", "output_file": "comparison of population by identity group.png", "rubrics": "1. The college student group has the largest number of participants (2,378), accounting for 47.2% of the total. The middle school student group ranks second (2,125), accounting for 42.2% of the total. The elementary school student group (339) and the out-of-school youth group (199) are relatively small in number.\n2. The college student and middle school student groups far exceed the average level (1,260.25), with significant differences (both exceeding 20%).\n3. The elementary school student and out-of-school youth groups are below the average level, also with relatively significant differences."} | |
| {"id": "DV_6", "question": "Based on the prize number data in the file \"sports_lottery_pattern.xlsx\", create a line chart: the horizontal axis represents the Draw number (arranged in chronological order), and the vertical axis represents the three digit positions of the drawn numbers for each draw (assuming the drawn number format is a 3-digit number, consisting of the Hundreds digit, Tens digit, and Units digit respectively). Use 3 lines of different colors to represent the trend of digit changes at each of these three positions across draws. The chart title is \"Trend Chart of Three-Digit Changes in Drawn Numbers\", the horizontal axis label is \"Draw number\", the vertical axis label is \"Digit Value (0-9)\", display a legend indicating that the three lines correspond to \"Hundreds digit\", \"Tens digit\", and \"Units digit\" respectively, the vertical axis range is fixed at 0-9, and the horizontal axis shows the most recent 50 draws.", "input_file": "sports_lottery_pattern.xlsx", "output_type": "Data Visualization", "output_file": "sports_lottery_last50_pattern.png", "rubrics": "1. The drawn number for Draw 1088 is 539. 2. The count of 0 in the Hundreds digit and Tens digit is greater than in the Units digit. 3. In Draw 1102, both the Hundreds digit and Tens digit are 9."} | |
| {"id": "DV_7", "question": "In \"e-commerce sales data.xlsx\", identify the variable most correlated with Sales Revenue (CNY), compute the linear regression equation y = ax + b (retain coefficients to 4 decimal places), and generate a scatter plot overlaid with the regression line. Chart requirements: the horizontal axis represents the independent variable, the vertical axis represents the dependent variable, the scatter points display the original data, the line displays the fitted result, the chart title is \"Linear Regression Fit\", the legend labels are \"Original Data\" and \"Regression Line y={a}x+{b}\", and the regression equation and R² value (retained to 4 decimal places) must be explicitly stated in the chart or output text.", "input_file": "e-commerce sales data.xlsx", "output_type": "Data Visualization", "output_file": "regression plot.png", "rubrics": "1. The chart displays the linear relationship between Advertising Spend (CNY) and Sales Revenue (CNY).\n2. The linear regression equation based on Advertising Spend (CNY) and Sales Revenue (CNY) is:\n y = 0.6902x + 6751.1769\n3. The coefficient of determination of the model R² = 0.3419"} | |
| {"id": "DV_8", "question": "Based on the data in table 3.xlsx, generate a bar chart showing the number of penalties and penalty amounts (in 10,000 yuan) under different penalty reasons. Specific requirements:\n1. The horizontal axis represents penalty reasons (all distinct penalty reason categories)\n2. The vertical axis represents values\n3. Use two independent bar chart subplots (arranged vertically): the first subplot shows the number of penalties for each penalty reason, and the second subplot shows the penalty amount for each penalty reason (unit: 10,000 yuan)\n4. The two subplots use different colors to distinguish them (number of penalties in blue, penalty amount in orange), both arranged in descending order.\n5. The overall chart title is \"2025 Q1-Q3 Penalty Reason Analysis\"\n6. The two subplots are labeled with subtitles respectively: \"Number of Penalties\" and \"Penalty Amount (10,000 yuan)\"\n7. Display specific values on top of each bar: the number of penalties rounded to integers, and the penalty amount retained to 2 decimal places", "input_file": "table 3.xlsx", "output_type": "Data Visualization", "output_file": "bar chart results.png", "rubrics": "1. Two bar charts arranged in a top-bottom layout. Penalty count uses blue, penalty amount uses orange; both are sorted in descending order.\n2. The value inside each bar is clearly labeled.\n3. \"Customer identity verification\" is the violation with the highest penalty count, reaching 703 cases. \"Identification and reporting of large-value and suspicious transactions\" ranks second, with a penalty count of 330 cases. \"Customers with unverified identity\" ranks third, with a penalty count of 252 cases.\n4. In terms of penalty amount, \"Customer identity verification\" also ranks first.\n5. \"Identification and reporting of large-value and suspicious transactions\" ranks second in penalty amount, reaching 279.1 million yuan."} | |
| {"id": "DV_9", "question": "Using all numerical data from both files, generate a line chart. Chart requirements: the horizontal axis represents dates, and the vertical axis represents \"Average Velocity m/s\". Note that each file contains multiple bridges.", "input_file": "River Section A.xlsx\nRiver Section B.xlsx", "output_type": "Data Visualization", "output_file": "River Section Average Flow Velocity Line Chart.png", "rubrics": "1. 5 line series for different bridges\n2. The horizontal axis has date labels, from 2025-04-01 to 2025-11-01\n3. The vertical axis is Average Velocity m/s"} | |
| {"id": "DV_10", "question": "Draw a heatmap using hierarchical clustering, top 50, use English throughout the figure.", "input_file": "1._Gene_Expression_Matrix.xlsx", "output_type": "Data Visualization", "output_file": "hierarchical_clustering_heatmap_top50.png", "rubrics": "1. Heatmap\n2. 5 columns representing different samples\n3. With correlation lines\n4. In English"} | |
| {"id": "DV_11", "question": "Based on the file \"Position Table 4.xlsx\", draw a grouped bar chart where the horizontal axis represents job title categories and the vertical axis represents values. Each job title corresponds to two side-by-side bars: the first bar represents the number of penalties for that job title (unit: cases), and the second bar represents the total penalty amount for that job title (unit: yuan, rounded to 2 decimal places). The two bars use different colors to distinguish them, and the legend labels them as \"Number of Penalties\" and \"Penalty Amount\". The horizontal axis job titles are sorted in descending order by number of penalties; ties in number of penalties are broken by descending penalty amount. The chart title is \"Distribution of Personal Anti-Money Laundering Penalty Departments for Q1–Q3 2025\", the vertical axis label is \"Value\", the horizontal axis label is \"Job Title\", the legend is displayed, and data labels are not displayed.", "input_file": "Position Table 4.xlsx", "output_type": "Data Visualization", "output_file": "226-2025 Q1-Q3 Individual Anti-Money Laundering Penalty Department Distribution.png", "rubrics": "1. Senior management has the highest number of penalties and penalty amounts, at 337 cases and 7.5092 million yuan respectively\n2. The operations department and business department rank second and third respectively\n3. The anti-money laundering management department, as the dedicated department responsible for anti-money laundering work, has 69 penalty cases with a total penalty amount of 1.9091 million yuan"} | |
| {"id": "DV_12", "question": "Compare the changes in the number of departing personnel per department between the 2024 and 2025 Resignation Summary Tables, and generate a bar chart. Specific requirements:\n1. Statistical scope: all departments that appear in either file (use the full department names as they appear in the original tables, without any merging or reclassification)\n3. Statistical dimension: group by department, and count the number of departing personnel for 2024 and 2025 respectively\n4. Output format: generate a grouped bar chart where the horizontal axis shows department names (sorted in descending order by 2025 headcount loss), the vertical axis shows the number of departing personnel, each department displays two bars (blue representing 2024, orange representing 2025), the chart title is \"Departmental Non-Key Position Attrition Comparison (2024 vs 2025)\", data labels are displayed, and a legend is included\n5. Missing data handling: if a department has data in only one year, record the other year as 0; records with empty/null department names are excluded from the statistics\n6. Department order should be sorted by 2024 attrition headcount from highest to lowest", "input_file": "Resignations2024.xlsx\nResignations2025.xlsx", "output_type": "Data Visualization", "output_file": "Department_Attrition_Comparison_2024_vs_2025.png", "rubrics": "1. Staff attrition statistics by department:\nDepartment 2024 Attrition Count 2025 Attrition Count\nDepartment8687 30 30\nDepartment0818 20 16\nDepartment9458 9 2\nDepartment9433 5 3\nDepartment4600 5 1\nDepartment3612 1 1\nDepartment6937 1 0\nDepartment7949 1 0\nDepartment3213 0 1\nDepartment8053 0 1\n2. Department8687 has had the highest number of non-key position attritions for two consecutive years and requires close attention\n3. Overall, the number of non-key position attritions in 2025 has decreased compared to 2024, indicating that talent retention measures may be beginning to show results"} | |
| {"id": "DV_13", "question": "Based on the student grade data from two files, generate a stacked bar chart where the horizontal axis shows subject names (Chinese, Math, English, Science, Ethics and Law), the vertical axis shows the number of students, and each bar is stacked by A/B/C/D grade levels (colors distinguish grades, legend labels grades). The chart title is \"Grade Distribution by Subject\". Display dual bars for Semester 1 and Semester 2, annotated with percentages rounded to 2 significant figures. Grading rules: Chinese/Math/English: ≥95 is A, [85, 95) is B, [75, 85) is C, <75 is D; Science/Ethics and Law: ≥55 is A, [45, 55) is B, <45 is C (no D grade). All students' scores are included in the statistics; students with missing scores for a subject are not counted toward any grade level for that subject.", "input_file": "2025 Second Semester.xlsx\n2025 First Semester.xlsx", "output_type": "Data Visualization", "output_file": "Grade Distribution by Subject.png", "rubrics": "1. Chart style is a stacked chart\n2. Specific values\nSemester 1:\nChinese:\n Grade A: 1 person (2.86%)\n Grade B: 4 people (11.43%)\n Grade C: 13 people (37.14%)\n Grade D: 17 people (48.57%)\n Total: 35 people\n\nMath:\n Grade A: 4 people (11.43%)\n Grade B: 24 people (68.57%)\n Grade C: 4 people (11.43%)\n Grade D: 3 people (8.57%)\n Total: 35 people\n\nEnglish:\n Grade A: 14 people (40.00%)\n Grade B: 14 people (40.00%)\n Grade C: 5 people (14.29%)\n Grade D: 2 people (5.71%)\n Total: 35 people\n\nScience:\n Grade A: 3 people (8.57%)\n Grade B: 28 people (80.00%)\n Grade C: 4 people (11.43%)\n Total: 35 people\n\nEthics and Law:\n Grade A: 11 people (31.43%)\n Grade B: 20 people (57.14%)\n Grade C: 4 people (11.43%)\n Total: 35 people\n\nSemester 2:\n\nChinese:\n Grade A: 0 people (0.00%)\n Grade B: 20 people (57.14%)\n Grade C: 8 people (22.86%)\n Grade D: 7 people (20.00%)\n Total: 35 people\n\nMath:\n Grade A: 27 people (77.14%)\n Grade B: 4 people (11.43%)\n Grade C: 2 people (5.71%)\n Grade D: 2 people (5.71%)\n Total: 35 people\n\nEnglish:\n Grade A: 25 people (71.43%)\n Grade B: 7 people (20.00%)\n Grade C: 2 people (5.71%)\n Grade D: 1 person (2.86%)\n Total: 35 people\n\nScience:\n Grade A: 25 people (71.43%)\n Grade B: 8 people (22.86%)\n Grade C: 2 people (5.71%)\n Total: 35 people\n\nEthics and Law:\n Grade A: 23 people (65.71%)\n Grade B: 10 people (28.57%)\n Grade C: 2 people (5.71%)\n Total: 35 people"} | |
| {"id": "DV_14", "question": "Based on the file Lhasa River Cross-sections 24-25.xls, generate a line chart with 6 subplots for the 2024 and 2025 data respectively, arranged in a 2-row by 3-column layout (3 subplots in the top row, 3 in the bottom row). Each subplot displays the trend of one indicator over time. Specific requirements:\n1. Data filtering: extract all data rows for 2024 and 2025 separately;\n2. Indicator selection: select the first 6 numeric columns in the data (if fewer than 6 numeric columns exist, use the actual count), with each indicator corresponding to one subplot;\n3. X-axis: the time column (use the \"Date\" column if present); Y-axis: the numeric values of the corresponding indicator;\n4. Line style: use dark orange (RGB: #FF8C00) for 2024 and dark blue (RGB: #00008B) for 2025, with line width set to 2.5;\n5. Legend: each subplot displays legends for \"2024\" and \"2025\", font size 14, legend box width set to 1.5 times the default;\n6. Overall figure title: do not display an overall title;\n7. Subplot borders: set the border line width of all subplots to 2;\n8. Subplot titles: each subplot title is the column name of the corresponding indicator, font size 12;\n9. Output: generate a PNG image file named Code_Generated_Image2.png at a resolution of 300 dpi.", "input_file": "Lhasa River Cross-sections 24-25.xls", "output_type": "Data Visualization", "output_file": "Code_Generated_Image2.png", "rubrics": "1. 2024 data fluctuations are more concentrated: the River Width, Area m2, and other indicators for 2024 (dark orange) show large fluctuations mainly in the early part of the year (April–June), with relatively few data points afterward; whereas the fluctuations for 2025 (dark blue) are distributed across multiple periods throughout the year.\n2. Consistent trends across variables: the change trends of River Width, Area m2, Total Flow Rate m3/s, and other indicators are highly synchronized — for example, when River Width increases, Area m2 and Total Flow Rate m3/s also rise accordingly, indicating strong correlations among these hydrological variables.\n3. More complete data coverage in 2025: the monitoring data for each indicator in 2025 spans a longer time range, with records from the beginning to the end of the year, whereas the 2024 data is concentrated in the first half of the year, with considerable missing data in the second half."} | |
| {"id": "DV_15", "question": "Read the files \"resignation_summary_2024_copy.xlsx\" and \"resignation_summary_2025_copy.xlsx\", merge the data, then group by the \"Position Series\" field to count the number of resignations per series. Calculate the percentage of each position series' resignation count relative to the total resignation count (rounded to 2 decimal places), sort in descending order by resignation count, and output the Top 10 position series. Generate a bar chart: the horizontal axis represents the position series names (sorted in descending order by resignation count), the vertical axis represents the resignation count, display data labels above each bar (resignation count + percentage), set the chart title to \"Distribution of Resignations by Position Series\", the vertical axis label to \"Resignation Count (persons)\", the horizontal axis label to \"Position Series\", and show the legend.", "input_file": "resignation_summary_2024_copy.xlsx\nresignation_summary_2025_copy.xlsx", "output_type": "Data Visualization", "output_file": "Code_Generated_Image3.png", "rubrics": "1. Frontline production employees account for over 60% of resignations: 48 frontline production employees resigned, representing 61.54% of total resignations, making it the job category with the highest number of departures.\n2. Production support and general staff are secondary resignation groups: 15 production support staff resigned (19.23%), and 13 general employees resigned (16.67%), together accounting for nearly 36%.\n3. Middle management has an extremely low resignation rate: only 2 middle management employees resigned, representing just 2.56%, making it the job category with the fewest resignations."} | |
| {"id": "DV_16", "question": "Generate a heatmap of knowledge section scores for the midterm exam, with the horizontal axis representing questions and the vertical axis representing students, with students sorted from top to bottom in the order they appear in the file.", "input_file": "202511 Midterm Data Analysis_(1).xlsx", "output_type": "Data Visualization", "output_file": "Grade Statistics Heatmap.png", "rubrics": "1. If the heatmap displays specific scores, the score corresponding to each student must be correct\n2. There are a total of 22 students and 23 sub-questions; every student and every question must be represented in the chart — none can be omitted"} | |
| {"id": "DV_17", "question": "Read the file \"buyer_identity_and_level.xlsx\". Based on the \"Identity\", \"Buyer Procurement Grade\", and month-related rows, group and aggregate by month (MM format). Count the number of valid inquiry users for each grade/category of \"Identity\" and \"Buyer Procurement Grade\" within each month. Generate two grouped bar charts: the horizontal axis represents months (sorted in ascending chronological order), the vertical axis represents the number of valid inquiry users, displaying the distribution of each category of Identity and Buyer Procurement Grade across each month. The chart titles are \"Monthly Identity User Inquiry Count Distribution Statistics\" and \"Monthly Buyer Procurement Grade User Inquiry Count Distribution Statistics\". Display data labels and include a legend. If data for a certain month is missing, skip that month.\nWhen reading Buyer Procurement Grade or Identity, first collect all categories that appear across all months; if a certain category does not appear in a given month, default the count for that category in that month to 0.", "input_file": "buyer_identity_and_level.xlsx", "output_type": "Data Visualization", "output_file": "monthly_buyer_procurement_level_user_inquiry_distribution_stats.png\nmonthly_identity_user_inquiry_distribution_stats.png", "rubrics": "1. Buyer procurement Grade has 6 categories in total, and Buyer Identity has 16 categories. The order of categories does not affect the chart result, but categories must not be missing or added.\n2. The horizontal axis represents the month, and the vertical axis represents the number of valid inquiry users. The titles of the two charts are \"Monthly Distribution Statistics of Inquiry Users by Buyer Identity\" and \"Monthly Distribution Statistics of Inquiry Users by Buyer Procurement Grade\"."} | |
| {"id": "DV_18", "question": "Based on the date column and attendance status column from 7 class files (Artificial Intelligence Class 1–7), draw a line chart to display the daily attendance count trend for each class. Specific requirements:\n1. Filter records where the attendance status is \"Signed\", and group by date and class to count the daily attendance\n2. The X-axis represents dates (sorted in ascending chronological order, format YYYY-MM-DD)\n3. The Y-axis represents the daily attendance count, with a scale range of 0–70 and intervals of 5 (i.e., 0, 5, 10, 15, ..., 70)\n4. Draw 7 lines, each representing one class, distinguished by different colors\n5. The legend is displayed inside the chart border at the top, labeled with each class name\n6. The chart type must be a line chart\n7. The chart title is \"Daily Attendance Count Trend by Class\", the X-axis label is \"Date\", and the Y-axis label is \"Attendance Count\"", "input_file": "Artificial_Intelligence_Class_1.xlsx\nArtificial_Intelligence_Class_2.xlsx\nArtificial_Intelligence_Class_3.xlsx\nArtificial_Intelligence_Class_4.xlsx\nArtificial_Intelligence_Class_5.xlsx\nArtificial_Intelligence_Class_6.xlsx\nArtificial_Intelligence_Class_7.xlsx", "output_type": "Data Visualization", "output_file": "Class_Attendance_Statistics.png", "rubrics": "1. The Y-axis represents the number of attendees on that day, with a scale range of 0–70 and intervals of 5 (i.e., 0, 5, 10, 15, ..., 70); the plotted chart must not exceed this range. The X-axis represents dates.\n2. For certain dates, because some classes have multiple sessions on the same day, attendance exceeds 70, and the line chart extends beyond the Y-axis range of this chart.\n3. The ordering of classes in the legend does not affect the correctness of the result.\n4. Class 4 has the lowest attendance rate among all classes across all dates on October 30, 2025."} | |
| {"id": "DV_19", "question": "Perform a word frequency analysis on all terms in the file \"8._biology_terms.xlsx\", counting the number of times each term appears across all entries, including occurrences as a substring (part of another term). Output a table of the Top 50 terms and their frequencies sorted in descending order of frequency. Additionally, based on the complete word frequency data, generate a word cloud image where the size of each term is proportional to its frequency, colors are randomly assigned, the background is white, the image dimensions are 800×600 pixels, and the title is \"biology_terms_frequency_word_cloud\".", "input_file": "8._biology_terms.xlsx", "output_type": "Data Visualization", "output_file": "biology_terms_frequency_word_cloud.png", "rubrics": "1. There are 340 vocabulary terms in total. The top six by word frequency are: \"Bioinformatics\" (25 times), \"Genomics\" (24 times), \"Proteomics\" (12 times), \"Synthetic Biology\" (12 times), \"Metabolomics\" (8 times), and \"Transcription Factor\" (8 times).\nAt minimum, the size proportions of these six words must reflect their rankings.\n2. The title must be \"biology_terms_frequency_word_cloud\" — no errors are acceptable."} | |
| {"id": "DV_20", "question": "Generate a pie chart based on this file", "input_file": "New_DOCX_Document_(5).docx", "output_type": "Data Visualization", "output_file": "pie chart.png", "rubrics": "1. The frequency of \"Never reads\" is 432, accounting for 22.05%\n2. The frequency of \"Occasionally reads\" is 1287, accounting for 65.70%\n3. The frequency of \"Always reads\" is 240, accounting for 12.25%"} | |
| {"id": "DV_21", "question": "Extract the top 5 most frequently occurring terms from the document as the subjects of analysis. For each dimension, extract its corresponding quantitative data or frequency statistics of qualitative descriptions, and generate a horizontal bar chart: the horizontal axis represents each dimension name, the vertical axis represents the importance score of that dimension in the text (calculation rule: number of occurrences × 2 + whether it appears in a title/section heading × 10), sorted in descending order of score. The chart title is \"Importance Analysis of Core Issues in Park-Based Development\", the vertical axis label is \"Importance Score\", the horizontal axis label is \"Issue Dimension\", display data labels (retain integers), and do not display a legend.", "input_file": "Exchange Center Park Development Analysis Report——Building a Borderless Platform Service Park_(3).docx", "output_type": "Data Visualization", "output_file": "chart37.png", "rubrics": "1. The top 5 most frequently appearing words are: platform, service, function, development, and space.\n2. The importance scores vary significantly across dimensions: platform has the highest importance score (112 points), followed by service (90 points), function (78 points), development (74 points), and space has a relatively lower score (50 points).\n "} | |
| {"id": "DV_22", "question": "Plot a \"Treatment Retention Curve\" — showing the proportion of patients still continuing treatment at months 3, 6, 9, and 12 after initiating treatment.", "input_file": "Payment Structure Analysis of 4 Project Pharmacies_desensitized.xlsx", "output_type": "Data Visualization", "output_file": "chart_39.png", "rubrics": "1. The 3-month patient retention rate is 70.87%\n2. The 6-month patient retention rate is 53.63%\n3. The 9-month patient retention rate is 11.76%"} | |
| {"id": "DV_23", "question": "Perform data analysis on the student attendance sheet and create a stacked bar chart to display each student's attendance status. The x-axis represents student names, and the y-axis represents the number of absences. Each student corresponds to one bar, with different colors distinguishing different absence statuses. The chart title should be \"Stacked Bar Chart of Class Student Attendance\", the y-axis label should be \"Number of Absences\", the x-axis label should be \"Student Name\", and a legend must be displayed indicating the attendance type corresponding to each color.", "input_file": "Grade24 AI Class2_desensitized.csv", "output_type": "Data Visualization", "output_file": "attendance_stacked_bar_chart.png", "rubrics": "1. The bar for Student 46 is the tallest\n2. The bars for Student 1 through Student 5 are all 0"} | |
| {"id": "DV_24", "question": "This is a dataset of primary school English scores, containing English scores for Grades 3, 4, 5, and 6. Please read the data from the file, and plot the average score for each grade (bar chart), pass rate (line chart), and excellence rate (line chart) all on a single figure, with the score axis (y-axis) on the left and the rate axis (y-axis) on the right.", "input_file": "Eighth Primary School (2025 Autumn) English Mid-term Test Score Statistics and Quality Analysis Table.xls", "output_type": "Data Visualization", "output_file": "chart41.png", "rubrics": "1. The bar for Grade 4 is the tallest, with an average score of 71.09\n2. Grade 5 has the lowest passing rate, at 58%.\n3. Grade 3 has an excellence rate of 19.44%."} | |
| {"id": "DV_25", "question": "Based on the air-quality-related fields in the data, select three dimensions — AQI (Air Quality Index), PM2.5 concentration, and PM10 concentration — to conduct a comparative analysis of three cities: Beijing, Shanghai, and Haikou. Then draw the following line chart: the horizontal axis represents time (aggregated by month, format YYYY-MM), the vertical axis represents monthly average PM2.5 concentration, with three lines representing Beijing, Shanghai, and Haikou respectively. Display the legend and set the chart title to \"Monthly Trend Comparison of PM2.5 Concentration in Three Cities\";\n ", "input_file": "air_quality_labeled_severe_pollution.xlsx", "output_type": "Data Visualization", "output_file": "month.png", "rubrics": "1. In the chart, Beijing's line is at the top, Shanghai's is in the middle, and Haikou's is at the bottom.\n2. Beijing will have a spike around June 23rd, because the AQI Index on that day reached as high as 137."} | |
| {"id": "DV_26", "question": "Using the data in for_plotting.xlsx, create a bar chart displaying four performance indicators for each class in the Chinese subject: Average Score, Excellence Rate %, Pass Rate, and Low Score Rate. The horizontal axis shows class names (sorted in ascending order by class number), and the vertical axis shows indicator values. The chart must include: (1) the four indicators for each class displayed as a grouped bar chart (the four indicators for the same class are shown side by side); (2) the grade-wide average of these four indicators added to the chart as reference lines or a separate comparison bar; (3) the name of the Chinese subject Instructor for each class annotated in the legend or data labels. Percentage-based indicators (Excellence Rate %, Pass Rate, Low Score Rate) should be displayed with 2 decimal places and a percent sign; Average Score should be displayed with 2 decimal places. The chart title is \"Chinese Subject Class Performance Indicators Comparison Chart\", the vertical axis label is \"Value\", and the horizontal axis label is \"Class\". Display data labels and show the legend.", "input_file": "for_plotting.xlsx", "output_type": "Data Visualization", "output_file": "chart43.png", "rubrics": "1. The chart must contain 4 lines.\n2. Class 2 has the lowest Average Score.\n3. Class 11 has the highest Excellence Rate %."} | |
| {"id": "DV_27", "question": "Please create a bubble chart from this data, where the x-axis represents CPC competition difficulty (the further left, the lower the competition); the y-axis represents Search Volume (the further up, the greater the traffic); Popularity Score determines the bubble size (the larger the value, the larger the bubble), and bubble data labels use Keyword.\nCreate four quadrants, labeled: High CPC High Search Volume; High CPC Low Search Volume; Low CPC High Search Volume; Low CPC Low Search Volume. Search Volume (Y-axis) ranges from a few hundred to tens of thousands — a regular axis scale would cause low-volume keywords to cluster together and become invisible, so apply a logarithmic scale to distribute the data more evenly. Keyword CPC Search Volume Popularity Score", "input_file": "new_XLS_worksheet.xlsx", "output_type": "Data Visualization", "output_file": "bubble_chart_analysis.png", "rubrics": "1. The number of points is consistent;\n2. The positions of the points are correct: after keyword matching, the x-coordinates and y-coordinates are approximately consistent\n3. The sorting by point size must match"} | |
| {"id": "DV_28", "question": "Read the file \"water_RQ.xlsx\". Using \"Sampling Point\" as the horizontal axis (X-axis) and \"Pesticide Name\" as the vertical axis (Y-axis), draw a heatmap. The color of each cell in the heatmap is mapped according to the RQ value for that sampling point–pesticide combination using the following rules: RQ > 1 displays red, 0.1 < RQ ≤ 1 displays purple, RQ ≤ 0.1 displays light green. The heatmap must display all sampling points and all pesticides (no Top-N truncation). The horizontal axis labels are the sampling point names, the vertical axis labels are the pesticide names. Add a color legend explaining the meaning of the three intervals. The chart title is \"Heatmap of Pesticide RQ Values in Farmland Water\".", "input_file": "water_RQ.xlsx", "output_type": "Data Visualization", "output_file": "image.png", "rubrics": "1. The number of rows and columns is the same;\n2. Spot-check sampling is acceptable (e.g., sample 50): cells at the intersection of the same column/row keywords have the same color (red/purple/green)"} | |
| {"id": "DV_29", "question": "Based on the Grade 7 math exam data in the attachment, generate a \"Student Knowledge Point Score Rate Heatmap\": the horizontal axis represents all students (sorted by total score in descending order, Top 30 only), the vertical axis represents all knowledge points, and the cell color indicates the student's score rate for that knowledge point (score / full score for that knowledge point, percentage retained to 2 decimal places). Color mapping: 0% is dark red, 100% is dark green, with a gradient in between. The chart title is \"Distribution of Student Knowledge Point Score Rates\", display a color bar legend, the X-axis label is \"Student Name\", and the Y-axis label is \"Knowledge Point\".", "input_file": "【Teaching Class】2025-2026 Academic Year Autumn Semester Final Exam (Grade 7)-Math-Item Analysis.xlsx\n【Teaching Class】2025-2026 Academic Year Autumn Semester Final Exam (Grade 7)-Grade 7 Student Scores.xlsx", "output_type": "Data Visualization", "output_file": "Student Knowledge Point Score Rate Distribution.png", "rubrics": "1. Correct number of rows/columns;\n2. The first column corresponds to student 11's category, and the colors match from the first question to the last question (0% is dark red, 100% is dark green, with a gradient in between; colors do not need to be exactly the same, but the green/red and gradient must correspond correctly)\n3. The 5th column corresponds to student 2's category, and the colors match from the first question to the last question (0% is dark red, 100% is dark green, with a gradient in between; colors do not need to be exactly the same, but the green/red and gradient must correspond correctly)"} | |
| {"id": "DV_30", "question": "Based on the information described in the document, generate an architecture diagram. Determine the type of architecture to generate (technical system architecture / personnel organizational architecture).\n1. Chart type: Use a hierarchical structure diagram (hierarchy chart), displaying the relationships between system levels from top to bottom\n2. Content extraction: Identify all node information and role information in the document\n3. Layout rules: Components at the same level are arranged horizontally; components at different levels are arranged vertically; connections between components are represented by arrows indicating call/dependency relationships (arrow direction: caller → callee)\n4. Visual optimization: Each component is represented by a rectangular box with its name labeled; different levels use different background colors to distinguish them; connections use solid lines, and critical paths may be bolded\n5. Annotation notes: The chart title is \"Organizational Structure Diagram\"; add a legend at the bottom of the chart explaining the meaning of each level and the meaning of the connecting lines\n6. Output format: Generate a new image file (PNG format, resolution of at least 1920x1080), with the file name \"organizational structure chart.png\"", "input_file": "ski rehabilitation tourism project team introduction.docx", "output_type": "Data Visualization", "output_file": "organizational structure chart.png", "rubrics": "1. Contains 8 teachers in total, details:\n- Teacher \"Guo**\" is the overall lead, and all other members report to him\n- \"Sun**\" and \"Gao**\" belong to \"Market Research and Model Innovation\" (any indication of this is acceptable, e.g., by color, or placing them in the same box)\n- \"Jiang**\" and \"Guo**\" belong to \"Technical R&D Team\" (any indication of this is acceptable, e.g., by color, or placing them in the same box)\n- \"Teacher Fang*\", \"Teacher Zhu**\", and \"Teacher Qiao**\" belong to \"Faculty Advisors\" (any indication of this is acceptable, e.g., by color, or placing them in the same box)"} | |
| {"id": "DV_31", "question": "Based on the two files \"resignation summary table 2024.xlsx\" and \"resignation summary table 2025.xlsx\", filter the resignation records for \"non-key positions\", perform a comparative analysis along the following dimensions, and generate visualization charts:\nBy department dimension, calculate the Top 5 departments by number of resignations from non-key positions for both years and display a side-by-side comparison (grouped bar chart, with department names on the horizontal axis, number of resignations on the vertical axis, two series representing 2024 and 2025 respectively, sorted in descending order by 2025 resignation count)\nFinal output: a visualization file containing the 3 charts described above (either PNG or a new sheet named \"Non-Key Position Attrition Comparative Analysis\" added to the original Excel file), with each chart including a title, axis labels, legend, and data labels.", "input_file": "resignation summary table 2024.xlsx\nresignation summary table 2025.xlsx", "output_type": "Data Visualization", "output_file": "Department Resignation Count Comparison.png", "rubrics": "1. Except for the last group, which can be labeled something other than \"Administrative Group\", the names of the first four groups and the heights of both bars must match the reference answer exactly."} | |
| {"id": "DV_32", "question": "Read the three files \"grade_7_exam_analysis.xlsx\", \"grade_8_exam_analysis.xlsx\", and \"grade_9_exam_analysis.xlsx\", extract difficulty data for all subjects in each file (if multiple sheets exist, merge all sheets; if the difficulty field name is inconsistent, select the column whose meaning is closest to \"difficulty\"), aggregate and calculate the average difficulty value by the \"grade-subject\" dimension (rounded to 2 decimal places), generate a summary table containing three columns: \"Grade\", \"Subject\", and \"Average Difficulty\", and based on this table draw a grouped bar chart: the horizontal axis represents subject names, the vertical axis represents average difficulty values, within each subject display three bars grouped by grade (representing Grades 7, 8, and 9 respectively), bars arranged in grade order, chart title is \"Average Difficulty Comparison by Grade and Subject\", vertical axis label is \"Average Difficulty\", horizontal axis label is \"Subject\", display a legend to distinguish grades, and show value labels above the bars.", "input_file": "grade_7_exam_analysis.xlsx\ngrade_8_exam_analysis.xlsx\ngrade_9_exam_analysis.xlsx", "output_type": "Data Visualization", "output_file": "subject_difficulty_bar_chart.png", "rubrics": "1. The order of subjects may vary\n2. The number of bars for each subject and the height of each bar (for different grades) must match"} | |
| {"id": "DV_33", "question": "Read the three files \"grade_7_exam_analysis.xlsx\", \"grade_8_exam_analysis.xlsx\", and \"grade_9_exam_analysis.xlsx\", and extract the discrimination index data for the subjects Chinese, Math, and English from each file. Generate a grouped bar chart containing three columns: \"Grade\", \"Subject\", and \"Discrimination Index\", arranged by grade (Grade 7 → Grade 8 → Grade 9). The horizontal axis represents subject names, the vertical axis represents discrimination index values (rounded to 2 decimal places), and bars for each grade are displayed side by side in different colors. The legend labels the grades, the chart title is \"Discrimination Index Comparison Across Grades and Subjects\", the vertical axis label is \"Discrimination Index\", the horizontal axis label is \"Subject\", and data labels are displayed.", "input_file": "grade_7_exam_analysis.xlsx\ngrade_8_exam_analysis.xlsx\ngrade_9_exam_analysis.xlsx", "output_type": "Data Visualization", "output_file": "Discrimination Comparison by Grade and Subject.png", "rubrics": "1. Chinese has the highest discrimination index in Grade 7;\n2. Math has the lowest discrimination index in Grade 9;\n3. English discrimination index in Grade 8 is higher than in Grade 9;"} | |
| {"id": "DV_34", "question": "Based on the data in \"Table 2-2.xlsx\", group by the \"Institution Type\" field and calculate the average \"Total Penalty Amount (10,000 yuan)\" for each institution type (rounded to 2 decimal places). Generate a vertical bar chart (bars extend from top to bottom, i.e., the Y-axis origin is at the top), with the horizontal axis representing institution type and the vertical axis representing the average total penalty amount (10,000 yuan), with all bars filled in orange. Sort the institution types in descending order of average total penalty amount; if there are more than 10 institution types, display only the Top 10. The chart title should be \"Average Total Penalty Amount by Institution Type\", the vertical axis label should be \"Average Total Penalty Amount (10,000 yuan)\", the horizontal axis label should be \"Institution Type\", and data labels (rounded to 2 decimal places) should be displayed at the top of each bar.", "input_file": "Table 2-2.xlsx", "output_type": "Data Visualization", "output_file": "Institution Penalty Amount Comparison Chart.png", "rubrics": "1. Large state-owned commercial banks have the highest average penalty amount: their average total penalty amount reaches 13.5736 million yuan.\n2. Joint-stock banks rank second: with an average penalty amount of 10.4728 million yuan.\n3. Rural and township banks have the lowest average penalty amount: only 1.0700 million yuan."} | |
| {"id": "DV_35", "question": "Based on the \"January\" data (i.e., all records where the month in the date column is 1), group the data by two dimensions: \"Project\" and \"Cost Category\", and calculate the total labor cost for each cost category under each project. Generate a grouped bar chart: the vertical axis represents the labor cost amount, the horizontal axis represents the cost category, with values rounded to 2 decimal places. The chart title is \"January Labor Cost Distribution by Project\", display data labels (rounded to 2 decimal places), and the legend shows the cost categories.", "input_file": "test.xlsx", "output_type": "Data Visualization", "output_file": "January labor cost distribution by project.png", "rubrics": "1. The \"Salary\" item for R&D is far higher than for Administration;\n2. The \"Labor Dispatch\" item for R&D is far lower than for Administration;\n3. The \"Work Injury\" item bar height is very low;"} | |
| {"id": "DV_36", "question": "Read the sheet named \"Trend Chart\" from the file \"HR System.xlsx\". Based on the data columns in that sheet (excluding totals), generate a line chart: the horizontal axis represents the time dimension column (if date/month/period or other time-related columns exist, arrange them in chronological order; if no explicit time column exists, use the original row order of the data); the vertical axis represents numeric indicator columns (if there are multiple numeric columns, plot all of them as multiple lines in the same chart). Use a red color scheme for the lines. Set the chart title to \"Trend Chart Data Changes\", the horizontal axis label to the column name of the time column, and the vertical axis label to \"Value\". Display the legend (if there are multiple indicators), do not display data labels, and save the output as an image file to the current directory.", "input_file": "HR System.xlsx", "output_type": "Data Visualization", "output_file": "HR System Trend Chart.png", "rubrics": "1. The number of proprietary staff shows a notable increase in July (from 144 to 165), then remains stable\n2. The number of outsourced staff remains stable after January (consistently 13 people)\n3. The trend in total headcount is broadly consistent with proprietary staff, with a significant increase in July"} | |
| {"id": "DV_37", "question": "Read the grade 8 midterm exam score data from the file for_plotting.xlsx, calculate the \"Average Score\" for each \"Class\", create a bar chart, and sort by \"Average Score\" from highest to lowest", "input_file": "for_plotting.xlsx", "output_type": "Data Visualization", "output_file": "class_average_score_comparison.png", "rubrics": "1、Class 7 has the highest Average Score；\n2、Class 3 has the lowest Average Score；\n3、Class 8's Average Score is higher than Class 2's；"} | |
| {"id": "DV_38", "question": "Please analyze the file visitor_analysis_week48.xlsx and identify the top 10 most frequently occurring entries in the \"Store Entry Keyword Source\" column. Sort the results in descending order by count and generate a bar chart with the x-axis labeled \"Store Entry Keyword Source\" and the y-axis labeled \"Count\".", "input_file": "visitor_analysis_week48.xlsx", "output_type": "Data Visualization", "output_file": "store_entry_keyword_source_statistics.png", "rubrics": "1. rims 22 inch wheel has the highest quantity;\n2. range rover vogue has the lowest quantity in the chart;\n3. the quantity of forged wheels is higher than that of wheels car"} | |
| {"id": "DV_39", "question": "Based on the \"Region\" field in the file 2Participant.xlsx, count the number of samples in each region, sort in descending order, and take the top 20 regions to plot a bar chart. The horizontal axis represents region names (sorted in descending order by count), and the vertical axis represents sample count. The chart title should be \"Sample Count Distribution by Region (Top 20)\", the vertical axis label should be \"Sample Count\", and the horizontal axis label should be \"Region\". Display the exact value on top of each bar. Also add a horizontal reference line in the chart representing the average sample count of these 20 regions, and annotate the mean value.", "input_file": "File 2 Participants.xlsx", "output_type": "Data Visualization", "output_file": "17-Sample quantity distribution by region (Top 20).png", "rubrics": "1. Shandong Province's sample size (1448) far exceeds all other regions, being the only region in the Top 20 to surpass 1000, while the last-ranked Xinjiang Uyghur Autonomous Region has only 19 samples — an extremely uneven distribution.\n2. The average sample size across the Top 20 regions is 248.8, yet only the top 5 regions (Shandong, Zhejiang, Hunan, Jiangsu, Jiangxi) exceed this average, while the remaining 15 regions all fall below it.\n3. The combined sample size of Shandong and Zhejiang alone (1448+796=2244) exceeds the total sample size of the other 18 regions combined, indicating that the leading regions contribute the majority of samples."} | |
| {"id": "DV_40", "question": "Based on the fields related to \"participant behavior change\" in the data, create a bar chart comparing two indicators: (1) the proportion of participants who \"actively seek health information and services\" is 66.5%, and (2) the proportion of participants who \"respect others and practice gender equality\" is 65.4%. The horizontal axis represents behavior categories (\"Seek Health Information and Services\", \"Respect Others / Gender Equality\"), the vertical axis represents percentage (0–100%), the bars are arranged in descending order by value (i.e., 66.5% first), each bar displays a data label at the top (one decimal place followed by a percent sign), the chart title is \"Comparison of Participant Behavior Change Proportions\", the vertical axis label is \"Proportion (%)\", the horizontal axis label is \"Behavior Category\", and no legend is displayed.", "input_file": "file1.pdf\nyouth_health_club_survey_data_descriptive_statistical_analysis_report-full_text-20251104.docx", "output_type": "Data Visualization", "output_file": "43-participant_behavior_change_proportion_comparison.png", "rubrics": "1. Overall high behavioral change engagement: The proportion of participants in both core behavioral change categories exceeded 65%, with \"seeking health information and services\" reaching 66.5% and \"respecting others / gender equality\" reaching 65.4%, reflecting a significant positive behavioral guidance effect of adolescent health club activities on participants.\n2. Health-related behavioral change holds a slight advantage: The proportion of \"seeking health information and services\" is 1.1 percentage points higher than \"respecting others / gender equality\", indicating that participants showed more prominent changes in meeting health needs and developing healthy behaviors.\n3. Balanced development of both behavioral change types: The gap between the two indicators is extremely small, reflecting a balanced cultivation effect of the activities across the two dimensions of \"health service guidance\" and \"social values formation\", with no obvious weaknesses emerging."} | |
| {"id": "DV_41", "question": "Based on Huangshi City's 2022–2024 digital economy data, generate a combination chart (bar chart + line chart): the left Y-axis represents \"Total Contribution Rate (%)\", the right Y-axis represents \"GDP Growth Driven (percentage points)\", and the X-axis represents the years (2022, 2023, 2024). The bar chart displays the total contribution rate (17.92% in 2022, 11.98% in 2023, 46.51% in 2024), and the line chart displays the GDP growth driven in percentage points (0.88 in 2022, 0.36 in 2023, 2.09 in 2024). The chart title is \"2022-2024 Huangshi City Digital Economy Total Contribution Rate and GDP Growth Driven (Percentage Points)\", the left Y-axis label is \"Total Contribution Rate (%)\", the right Y-axis label is \"GDP Growth Driven (Percentage Points)\", and the X-axis label is \"Year\". The bar chart uses a gradient blue color scheme (#4A90E2), the line chart uses orange-red (#E74C3C), line thickness 2.5pt, and data point markers are solid circles (size 8pt). All values are retained to 2 decimal places and data labels are displayed above the bar tops and data points (font size 10pt, bold). The legend is located in the upper right corner, gridlines are light gray dashed lines, the background is white, and the overall design adopts a flat design style. Output as a PNG image file at a resolution of 1200×800 pixels, ensuring that the values are completely consistent with the input data.", "input_file": "Huangshi Digital Economy and High-Quality Development Analysis_(1).png", "output_type": "Data Visualization", "output_file": "197-2022-2024 Huangshi Digital Economy Total Contribution Rate and GDP Growth Percentage Points.png", "rubrics": "1. The overall contribution rate of the digital economy surged dramatically after initial fluctuation: from 2022 to 2024, Huangshi's overall digital economy contribution rate first declined from 17.92% to 11.98%, then climbed directly to 46.51% in 2024 — an increase of more than 3 times — reflecting a breakthrough development of the digital economy sector in 2024.\n2. The contribution to GDP growth moved in sync with the overall contribution rate: the percentage points contributed to GDP growth fluctuated in line with the overall contribution rate, reaching a low of 0.36 percentage points in 2023, then surging to 2.09 percentage points in 2024 alongside the rising contribution rate, indicating that the digital economy's pull effect on GDP is highly correlated with its own contribution scale.\n3. In 2024, the digital economy became the core driver of economic growth: with an overall contribution rate approaching 50% and a GDP growth pull of more than 2 percentage points in 2024 — a substantial increase over the preceding two years — the digital economy has transitioned from a supplementary growth force to a key pillar of Huangshi's economic growth."} | |
| {"id": "DV_42", "question": "Based on the provided data, redraw the \"2024 Huangshi GDP Increment Source Decomposition Chart\" (pie chart) with the following requirements:\n1. Data accuracy: Must include three segments with their exact values and proportions: direct contribution of digital economy 45.1 billion yuan (45.1%), indirect contribution of digital economy 1.41 billion yuan (1.41%), contribution of other industries 53.49 billion yuan (53.49%)\n2. Chart type: Pie chart\n3. Visual optimization requirements: Use gradient colors (distinguish digital economy-related segments by light and dark shades of the same color family), display data labels on each sector (including amount and percentage, formatted as \"XX billion yuan (XX.XX%)\"), add a legend, set a clear chart title \"2024 Huangshi GDP Increment Sources Decomposition (Total Increment 100 billion yuan)\"\n4. Layout optimization: Arrange sectors in descending order of proportion (Other Industries → Digital Economy Direct → Digital Economy Indirect), ensure labels do not overlap, and keep the chart centered overall with balanced proportions\n5. Output format: Generate a new PNG image file named 198-Huangshi City GDP Increment Source Decomposition Chart_Optimized Version.png with a resolution of no less than 1920×1080 pixels", "input_file": "Huangshi City Digital Economy and High-Quality Development Analysis_(2).png", "output_type": "Data Visualization", "output_file": "198-Huangshi City GDP Increment Source Decomposition Chart_Optimized Version.png", "rubrics": "1. Other industries remain the core driver of GDP increment: In 2024, other industries contributed 5.349 billion yuan to Huangshi's GDP increment, accounting for 53.49% — more than half of the total increment — reflecting that traditional and non-digital industries are still the foundational leading force of economic growth.\n2. The direct contribution of the digital economy accounts for nearly half: The direct contribution of the digital economy reached 4.51 billion yuan, accounting for 45.1%, and has become a major driver of GDP growth, reflecting that the digital economy sector in Huangshi has achieved considerable scale and growth vitality.\n3. The indirect spillover effect of the digital economy has yet to be fully realized: The indirect contribution of the digital economy was only 141 million yuan, accounting for 1.41%, indicating that the digital economy's radiation and enabling effect on other industries has not yet been fully leveraged, and there is substantial room to improve industrial integration."} | |
| {"id": "DV_43", "question": "Perform data analysis on the student attendance records of 7 classes and create a line chart showing the trend of \"personal leave\" counts for each class over time. Specific requirements:\nData processing rules:\n1. Determination of \"personal leave\": only count records where the leave type field is explicitly \"personal leave\"; \"official leave\", \"sick leave\", and other leave types are not included.\n2. If the same student has multiple records on the same day (e.g., both \"official leave\" and \"personal leave\"), as long as that student has at least one \"personal leave\" record on that day, they are counted as 1 person.\n3. For each date and each class, count the number of distinct students who took personal leave that day (i.e., if the same student has multiple personal leave records on the same day, count them only once).\n4. The horizontal axis dates are the union of all dates across all classes (i.e., any date that appears in any class is included on the horizontal axis). If a class has no attendance records on a given date, no data point is plotted for that class on that date (the line naturally skips it, with no zero-filling).\n\nChart style requirements:\n1. The horizontal axis represents dates (sorted in ascending chronological order, formatted as \"M/D\", e.g., \"9/22\", \"10/13\"); the vertical axis represents the number of students on personal leave, with a fixed range of 0–35 and tick interval of 5.\n2. The chart contains 7 lines, each representing one class, distinguished by different colors.\n3. The legend is arranged horizontally, positioned at the top inside the chart border, with a font size of 12 and a spacing of 10 between legend items.\n4. The chart title is \"Trend Comparison of Personal Leave Counts by Class\", with a font size of 18 and a spacing of 20 between the title and the chart body.", "input_file": "Grade24_AI_Class1_desensitized.xlsx\nGrade24_AI_Class2_desensitized.xlsx\nGrade24_AI_Class3_desensitized.xlsx\nGrade24_AI_Class4_desensitized.xlsx\nGrade24_AI_Class5_desensitized.xlsx\nGrade24_AI_Class6_desensitized.xlsx\nGrade24_AI_Class7_desensitized.xlsx", "output_type": "Data Visualization", "output_file": "student_attendance_data_statistical_analysis.jpg", "rubrics": "1. 7 line charts;\n2. The horizontal axis represents dates (arranged in ascending chronological order, formatted as \"M/D\", e.g. \"9/22\" \"10/13\"), the vertical axis represents the number of personal leave absences, the vertical axis range is fixed at 0–35, with tick intervals of 5"} | |
| {"id": "DV_44", "question": "Please draw a metabolite–bacteria association network diagram. The detailed requirements are as follows:\n1. Screen key metabolites: Calculate the Pearson correlation coefficient between all metabolites (LC-MS, GC-MS) and the sensory evaluation \"Composite Score\", and select the Top 10 metabolites significantly correlated with the sensory evaluation score (|r| ≥ 0.3 and p < 0.1).\n2. Calculate metabolite–bacteria correlations: For the Top 10 metabolites obtained in Step 1, calculate their correlations with each bacterium in the document, and retain significantly correlated pairs (|r| ≥ 0.3 and p < 0.1).\n3. Network diagram display (save the image as \"metabolite_microbe_network.png\"):\n- Two types of nodes: metabolites (blue) and bacteria (red)\n- Edges represent significant correlations between metabolites and bacteria\n- Positive correlations shown with red edges, negative correlations shown with blue edges", "input_file": "01_sensory_evaluation_results.xlsx\n02_LC-MS_metabolite_data.xlsx\n03_GC-MS_metabolite_data.xlsx\n04_bacterial_abundance_data.xlsx", "output_type": "Data Visualization", "output_file": "metabolite_microbe_network.png", "rubrics": "1. Blue nodes in the figure are labeled: GC_Metabolite_28, GC_Metabolite_32, GC_Metabolite_45, LC_Metabolite_20, LC_Metabolite_32, LC_Metabolite_48;\n2. Red nodes in the figure are labeled: Bacteria_1, Bacteria_2, Bacteria_4, Bacteria_12, Bacteria_14, Bacteria_17, Bacteria_18, Bacteria_24, Bacteria_26, Bacteria_29;\n3. The edges in the figure are:\n- Blue edges (negative correlation): (GC_Metabolite_45, Bacteria_4), (GC_Metabolite_28, Bacteria_29), (LC_Metabolite_32, Bacteria_17)\n- Red edges (positive correlation): (GC_Metabolite_45, Bacteria_26), (GC_Metabolite_45, Bacteria_12), (GC_Metabolite_28, Bacteria_26), (GC_Metabolite_28, Bacteria_17), (GC_Metabolite_28, Bacteria_12), (GC_Metabolite_32, Bacteria_14), (GC_Metabolite_32, Bacteria_2), (LC_Metabolite_20, Bacteria_1), (LC_Metabolite_48, Bacteria_14), (LC_Metabolite_48, Bacteria_2), (LC_Metabolite_32, Bacteria_24)"} | |
| {"id": "DV_45", "question": "Based on the file gold historical price data (daily).csv, use the moving average method to forecast closing prices. Specific requirements:\n 1. Filter all data records for the year 2024 (2024-01-01 to 2024-12-31)\n 2. Compute the moving average for the closing price field (the column whose semantics best match \"closing price\"), with a window size of 30 days (1 month = 30 days)\n 3. The forecast value is defined as: the forecasted closing price for each date equals the arithmetic mean of the closing prices over the 30 days ending on (and including) that date; for the first 29 days where insufficient data exists, the forecast value is recorded as null\n 4. Plot a line chart with the horizontal axis representing dates (arranged in ascending chronological order) and the vertical axis representing price values (rounded to 2 decimal places)\n 5. The chart contains two lines: one for the actual closing prices in 2024 (labeled \"Actual Closing Price\") and one for the corresponding moving average forecast values (labeled \"Forecasted Closing Price\")\n 6. The chart title is \"2024 Gold Closing Price: Actual vs. Moving Average Forecast\", the horizontal axis label is \"Date\", and the vertical axis label is \"Price\"\n 7. Display a legend to distinguish the two lines; do not display data labels\n 8. The output is a generated visualization chart file", "input_file": "gold historical price data (daily).csv", "output_type": "Data Visualization", "output_file": "2024 gold closing price forecast comparison chart.png", "rubrics": "1. The horizontal axis represents dates, and the vertical axis represents prices;\n2. Two line series: one for the actual closing price, and one for the predicted closing price"} | |
| {"id": "DV_46", "question": "Read the data from the file \"2.Statistics by school.xlsx\", and plot a grouped bar chart sorted by school index (ascending order). The horizontal axis represents the school index, and the vertical axis represents the value. Display three metrics simultaneously: average score, pass rate, and excellence rate. The three metrics are shown as different series (bars of different colors) placed side by side, with the legend clearly labeling each series name. The vertical axis range adapts automatically to the data; the average score is retained to 2 decimal places, and the pass rate and excellence rate are displayed as percentages (retained to 2 decimal places, e.g., \"85.50%\"). The chart title is \"Comparison of Average Score, Pass Rate, and Excellence Rate by School\", the horizontal axis label is \"School Index\", the vertical axis label is \"Value\", and data labels are displayed.", "input_file": "2.Statistics by school.xlsx", "output_type": "Data Visualization", "output_file": "Art literacy comparison.jpg", "rubrics": "There are 21 schools, each with 3 bars"} | |
| {"id": "DV_47", "question": "Draw a histogram that shows the TEU volume for the top 5 salespersons by \"Number of Shippers\" for each week.\n- The TEU volume data for each week is in the columns named \"Week x\" (e.g., \"Week 1\", \"Week 2\");\n- Save the final chart as top5_sales_volume_TEU.png:\nThe horizontal axis is \"Week\" (arranged in chronological order), the vertical axis is \"TEU Volume\", with 5 bars displayed per week representing the top 5 salespersons for that week, using different colors to distinguish different salespersons, and the legend shows the salesperson names. The chart title is \"Weekly Top 5 Salesperson TEU Volume Statistics\", the vertical axis label is \"TEU Volume\", and the horizontal axis label is \"Week\". TEU values are rounded to 2 decimal places. If a given week has fewer than 5 salespersons, display the actual number.", "input_file": "salesperson_dimension_statistical_analysis_desensitized.xlsx", "output_type": "Data Visualization", "output_file": "top5_sales_volume_TEU.png", "rubrics": "1. The vertical axis label is \"TEU Volume\"; the horizontal axis label is \"Week\";\n2. There are 49 weeks, with 0 to 5 bars per week"} | |
| {"id": "DV_48", "question": "Read the litigation ledger data from the file \"New_XLSX_Worksheet_(2)_Desensitized.xlsx\", count the number of cases for each case handler (employee), sort in descending order by case count (for ties, place the one with the smaller name first), take the top 10 (display all if fewer than 10), and draw a bar chart: the horizontal axis represents the case handler's name, the vertical axis represents the number of cases, bars are arranged from highest to lowest case count (for ties, place the one with the smaller name first), the chart title is \"Case Handler Case Count Statistics (Top10)\", the vertical axis label is \"Number of Cases\", the horizontal axis label is \"Case Handler\", and display the specific value on top of each bar.", "input_file": "New_XLSX_Worksheet_(2)_Desensitized.xlsx", "output_type": "Data Visualization", "output_file": "chart_72.png", "rubrics": "1. The top ten are: Name4 (31 cases), Name5 (22 cases), Name2 (19 cases), Name1 (18 cases), Name6 (18 cases), Name9 (14 cases), Name8 (7 cases), Name3 (2 cases), Name7 (1 case), Name10 (1 case)\n2. The chart title must be \"Case Count Statistics by Case Handler (Top10)\", the vertical axis label must be \"Number of Cases\", and the horizontal axis label must be \"Case handler\"\n3. Name1 and Name6 both have 18 cases, with Name1 listed first; Name7 and Name10 both have 1 case, with Name7 listed first"} | |
| {"id": "DV_49", "question": "Plot the singular values in descending order, showing only the top 10. The x-axis label should be \"Singular Values\", the y-axis label should be empty, and the title should be \"Top 10 Singular Values\". The y-axis should start at 20. Display the corresponding singular value on top of each bar, rounded to two decimal places.", "input_file": "2._Single_Cell_Simulation_Data.xlsx", "output_type": "Data Visualization", "output_file": "chart_73.png", "rubrics": "1. The top ten singular values are: 41.44, 41.22, 40.19, 40.06, 39.51, 39.37, 39.03, 38.85, 38.63, 38.54\n2. The horizontal axis label is \"Singular Values\", the vertical axis label is empty, and the title is \"Top 10 Singular Values\"\n3. The vertical axis starts at a minimum tick of 20"} | |
| {"id": "DV_50", "question": "Draw two subplots in one figure:\n1. On the left is a table summarizing the counts of different classes and different subjects (ignoring \"None\") for Question 10 in the \"Merged Information\" column, with subjects on the horizontal axis and classes on the vertical axis. Add a \"Total\" row at the bottom and a \"Total\" column on the right. Data sourced from \"Merged Information\".\n2. On the right is a pie chart showing the total count and proportion of each subject (as a percentage, kept to two decimal places; if the second decimal place is 0, only one decimal place is retained). Title: \"Subject Proportion\", data labels are required, and the legend is displayed below.", "input_file": "grade12_class1-18_homework_duration_survey_results_summary.xlsx\nquestionnaire_items.docx", "output_type": "Data Visualization", "output_file": "chart_74.png", "rubrics": "1. There are 18 classes in total (Class 1, Class 2, ..., Class 18)\n2. There are 9 subjects in total (Chinese, Mathematics, English, Physics, Chemistry, Biology, History, Politics, Geography)\n3. The count and proportion by subject are: Chinese: 79 (12.68%), Mathematics: 200 (32.1%), English: 111 (17.82%), Physics: 30 (4.82%), Chemistry: 70 (11.24%), Biology: 24 (3.85%), History: 32 (5.14%), Politics: 57 (9.15%), Geography: 20 (3.21%)"} | |
| {"id": "DV_51", "question": "Read the Azimuth data from column F in the file \"Processing2.xlsx\" and draw a polar coordinate chart (circular chart): with the center of the circle as the origin, each azimuth corresponds to a ray pointing from the center to the circumference, with the Serial Number of that data row labeled at the end of the ray. Chart requirements: use a polar coordinate system, angular axis range 0–360 degrees (0 degrees = due North, clockwise), all rays have the same length, label the corresponding row number as text at the end of each ray, chart title is empty, do not display radial tick values, retain only the angular tick marks (one tick label every 30 degrees).", "input_file": "Processing2.xlsx", "output_type": "Data Visualization", "output_file": "chart_75.png", "rubrics": "1. There are 16 rows of data in total; starting from the center of the circle in the chart, exactly 16 rays or line segments must be drawn\n2. The end of each of the 16 rays or line segments must be correctly labeled with the corresponding Serial Number\n3. Strictly follow the convention of \"0 degrees = due North, clockwise\" with tick marks labeled at every 30 degrees"} | |
| {"id": "DV_52", "question": "Based on the two core numerical fields related to customer value (Total Spending Amount, Number of Purchases) in \"customer_behavior_data.xlsx\", generate a scatter plot: the horizontal axis represents the first numerical field, the vertical axis represents the second numerical field, and each point represents one customer. Customer tiers are distinguished by different colors: high-value (Number of Purchases greater than 2), mid-value (Number of Purchases equal to 2), and low-value (Number of Purchases equal to 1). The chart title is \"Customer Behavior Analysis Scatter Plot\", the horizontal and vertical axis labels are the column names of the selected fields respectively, the customer tier legend is displayed in the upper-left corner, and data labels are not shown. If the number of customers exceeds 500, randomly sample 500 customers to display in order to ensure readability.", "input_file": "customer_behavior_data.xlsx", "output_type": "Data Visualization", "output_file": "chart_76.png", "rubrics": "1. The title must be \"Customer Behavior Analysis Scatter Plot\", the horizontal axis must be \"Total Spending Amount\", and the vertical axis must be \"Number of Purchases\"\n2. There are 9 points in total, of which two points overlap at (289, 1).\n3. The customer tier legend must be displayed in the upper left corner, with three different colors distinguishing different customer tiers. The colors in the legend must match the colors of the points in the chart."} | |
| {"id": "DV_53", "question": "Based on the price-related fields in \"price_sales_data.xlsx\", generate a bar chart: the horizontal axis represents product names or product categories (select the categorical dimension field that exists in the data), the vertical axis represents average price, sorted in descending order by average price, displaying a price comparison of the Top 10 products/categories (products with the same name count as 1; if fewer than 10 exist, display all). The chart title is \"Product Price Analysis (Top 10)\", the vertical axis label is empty, and the horizontal axis must display the specific product names. Display data labels in red above each bar — if the value is an integer, do not show decimal places; if it is a decimal, retain 1 decimal place. Grid lines must be drawn.", "input_file": "price_sales_data.xlsx", "output_type": "Data Visualization", "output_file": "chart_77.png", "rubrics": "1. The chart title must be \"Product Price Analysis (Top 10)\", the y-axis label must be empty, and the x-axis must display the specific product names (from left to right: Fashion Trench Coat, Slim-fit Round-neck Knit Dress, Mid-length A-line Skirt, Lady-style Floral Dress, High-waist Jeans, Knitted Cardigan, Simple Commuter Shirt, Casual Hoodie)\n2. There must be exactly 8 bars, with the corresponding average prices displayed in red above each bar, from left to right: 689, 459, 445, 428, 359, 329, 289, 199\n3. Grid lines must be drawn"} | |
| {"id": "DV_54", "question": "Based on the data in \"competitor_category_data.xlsx\", create a bar chart comparing the monthly sales revenue of Own Store and Competitor Store across different product categories. The horizontal axis must display specific category names, sorted in descending order by Own Store monthly sales revenue, with missing values treated as 0. The title should be \"Monthly Sales Revenue Comparison\" and centered at the top. The legend should be centered at the bottom, using different colors to represent Own Store and Competitor Store data. Display the corresponding sales revenue values above each bar.", "input_file": "competitor_category_data.xlsx", "output_type": "Data Visualization", "output_file": "chart_78.png", "rubrics": "1. The title must be \"Monthly Sales Comparison\" and centered at the top; the legend must be centered at the bottom, using different colors to represent the data for Own Store and Competitor Store\n2. There are 4 categories in total, from left to right: Dress, Top, Pants, Skirt\n3. The monthly sales for each category are: Dress (Own Store 239155, Competitor Store 330044), Top (Own Store 105237, Competitor Store 209569), Pants (Own Store 95853, Competitor Store 77022), Skirt (Own Store 0 or empty, Competitor Store 66677)"} | |
| {"id": "DV_55", "question": "Based on the file \"25Q3 outsourcing assessment user data.xlsx\", group and aggregate the data by the \"Role\" field, calculating the \"Overall Average Score\" for each role (first compute the average of all numeric columns related to scores, then compute the mean for each role). Identify the two roles with the highest and lowest Overall Average Score, and explicitly output their role names and corresponding Overall Average Score values (rounded to 2 decimal places). Also generate a bar chart where the horizontal axis shows all role names, the vertical axis shows the Overall Average Score for each role, sorted in descending order of Overall Average Score, with the chart title \"Overall Average Score Comparison by Role\", the vertical axis label \"Overall Average Score\", the horizontal axis label \"Role\", and data labels displayed above each bar (rounded to 2 decimal places).", "input_file": "25Q3 outsourcing assessment user data.xlsx", "output_type": "Data Visualization", "output_file": "Overall Average Score Comparison by Role.png", "rubrics": "1. The chart clearly displays the descending distribution of average assessment scores by role, ranging from 4.27 to 2.85.\n2. The FDE/VPM/XPM role scores significantly lead, at 4.27.\n3. The structural role has the lowest average assessment score among all roles, at 2.85."} | |
| {"id": "DV_56", "question": "Read the file \"25Q3Outsourced Assessment User Data.xlsx\", group the data by the \"System Name\" field, and calculate the \"Overall Average Score\" for each system (compute the average of all numeric scoring columns for each record, then average those values across all records within that system, rounded to 2 decimal places). Identify the 1 system with the highest Overall Average Score and the 1 system with the lowest Overall Average Score, and output the names of these two systems along with their corresponding Overall Average Score values. Also generate a bar chart: the horizontal axis represents each system name, the vertical axis represents the Overall Average Score, sorted in descending order of Overall Average Score, with data labels displayed (rounded to 2 decimal places), the chart title set to \"Overall Average Score Comparison by System\", the vertical axis label set to \"Overall Average Score\", and the horizontal axis label set to \"System Name\".", "input_file": "25Q4 outsourcing assessment user data.xlsx", "output_type": "Data Visualization", "output_file": "comparison of overall average scores across systems.png", "rubrics": "1. System_01 ranks first with a score of 3.70, nearly 0.24 points ahead of the second-ranked System_02.\n2. System_06 has an overall average score of 2.73, making it the only system among all units with a score below 2.8.\n3. Performance differences across systems are significant, with a gap of 0.97 points between the highest score of 3.70 and the lowest score of 2.73."} | |
| {"id": "DV_57", "question": "Using the data from \"File 2 Participants.xlsx\",\nGroup by province and calculate the mean activity usefulness score for each province (first compute the mean of all usefulness scores for each user, then compute the mean across users by province), and plot a line chart: the horizontal axis represents provinces (sorted in descending order by the indicator mean, taking the Top 10 provinces), the vertical axis represents the mean of the indicator, and each line represents the value trend of one province.\nThe chart title is \"Group Characteristic Comparison by Province (Top 10)\", display data labels (rounded to 2 decimal places), and add a legend indicating province names.", "input_file": "File 2 Participants.xlsx", "output_type": "Data Visualization", "output_file": "Comparison of Group Characteristics by Province.png", "rubrics": "1. Anhui Province ranked highest at 4.88, with Jilin Province close behind at 4.78.\n2. Shanghai, Tianjin, and Heilongjiang decreased in sequence with minimal differences, while Zhejiang ranked last.\n3. The visualization line chart is sorted in descending order by mean score, intuitively presenting the rating trends of the leading provinces, with precise scores annotated to two decimal places."} | |
| {"id": "DV_58", "question": "Based on the age data in \"File 2 Participants.xlsx\", group the data by age range and calculate the percentage of each group,\n then draw a line chart to display the distribution trend across different age groups.\n The horizontal axis represents age ranges (grouped in 10-year intervals: 0-9, 10-19, 20-29, and so on), and the vertical axis represents the percentage of each age group as a proportion of the total number of people (rounded to 2 decimal places).\n Connect the data points in ascending order of age range, with the chart title set to \"Age Range Distribution Trend\", the horizontal axis label set to \"Age Range\", the vertical axis label set to \"Percentage (%)\", display data labels, and hide the legend.", "input_file": "File 2 Participants.xlsx", "output_type": "Data Visualization", "output_file": "Age Group Distribution Trends.png", "rubrics": "1. The line chart shows a trend of first rising then falling.\n2. The 30-39 age group has the lowest proportion, at 0.73%, followed by the 0-9 age group.\n3. The 10-19 age group has the highest proportion, at 69.69%."} | |
| {"id": "DV_59", "question": "Based on the \"9 types checkbox\" related fields in \"File 2 Participants.xlsx\",\n\nCount the total number of times each type was checked, and generate a bar chart: the horizontal axis shows the names of the 9 types, the vertical axis shows the number of times each type was checked, sorted in descending order by count, with data labels displayed. The chart title is \"9 Types Checkbox Distribution\", the vertical axis label is \"Checkbox Count\", and the horizontal axis label is \"Type Name\".", "input_file": "File 2 Participants.xlsx", "output_type": "Data Visualization", "output_file": "9 types checkbox distribution.png", "rubrics": "1. Overall, it is a bar chart with values decreasing from left to right, showing the frequency distribution of checkbox-type selections\n2. The most frequently occurring option is health lectures, with up to 4365 occurrences\n3. The least frequent, excluding others, is online services, with 876 occurrences"} | |
| {"id": "DV_60", "question": "Based on enterprise_data.xlsx, generate an optimized dual-column layout chart containing: a donut chart on the left displaying \"Distribution of Enterprise Count Across Sub-sectors of the Digital Core Industry\", and a bar chart on the right displaying \"High-Tech Enterprise Proportion Across Sub-sectors of the Digital Core Industry\". The donut chart must show data labels (count + percentage, rounded to 1 decimal place), with sectors arranged in descending order by proportion; the bar chart must show value labels (percentage rounded to 1 decimal place), with bars arranged in descending order by proportion, and the vertical axis tick interval set to 20%. The overall chart should use an academic-style color scheme (harmonious color palette, moderate contrast), with titles in 14pt bold, axis labels in 10pt, legends positioned below each subplot. Output as a PNG image file with a resolution of no less than 1200×600 pixels.", "input_file": "enterprise_data.xlsx", "output_type": "Data Visualization", "output_file": "digital_core_industry_statistics.png", "rubrics": "1. The donut chart on the left shows that the combined \"Enterprise Count Proportion (%)\" of the Digital Product Manufacturing sector (45.5%) and the Digital Factor-Driven sector (40.7%) exceeds 80%, making them the two \"Sub-sector\"s with the highest number of enterprises.\n2. The bar chart on the right displays the \"High-Tech Enterprise Proportion (%)\", where Digital Product Manufacturing ranks first at 81.6%, followed closely by Digital Technology Application at 61.5%.\n3. The \"High-Tech Enterprise Proportion (%)\" for the Digital Factor-Driven sector is 14.7%, while that figure is currently 0.0% for the Digital Product Services sector."} | |
| {"id": "DV_61", "question": "Based on the personnel attrition data in the two files \"resignation summary table 2024.xlsx\" and \"resignation summary table 2025.xlsx\", generate a bar chart comparing the number of attritions in 2024 vs. 2025. Specific requirements: the horizontal axis represents the time dimension (2024, 2025), and the vertical axis represents the total attrition headcount; if the data contains monthly breakdowns, aggregate by month and compare the attrition headcount for each corresponding month across the two years (horizontal axis shows months 1–12, with two groups of bars representing 2024 and 2025 respectively); the chart title is \"2024 vs 2025 Employee Attrition Comparison\"; display data labels; the legend should indicate the year; arrange in chronological month order; retain integers (headcount).", "input_file": "resignation summary table 2024.xlsx\nresignation summary table 2025.xlsx", "output_type": "Data Visualization", "output_file": "2024 vs 2025 staff turnover comparison.png", "rubrics": "1. Monthly attrition in 2024 was relatively volatile, with July and September showing higher headcount losses.\n2. 2025 data shows generally lower attrition numbers, with some months recording 0.\n3. The highest single-month attrition in 2024 reached 10 employees, while the highest single-month attrition in 2025 was 5 employees."} | |
| {"id": "DV_62", "question": "Chart: The overall satisfaction average score is 8.98, 67.8% of participants gave high ratings of 9-10, and 95.68% of participants are willing to recommend the club.", "input_file": "youth_health_club_survey_data_descriptive_statistical_analysis_report-full_text-20251104.docx\nfile1.pdf", "output_type": "Data Visualization", "output_file": "Plot Club Satisfaction Data Chart.png", "rubrics": "1. Contains 3 parts: overall satisfaction, evaluation, and willingness to recommend.\n2. The average overall satisfaction score is 8.98.\n3. 67.8% of participants gave a high rating of 9–10, displayed using a pie chart / donut chart, etc.\n4. 95.68% of participants are willing to recommend the club, displayed using a pie chart or donut chart, etc."} | |
| {"id": "DV_63", "question": "Based on the file AVT.xls, create a line chart showing the trend of KBT values for different product models under different temperature conditions. Specific requirements: the horizontal axis represents temperature (sorted in ascending numerical order), the vertical axis represents KBT values, each model is represented by a separate line (distinguished by different colors), the legend displays all model names, the chart title is \"Trend of KBT Values for Different Models at Different Temperatures\", the horizontal axis label is \"Temperature (℃)\", the vertical axis label is \"KBT\", and data labels are displayed (rounded to 2 decimal places).", "input_file": "AVT.xls", "output_type": "Data Visualization", "output_file": "KBT change trends for different models at different temperatures.png", "rubrics": "1. The KBT values of all models show an increasing trend as temperature rises\n2. The temperature increase of product model 2400k is the most significant\n3. The KBT values are positively correlated with the model power rating\n4. A total of 8 models and 7 temperatures."} | |
| {"id": "DV_64", "question": "Based on the data in the file \"Experiment 2.xlsx\", use the date as the horizontal axis (X-axis) to plot a line chart showing \"the trend of Oversize (%) and Undersize (%) over time for the 4.75-9.5 specification\". Chart requirements: the horizontal axis represents Date (sorted in ascending chronological order), the vertical axis represents percentage (%), the chart contains two lines (one representing Oversize (%) and one representing Undersize (%)), each line displays data labels, the legend position is at the top, the chart title is \"4.75-9.5mm Specification Oversize (%) and Undersize (%) Trend Chart\", the X-axis label is \"Date\", and the Y-axis label is \"Percentage (%)\". If a given Date–percentage combination has multiple results, use the average. If a date is missing, skip and ignore it.", "input_file": "Experiment 2.xlsx", "output_type": "Data Visualization", "output_file": "4.75-9.5mm spec oversize and undersize rate trend chart.png", "rubrics": "1. Oversize rate and Undersize rate exhibit approximately inverse fluctuation relationship\n2. The Undersize (%) on October 28 is 17.5%\n3. The highest point of Undersize (%) is 26.9%."} | |
| {"id": "DV_65", "question": "Read the file \"resignation summary table 2024.xlsx\", and classify positions into two categories: \"Key Positions\" and \"General Positions\". Count the number of attritions for each of the two position categories. Generate a bar chart: the horizontal axis represents position type (\"Key Positions\", \"General Positions\"), the vertical axis represents the number of attritions, data labels (attrition counts) are displayed above each bar, the chart title is \"Comparison of Attrition Count Between Key Positions and General Positions\", the Y-axis label is \"Number of Attritions\", and the X-axis label is \"Position Type\".", "input_file": "resignation summary table 2024.xlsx", "output_type": "Data Visualization", "output_file": "Key Positions vs General Positions Turnover Comparison.png", "rubrics": "1. General position employees have higher turnover, with 32 people.\n 2. Key position turnover is 22 people.\n 3. The chart X-axis label is \"Position Type\"."} | |
| {"id": "DV_66", "question": "Generate a visualization chart based on the data file:\nCustomer distribution scatter plot: Use the customers' longitude and latitude coordinates as the X-axis (longitude) and Y-axis (latitude) to plot a scatter plot, where each point represents a customer location. The chart title should be \"Customer Geographic Distribution Map\", the X-axis label should be \"Longitude\", and the Y-axis label should be \"Latitude\". Each point should be colored differently to represent the customer demand (in tons), and each point should be labeled with the store identifier (e.g., C1, C2, ...).", "input_file": "Copy_Supply_Chain_Analysis_Final_Document.xlsx", "output_type": "Data Visualization", "output_file": "Customer_Distribution_Scatter_Plot.png", "rubrics": "1. The point closest to C11 is C3.\n2. C11 and C9 have the same color.\n3. C6 is located at coordinates (16.4, 23.5).\n \n "} | |
| {"id": "DV_67", "question": "Based on \"AI_literacy_assessment-final_integrated_version.xlsx\" and \"AI_literacy_assessment-original_questionnaire_items_and_scoring_rules.docx\" (questions and scoring rules), perform the following tasks:\nGenerate a horizontal bar chart for the first question, with the following requirements:\n - Chart type: horizontal bar chart (Y-axis shows the full text of each option, X-axis shows the number of respondents who selected it)\n - Display data labels on each bar in the format: \"{number of selections} ({percentage})\", e.g. \"150 (25.3%)\"\n - Sort in descending order by number of selections (highest count at the top)\n - Chart title: \"Question 1 Option Distribution\"\n - X-axis label: \"Number of Selections\", Y-axis label: \"Option Content\"\n - Save as a standalone image file named \"question_1_option_distribution.png\"", "input_file": "AI_literacy_assessment-final_integrated_version.xlsx\nAI_literacy_assessment-original_questionnaire_items_and_scoring_rules.docx", "output_type": "Data Visualization", "output_file": "question_1_option_distribution.png", "rubrics": "1. The number of people who chose option B is 1064, and the percentage is 84.2%.\n2. Option E has the fewest number of people.\n3. Option C ranks in the middle."} | |
| {"id": "DV_68", "question": "Based on the columns related to participation frequency in \"File 2 Participants.xlsx\", count the number of each of the 3 participation frequency types and generate a bar chart. The horizontal axis shows the names of the 3 participation frequency types, and the vertical axis shows the corresponding counts, sorted in descending order by count. The chart title is \"Participation Frequency Type Distribution\", the vertical axis label is \"Count\", the horizontal axis label is \"Participation Frequency Type\", display data labels (show the specific value at the top of each bar), and do not display a legend.", "input_file": "Question 7 Format.xlsx\nfile1.pdf\nFile 2 Participants.xlsx", "output_type": "Data Visualization", "output_file": "Participation Frequency Type Distribution Bar Chart.png", "rubrics": "1. Participation frequency is inversely related to the number of participants\n 2. Low-frequency participants constitute the majority group\n 3. There are significant order-of-magnitude differences among the three frequency categories"} | |
| {"id": "DV_69", "question": "Based on the \"Gender\" field in \"File 2 Participants.xlsx\", count the number and proportion of males and females respectively, and create a bar chart for comparison. The horizontal axis represents gender categories (Male, Female), and the vertical axis represents the count. The chart title is \"Gender Distribution Comparison\". Display the specific count and proportion (percentage rounded to 2 decimal places) on top of each bar. Additionally, output the p-value of the chi-square test in text form below or beside the chart, to determine whether the difference in gender distribution is significant (p < 0.05 is considered significant). If there are missing values in the gender field, ignore those rows during the statistical analysis.", "input_file": "Question 7 Format.xlsx\nfile1.pdf\nFile 2 Participants.xlsx", "output_type": "Data Visualization", "output_file": "Gender distribution comparison chart.png", "rubrics": "1: It is clearly visible that the proportion of female participants is higher than that of male participants\n 2: The number of both male and female participants each exceeds 2000\n 3: There are 977 more female participants than male participants"} | |
| {"id": "DV_70", "question": "Based on the \"Province\" field and relevant group characteristic indicators in \"File 2 Participants.xlsx\", create a bar chart: the horizontal axis shows each province (sorted in descending order by number of participants, Top 10 only), and the vertical axis shows the mean value of the core group characteristic indicator for that province (select the numeric column in the data that best semantically matches \"group characteristics\"; if multiple columns exist, prioritize demographic indicators such as \"Age\" or \"income\"). Each province displays one bar. The chart title should be \"Group Characteristics Comparison by Province (Top 10)\", the vertical axis label should be the selected indicator name plus its unit, the horizontal axis label should be \"Province\", numerical labels (rounded to 2 decimal places) should be displayed above each bar, and the overall mean should be noted below the chart or in a subtitle as a reference baseline.", "input_file": "Question 7 Format.xlsx\nfile1.pdf\nFile 2 Participants.xlsx", "output_type": "Data Visualization", "output_file": "Provincial Group Characteristics Comparison Chart.png", "rubrics": "1: Guangxi Zhuang Autonomous Region has the lowest group characteristic ratio\n 2: Jiangsu Province, Hebei Province, and Henan Province have an average age exceeding 18 years\n 3: Jiangsu Province has the highest average age"} | |
| {"id": "DV_71", "question": "Based on the data in File 2 Participants.xlsx, compute the Pearson correlation coefficient matrix among all numeric columns and generate a correlation heatmap. Heatmap requirements: use color blocks to represent correlation coefficient strength (ranging from -1 to 1), with the color scheme red for positive correlation, blue for negative correlation, and white for no correlation; display the correlation coefficient value on each color block (rounded to 2 decimal places); both the X-axis and Y-axis should show variable names; add a color bar legend annotating the correlation coefficient range; set the chart title to \"Variable Correlation Heatmap\". If the data contains fewer than 2 numeric columns, output a message indicating that correlation analysis cannot be performed.", "input_file": "Question 7 Format.xlsx\nfile1.pdf\nFile 2 Participants.xlsx", "output_type": "Data Visualization", "output_file": "variable_correlation_heatmap.png", "rubrics": "1: The correlation coefficient between every variable and itself equals 1 (shown as the darkest red block)\n 2: Some pairs of variables exhibit relatively high positive correlations — for example, the correlation coefficient between \"Interpersonal relationships\" and \"Life planning\" reaches 0.48, and the correlation coefficient between \"Professional competence\" and \"Service attitude\" is 0.44, indicating fairly pronounced positive associations among these variables.\n 3: The correlation coefficients among most variable pairs are concentrated near 0 (colors close to light orange or white) — for example, the correlation coefficient between \"Age\" and \"Reproductive health knowledge\" is only -0.04, and the correlation coefficient between \"Psychological reconstruction\" and \"Life planning\" is -0.00, indicating that the degree of association among these variables is relatively weak."} | |
| {"id": "DV_72", "question": "Read the data from the file \"two types of headphone data.png\", and extract the following 5 battery-life metrics for the two earphone models Product_A and Product_B: (1) single-charge battery life, (2) charging-case total battery life, (3) 10-minute quick-charge battery life, (4) call battery life, (5) battery life in noise-cancelling mode. Draw a radar chart containing 5 dimension axes (corresponding to the 5 metrics listed above). The scale range of each dimension axis is [0, max(the maximum value of that metric across both earphone models) * 1.2]. The two earphone models are represented by polygons in different colors (Apple in blue, Sony in orange). The polygon area fill transparency is 0.3 and the border line width is 2. The chart title is \"Product_A and Product_B Battery Life Performance Comparison\". Display a legend labeling both earphone model names. All battery-life values must use the unified unit \"hours\"; if the original data unit is minutes, convert to hours and retain 1 decimal place.", "input_file": "two types of headphone data.png", "output_type": "Data Visualization", "output_file": "5-dimension radar chart.png", "rubrics": "1: In the \"Quick Charge 10-min Playback Time\" dimension, Product_B's value is only 0.3, far below Product_A, making it a clear weak point in its battery performance.\n 2: In the \"Battery Life in Noise-Cancellation Mode\" dimension, both products have identical values, indicating that once noise cancellation is enabled, the single-charge endurance of the two earphones is the same.\n 3: In the three dimensions of \"Charging Case Total Battery Life\", \"Total Call Battery Life\", and \"Quick Charge 10-min Playback Time\", Product_A's performance (corresponding to the blue polygon) is superior to Product_B's in all three."} | |
| {"id": "DV_73", "question": "Based on the provided Nanjing Pharmaceutical AI Project Set Excel file, generate three separate four-quadrant bubble charts, one for each \"Launch Year\" value (2026, 2027, 2028). The specific requirements for each chart are as follows:\n 1. X-axis: The project's \"Total Feasibility Dimension Score\" (if the field name in the file does not match exactly, select the semantically closest column, such as \"Total Feasibility Score\" or a summary column containing \"Feasibility\")\n 2. Y-axis: The project's \"Total Importance Dimension Score\" (similarly, select the semantically closest column)\n 3. Bubble size: Determined by the \"Total Urgency Dimension Score\" (bubble area is proportional to that score)\n 4. Bubble labels: Display the project's \"Serial Number\" field (if no \"Serial Number\" column exists, use the row number or project number)\n 5. Bubble color: Mapped according to the \"Business Segment\" field, with the following rules: Supply Chain = green (#00FF00), Logistics = blue (#0000FF), Retail = yellow (#FFFF00), Operations Management = red (#FF0000); any other segments should use gray (#808080)\n 6. Axis ranges: The minimum value for both the X-axis and Y-axis should be the floor of the minimum score across all projects (combined across all three years) for the corresponding dimension, and the maximum value should be the ceiling of the maximum score, ensuring all bubbles are fully displayed within the chart; tick intervals should be automatically divided into 4–6 equal segments\n 7. Chart titles: \"Four-Quadrant Analysis of Projects Launched in 2026\", \"Four-Quadrant Analysis of Projects Launched in 2027\", and \"Four-Quadrant Analysis of Projects Launched in 2028\", respectively\n 8. Four-quadrant dividing lines: The midline positions for both the X-axis and Y-axis should be the median of the respective dimension scores (calculated across all projects combined)\n 9. Output format: Generate 3 bubble charts for the corresponding years, named \"bubble_chart_2026\", \"bubble_chart_2027\", and \"bubble_chart_2028\", respectively\n 10. Missing value handling: If any dimension score is missing for a project, that project should be excluded from the chart for the corresponding year; if the \"Launch Year\" is missing, the project should be categorized as \"Unclassified\" and not plotted", "input_file": "AI_digital_transformation_project_portfolio_and_roadmap_(12.18).xlsx", "output_type": "Data Visualization", "output_file": "bubble_chart_2026.png\nbubble_chart_2027.png\nbubble_chart_2028.png", "rubrics": "1. 2026 (42 projects): Dominated by supply chain (green) and operations management (red) projects; high feasibility + high importance (Quadrant I) projects account for approximately 35%;\n2. 2027 (47 projects): Logistics (blue) projects increase significantly; low feasibility + high importance (Quadrant II) projects require focused attention on feasibility improvement;\n3. 2028 (11 projects): Fewer projects overall, dominated by retail (yellow) and supply chain projects, with a relatively dispersed distribution — further priority planning is needed."} | |
| {"id": "DV_74", "question": "Read the file \"data_analysis2025.12.19.xlsx\", filter for 2025 closed-deal records, and aggregate the total transaction amount by province for each client type (state-owned enterprise, government, private enterprise, individual). For each client type, generate a China map heatmap where the color intensity of each province represents the total transaction amount for that client type in that province (darker color indicates higher amount), with province names labeled on the map. Name the four heatmaps \"state_owned_enterprise_client_transaction_amount_distribution\", \"government_client_transaction_amount_distribution\", \"private_enterprise_client_transaction_amount_distribution\", and \"individual_client_transaction_amount_distribution\" respectively. Each map must use the same color scheme (e.g., blue gradient), with the color mapping range spanning from the minimum to the maximum provincial amount within that client type.", "input_file": "data_analysis2025.12.19.xlsx", "output_type": "Data Visualization", "output_file": "individual_client_transaction_amount_distribution.png\nstate_owned_enterprise_client_transaction_amount_distribution.png\nprivate_enterprise_client_transaction_amount_distribution.png\ngovernment_client_transaction_amount_distribution.png", "rubrics": "Individual Client Transaction Amount Distribution Chart 1: Guangdong has the highest individual client transaction amount\n 2: Shaanxi has the lowest individual client transaction amount\n 3: All provinces show a positive correlation in individual client transaction amounts\n Private Enterprise Client Transaction Amount Distribution Chart 1: Among the four charts, the Private Enterprise Client Transaction Amount Distribution Chart has the most provinces\n 2: Shandong has the highest private enterprise client transaction amount at 918\n 3: All province ratios show a positive correlation\n Government Client Transaction Amount Distribution Chart 1: All province ratios show a positive correlation\n 2: Among the four charts, this chart has the fewest provinces\n 3: Xinjiang has the highest government client transaction amount\n State-Owned Enterprise Client Transaction Amount Distribution Chart 1: All province ratios show a positive correlation\n 2: Jiangsu has the highest state-owned enterprise client transaction amount\n 3: Jilin has the lowest state-owned enterprise client transaction amount"} | |
| {"id": "DV_75", "question": "Read the file \"2025 transaction_information_-_copy.xlsx\", sum the transaction amounts for 2025 by province field to obtain the total transaction amount for each province. Sort the results in descending order by total transaction amount. Generate a bubble chart named \"Total Transaction Amount by Province.png\", arranged according to a Chinese map layout (provincial-level administrative divisions), using color intensity to represent the total transaction amount of each province: the province with the highest total transaction amount uses the darkest color, the province with the lowest total transaction amount uses the lightest color, with a monochromatic color gradient scheme (e.g., blue ranging from light blue to dark blue). Label each province's region with the province name and value, and display a legend showing the correspondence between color and total transaction amount (rounded to 2 decimal places, with units in yuan or automatically adjusted to 10,000 yuan / 100 million yuan based on the order of magnitude).", "input_file": "2025 transaction_information_-_copy.xlsx", "output_type": "Data Visualization", "output_file": "Total Transaction Amount by Province.png", "rubrics": "1: Xinjiang leads in total transaction amount: Xinjiang's total transaction amount reaches 1,300,117 (10,000 yuan), making it the province with the highest total transaction amount in the chart (corresponding to the darkest color in the legend).\n 2: Guizhou ranks last in total transaction amount: Guizhou's total transaction amount is only 100,000 (10,000 yuan), making it the province with the lowest total transaction amount in the chart (corresponding to the lightest color in the legend).\n 3: Significant regional transaction disparities: The total transaction amounts vary greatly across provinces, exhibiting pronounced regional imbalance characteristics.\n4. Bubble chart"} | |
| {"id": "DV_76", "question": "Beautify the table above: center and bold the text in cells, color the cells blue, following a blue-light blue-blue-light blue pattern. Output image name: after_beautification.png", "input_file": "2c82d54cf92ad14872831615fc7981fb.png", "output_type": "Data Visualization", "output_file": "after_beautification.png", "rubrics": "1. Text is centered and bolded in the cell;\n2. Cells are colored blue, following the pattern of blue-light blue-blue-light blue"} | |
| {"id": "DV_77", "question": "What is the gender distribution in the survey sample? Present it as a pie chart with white numeric labels on each corresponding slice, where the slice for male is blue, female is red, and other options are green. Name the file Gender_Proportion.png, and the chart should have the title \"Gender_Proportion\" centered at the top.", "input_file": "Copy_Group_Assignment.docx", "output_type": "Data Visualization", "output_file": "Gender_Proportion.png", "rubrics": "1. Male respondents account for 41.67%, female for 37.5%, and another 20.83% selected \"Other\" or \"All\" options, showing a relatively balanced overall distribution.\n2. In the chart, the range corresponding to males is blue, females is red, and other options is green.\n3. The chart must have a centered title \"Gender Distribution\" at the top.\n4. Use white numbers labeled on the corresponding chart segments."} | |
| {"id": "DV_78", "question": "Based on the data in \"Thesis Questionnaire Data.xlsx\", divide the samples into three groups — \"High Deviation\", \"Moderate Deviation\", and \"Low Deviation\" — according to the \"deviation\" field (or the categorization field most semantically similar to it). Calculate the corresponding percentages and present them as a pie chart. The numbers must be displayed on the chart segments in black font. Segment colors: High Deviation – light green, Moderate Deviation – light blue, Low Deviation – light pink. The chart title should be: \"Major selection deviation level distribution\". Save the generated chart as \"Major Selection Bias Level Distribution Pie Chart.png\".", "input_file": "Thesis Questionnaire Data.xlsx", "output_type": "Data Visualization", "output_file": "Major Selection Bias Level Distribution Pie Chart.png", "rubrics": "Numbers must be labeled on the tiles in black font, tile colors: high deviation - light green 10.2%, moderate deviation - light blue 32.8%, low deviation - light pink 57.0%, title: Major Selection Deviation Level Distribution"} | |
| {"id": "DV_79", "question": "Sum the leads and leads % of total data corresponding to the same day, and plot a bar chart where the horizontal axis represents dates in ascending order from left to right, the vertical axis represents values, the legend corresponds to leads and leads % of total, the title is \"Daily Leads and Leads % of Total Sum\", and save the output file as daily_leads_summary.png", "input_file": "Traffic_Daily_-_superset.xlsx", "output_type": "Data Visualization", "output_file": "daily_leads_summary.png", "rubrics": "The horizontal axis represents dates, ascending from left to right from 2025-11-01 to 2025-12-04, the vertical axis represents values, the legend corresponds to leads and leads % of total, and the title is \"Daily Leads and Leads % of Total Sum\""} | |
| {"id": "DV_80", "question": "Based on the 3 form datasets, analyze the geographic distribution of customers who have placed orders. Aggregate the number of ordering customers by geographic dimension, and generate a bar chart for visualization. The horizontal axis represents geography (country or region), the vertical axis represents the number of ordering customers, sorted in descending order by customer count, displaying the Top 10 regions. The chart title is \"Top 10 Geographic Distribution of Ordering Customers\", the vertical axis label is \"Number of Ordering Customers\", the horizontal axis label is \"Region\", and data labels are displayed.", "input_file": "inquiry_followup_table_desensitized(111).xlsx\ndata_overview_data_overview_report_October.xlsx\ndata_overview_data_overview_report_November.xlsx", "output_type": "Data Visualization", "output_file": "province.png\nprefecture_level_city.png", "rubrics": "1. Zhejiang Province customers hold an absolute dominant position\n2. Jiangsu Province ranks second\n3. Customers from other regions are distributed relatively evenly"} | |
| {"id": "DV_81", "question": "Based on the sales data for January–October of years 24 and 25 in the file (excluding e-commerce), calculate the monthly sales amount (or sales volume — use whichever numeric field in the file best matches the semantic meaning of \"offtake\" or \"sales\"), and aggregate the monthly total sales for both year 24 and year 25 separately by month (January through October). Plot a line chart where the horizontal axis represents the month (1–10), the vertical axis represents sales amount, with two lines representing year 24 and year 25 respectively. The legend should be labeled \"2024Year\" and \"2025Year\", the chart title should be \"2024Year vs 2025Year Monthly Sales Trend Comparison (1-10 Month)\", the vertical axis label should be \"Sales Revenue\", values should be rounded to 2 decimal places, and the data should be sorted in ascending order by month.", "input_file": "24-25 Wyeth Tianji Two Channels (Excl. E-commerce) Jan-Oct Offtake_Jan-Oct YoY.xlsx", "output_type": "Data Visualization", "output_file": "Sales Trend Comparison.png", "rubrics": "1. Sales in 2025 \"lead in the vast majority of months\", with only October sales falling below the same period in 2024\n2. February–March are peak sales months - February 2025: 1.7078 million, March 2025: 1.8526 million\n3. October shows the best sales performance - October 2024 reached 1.7030 million, the annual high"} | |
| {"id": "DV_82", "question": "Based on the CAT-related data in the data file, create a bar chart: the horizontal axis should use the column containing CAT-related exposure concentrations (e.g., \"xxx xx mg/L\"), with the axis title labeled \"Exposure Concentration\" translated as \"Exposure Concentration\"; the vertical axis should display the \"CAT\" indicator, with the axis title labeled \"CAT\"; sort in descending order by the vertical axis values. The chart title should be \"CAT by Exposure Concentration Distribution\", display data labels, retain 2 decimal places, and do not include \"± xx\".", "input_file": "new_XLSX_worksheet.xlsx", "output_type": "Data Visualization", "output_file": "bar_chart.png", "rubrics": "1. The PtNP25 0.1 mg/L group has the highest CAT mean (86.69 U/mg FW)\n2. The Control 0.0 mg/L group ranks second (80.87 U/mg FW)\n3. The PtNP70 1.0 mg/L group has the lowest CAT value (6.55 U/mg FW), with significant differences among groups"} | |
| {"id": "DV_83", "question": "Based on the data in online_courses.csv, count the number of courses for each course category (the \"category\" field) and each platform (the \"platform\" field) respectively. Generate horizontal bar charts (bar charts) where the x-axis represents the number of courses and the y-axis represents the category or platform name, sorted in descending order by number of courses. Each chart must include the x-axis label \"Number of Courses\", the y-axis label \"Category/Platform\", and the chart title \"Course Count Statistics (by Category/Platform)\", and display data labels on each bar. Since two dimensions are being visualized, generate two separate horizontal bar charts.", "input_file": "online_course.csv", "output_type": "Data Visualization", "output_file": "result.png", "rubrics": "I. Statistics by Category\n 1. Finance courses are the most numerous (122 courses)\n 2. The middle tier (Design, Data Science, etc.) have similar counts\n 3. Technology courses are the fewest (88 courses)\nII. Statistics by Platform\n 1. The four major platforms are evenly distributed\n 2. The maximum gap between platforms is only 32 courses"} | |
| {"id": "DV_84", "question": "Shortcomings of Short-Video Platforms in Disseminating Local Culture and Tourism Information (Unit: %)\\r\\nData Distribution:\\r\\nExcessive Commercialization: 30%\\r\\nSevere Content Homogenization: 20%\\r\\nDifficulty Ensuring Information Authenticity: 30%\\r\\nOthers: 10%.\nCreate a bar chart based on the above information", "input_file": null, "output_type": "Data Visualization", "output_file": "res.png", "rubrics": "1. The chart is a vertical bar chart displaying 4 problem categories in total.\n2. \"Over-commercialization\" and \"Difficulty guaranteeing information authenticity\" are tied for the highest proportion, each at 30%.\n3. The \"Other\" category has the lowest proportion, at only 10%.\n4. \"Severe content homogenization\" has a moderate proportion of 20%."} | |
| {"id": "DV_85", "question": "I am a member of the company's data analytics team. We have a real estate dataset that needs to be analyzed by your team. The dataset contains 545 real estate records (already cleaned), with attributes including house price, house area, number of bedrooms, number of bathrooms, number of floors, whether it is located on a main road, whether it has a guest room, and other properties. Please convert the binary data fields (i.e., fields whose values are \"yes\" or \"no\") so that \"yes\" is encoded as 1 and \"no\" is encoded as 0. Output the converted dataset (must be submitted in Excel format).\nBased on the processed data, please plot a heatmap to display the correlations between all fields, and provide analytical conclusions.", "input_file": "CAIP-6091.xlsx", "output_type": "Data Visualization", "output_file": "Q37_vis.png", "rubrics": "1. Both the horizontal and vertical axes contain 12 feature variables, such as price, area, bedrooms, etc.\n2. Excluding the diagonal, the correlation coefficient between house price (price) and house area (area) is the highest, at 0.54.\n3. The number of stories (stories) and basement (basement) show a negative correlation, with a coefficient of -0.17."} | |
| {"id": "DV_86", "question": "Calculate the average tenure for each department and generate a bar chart. The department names must be clearly visible, and the average tenure value for each department should not be labeled on the chart.", "input_file": "Personnel Information Simulation Table-Visualization.xlsx", "output_type": "Data Visualization", "output_file": "Q50_vis.png", "rubrics": "1. The chart displays a comparison of Average Tenure across 8 departments from A to H.\n2. Department B has the highest Average Tenure, with a value exceeding 4.\n3. Department A has the lowest Average Tenure, with a value of approximately 1.5.\n4. Do not label the Average Tenure data for each department."} | |
| {"id": "DV_87", "question": "Calculate the average tenure for each department, and generate a bar chart with department names clearly visible, with the average tenure data labeled for each department.", "input_file": "Personnel Information Simulation Table-Visualization.xlsx", "output_type": "Data Visualization", "output_file": "Q51_vis.png", "rubrics": "1. The department with the highest Average Tenure is B, with a value of 4.38.\n2. The value for Department F is 2.38."} | |
| {"id": "DV_88", "question": "Create a bar chart showing the number of competitions for each department", "input_file": "2024 College Student Competition Projects.xlsx", "output_type": "Data Visualization", "output_file": "Q60_vis.png", "rubrics": "1. The School of Modern Business has the highest number of competitions, with a value of 43.\n2. There are 7 departments in the chart, and the data is arranged in descending order by value.\n3. The Teaching Service Center and the Network and Information Service Center have the smallest values, both being 2."} | |
| {"id": "DV_89", "question": "Create a pie chart based on the data in Sheet1 of the spreadsheet", "input_file": "data.xlsx", "output_type": "Data Visualization", "output_file": "Q94_vis.png", "rubrics": "1. The pie chart contains two sectors, where \"believe there is an impact\" has the highest proportion, with a value of 0.9455.\n2. \"believe there is no impact\" has a smaller proportion, with a value of 0.0545."} | |
| {"id": "DV_90", "question": "Based on the data for Tulong Information Co., Ltd. in the table, create a bar chart using the population count and actual elderly population data", "input_file": "2024 physical examination rate spring compilation.xlsx", "output_type": "Data Visualization", "output_file": "Q112_vis.png", "rubrics": null} | |
| {"id": "DV_91", "question": "Help me generate a project plan Gantt chart (each process defaults to a duration of 3 months). Tasks under the same objective should use the same color and be placed on the same row. The horizontal axis is \"Time\", starting from 2024-12, with every month displayed. The vertical axis is \"Project Module\". The title should be \"Drone Project Plan Gantt Chart\". Add a legend in the upper-right corner explaining the color corresponding to each project module.", "input_file": "Workbook.xlsx", "output_type": "Data Visualization", "output_file": "old_chart_10.png", "rubrics": "1. There must be exactly four rows, each in a different color\n2. The horizontal axis must be \"Time\", starting from 2024-12, with every month displayed. The vertical axis must be \"Project Module\". The title must be \"Drone Project Plan Gantt Chart\". There must be a legend in the upper right corner indicating the project module corresponding to each color\n3. The text inside each block in the chart must be correct"} | |
| {"id": "DV_92", "question": "Based on the crop data in \"Attachment 2.xlsx\", calculate the correlation coefficients among yield per mu, planting cost, and selling unit price, and generate a correlation heatmap.\nSpecific requirements:\nBoth the horizontal and vertical axes represent the three indicators involved in the analysis (Yield per Mu / Jin, Planting Cost / (Yuan/Mu), Selling Unit Price / (Yuan/Jin));\nIf the selling unit price data is in interval format (e.g., \"2.50-4.00\"), use the average of the interval as the unit price for that crop in the calculation;\nThe chart title should be \"Correlation Heatmap of Crop Yield per Mu, Cost, and Unit Price\"; display correlation coefficient data labels inside the matrix cells; retain 2 decimal places; colors should clearly distinguish between positive and negative correlations.", "input_file": "Attachment 2.xlsx", "output_type": "Data Visualization", "output_file": "Q130_vis.png", "rubrics": "1. Yield per Mu vs Planting Cost (0.79): Shows a strong positive correlation — crops with higher yield per mu tend to have higher planting costs.\n2. Yield per Mu vs Selling Unit Price (-0.36): Shows a weak negative correlation — crops with higher yields tend to have lower unit prices.\n3. Planting Cost vs Selling Unit Price (-0.21): Shows a weak negative correlation — crops with higher costs do not necessarily sell at higher prices."} | |
| {"id": "DV_93", "question": "Please create a scatter analysis chart titled 'Crop Planting Scale and Production Efficiency', with the following specific requirements:\nData Processing: Use 'Planting Area (mu)' as the horizontal axis (X-axis) and 'Yield per Mu / Jin' as the vertical axis (Y-axis).\nData Labels: Clearly label the corresponding Crop Name next to each data point in the chart.\nQuadrant Division: Draw two reference lines passing through the mean values of the X-axis and Y-axis respectively, dividing the chart into four quadrants to visually distinguish which crops fall into categories such as 'High-Yield Large-Scale' or 'Low-Yield Niche'.\nChart Legend: The chart title is 'Scatter Analysis Chart of Crop Planting Scale and Production Efficiency'. In the upper-right corner, display the average Planting Area and average Yield per Mu / Jin in the form of a legend.", "input_file": "planting area and yield per mu.xlsx", "output_type": "Data Visualization", "output_file": "scatter analysis of crop planting scale and production efficiency.png", "rubrics": "1. Most points are located in the lower-left corner.\n2. There are no points in the high-yield large-area region.\n3. The lowest-yield niche crop is oat (Oat (Youmai)).\n4. Mung bean and millet are very close to each other.\n5. Water spinach falls in the upper-left quadrant."} | |
| {"id": "DV_94", "question": "First calculate the total Payment Amount for each of the top 7 Withdrawing Merchants, then generate a bar chart displayed in descending order by amount", "input_file": "merchant_weekly_withdrawal.xlsx", "output_type": "Data Visualization", "output_file": "old_chart_13.png", "rubrics": "1. All merchants are ranked in descending order by Payment Amount.\n2. The merchant with the highest Payment Amount is \"user_6\", with an amount of 29455.00.\n3. The merchant with the lowest Payment Amount is \"user_19\", with an amount of only 6936.00."} | |
| {"id": "DV_95", "question": "Based on sheet3, generate a pie chart with the title \"Category Proportion Pie Chart\". Display the proportion values rounded to two decimal places in black font on the sectors, with the category names shown outside their corresponding sectors.", "input_file": "2024 middle school exam math analysis data archive.xlsx", "output_type": "Data Visualization", "output_file": "Q141_vis.png", "rubrics": "1. Contains 4 category sectors in total.\n2. \"Mathematics Integrated into Life\" has the highest proportion, reaching 52.94%.\n3. \"The Intersection of Mathematics and Technology\" ranks second, accounting for 23.53%.\n4. The smallest proportion is \"The Integration of Mathematics and Humanities & Arts\", accounting for only 5.88%."} | |
| {"id": "DV_96", "question": "In the \"Crop Planting Situation in 2023\" table, for the \"Crop Name\" column, generate a pie chart (top 4 only, plus Others). Remove non-crop-name entries.", "input_file": "Attachment 2.xlsx", "output_type": "Data Visualization", "output_file": "Q144_vis.png", "rubrics": "1. The largest share belongs to the \"Others\" category, reaching 61.8%.\n2. Among specific crops, \"Wheat\" has the highest share at 11.8%.\n3. Corn, Millet, and Chinese Cabbage have the same share, each at 8.8%.\n4. The pie chart is divided into a total of 5 sectors."} | |
| {"id": "DV_97", "question": "Help me create a trend chart of the Member TC achievement rate for Store 2 in August, and add a trendline. The chart title should be \"August 2024 Store 2 Member TC Achievement Rate Trend Chart\", the vertical axis title should be \"TC Achievement Rate\", and the horizontal axis title should be \"Date\". No need to provide statistical data.", "input_file": "member_data.xlsx", "output_type": "Data Visualization", "output_file": "Q145_vis.png", "rubrics": "1. The chart is a line chart with one trendline\n2. The TC achievement rate fluctuates significantly throughout August, with the lowest value appearing on August 5 (approximately 0.408) and the highest value appearing on August 19 (approximately 0.833)\n3. The trendline shows a slight upward trend"} | |
| {"id": "DV_98", "question": "In chronological order, present the Minute-level flow rate and Product price in the form of a line chart, and display the minute-level node data points on the chart.", "input_file": "product_ranking.xlsx", "output_type": "Data Visualization", "output_file": "product_ranking_line_chart.png", "rubrics": "1. The chart shows the trends of Minute-level flow rate and Product price over time (1 minute to 40 minutes).\n2. Minute-level flow rate reaches its peak at minute 23, with a value of 1000.\n3. The Minute-level flow rate line remains stable from minute 24 to minute 26, with a value of 666 throughout.\n4. The red line (Product price) is not continuous and only displays data for certain time periods."} | |
| {"id": "DV_99", "question": "Help me analyze the workload comparison of three people in July and August 2024, using a combination chart (stacked bar chart + line chart). Use the stacked bar chart to display the workload of the three people, and use the line chart to show the trend.\nNote:\n1. There are four bars in total: July 2024 Proofreading, July 2024 Review, August 2024 Proofreading, and August 2024 Review. Two line series.\n2. The same employee uses the same color in both Proofreading and Review.\n3. Place the legend in the upper right corner.\n4. Both vertical axes use the same range.\n5. Label the stacked bar chart with values.", "input_file": "workload_statistics.xlsx", "output_type": "Data Visualization", "output_file": "workload_comparative_analysis.png", "rubrics": "1. A total of four bar groups: July 2024 Proofreading, July 2024 Review, August 2024 Proofreading, and August 2024 Review. Two line series.\n2. The same employee uses the same color in both Proofreading and Review.\n3. Place the legend in the upper-right corner.\n4. Both vertical axes use the same range.\n5. Label the values on the stacked bar chart.\n6. Trend Analysis:\nProofreading workload: July 46 → August 41, change -5 (-10.9%)\nReview workload: July 27 → August 28, change +1 (+3.7%)"} | |
| {"id": "DV_100", "question": "Using 202401-202408 as the horizontal axis and the data in the third row as the vertical axis, create a line chart displaying three separate lines for: Sum of Sales Amount, Sum of Target, and Sum of Same Period Last Year", "input_file": "Workbook.xlsx", "output_type": "Data Visualization", "output_file": "Q153_vis.png", "rubrics": "1. The chart displays the trends of three lines during the period from 202401 to 202408: \"Sum of Sales Amount\", \"Sum of Target\", and \"Sum of Same Period Last Year\".\n2. \"Sum of Sales Amount\" reaches its highest point in 202401 (exceeding 900000), followed by a sharp decline in 202402.\n3. \"Sum of Target\" starts at 0 in 202401, shows an overall upward trend, and reaches its highest value in 202408."} | |
| {"id": "DV_101", "question": "Draw distribution charts of BMI for different age groups", "input_file": "2024 physical examination rate spring compilation.xlsx", "output_type": "Data Visualization", "output_file": "Q164_vis.png", "rubrics": "1. The number of overweight people aged 65-69 years old exceeds 700.\n2. The number of obese people aged 75-79 years old is between 100 and 200."} | |
| {"id": "DV_102", "question": "Please create a line chart using the data from the table, with the title \"Unit Cost and Unit Selling Price per Mu for Ordinary Greenhouse Crops\"", "input_file": "Workbook.xlsx", "output_type": "Data Visualization", "output_file": "Q190_vis.png", "rubrics": "1. The chart title is \"Cost per Mu vs. Sale Price per Mu for Ordinary Greenhouse Crops\", containing two lines: \"Planting Cost / (Yuan/Mu)\" and \"Flat Dry Land Price per Mu\".\n2. The sale price of Yuhuang mushroom is the highest among all crops, with a peak close to 300,000 Yuan.\n3. The overall trend shows that the sale price per Mu (orange line) for all listed crops is higher than their planting cost (blue line)."} | |
| {"id": "DV_103", "question": "\nStatistical Chart of the Relationship Between Gender and BMI", "input_file": "Workbook.xlsx", "output_type": "Data Visualization", "output_file": "Q194_vis.png", "rubrics": "1. The median BMI of males (green line position, approximately 19.8) is noticeably higher than the median BMI of females (approximately 17.0).\n2. The upper edge (maximum value) of both groups is close to 25, but the overall box position of males is higher than that of females, indicating a higher distribution."} | |
| {"id": "DV_104", "question": "BMI and Test Score relationship, create a chart", "input_file": "Workbook.xlsx", "output_type": "Data Visualization", "output_file": "Q195_vis.png", "rubrics": "1. The person with the highest Test Score is a male, with a BMI close to 16.\n2. Most females have a Test Score around 110.\n3. The person with the lowest Test Score is a male, with a BMI exceeding 24."} | |
| {"id": "DV_105", "question": "Help me create a line chart of Body weight based on the Sheet1 data in the document", "input_file": "September_2_desensitized.xlsx", "output_type": "Data Visualization", "output_file": "Q227_vis.png", "rubrics": "1. The chart title is \"Body weight\", showing the trend of body weight changes from data point 1 to data point 40.\n2. The overall data presents a steady upward trend, with no obvious downward inflection point.\n3. The body weight value increases from an initial 127.2 jin to a maximum of 131.5 jin."} | |
| {"id": "DV_106", "question": "Draw a box plot, mark the upper bound of outliers with a green dashed line and label its value (rounded to two decimal places); mark the lower bound of outliers with a red dashed line and label its value (rounded to two decimal places). Add a legend in the upper right corner explaining what each colored line represents. The title should be \"Box Plot of Data (Outlier Bounds Marked)\"", "input_file": "Workbook.xlsx", "output_type": "Data Visualization", "output_file": "old_chart_26.png", "rubrics": "1. A green dashed line must be used to mark the upper bound of outliers, with the value 70.77 labeled\n2. A red dashed line must be used to mark the lower bound of outliers, with the value -13.82 labeled\n3. There must be eight points (or circles) above the green dashed line"} | |
| {"id": "DV_107", "question": "Based on the \"Export Count_MS2class\" data sheet in \"combine_intensity_select.xlsx\", extract the proportions for the two categories \"Flavonoids\" and \"Isoquinolines and derivatives\".\nCombine all remaining categories in the table into \"Others\", ensuring that the sum of the three proportions equals 100%.\nPlease draw a pie chart to display the distribution of these three parts, with the chart title \"Proportion Distribution of Key Compound Classifications\", and show data labels (category name + percentage).", "input_file": "combine_intensity_select.xlsx", "output_type": "Data Visualization", "output_file": "Q274_vis.png", "rubrics": "1. The \"Others\" category has the largest proportion, with a value of 93.4%.\n2. \"Flavonoids\" has a proportion of 6.0%.\n3. \"Isoquinolines and derivatives\" has the smallest proportion at 0.6%."} | |
| {"id": "DV_108", "question": "Generate a clustered bar chart based on the table data. Requirements:\nChart title: set to 'Statistical Comparison of Key and General Student Counts by Subject';\nHorizontal axis (X-axis): categorized by 'Subject', with axis title named 'Subject';\nVertical axis (Y-axis): displays values, with axis title named 'Count';\nData series: display four metrics in parallel under the same subject — 'Key Count', 'Key Valid Count', 'General Count', and 'General Valid Count';\nLegend: clearly label the colors of the four metrics, placed to the right or above the chart.", "input_file": "Workbook.xlsx", "output_type": "Data Visualization", "output_file": "Comparison Chart of Key vs Regular Student Count Statistics by Subject.png", "rubrics": "1. There are 4 legend entries on the right or top of the chart, which are Key Count, Key Valid Count, General Count, and General Valid Count.\n2. The four bar values in the Math cluster are 13, 7, 38, and 31.\n3. The highest value in General Count is 40, in the English subject.\n4. The chart title is \"Comparison of Key and General Student Counts by Subject\"."} | |
| {"id": "DV_109", "question": "Based on students' \"Junior High School Graduated From\", create a pie chart", "input_file": "2024 xx No.1 High School College Entrance Exam Admission Honor Roll.xlsx", "output_type": "Data Visualization", "output_file": "old_chart_29.png", "rubrics": "Junior High 21 (14 people), Junior High 9 (9 people), Junior High 5 (4 people), Junior High 22 (3 people), Junior High 12 (3 people), Junior High 37 (3 people), Junior High 17 (2 people), Junior High 14 (1 person), Junior High 31 (1 person)"} | |
| {"id": "DV_110", "question": "Based on the scores of the knowledge points examined in sheet2, generate a stacked bar proportion chart with the title \"Proportion of Mathematics Culture Questions and Other Questions Each Year\", the horizontal axis representing \"Year\", the vertical axis representing proportion, the legend displaying \"Mathematics Culture Questions Proportion\" and \"Other Questions Proportion\", and the vertical axis unit interval set to 0.2.", "input_file": "2024 middle school exam math analysis data archive.xlsx", "output_type": "Data Visualization", "output_file": "Q320_vis.png", "rubrics": "1. The chart covers three years: 2022, 2023, and 2024.\n2. The legend displays \"Mathematics Culture Questions Proportion\" and \"Other Questions Proportion\".\n3. The sum of the proportions of the two question types for each year equals 1.0, with the Mathematics Culture Questions Proportion in 2023 and 2024 being roughly equal and higher than in 2022."} | |
| {"id": "DV_111", "question": "Draw a line chart for bid-rigging crimes, without labeling the data values.", "input_file": "Workbook.xlsx", "output_type": "Data Visualization", "output_file": "Q325_vis.png", "rubrics": "1. There is a noticeable decline between 2016 and 2017, dropping from around 80 to around 30.\n2. The number of cases reached its peak in 2020, with the annotated value of 136.0.\n3. Each data point on the line is labeled with a specific number, and the horizontal axis spans from 2010 to 2024.\n4. No numerical labels are required."} | |
| {"id": "DV_112", "question": "Draw a line chart for the crime of bid-rigging collusion, with year on the x-axis and number of cases on the y-axis. Label each point on the line chart with its value.", "input_file": "Workbook.xlsx", "output_type": "Data Visualization", "output_file": "Q325-1_vis.png", "rubrics": "1. There is a notable decline between 2016 and 2017, dropping from around 80 to around 30.\n2. The number of cases reaches its peak in 2020, with the annotated value of 136.0.\n3. Each data point on the line chart is labeled with a specific number, and the horizontal axis spans from 2010 to 2024.\n4. Each point is labeled with a number."} | |
| {"id": "DV_113", "question": "Please create a bar chart showing the Yield per Mu, planting cost per Mu, and Price per Mu for each crop in the table.\nThe left axis scale represents Yield per Mu, and the right axis scale represents Planting Cost and Price per Mu.", "input_file": "Workbook.xlsx", "output_type": "Data Visualization", "output_file": "crop_bar_chart.png", "rubrics": "1. The left axis scale represents Yield per Mu / Jin, and the right axis scale represents Planting Cost / (Yuan/Mu) and Price per Mu. The horizontal axis displays 15 crops including peas, sweet potatoes, etc.\n2. The Price per Mu of sweet potatoes is the highest among all crops, with a value exceeding 7000.\n3. Pumpkin has the highest Yield per Mu / Jin, with a value approaching 3000 Jin."} | |
| {"id": "DV_114", "question": "In \"Related Statistics for 2023\", first remove the data where the crop is a person's name, then generate a line chart using the Crop Name of terraced fields as the x-axis, and Yield per Mu / Jin and Planting Cost / (Yuan/Mu) as the 2 y-axes.", "input_file": "2023 statistical data attachment 2.xlsx", "output_type": "Data Visualization", "output_file": "Q347_vis.png", "rubrics": "1. The chart uses dual Y-axes to display Yield per Mu / Jin and Planting Cost / (Yuan/Mu).\n2. The Yield per Mu / Jin shows that \"Pumpkin\" has the highest yield per mu, significantly higher than other crops, with a value approaching 3000 Jin.\n3. The Planting Cost / (Yuan/Mu) shows that \"Sweet Potato\" has the highest planting cost, with a peak value of approximately 2000 Yuan/Mu."} | |
| {"id": "DV_115", "question": "Plot a histogram of all Change % data", "input_file": "data_tracking.xlsx", "output_type": "Data Visualization", "output_file": "Q384_vis.png", "rubrics": "1. The X-axis represents the Change % intervals, and the Y-axis represents frequency.\n2. The interval with the highest frequency is the first interval [9.36%, 11.36%], with a value exceeding 120.\n3. There is a significant secondary peak at the interval [29.36%, 31.36%], with a frequency close to 40.\n4. The data distribution exhibits a clear long-tail characteristic, with the vast majority of data concentrated on the left side, while most intervals on the right side have a frequency of 0."} | |
| {"id": "DV_116", "question": "Create a bar chart of shipping performance", "input_file": "Application Form.xlsx", "output_type": "Data Visualization", "output_file": "Q412_vis.png", "rubrics": "1. The horizontal axis spans from June 2022 to August 2024.\n2. The month with the highest performance is November 2022, with a value of 1835960.67.\n3. The month with the lowest performance is January 2023, with a value of 454729.09."} | |