type: Data Visualization

instruction: Identify the top ten artists based on sales from this dataset and plot their sales figures in a bar chart. Save the image as sales.jpg, with the title "Top Ten Artists Based on Sales," a size of (6, 6), y-axis labeled as "Artist," and x-axis labeled as "Total Sales."

hardness: Easy


type: Data Visualization

instruction: Draw a stacked bar chart that displays the percentage of restaurants offering online ordering options versus those not offering online ordering options across different rating levels. Save this chart as 'result.jpg'. The title of the image should be "Percentage of Restaurants' Online Order Option by Rating", with the xlabel as "Rating", and the ylabel as "Percentage of Online Orders".

hardness: Medium


type: Data Visualization

instruction: Identify the top ten authors with the highest average book prices. Create a bar chart with dimensions of 18 by 12, label the y-axis as 'Author', the x-axis as 'Average Price', and the title as 'Most Expensive Author', and save the chart in 'result.jpg'

hardness: Easy


type: Data Visualization

instruction: Enhance the provided analysis.py, correct any potential errors, plot the images and save them in 'result.png' following the form of 'plot.yaml'

hardness: Hard


type: Data Visualization

instruction: Draw a bar chart showing the number of people in each age group who filled out the survey. Follow the method for dividing age groups as outlined in AgeGroup.md. Save the bar chart as 'result.png' with the title 'Age Group Distribution', the x-axis labeled as 'Age Group', and the y-axis labeled as 'Count'.

hardness: Hard


type: Data Visualization

instruction: Plot a bar chart for a clearer visual representation of each team's performance over the specified period. Save the resulting chart as 'team.png', adhering to the settings specified in 'plot.yaml'.

hardness: Hard


type: Data Visualization

instruction: Create a bar chart showing the distribution of Amazon Prime movies grouped by duration, displaying the number of movies in each duration range. The chart should be formatted according to the specifications given in plot.yaml and saved as result.png.

hardness: Medium


type: Data Visualization

instruction: Calculate the gender distribution by country, create a bar chart to visualize the results, and save the chart as 'distribution.png'. Please refer to 'plot_details.txt' for additional specifications.

hardness: Medium


type: Data Visualization

instruction: Analyze and visualize the top 10 countries by gold medal count in descending order using a bar chart, while simultaneously comparing their counts of athletes and coaches with separate bars on the same chart. Refer to 'plot.yaml' for specific requirements.

hardness: Medium


type: Data Visualization

instruction: Calculate the mutual information between all variables and the house price using the provided dataset. Follow the instructions in guidance.txt and create a bar chart according to plot.yaml. Save the chart as result.png.

hardness: Medium


type: Data Visualization

instruction: You are tasked with analyzing the "AB_NYC_2019.csv" dataset. Compare the total number of Airbnb listings with the number of listings priced between 50 and 250 USD across different suburbs in New York City. Follow the format specified in plot.yaml to create a bar chart, and save it as 'result.png'.

hardness: Medium


type: Data Visualization

instruction: Investigate the living arrangements of students aged 15-22 within the dataset, distinguishing between teenagers and adults, to determine whether they reside with their parents or independently. Create a bar chart of the results with a size of (15, 10), titled \"Where do children between 15 and 22 live\", with the x-axis title \"age_category\" and the y-axis title \"number\" Save the chart as result.png.

hardness: Hard


type: Data Visualization

instruction: Analyze the ICC Hall of Fame dataset to visualize the player roles. Create a bar chart based on the format requirements in plot.yaml and save it as 'result.png'.

hardness: Medium


type: Data Visualization

instruction: You are analyzing the Synthetic Indian Automobile Crash Data dataset. Your task is to count the missing values for each feature and create a bar chart showing these values in descending order. Ignore features with zero missing values. Save the chart as 'output.png'.

hardness: Medium


type: Data Visualization

instruction: Collect sales data for each product size, quantified in increments of $10,000 units. Create a bar chart to visualize this data, sorting the sizes in ascending order with 'free size' considered the smallest. Save the chart to 'result.png'

hardness: Hard


type: Data Visualization

instruction: Examine the E-commerce dataset by Olist and determine the top 10 product categories by count. Create a bar chart to display these categories, ensuring it adheres to the format outlined in plot.yaml. Save the chart as 'result.png'.

hardness: Medium


type: Data Visualization

instruction: Plot the total donations to each political party as a bar chart and save the resulting plot as 'party_sums_plot.png'. The image should have a size of (14, 8), with the title 'Total Denominations by Political Party', x-axis labeled as 'Total Denominations', and y-axis labeled as 'Political Party'.

hardness: Medium


type: Data Visualization

instruction: Generate a bar chart that shows the "average number of days from the submission of a project to the issuance of a permit," categorized by working days. Ensure that the chart's format adheres to the specifications outlined in `plot.yaml`. After creating the chart, save it as `result.png`.

hardness: Medium


type: Data Visualization

instruction: Filter the top 10 sub-categories by quantity in the Amazon Sales dataset and count their numbers. Create a bar chart with a size of (16, 6), titled "Most Amount of Products by Category," with "Count" on the x-axis and "Product Sub-Category" on the y-axis. Label the y-axis with the sub-category names. Save the chart as result.png.

hardness: Medium


type: Data Visualization

instruction: To analyze the distribution of free and paid apps across different categories in the mobile app market, please count the number of free and paid apps in each category. Based on the format requirements in `plot.yaml`, create a bar chart of the results, and save the generated image to the `result.png` file.

hardness: Medium


type: Data Visualization

instruction: Based on the contents of tips.md, I need help calculating the number of positive and negative sales figures for each category among the top 250 restaurants. Positive sales are those greater than zero. Please plot this data in a bar chart according to the format specified in plot.yaml, labeling each category on the x-axis. Save the resulting image as 'category.png'.

hardness: Medium


type: Data Visualization

instruction: Help me compile the Top 10 maximum number of students present in one day for each school. Plot the results in a bar chart format as specified in plot.yaml. Save the plotted result as 'result.png'.

hardness: Easy


type: Data Visualization

instruction: Following the guidelines for grading in tips.md, please compile the number of people in each grade. Plot the results in a bar chart as specified in plot.yaml. Save the resulting chart as 'result.png'

hardness: Medium


type: Data Visualization

instruction: Create a line graph from the NBA database showing average game scores per year. Ensure to check for and remove any invalid data, such as cases where three-point shots made exceed attempts. Follow the formatting guidelines specified in plot.yaml and save the image as point.png.

hardness: Medium


type: Data Visualization

instruction: Using the Indian Premier League dataset, identify teams with more than 100 matches. Create a line graph showing the total runs scored in each over by these teams. Label the teams using abbreviations from teamabbreviations.md. Save the graph as result.png.

hardness: Medium


type: Data Visualization

instruction: You are tasked with analyzing the "Getting Real about Fake News" dataset. Calculate the spam scores for each day based on both crawled and published dates. Plot these scores as a line graph according to the format specified in plot.yaml and save your graph as 'output.png'.

hardness: Medium


type: Data Visualization

instruction: Analyze the E-commerce dataset by Olist to plot multiple lines for different order_status values in chronological order. Ensure that if data for a particular order_status is missing in any year, it is ignored rather than treated as zero. Follow the format specified in plot.yaml and save the plot as 'result.png.

hardness: Medium


type: Data Visualization

instruction: Calculate the average Happiness Score from 2015 to 2019 for regions listed in Region.md using the World Happiness Report dataset. Plot these scores in a line graph in chronological order as specified in plot.yaml. Save the image as result.png.

hardness: Medium


type: Data Visualization

instruction: From the Climate Change Dataset, create a line graph showing global seasonal average temperature changes as instructed in tips.txt and formatted according to plot.yaml. Save the graph as result.png.

hardness: Hard


type: Data Visualization

instruction: Using the 17K Mobile Strategy Game dataset, plot the yearly average size (MB) of four major game genres from 2008 to 2019. Follow the instructions in guidance.txt and the formatting in plot.yaml. Save the plot as result.png.

hardness: Medium


type: Data Visualization

instruction: Calculate the average BMI for each season using the NBA Players dataset and BMI.txt guidelines. Plot the data as a line chart, size (16, 8), titled "Average BMI Each Season". Label the x-axis "Season" and the y-axis "BMI", with the line in dark blue (#17408b). Save the chart as result.png.

hardness: Medium


type: Data Visualization

instruction: Using the Daily Temperature of Major Cities dataset, plot the yearly average temperatures (in Celsius) for Karachi and Islamabad from 1995 to 2019. Follow the instructions in tips.txt and adhere to the formatting guidelines in plot.yaml. Save the chart as result.png.

hardness: Medium


type: Data Visualization

instruction: Calculate the proportion of Nobel Prize winners born in the most common birth country for each awarding decade, and plot it as a line graph. Set the image size to (10,5), and save the plot as result.jpg. Title the plot 'Proportion of Nobel Prize Winners Born in the most common birth country of Nobel laureates by Decade,' with the x-axis labeled 'Decade' and the y-axis labeled 'Proportion of USA-born Winners.

hardness: Medium


type: Data Visualization

instruction: Calculate the percentage of Nobel Prize winners of the gender with the most laureates for each decade and category, and then plot this as a line graph. Save the image as result.jpg. The image should be sized (10,6), with the title “Proportion of Top Gender Nobel Prize Winners by Decade and Category”, the x-axis labeled “Decade”, and the y-axis labeled “Percentage of Top Gender Winners”. Also, include the category names in the legend.

hardness: Medium


type: Data Visualization

instruction: Draw a line plot to visualize the average withdrawal and deposit amounts for each of the 12 months (January to December), calculated across all available years. Save the resulting plot as result.jpg. The title of the line plot should be 'Average Withdrawal and Deposit Amounts per Month', the legend labels should be 'Withdrawal' and 'Deposit', the x-axis title should be 'Month', and the y-axis title should be 'Average Amount'.

hardness: Easy


type: Data Visualization

instruction: Calculate a 30-day moving average of orders by store, and then visually represent this data using a line plot. Save the resulting plot as result.jpg with a size of (10,4). The title of the plot should be “30-Day Moving Average of Orders”, and the legend title should be “store_id”.

hardness: Medium


type: Data Visualization

instruction: Please use the 2017 stock return data for the 9 largest companies to calculate the daily cumulative returns for three portfolio strategies: equal-weight, market value-weighted, highest Sharpe ratio, and global minimum volatility. Generate a line plot as specified in 'plot.yaml' and save it as 'result.jpg'. You can refer to 'analysis.py' for relevant code

hardness: Hard


type: Data Visualization

instruction: You are tasked with analyzing the Interesting Data to Visualize dataset. Plot a line chart to show how the number of visitors to Avila Adobe has changed over time. Ensure the chart follows the format specified in plot.yaml and save it as 'result.png'.

hardness: Medium


type: Data Visualization

instruction: Please follow the instructions in README.md to plot a timeline line chart for Markdown1-5, showing the trend of promotions over time. Format the chart according to plot.yaml and save the resulting image as 'result.png'.

hardness: Medium


type: Data Visualization

instruction: Please compile the total scores for each season and plot them in a line chart to show the trend over different seasons. Follow the format specified in plot.yaml and save the chart as 'result.png'.

hardness: Medium


type: Data Visualization

instruction: Please compile the total scores for each year from 1950 to 2018. Plot the results in a line chart according to the format specified in plot.yaml and save the chart as 'result.png'.

hardness: Hard


type: Data Visualization

instruction: Please compile the total scores of players from the Eastern and Western conferences for each year. Plot the results according to the format specified in plot.yaml to show the trend over the years for both conferences. Save the chart as 'result.png'.

hardness: Hard


type: Data Visualization

instruction: Calculate the percentage of missing values for each column in the dataset. Plot columns with non-zero missing values and their percentages in a line chart formatted according to plot.yaml, with the x-axis labels set to the column names. Save the chart as 'result.png'.

hardness: Medium


type: Data Visualization

instruction: Draw a line chart depicting the electricity consumption of various Southeast Asian countries over time, based on the format given in plot.yaml. Highlight the total electricity consumption for each country and save the chart as result.png.

hardness: Medium


type: Data Visualization

instruction: Analyze the UPI Transactions Dataset to identify the top four categories with the largest number of transactions. Based on the format specified in plot.yaml, create a pie chart and save it as answer.png.

hardness: Easy


type: Data Visualization

instruction: Analyze the IPL Complete Dataset (2008-2023) to filter the runs (1-6) scored by V Kohli and calculate their distribution. Use the plot.yaml file to load chart configurations, including labels, colors, and the title. Based on the format specified in plot.yaml, save the distribution data to a CSV file and create a pie chart, saving it as distribution.png.

hardness: Medium


type: Data Visualization

instruction: Identify the top 4 categories with the highest number of transactions and create a pie chart to display these four categories and their distribution. Save the image as "result.jpg", with a size of (8, 6), and the title "Transaction Distribution by Category".

hardness: Medium


type: Data Visualization

instruction: Use the Student Alcohol Consumption dataset to create a pie chart showing the distribution of final grades (G3) for students with weekly alcohol consumption levels (2 to 10). Combine weekday and weekend alcohol consumption into a single value. Use the colors ‘lime’, ‘blue’, ‘orange’, ‘cyan’, ‘grey’, ‘purple’, ‘brown’, ‘red’, and ‘darksalmon’. Save the chart as ‘result.png’.

hardness: Medium


type: Data Visualization

instruction: Implement the analysis.py script as per guidance.txt to tally the number of games across the four major genres in the Mobile Strategy Game dataset. Generate a pie chart depicting genre proportions, sized (12, 8), with Green, Orange, Blue, and Red used as pie colors. Include genre names in the legend and save the image as result.png.

hardness: Hard


type: Data Visualization

instruction: Analyze the UFO Sightings dataset to count sightings in the ‘USA’, ‘Canada’, ‘United Kingdom’, ‘Australia’, and ‘Germany’. Clean the data by removing rows with missing values and converting columns to appropriate data types. Based on the format requirements in plot.yaml, create a pie chart showing the proportion of sightings for each country and save it as result.png.

hardness: Medium


type: Data Visualization

instruction: Identify the CSV file from the Diabetes Health Indicators Dataset where the target variable has three categories. Count the proportions of 'Healthy', 'Diabetic', and 'Pre-Diabetic' individuals and plot them in a pie chart. Size the chart as (8, 8) and use colors '#1f77b4', '#ff7f0e', and '#2ca02c'. Title the chart "Proportion of Different Diabetes States" and save it as "result.png".

hardness: Medium


type: Data Visualization

instruction: Please find out which hub city has the largest biker average delivery distance. Then, generate a pie chart illustrating the distribution of order deliveries by driver modes in that city, adhering to the guidelines in 'plot.yaml', and save the chart in `result.jpg`

hardness: Hard


type: Data Visualization

instruction: Compile the distribution of places with below- and above-average reviews from the provided dataset. Clean the dataset by removing irrelevant columns and rows with zero availability. Based on the plotting specifications in plot.yaml, visualize the distribution as a pie chart and save it as result.png.

hardness: Easy


type: Data Visualization

instruction: Compile statistics to determine if the coin toss winner also wins the match from the provided dataset. Clean the dataset by removing irrelevant columns and replacing team names with their abbreviations. Then, based on the plotting specifications in plot.yaml, visualize the results in a pie chart and save the image as result.png.

hardness: Medium


type: Data Visualization

instruction: Calculate GDP for the top 7 countries with the highest literacy rates. Use the mean for any missing numerical data. Produce a pie chart following the format in plot.yaml and save the output as 'result.png'.

hardness: Hard


type: Data Visualization

instruction: Create a list of the top 10 highest-grossing movies of all time and visualize their box office revenues as segments in a pie chart formatted as per plot.yaml. Ensure the legend includes movie names and save the chart as 'result.png'.

hardness: Easy


type: Data Visualization

instruction: Analyze the cancellation proportions for the top 10 countries with average room rates per night under 5000. Focus on the number of cancellations per country. Produce a pie chart according to plot.yaml specifications and save it as ‘result.png’.

hardness: Easy


type: Data Visualization

instruction: Utilize the sales dataset to generate a pie chart that displays the percentage of each discount level, following the format specified in plot.yaml. Save the output as result.png.

hardness: Easy


type: Data Visualization

instruction: Generate a pie chart depicting the top 10 most popular genres on Netflix, after cleaning and processing the dataset. Focus on Netflix data only, and use the specified colors from plot.yaml. Save the chart as ‘result.png'

hardness: Medium


type: Data Visualization

instruction: Analyze the distribution of coupon statuses in the dataset and create a pie chart according to the format specified in plot.yaml. Save the chart as result.png.

hardness: Medium


type: Data Visualization

instruction: Analyze the Google Play Store Apps dataset to calculate the ratio of free to paid apps. Clean the dataset by handling missing values, removing rows with missing data, replacing “Varies with device” in the Size column with NaN, and standardizing the Size and Installs columns. Specifically, convert sizes to numerical values (e.g., replace ‘k’ and ‘M’ with 10^3 and 10^6) and remove commas and plus signs from Installs. Create a pie chart based on the app type distribution and save it as ‘result.png’.

hardness: Medium


type: Data Visualization

instruction: You are tasked with visualizing the distribution of experience levels in data science. Plot a pie chart showing Entry-level/Junior, Mid-level/Intermediate, Senior-level/Expert, and Executive-level/Director roles. Ensure the chart follows the format specified in plot.yaml and save it as 'result.png'.

hardness: Medium


type: Data Visualization

instruction: Analyze the Netflix dataset and extract the highest IMDb-rated title for each genre. Generate a scatter plot that shows the IMDb scores of the top-rated titles by genre. Color the points by genre and save the plot as ‘result.png’.

hardness: Medium


type: Data Visualization

instruction: You are tasked with analyzing the Netflix dataset. Identify the countries with the most titles in each genre and plot the results in a scatter graph. Follow the format specified in plot.yaml and save it as 'result.png'.

hardness: Medium


type: Data Visualization

instruction: Create a stacked horizontal bar chart, which illustrates the average days per order stage forthe top 10 cities by sales. Save the chart as "result.png" with settings from "plot.yaml".

hardness: Hard


type: Data Visualization

instruction: Using the 120 years of Olympic history: athletes and results dataset, plot a scatter plot of the number of athletes participating each year. Group the data by year and city, and consider both Summer and Winter Olympics. Follow the format specified in plot.yaml and save the plot as ‘result.png’.

hardness: Medium


type: Data Visualization

instruction: Using the 17K Mobile Strategy Game dataset, create a scatter plot to illustrate the relationship between the time since release and user rating count. Clean the data by removing games with fewer than 200 user ratings and an update gap of less than 6 months. Reclassify the game genres, and only include games from popular genres (Puzzle, Action, Adventure, etc.). Follow the instructions in tips.txt and format according to plot.yaml. Save the plot as ‘result.png’.

hardness: Hard


type: Data Visualization

instruction: Calculate the Pearson correlation coefficient between the standardized Average Playtime and standardized Positive Ratings using the Steam Store Games dataset. Filter the data to only include games with positive ratings and positive playtime. Plot the results in a scatter plot following plot.yaml requirements and save it as result.png.

hardness: Medium


type: Data Visualization

instruction: Complete the given analysis.py to draw a scatter plot of book price versus rating. Ensure that the Price column is cleaned by removing currency symbols and converted to numeric. For better visualization, plot the log of the price on the x-axis. Save the image as ‘result.jpg’, with the image size set to (12, 8), the scatter plot color set to blue, the title as “Price vs. Rating of Books”, the x-axis labeled as “Log of Price”, and the y-axis labeled as “Rating”.

hardness: Medium


type: Data Visualization

instruction: Please compile the sales data for the top 100 best-selling games in North America, Europe, Japan, and Other regions. Plot the results in a scatter plot as specified in plot.yaml. Save the plotted result as 'result.png'.

hardness: Easy


type: Data Visualization

instruction: Please compile the height and weight data for players in different positions (point guard, shooting guard, small forward, power forward, center) for the year 2000. Plot this data in a scatter plot according to the format specified in plot.yaml and save the chart as 'result.png'.

hardness: Medium


type: Data Visualization

instruction: Compare the engine horsepower and highway miles per gallon of Ford and Volkswagen vehicles using the given dataset. Use the vehicle year to color the scatter points and differentiate between the two brands. Follow the specifications outlined in plot.yaml, and save the chart as ‘result.png’.

hardness: Easy


type: Data Visualization

instruction: Examine how wine consumption correlates with the number of children among respondents who have a Response value of 1. Clean the data by calculating the total number of children (Kidhome + Teenhome) and ensure the dataset has no duplicates. Generate a scatter plot as per the specifications in plot.yaml, saving the chart as ‘result.png’.

hardness: Medium


type: Data Visualization

instruction: Examine how streams relate to the number of times the most popular songs of 2023 are added to Spotify playlists. Clean the dataset by removing unnecessary columns, handling duplicates, and converting relevant columns to numeric. Generate a scatter plot as per the specifications outlined in plot.yaml, and save the chart as ‘result.png’.

hardness: Medium


type: Data Visualization

instruction: Complete missing data with mean interpolation, then explore how population density correlates with GDP. Produce a scatter plot formatted according to plot.yaml guidelines and save it as 'result.png'.

hardness: Medium


type: Data Visualization

instruction: Examine how job categories are distributed across different years of experience. Clean the dataset by grouping the job categories and calculating the frequency of each category by year. Convert job categories to numerical values for plotting. Create a scatter plot following plot.yaml guidelines to visualize this relationship, and save it as ‘result.png’.

hardness: Medium


type: Data Visualization

instruction: Generate a scatter plot with sales on one axis and profit on the other, following the format specified in plot.yaml. Save the chart as result.png.

hardness: Easy


type: Data Visualization

instruction: Create a scatter plot showing the average movie durations from 2011 to 2021. Clean the dataset by extracting the duration in minutes and grouping the data by year. Follow the format specified in plot.yaml and save the chart as ‘result.png’.

hardness: Medium