Instruction: Calculate the correlation between other variables (excluding ‘id’) and the ‘outcome’ column. Find the variable with the highest correlation and fill in the corresponding column name into result.csv according to the format provided in result.csv.


Instruction: It is observed that the beaks of G. scandens birds on Daphne Major appear to have become deeper. Assuming that the means of two groups of data are the same, you need to calculate the probability of observing the difference in means as per the instructions in tips.md. Under the null hypothesis that the means of the two groups are equal, calculate the probability (p-value) of observing the actual difference in means or a more extreme value.Write the calculated results in the format specified in sample_result.csv into the result.csv file.


Instruction: Using the beak length and depth data of G. scandens birds from Daphne Major in 1975 and 2012, compute the beak length-to-depth ratios for each bird in both years. Calculate the mean ratio for each year and compute the 99% confidence intervals using bootstrap replicates (size = 10,000). Finally, write the results into result.csv, following the format specified in sample_result.csv.


Instruction: Please estimate the 99% Conditional Value at Risk (CVaR) for a portfolio using two methods: a Student's t-distribution approach and a Gaussian Kernel Density Estimation (KDE) approach; save the results to "result.csv" in the required format with exactly two columns named "99% CVaR for T" and "99% CVaR for KDE".


Instruction: Please perform a Bootstrap analysis to determine the 95% confidence interval for the reduction in deaths due to handwashing. Enter your findings into `result.csv`, following the provided format.


Instruction: Please calculate the annualized Sharpe ratio for Amazon and Facebook from the dataset "Risk and Returns: The Sharpe Ratio." This ratio measures the additional return per unit of risk, with the annual factor being the square root of 252. Enter your results in `result.csv` as specified.


Instruction: Analyze the returns of FAANG stocks from the dataset. Calculate the expected annual return and annualized Sharpe ratio for an equally weighted portfolio of these stocks. Save the results in result.csv according to the provided format


Instruction: Identify the geographic region and fuel source with the highest median CO₂ emissions. Then, within each geographical region, perform ANOVA tests to determine if there are significant differences in CO₂ emissions among the different fuel sources. If the ANOVA results are significant, conduct pairwise t-tests between fuel sources within each region, applying the Bonferroni correction for multiple comparisons. Record your analysis results in the file named `anova_results.csv`, following the provided format.


Instruction: Analyze CO2 emissions from various fuel sources across different geographical regions, compare fuel sources using Bonferroni-corrected p-values, and save the results to "bonferroni_corrected_p_values.csv" using the provided template with column names exactly "Comparison" and "Bonferroni_Corrected_P_Value".


Instruction: You need to compute the heritability for G. scandens and G. fortis, where heritability is defined as covariance(parent trait, offspring trait) divided by variance(parent trait). Then compute a 95% confidence interval using pairs bootstrap with 1000 bootstrap replicates for each species (set random seed to 42). Save the results to "result.csv" following the provided format with column names exactly "me","heritability","conf"; the "conf" field must be formatted as a string like "[lower, upper]" with both bounds rounded to 8 decimal places.


Instruction: Calculate 10,000 bootstrap replicates to determine the variance in annual rainfall at the Sheffield Weather Station. Save your findings in "result.csv" with the column names exactly "Bin", "Center", and "PDF". (Set random seed to 42)


Instruction: The average strike force of Frog B was 0.71 Newtons (N), and that of Frog D was 0.42 N, for a difference of 0.29 N. It is possible that the frogs strike with the same force and this observed difference was by chance. You will compute the probability of getting at least a 0.29 N difference in mean strike force under the hypothesis that the distributions of strike forces for the two frogs are identical. Use a permutation test with a test statistic of the difference of means to test this hypothesis and print the p-value. (Set seed to 42). Save the results in the format of sample_result.csv to a file named "result.csv" with the column name exactly "result".


Instruction: Determine if Frog D and Frog E have similar impact forces. Compute the corresponding p-value and save the result in the format of sample_result.csv to a file named result.csv. (Set the random seed to 42)


Instruction: Perform a bootstrap hypothesis test to determine if Frog B and Frog D have the same mean impact force, without assuming their distributions are identical. Calculate the p-value and save the result to `result.csv`, following the format provided in `sample_result.csv`.


Instruction: Perform a permutation test to evaluate the hypothesis that a House member's party affiliation has no effect on their vote. Use the fraction of Democrats voting in favor as the test statistic. Calculate the p-value for observing a fraction of Democrats voting in favor less than or equal to the observed fraction (153/244). Set the random seed to 42. Save the p-value to a file named result.csv, following the format of sample_result.csv


Instruction: Using the beak depth data (bd_1975 and bd_2012) for G. scandens from 1975 and 2012, generate 10,000 bootstrap replicates of the mean for each year (set random seed to 42), compute the difference in mean beak depth between 2012 and 1975, and calculate a 95% confidence interval for this difference; save the results to "result.csv" following sample_result.csv with column names exactly "diff" and "conf", where "conf" is formatted as a string "[lower, upper]" with both bounds rounded to 8 decimal places.


Instruction: Calculate the sum of squared residuals (SSR) from a regression analysis predicting quarterly minimum portfolio returns based on mortgage delinquencies using data from 2005 to 2010. Save the results in result.csv


Instruction:
You are working on a project for a food industry analytics company. Your task is to analyze the data to determine the average ratings for restaurants offering pickup and delivery, along with the T-Statistic and P-Value. Report your findings with three significant digits and format your response as follows:
"""
{
    "Average rating for pickup restaurants": [3.81],
    "Average rating for delivery restaurants": [3.73],
    "T-Statistic": [2.31],
    "P-Value": [0.0207]
}
"""


Instruction:
As a data scientist for a medical research institute, you need to examine the data to find the survival rate statistics, including the number of observations, mean, minimum, maximum, variance, skewness, and kurtosis. Your response should be formatted like this:
"""
{
    "Number of observations (Nobs)": [5191],
    "Mean": [...],
    "Minimum": [...],
    "Maximum": [...],
    "Variance": [...],
    "Skewness": [...],
    "Kurtosis": [...]
}
"""


Instruction:
You are working on a project for an aviation industry analytics firm. Your task is to interpret the output of a two-sample t-test comparing the mean number of fatalities between aircraft manufactured by Boeing and Douglas. Provide the conclusion regarding the null hypothesis ("reject" or "accept") and the implications of the findings ("statistically significant difference" or "no statistically significant difference"). Format your response as follows:
"""
{
    "T-statistic": [13.7262535],
    "P-value": [3.196323892903482e-40],
    "Conclusion": ["reject"/"accept"],
    "Implication": ["statistically significant difference"/"no statistically significant difference"]
}
"""


Instruction:
Calculate the Pearson correlation coefficient to evaluate the relationship between BMI and medical expenses to determine if there is a significant linear relationship. Provide your results in the following format:

"""
{
    "Pearson's correlation coefficient": [...],
    "P-Value": [...]
}
"""


Instruction:
Perform a chi-square test to assess whether the app category significantly affects the rating distribution. Use a significance level of 0.05. Based on the p-value, select an appropriate conclusion between "Fail to reject the null hypothesis: No significant effect of category on rating distribution." and "Reject the null hypothesis: The category of an app affects its rating distribution." Provide your response in the following format:

"""
{
    "Chi2 Statistic": [...],
    "P-value": [...],
    "result": [...]
}
'""


Instruction:
Conduct a t-test to compare the average ratings of free and paid apps to determine if there is a significant difference. Use a significance level of 0.05. Based on the p-value, choose the appropriate conclusion between "Reject the null hypothesis: Free apps have a higher average rating than paid apps." and "Fail to reject the null hypothesis: No significant difference in ratings between free and paid apps." Provide your answer in the following format:

"""
{
    "T-Statistic": [...],
    "P-value": [...],
    "result": [...]
}
"""


Instruction:
With a random seed of 42, randomly select 100 data points from the dataset and conduct a Z-test on Canada's CO2 emission data. Determine if the sample mean significantly differs from the population mean at a 0.05 significance level. Based on the Z-value, choose between "Fail to reject null hypothesis" and "reject null hypothesis." Format your findings as follows:
"""
{
    "Z-value": [...],
    "Sample Mean": [...],
    "Sample Standard Deviation": [...],
    "result": [...]
}
"""


Instruction:
Perform a t-test to assess whether there is a significant difference in average medical expenses between males and females. Apply a 0.05 significance level. Depending on the p-value, choose between "Fail to reject null hypothesis" and "reject null hypothesis" for the result. Format your answer as follows:

"""
{
    "T-Statistic": [...],
    "P-Value": [...],
    "result": [...]
}
"""


Instruction:
Perform a t-test to compare the average medical expenses between smokers and non-smokers to determine if smoking status has a significant impact on medical expenses. Use a significance level of 0.05. Based on the p-value, select either "Fail to reject null hypothesis" or "reject null hypothesis" as the result. Provide your results in the following format:

"""
{
    "T-Statistic": [...],
    "P-Value": [...],
    "result": [...]
}
"""


Instruction:
From the January 2018 dataset, select samples with a status of 'failed' and 'successful'. Apply a logarithmic transformation to the 'usd_goal_real' feature. Perform a Shapiro-Wilk normality test on the transformed data, calculating both the test statistic and p-value. Based on the p-value, conclude whether the data exhibits a "Normal Distribution" or a "Non-Normal Distribution". Present your findings as follows:

"""
{
    "Shapiro stat": [...],
    "P-value": [...],
    "Conclusion": [...]
}
"""


Instruction:
Use an independent two-sample t-test to determine if there is a significant difference between males and females in their reasons for eating comfort food. With a p-value threshold of 0.05, select the appropriate conclusion from "There is a significant difference" and "There is no significant difference". Provide your response in the following format:

"""
{
    "T-Statistic": [...],
    "P-Value": [...],
    "Conclusion": [...]
}
"""


Instruction:
Analyze the data to calculate the mean and standard deviation of purchase amounts for two age groups: those over 65 years old and those under 25 years old. Perform an independent two-sample t-test to determine if there is a significant difference in purchase amounts between the two groups. Provide your response in the following format:

"""
{
    "Oldage Purchase Amount Mean": [...],
    "Youngage Purchase Amount Mean": [...],
    "Oldage Purchase Amount std": [...],
    "Youngage Purchase Amount std": [...],
    "T_Statistic": [...],
    "P_Value": [...]
}
"""


Instruction:
Filter the data for each season by calculating the quartiles and outlier range, excluding any data outside this range. Next, perform an independent two-sample t-test to assess whether there is a significant difference in bike rental counts between weekdays and weekends. Use the resulting p-value to draw a conclusion, choosing from "Accept the null hypothesis" or "Reject the null hypothesis". Present your findings as follows:

"""
{
    "p_value": [...],
    "conclusion": [...]
}
"""


Instruction: For each season, filter the data by calculating the quartiles and outlier range, removing any data outside this range. Next, perform the Shapiro-Wilk test for normality on the filtered data for each season, and record the statistics and p-values. Ensure the results are saved in result.csv with the columns "Season", "Stats", and "P-Value".


Instruction:
Calculate the quartiles and outlier range for each season's data, filtering out the outliers. Then, use the Levene test to compare the rental count variances across the four seasons. Based on the p-value obtained, decide between "Variances are not equal" and "Variances are equal among different seasons". Format your response like this:

"""
{
    "p-value": [...],
    "conclusion": [...]
}
"""


Instruction:
Calculate the quartiles and outlier range for each season's data, filtering out the outliers. Then, use the Kruskal-Wallis test to determine if the number of bike rentals is significantly impacted by the season. Based on the p-value obtained, decide between "Season does impact the count of cycles used" and "Season does not impact the count of cycles used". Format your response like this:

"""
{
    "p-value": [...],
    "conclusion": [...]
}
"""


Instruction:
Filter the data for each season by calculating the quartiles and outlier range, excluding any data outside this range. Then, perform the Shapiro-Wilk normality test to calculate the normality of bike rental counts under different weather conditions (Clear, Misty, Rainy). Based on the p-values obtained, select the final conclusion from "Data is not normally distributed" and "Data is normally distributed". Provide your response in the following format:

"""
{
    "Clear weather P-value": [...],
    "Misty weather P-value": [...],
    "Rainy weather P-value": [...],
    "conclusion": [...]
}
"""


Instruction:
First, filter the data for each season by calculating the quartiles and outlier range, removing any data outside this range. Then, apply the Levene test to assess whether the variances of bike rental counts differ under the three weather conditions (Clear, Misty, Rainy). Use the obtained p-values to draw a conclusion, choosing between "Variances are not equal" and "Variances are equal". Present your findings as follows:

"""
{
    "P-value": [...],
    "conclusion": [...]
}
"""


Instruction:
Filter the data for each season by calculating the quartiles and outlier range, excluding any outliers. Next, use the Chi-square test to assess the correlation between season and weather conditions. Based on the p-values obtained, determine if there is an impact of season on weather. Format your response like this:

"""
{
    "P-value": [...],
    "Conclusion": [...]
}
"""


Instruction:
Use one-way ANOVA to evaluate if there are significant differences in total rating counts (rating_count_tot) among different snapshot screen numbers (ipadSc_urls.num) at a 0.05 significance level. Based on the p-value, determine if "There is a significant difference" or "There is no significant difference". Format your response like this:
"""
{
    "One-way ANOVA P": [...],
    "Conclusion": [...]
}
"""


Instruction:
Use the descriptions from tips.md to calculate the effect size for height and weight for males and females. Provide your response in the following format:

"""
{
    "height's effect size": [...],
    "weight's effect size": [...]
}
"""


Instruction:
Perform a two-sample t-test to compare the net worth of self-made billionaires and inherited billionaires to determine if there is a significant difference. Provide your response in the following format:

"""
{
    "T-statistic": [...],
    "P-value": [...]
}
"""


Instruction:
Calculate the mean, variance, and standard deviation for the win_by_wickets data according to the instructions in tips.md. Provide your response in the following format:

"""
{
    "Mean": [...],
    "Variance": [...],
    "Standard deviation": [...]
}
"""


Instruction:
Use a two-sample t-test to calculate the t-statistic and p-value for the technical abilities between left-footed and right-footed players using FIFA20 data. With a p-value threshold of 0.05, choose the appropriate conclusion from "There is a significant difference" and "There is no significant difference". Provide your response in the following format:

"""
{
    "T-Statistic": [...],
    "P-Value": [...],
    "Conclusion": [...]
}
"""


Instruction:
Perform a two-sample t-test to assess the shot power of left-footed and right-footed players using FIFA20 data. Calculate the t-statistic and p-value. With a threshold of 0.05, determine if "There is a significant difference" or "There is no significant difference". Format your response like this:

"""
{
    "T-Statistic": [...],
    "P-Value": [...],
    "Conclusion": [...]
}
"""


Instruction:
Compute the mean, standard deviation, sample size, and standard error for the natural log of total medals for Olympic countries when they are hosting and when they are not hosting. Calculate the standard error between the means by comparing the difference in means to the sum of standard errors. Present your findings as follows:

"""
{
    "Mean Natural Log of Total Medals when Not Hosting": [...],
    "Mean Natural Log of Total Medals when Hosting": [...],
    "Standard Error of Natural Log of Total Medals when Not Hosting": [...],
    "Standard Error of Natural Log of Total Medals when Hosting": [...],
    "Standard Errors between means": [...]
}
"""


Instruction:
Conduct a two-sample t-test to evaluate whether there is a significant difference in the natural log of total medals between countries that host the Olympics and those that do not. Based on a p-value threshold of 0.05, determine the conclusion: "Same distributions (fail to reject H0)" or "Different distributions (reject H0)". Present your findings as follows:

"""
{
    "T-Statistics": [...],
    "P-Value": [...],
    "Conclusion": [...]
}
"""


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."


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".


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'


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'


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'.


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'.


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.


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.


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.


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.


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'.


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.


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'.


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'.


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'


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'.


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'.


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`.


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.


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.


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'.


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'.


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'


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.


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.


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'.


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.


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.


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.


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.


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.


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.


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.


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.


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'.


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”.


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


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'.


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'.


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'.


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'.


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'.


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'.


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.


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.


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.


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".


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’.


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.


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.


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".


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`


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.


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.


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'.


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'.


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’.


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.


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'


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.


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’.


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'.


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’.


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'.


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".


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’.


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’.


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.


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”.


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'.


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'.


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’.


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’.


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’.


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'.


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’.


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.


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’.