Question: Can you tell me the name of the most popular female baby in Wyoming for the year 2021, based on the proportion of female babies given that name compared to the total number of female babies given the same name across all states?


Question: For bladder cancer patients who have mutations in the CDKN2A (cyclin-dependent kinase inhibitor 2A) gene, using clinical data from the Genomic Data Commons Release 39, what types of mutations are they, what is their gender, vital status, and days to death - and for four downstream genes (MDM2 (MDM2 proto-oncogene), TP53 (tumor protein p53), CDKN1A (cyclin-dependent kinase inhibitor 1A), and CCNE1 (Cyclin E1)), what are the gene expression levels for each patient?


Question: What are the RNA expression levels of the genes MDM2, TP53, CDKN1A, and CCNE1, along with associated clinical information, in bladder cancer patients with CDKN2A mutations in the 'TCGA-BLCA' project?  Use clinical data from the Genomic Data Commons Release 39, data about somatic mutations derived from the hg19 human genome reference in Feb 2017.


Question: Use CPTAC proteomics and RNAseq data for Clear Cell Renal Cell Carcinoma to select 'Primary Tumor' and 'Solid Tissue Normal' samples. Join the datasets on sample submitter IDs and gene symbols. Calculate the correlation between protein abundance (log2 ratio) and gene expression levels (log-transformed+1 FPKM) for each gene and sample type. Filter out correlations with an absolute value greater than 0.5, and compute the average correlation for each sample type.


Question: Can you identify the TCGA breast cancer cases from the RNA sequencing hg38 r35` where the protein_coding gene and the project TCGA-BRCA, and which have RNA sequencing samples of multiple tissue types—including "Solid Tissue Normal"—within the same case?


Question: Could you identify the top five protein-coding genes that exhibit the highest variance in their expression levels (measured as fpkm_uq_unstranded) specifically within 'Solid Tissue Normal' samples? Please limit the analysis to TCGA-BRCA project cases that include at least one 'Solid Tissue Normal' sample type.


Question: Identify cytoband names on chromosome 1 in the TCGA-KIRC segment allelic dataset where the frequency of amplifications, gains, and heterozygous deletions each rank within the top 11. Calculate these rankings based on the maximum copy number observed across various genomic studies of kidney cancer, reflecting the severity of genetic alterations.


Question: Identify the case barcodes from the TCGA-LAML study with the highest weighted average copy number in cytoband 15q11 on chromosome 15, using segment data and cytoband overlaps from TCGA's genomic and Mitelman databases.


Question: For breast cancer cases (TCGA-BRCA) from Release 23 of the active GDC archive, identify and categorize copy number variations (CNVs) across all cytobands on every chromosome. For each cytoband and each case, determine the overlap between the cytoband region and the case's copy number segments, and compute the overlap-weighted average copy number for that cytoband in the case, rounding to the nearest whole number. Classify the rounded copy number into CNV types as follows: homozygous deletions (0), heterozygous deletions (1), normal diploid state (2), gains (3), and amplifications (greater than 3). For each cytoband, provide its name and start/end positions, and calculate the frequency of each CNV type across all cases as a percentage of the total number of cases, rounded to two decimal places.


Question: Assess whether different genetic variants affect the log10-transformed TP53 expression levels in TCGA-BRCA samples using sequencing and mutation data. Provide the total number of samples, the number of mutation types, the mean square between groups, the mean square within groups, and the F-statistic.


Question: For breast cancer cases (TCGA-BRCA) from Release 23 of the active GDC archive, identify and categorize copy number variations (CNVs) across all cytobands on every chromosome. For each cytoband and each case, determine the overlap between the cytoband region and the case's copy number segments, and compute the overlap-weighted average copy number for that cytoband in the case, rounding to the nearest whole number. Classify the rounded copy number into CNV types as follows: homozygous deletions (0), heterozygous deletions (1), normal diploid state (2), gains (3), and amplifications (greater than 3). For each cytoband, provide its name and start/end positions, and calculate the frequency of each CNV type across all cases as a percentage of the total number of cases, rounded to two decimal places.


Question: In the TCGA-BRCA cohort of patients who are 80 years old or younger at diagnosis and have a pathological stage of Stage I, Stage II, or Stage IIA, calculate the t-statistic derived from the Pearson correlation between the log10-transformed average RNA-Seq expression levels (using HTSeq__Counts + 1) of the gene SNORA31 and the average microRNA-Seq expression levels of all unique microRNAs, only considering pairs with more than 25 samples and where the absolute Pearson correlation coefficient is between 0.3 and 1.0


Question: Using the TCGA-KIRP dataset, select patients from the 'TCGA_bioclin_v0.Clinical' table who have a non-null clinical_stage and a disease_code of 'KIRP.' Retrieve their gene expression data from the 'TCGA_hg38_data_v0.RNAseq_Gene_Expression' table for the genes 'MT-CO3,' 'MT-CO1,' and 'MT-CO2,' and randomly split the patients into a training set (90%) and a test set (10%) based on their case_barcode via the FARM_FINGERPRINT method. For each clinical stage in the training set, calculate the average HTSeq__FPKM_UQ expression of the three genes. For each patient in the test set, compute the Euclidean distance between the patient’s expression values and the stage-specific averages, and assign that patient to the clinical stage whose average is closest. Finally, output the case_barcode and the predicted clinical stage.


Question: Find case barcodes and their corresponding GDC file URLs for female patients aged 30 or younger diagnosed with breast cancer (BRCA) in TCGA database. The query should first identify patients from the Annotations table where entity_type is "Patient" and either category is "History of unacceptable prior treatment related to a prior/other malignancy" or classification is "Redaction". Also identify patients from the Clinical table with disease_code "BRCA", age_at_diagnosis less than or equal to 30, and gender "FEMALE". Perform a FULL JOIN between these two sets, but only keep patients where both categoryName and classificationName are NULL in the final result. Use GDC metadata from archive release 14 (specifically rel14_caseData, rel14_fileData_current, and rel14_GDCfileID_to_GCSurl_NEW tables) to get the corresponding file URLs.


Question: Calculate, for each histology type specified in the 'icd_o_3_histology' field (excluding those enclosed in square brackets), the average of the per-patient average log10(normalized_count + 1) expression levels of the IGF2 gene among LGG patients with valid IGF2 expression data. Match gene expression and clinical data using the ParticipantBarcode field.


Question: Calculate the Kruskal-Wallis H-score among groups of LGG patients for IGF2 gene expression, where each patient’s IGF2 expression is determined by applying log10(normalized_count + 1) and then averaging across samples. Group the patients by ICD-O-3 histology codes, exclude any codes fully enclosed in square brackets, only include groups with more than one patient, and ensure that normalized count is not null. Finally, return the total number of groups, the total number of samples, and the Kruskal-Wallis H-score in descending order.


Question: Compute the t-score (rounded to 2 decimals) to compare the difference in mean expression levels of the gene DRG2 between two groups (TP53 mutated vs. non-mutated) in the Lower Grade Glioma (LGG) study, where the expression levels are calculated as the average of log10(normalized_count + 1) for each participant, only considering samples with TP53 mutations that have a 'FILTER' status of 'PASS' in the mutation data, and ignoring any groups with fewer than 10 samples or with zero variance; refer to `t_score.md` for the method of computing the t-score.


Question: Please compute the T-score to determine the statistical difference in the expression of the DRG2 gene between LGG patients with and without TP53 mutation: for each patient, calculate the average of log10(normalized_count + 1) of DRG2 expression across all their samples, using only samples present in the `MC3_MAF_V5_one_per_tumor_sample` table for the LGG study; identify patients with TP53 mutations from this table where `Hugo_Symbol` is 'TP53' and `FILTER` is 'PASS'; then perform a T-test comparing the mean averaged log-transformed DRG2 expression between patients with and without TP53 mutation.


Question: Which top five histological types of breast cancer (BRCA) in the PanCancer Atlas exhibit the highest percentage of CDH1 gene mutations?


Question: Calculate the chi-square value to assess the association between histological types and the presence of CDH1 gene mutations in BRCA patients using data from the PanCancer Atlas. Focus on patients with known histological types and consider only reliable mutation entries.  Exclude any histological types or mutation statuses with marginal totals less than or equal to 10. Match clinical and mutation data using ParticipantBarcode


Question: Calculate the net difference between the number of pancreatic adenocarcinoma (PAAD) patients in TCGA's dataset who are confirmed to have mutations in both KRAS and TP53 genes, and those without mutations in either gene. Utilize patient clinical and follow-up data alongside genomic mutation details from TCGA’s cancer genomics database, focusing specifically on PAAD studies where the mutations have passed quality filters.


Question: Using TCGA dataset, calculate the chi-squared statistic to evaluate the association between KRAS and TP53 gene mutations in patients diagnosed with pancreatic adenocarcinoma (PAAD). Incorporate clinical follow-up data and high-quality mutation annotations to accurately determine the frequency of patients with co-occurring KRAS and TP53 mutations compared to those with each mutation occurring independently. Ensure that patient records are meticulously matched based on unique identifiers to maintain data integrity. This analysis aims to identify and quantify potential correlations between KRAS and TP53 genetic alterations within the PAAD patient population.


Question: Based on the 5th revision (r5) of the HTAN data, list the imaging assay types available at the HTAN WUSTL center that have Level2 data and any associated higher-level data (Level3, Level4) derived from them through 'entityId' relationships in the 'id_provenance_r5' table; exclude any records where the 'Component' is NULL or contains 'Auxiliary' or 'OtherAssay'; for each imaging assay type, provide the available data levels (Level2, Level3, Level4), and do not include Level1 data or Electron Microscopy assay types.


Question: Which 20 genes in the HTAN scRNAseq MSK-SCLC combined samples dataset show the greatest difference in average X_value expression between female and male epithelial cells specifically in cluster 41 of 74-year-old human stage patients? Please calculate the difference by subtracting male average X_value from female average X_value for each gene


Question: Consolidate metadata from spatial transcriptomics and scRNAseq datasets—including levels 1 through 4 and auxiliary files—for the run ID 'HT264P1-S1H2Fc2U1Z1Bs1-H2Bs2-Test'. Include Filename, HTAN Parent Biospecimen ID, Component, File Format, Entity ID, and Run ID.


Question: Using segment-level copy number data from the copy_number_segment_allelic_hg38_gdc_r23 dataset restricted to 'TCGA-KIRC' samples, merge these segments with the cytogenetic band definitions in 'CytoBands_hg38' to identify each sample’s maximum copy number per cytoband. Classify these maximum copy numbers into amplifications (>3), gains (=3), homozygous deletions (=0), heterozygous deletions (=1), or normal (=2), then calculate the frequency of each subtype out of the total number of distinct cases, and finally present these frequencies as percentages sorted by chromosome and cytoband.


Question: Can you use CytoConverter genomic coordinates to calculate the frequency of chromosomal gains and losses across a cohort of breast cancer (morphology='3111') and adenocarcinoma (topology='0401') samples? Concretely, please include the number and frequency (2 decimals in percentage) of amplifications (gains of more than 1 copy), gains (1 extra copy), losses (1 copy) and homozygous deletions (loss of 2 copies) for each chromosomal band. And sort the result by the ordinal of each chromosome and the starting-ending base-pair position of each band in ascending order.


Question: Retrieve distinct case references, case numbers, investigation numbers, and clone information where a single clone simultaneously exhibits all three of the following genetic alterations: (1) a loss on chromosome 13 between positions 48,303,751 and 48,481,890, (2) a loss on chromosome 17 between positions 7,668,421 and 7,687,490, and (3) a gain on chromosome 11 between positions 108,223,067 and 108,369,102. For each matching clone, display the chromosomal details for each of these three regions (including chromosome number represented by ChrOrd, start position, and end position) and the corresponding karyotype short description from the KaryClone table. Use the CytoConverted and KaryClone.


Question: Could you compute, by chromosome, the Pearson correlation between the frequency of copy number aberrations (including amplifications, gains, losses, and deletions) from the Mitelman database for cases with morph = 3111 and topo = 0401, and those computed from TCGA data, returning correlation coefficients and corresponding p-values for each aberration type, ensuring only results with at least five matching records are shown.


Question: Extract genotype data for single nucleotide polymorphisms (SNPs) on chromosome X, excluding positions where the `start` value is between 59999 and 2699519 or between 154931042 and 155260559. For each sample, identify genotype calls where the genotype array has at least one allele. Classify each genotype call into one of the following categories: homozygous reference alleles (both alleles are 0), homozygous alternate alleles (both alleles are the same and greater than 0), or heterozygous alleles (alleles are different, or any allele is null, and at least one allele is greater than 0). Compute the total number of callable sites (the sum of all three genotype categories), the number of homozygous reference, homozygous alternate, and heterozygous genotype calls, the total number of single nucleotide variants (SNVs) as the sum of homozygous alternate and heterozygous genotype calls, the percentage of heterozygous genotype calls among all SNVs, and the percentage of homozygous alternate genotype calls among all SNVs. Output the sample ID along with these computed counts and percentages, and order the results by the percentage of heterozygous genotype calls among SNVs in descending order, then by sample ID.


Question: Identify variants on chromosome 12 and, for each variant, calculate the chi-squared score using allele counts in cases and controls, where cases are individuals from the 'EAS' super population and controls are individuals from all other super populations. Apply Yates's correction for continuity in the chi-squared calculation, ensuring that the expected counts for each allele in both groups are at least 5. Return the start position, end position, and chi-squared score of the top variants where the chi-squared score is no less than 29.71679.


Question: In chromosome 17 between positions 41196311 and 41277499, what are the reference names, start and end positions, reference bases, distinct alternate bases, variant types, and the chi-squared scores (calculated from Hardy-Weinberg equilibrium) along with the total number of genotypes, their observed and expected counts for homozygous reference, heterozygous, and homozygous alternate genotypes, as well as allele frequencies (including those from 1KG), for each variant?


Question: For the 1000 Genomes dataset, analyze common autosomal variants (those with an allele frequency of at least 0.05) across different super populations. For each super population, count how many variants are shared by each specific number of samples within that super population. Include in your results the total population size of each super population, whether the variant is common (allele frequency ≥ 0.05), the number of samples having each variant, and the total count of variants shared by that many samples. Only include autosomal variants by explicitly excluding sex chromosomes (X, Y) and mitochondrial DNA (MT) from the analysis. Consider only samples that have at least one alternate allele (non-reference) for the variant.


Question: List the top 10 samples in the genome data that have the highest number of positions where there is exactly one alternate allele and the sample's genotype is homozygous for the reference allele (both alleles are 0). Order the results in descending order of these counts.


Question: Can you provide the number of distinct active and closed bike share stations for each year 2013 and 2014?


Question: What is the highest number of electric bike rides lasting more than 10 minutes taken by subscribers with 'Student Membership' in a single day, excluding rides starting or ending at 'Mobile Station' or 'Repair Shop'?


Question: Can you tell me the numeric value of the active council district in Austin which has the highest number of bike trips that start and end within the same district, but not at the same station?


Question: What is the date with the second highest Z-score for daily counts of 'PUBLIC INTOXICATION' incidents in Austin for the year 2016? List the date in the format of '2016-xx-xx'.


Question: Among all stations that are currently active, identify those that rank in the top 15 (including ties) based on the total number of trips that start at each station. For each of these stations, return the station ID, the total number of starting trips, the percentage of those trips out of the overall starting trips from active stations, and the average trip duration in minutes. Order the results by the station’s rank.


Question: Can you provide a breakdown of the total number of articles into different categories and the percentage of those articles that mention "education" within each category from the BBC News?


Question: Retrieve the venue titles of publications that have a `date_inserted` from the year 2021 onwards and are associated with a grid whose address city is 'Qianjiang'. For each publication, prioritize the venue title by selecting the journal title first if it exists; if not, then the proceedings title; if that's also unavailable, then the book title; and finally, if none of those are available, the book series title.


Question: Using data from ChEMBL Release 23, retrieve all distinct molecules associated with the company 'SanofiAventis,' listing the trade name and the most recent approval date for each molecule. Make sure to keep only the latest approval date per molecule and ensure the company field precisely matches 'SanofiAventis' without relying on other fields.


Question: Find pairs of different molecules tested in the same assay and standard type, where both have 10–15 heavy atoms, fewer than 5 activities in that assay, fewer than 2 duplicate activities, non-null standard values, and pChEMBL values over 10. For each pair, report the maximum heavy atom count, the latest publication date (calculated based on the document's rank within the same journal and year, and map it to a synthetic month and day), the highest document ID, classify the change in standard values as 'increase', 'decrease', or 'no-change' based on their values and relations, and generate UUIDs from their activity IDs and canonical SMILES.


Question: Using the 2018 5-Year American Community Survey (ACS) for median incomes at the census tract level and the 2020 Federal Election Commission (FEC) individual contributions dataset filtered for donors in New York, matched to census tract geographies via a ZIP code to census tract crosswalk, calculate and list the average political donation amount and the median income for each census tract located in Kings County (Brooklyn), New York.


Question: Please provide a list of all 2016 committees that supported at least one candidate and received a total amount of individual contributions between $0 and $200 (inclusive of more than $0 and less than $200) where these small-dollar contributions sum to more than $0 overall. For each qualifying committee, include its name, the number of unique candidates it supported, the candidates’ names in alphabetical order (separated by commas), and the total sum of these small-dollar donations received by the committee.


Question: Among all Utah ZIP codes, what is the 2017 American Community Survey employment rate for the population aged 16 or older in the ZIP code that has the fewest FDIC-insured bank locations?


Question: Please provide the food events data where both \"date_created\" and \"date_started\" are between January 1 and January 31, 2015, apply the following data cleansing steps: split reactions and outcomes fields into arrays by commas, handle special numeric patterns in the products_brand_name field (where a digit is followed by comma and another digit) by preserving those numeric patterns while replacing other ", " with " -- ", replace ", " with " -- " in products_industry_code, products_role, and products_industry_name fields, and calculate industry_code_length and brand_name_length as the array lengths after splitting.


Question: Could you provide me with the zip code of the location that has the highest number of bank institutions in Florida?


Question: What is the total number of all banking institutions in the state that has the highest sum of assets from banks established between January 1, 1900, and December 31, 2000, with institution names starting with 'Bank'?


Question: Please retrieve the page URLs, first shown time, last shown time, removal reason, violation category, and the lower and upper bounds of times shown for the five most recently removed ads in the Croatia region (region code 'HR'), where the times shown availability date is null, the times shown lower bound exceeds 10,000, the times shown upper bound is below 25,000, and the ads used at least one non-unused audience selection approach among demographics, geographic location, contextual signals, customer lists, or topics of interest, ordering the resulting ads by their last shown time in descending order.


Question: Between January 1, 2023, and January 1, 2024, which image-type advertisement on the topic of Health, published by a verified advertiser located in Cyprus, was shown in Croatia, has times_shown_availability_date as NULL (meaning the times shown data is available), utilized demographic information, geo-location targeting, contextual signals, customer lists, and topics of interest without any of these selection methods being unused, and additionally had its first shown date strictly after January 1, 2023, and last shown date strictly before January 1, 2024? Among such ads, provide the page URL of the one with the highest upper bound of times shown.


Question: Could you provide a clean, structured dataset from dicom_all table that only includes SM images marked as VOLUME from the TCGA-LUAD and TCGA-LUSC collections, excluding any slides with compression type “other,” where the specimen preparation step explicitly has “Embedding medium” set to “Tissue freezing medium,” and ensuring that the tissue type is only “normal” or “tumor” and the cancer subtype is reported accordingly?


Question: In the dicom_pivot table, how many unique StudyInstanceUID values exactly match the SegmentedPropertyTypeCodeSequence of "15825003" (case-insensitive) and also have a collection_id of either "Community" or "nsclc_radiomics"?


Question: How many unique StudyInstanceUIDs are there from the DWI, T2 Weighted Axial, Apparent Diffusion Coefficient series, and T2 Weighted Axial Segmentations in the 'qin_prostate_repeatability' collection?


Question: Within the 'prostatex' collection, for MRI sequences where the Modality is 'MR', assign the label 't2w_prostateX' to sequences whose SeriesDescription contains 't2_tse_tra' and 'adc_prostateX' to sequences whose SeriesDescription contains 'ADC'. For all sequences labeled as 't2w_prostateX' or 'adc_prostateX', calculate the average Repetition Time, the average Echo Time, and the average Slice Thickness, and then compute the sum of these averages to obtain the combined overall average.


Question: Please provide identification details, study and series information, storage location, and total size in MB for the medical images belonging to male patients who are exactly 18 years old based on the numeric portion of the PatientAge field, where the BodyPartExamined is set to 'MEDIASTINUM' and the study date is strictly after September 1, 2014.


Question: Identify the top five CT scan series by size (in MiB), including their SeriesInstanceUID, series number, patient ID, and series size. These series must be from the CT modality and not part of the 'nlst' collection. Exclude any series where the ImageType is classified as 'LOCALIZER' or where the TransferSyntaxUID is either '1.2.840.10008.1.2.4.70' or '1.2.840.10008.1.2.4.51' (i.e., JPEG compressed). The selected series must have consistent slice intervals, exposure levels, image orientation (with only one unique ImageOrientationPatient value), pixel spacing, image positions (both z-axis and xy positions), and pixel dimensions (rows and columns). Ensure that the number of images matches the number of unique z-axis positions, indicating no duplicate slices. Additionally, the z-axis component of the cross product of the x and y direction cosines from ImageOrientationPatient must have an absolute value between 0.99 and 1.01, ensuring alignment with the expected imaging plane. Finally, order the results by series size in descending order and limit the output to the top five series satisfying these conditions.


Question: Please retrieve from the dicom_all table each PatientID, StudyInstanceUID, StudyDate, and the CodeMeaning of the FindingSite for patients whose StudyDate is in the year 2001, along with the maximum values of each of the following measurements identified by their CodeMeaning (Elongation, Flatness, Least Axis in 3D Length, Major Axis in 3D Length, Maximum 3D Diameter of a Mesh, Minor Axis in 3D Length, Sphericity, Surface Area of Mesh, Surface to Volume Ratio, Volume from Voxel Summation, and Volume of Mesh), ensuring that the quantitative_measurements table is joined on segmentationInstanceUID matching the SOPInstanceUID in dicom_all, and grouping by PatientID, StudyInstanceUID, StudyDate, and FindingSite CodeMeaning.


Question: How many frames in total are present across all whole slide microscopy images from the TCGA-BRCA collection that use the SM modality and include an eosin-based staining step in their SpecimenPreparationSequence?


Question: Determine which three lowest-level Reactome pathways (with TAS evidence) have the highest chi-squared statistics, considering only Homo sapiens targets associated with sorafenib under the conditions that the median assay value is ≤ 100 and both low and high assay values are ≤ 100 or null. For each of these three pathways, how many of these targets and non-targets lie within the pathway and outside it?


Question: Which Colorado zip code has the highest concentration of bank locations per block group, based on the overlap between zip codes and block groups?


Question: What are the top three debt indicators for Russia based on the highest debt values?


Question: Which high-income country had the highest average crude birth rate respectively in each region, and what are their corresponding average birth rate, during the 1980s?


Question: Get details of repositories that use specific feature toggle libraries. For each repository, include the full name with owner, hosting platform type, size in bytes, primary programming language, fork source name (if any), last update timestamp, the artifact and library names of the feature toggle used, and the library's programming languages. Include repositories that depend on the specified feature toggle libraries, defined by their artifact names, library names, platforms, and languages.


Question: Could you provide the annual percentage shares, rounded to two decimal places, of the top 5 minor crime categories from 2008 in London's total crimes, with each year displayed in one row?


Question: Could you provide the total number of 'Other Theft' incidents within the 'Theft and Handling' category for each year in the Westminster borough?


Question: Please provide a list of the top three major crime categories in the borough of Barking and Dagenham, along with the number of incidents in each category.


Question: Using the bigquery-public-data.open_images dataset, can you provide a count of how many distinct image URLs are categorized as 'cat' (where the image has label '/m/01yrx' with confidence=1) and how many distinct image URLs are categorized as 'other' (meaning they have no cat label '/m/01yrx' at all)?


Question: Using the crops dataset, find the total 2022 production figures, measured in bushels, for corn from the 'FIELD CROPS' category and mushrooms from the 'HORTICULTURE' group for each U.S. state. Only include data rows where 'statisticcat_desc' is 'PRODUCTION', 'agg_level_desc' is 'STATE', 'value' is not null, and ensure that for corn the 'unit_desc' is 'BU'. Combine both results so that each state’s 2022 corn and mushroom totals are presented.


Question: Based on the World Bank global population dataset and the World Bank health nutrition population dataset, how many countries experienced an increase of more than 1% from the previous year to 2018 in both their total population and per capita current health expenditure (PPP)?


Question: List the top 10 countries with respect to the total amount of long-term external debt in descending order, excluding those without a specified region.


Question: How many debt indicators for Russia have a value of 0, excluding NULL values?


Question: Which region has the highest median GDP (constant 2015 US$) value?


Question: Find the top three users who have authored the first message in forum topics, ranked in descending order by their message scores, where a message score is defined as the number of distinct users who voted on that message. For each of these users, provide their username and the absolute difference between their message score and the average message score across all first messages in forum topics.


Question: Using the data from Forum Message Votes and Users tables, find the top three users who have received the most distinct upvotes on the Kaggle forum. For each of these users, list their username, the total number of distinct upvotes they have received (based on the ToUserId field), and the total number of distinct upvotes they have given (based on the FromUserId field), sorted by the number of upvotes received in descending order and restricted to only the top three results.


Question: How many customers have orders and invoices that match at the line-item level and, when aggregated, result in each customer having an equal count of orders and invoices as well as an identical total value for the orders and invoices?


Question: In the year 2013, considering each invoice’s total value as the product of unit price and quantity and grouping by the quarter (Q1, Q2, Q3, Q4) in which the invoice date occurs, what is the difference between the maximum and minimum average invoice values across these quarters?


Question: Among all orders that do not appear in the invoice table, for each customer category calculate the maximum lost order value, then determine which customer category’s maximum lost order value is closest to the overall average of these maximum lost order values across all categories?


Question: Using the invoice date to determine each month of the year 2014, and summing the total invoice line amounts for each customer across these months, what is the median of the resulting average monthly spending across all customers?


Question: I want to identify users who had activity followed by inactivity. Specifically, find the user ID and their corresponding month number (counting from their first activity month) for the user with the highest month number who became inactive (no activity recorded) after their last recorded activity month. For this analysis, only consider data up until September 10, 2024, and ensure the month number represents the count of months since the user's first activity. The user should have at least one month where they were expected to be active (within their activity span) but actually had no records.


Question: Which three years in 2012-2017 have the smallest absolute difference between median revenue and median functional expenses for organizations filing IRS 990 forms? Please output three years and respective differences.


Question: After removing any duplicate records from the rev_transactions dataset, identify each channel grouping that has transactions from more than one country. For each such channel grouping, find the country with the highest total number of transactions and report both the country name and the sum of transactions for that channel grouping.


Question: Calculate the conversion rate from unique visitors to purchasers by using data exclusively from the `web_analytics` table in the `data-to-insights.ecommerce` dataset. A visitor is defined as a unique `fullVisitorId` present in the table, while a purchaser is a visitor who has at least one transaction recorded (`totals.transactions` is not null). The conversion rate is computed by dividing the number of unique purchasers by the total number of unique visitors. Additionally, calculate the average number of transactions per purchaser, considering only those visitors who have made at least one transaction.


Question: Please provide the creation date, title, parent forum title, reply count, distinct user replies count, total upvotes, and total views for the earliest five forum topics that belong to any sub-forum under the parent forum titled "General". The reply count should be calculated as the total number of messages associated with each topic, and the distinct user replies count should be the number of unique users who have posted messages in the topic. The upvotes should be calculated as the total number of upvotes on all messages within each topic. If any values are missing or None, please treat them as zero


Question: Identify the pair of Kaggle users involved in ForumMessageVotes such that one user has given the other the greatest distinct number of upvotes, then also display how many upvotes that recipient returned. Present the usernames of both users, the total distinct upvotes one received from the other, and the upvotes they gave back, sorting by the highest received count and then by the highest given count, and show only the top result.


Question: Whose Forum message upvotes are closest to the average in 2019? If there’s a tie, tell me the one with the alphabetically first username.


Question: Among individuals identified as white, how much higher is the average number of deaths from ICD-10 codes whose descriptions contain the word “discharge” (specifically excluding “Urethral discharge,” “Discharge of firework,” and “Legal intervention involving firearm discharge”) compared to the average number of deaths from ICD-10 codes whose descriptions contain the word “vehicle,” when aggregated by age groups?


Question: Please provide, for each age from 12 through 18 (inclusive), the total number of deaths and the number of deaths among individuals identified as Black (based on race descriptions containing the word ‘black’), specifically for deaths associated with ICD-10 codes whose descriptions include the word ‘vehicle’ and for deaths associated with ICD-10 codes whose descriptions include the word ‘firearm.’ Use the EntityAxisConditions table to determine which ICD-10 codes were involved in each death, rather than joining ICD-10 code information directly on the death records.


Question: I want to know the preferences of customers who purchased the Google Navy Speckled Tee in December 2020. What other product was purchased with the highest total quantity alongside this item?


Question: Tell me the most purchased other products and their quantities by customers who bought the Google Red Speckled Tee each month for the three months starting from November 2020.


Question: I'm trying to evaluate which board types were most effective on September 15, 2018. Can you find out the average scores for each board type from the quick play mode completions on that day?


Question: Can you figure out the average difference in pageviews between users who bought something and those who didn’t in December 2020? Just label anyone who was involved in purchase events as a purchaser.


Question: Could you provide the total number of page views for each day in November 2020 as well as the average number of page views per user on those days, restricted to users who made at least one purchase in November 2020?


Question: How many distinct users viewed the most frequently visited page during January 2021?


Question: Please find out what percentage of the page views on January 2, 2021, were for PDP type pages.


Question: I want to know all the pages visited by user 1402138.5184246691 on January 2, 2021. Please show the names of these pages and adjust the names to PDP or PLP where necessary.


Question: On January 2nd, 2021, I want to determine the percentage of times users transition from a product list page (PLP) view to a product detail page (PDP) view within the same session, using only page_view events. Could you calculate how many PLP views eventually led to a PDP view in the same session on that date, and then provide the resulting percentage of PLP-to-PDP transitions?


Question: Can you generate the navigation flow for user with pseudo_id '1362228.4966015575' on January 28th 2021, showing only the page_view events? Please connect the page titles with '>>' between each step, convert product detail pages to 'PDP' and product listing pages to 'PLP' based on the URL structure, and merge adjacent identical pages so they only appear once in the sequence. I need to understand how this specific user navigated through our website on that day.


Question: I want to know the user session conversion rate on January 2nd, 2021, using only 'page_view' events. The conversion rate should be calculated as the percentage of user visits that reached both the Home and Checkout Confirmation pages in one session, relative to those that landed on the Home page.


Question: For the date range November 1–30, 2020, can you retrieve each user_pseudo_id and its average purchase revenue in USD per session for users who had more than one purchase session, considering only events with event_name='purchase' and a non-null ecommerce.purchase_revenue_in_usd, grouping sessions by the ga_session_id from event_params


Question: Could you tell me the average number of engaged sessions per user for December 2020, counting only those sessions where the event parameter 'session_engaged' is equal to '1' and using 'user_pseudo_id' combined with the 'ga_session_id' to identify distinct sessions?


Question: Can you give me an overview of our website traffic for December 2020? I'm particularly interested in the channel with the fourth highest number of sessions.


Question: Can you provide the total number of sessions for each traffic channel in December 2020, using the information from the 'event_params'


Question: What is the page with the second highest total page views, after cleaning up its URL (removing extra slashes) and extracting the correct page path, on the website 'shop.googlemerchandisestore.com' during December 2020?


Question: On November 30, 2020, identify the item category with the highest tax rate by dividing tax value in usd by purchase revenue in usd for purchase events, and then retrieve the transaction IDs, total item quantities, and both purchase revenue in usd and purchase revenue for those purchase events in that top-tax-rate category.


Question: Could you determine what percentage of users either did not uninstall our app within seven days or never uninstalled it after installing during August and September 2018?


Question: Can you group users by the week of their first session start, starting from July 2, 2018? For each group, calculate the retention rate in the fourth week (i.e., the percentage of users from the original group who returned in the fourth week after their first session). Please identify the cohort with the highest retention rate in the fourth week, and name the group by the Monday date of the cohort's first session week. Return the result in the format "YYYY-MM-DD".


Question: Conduct a weekly cohort analysis for user retention, starting from July 9, 2018, and ending on October 2, 2018. Group users by the week of their first session_start event (with weeks starting on Monday), and identify new users as those where the event_date matches the date of their user_first_touch_timestamp. Calculate the Week 2 retention rate for each weekly cohort, defined as the percentage of users who had a session_start event exactly 2 weeks after their first week. Only include cohorts from July 9, 2018 through September 17, 2018 (the last cohort that can be analyzed for 2-week retention given the available data). Present the results with each weekly cohort and its corresponding Week 2 retention rate, ordered by cohort date.


Question: Please perform a 7-day retention analysis for users who first session start the app during the week starting on July 2, 2018. For each week from Week 0 (the week of their first session) to Week 4, provide the total number of new users in Week 0 and the number of retained users for each subsequent week. Ensuring that you only count events up to October 2, 2018, and group dates by Monday-based weeks


Question: Which quickplay event type had the lowest user retention rate during the second week after their initial engagement, for users who first engaged between August 1 and August 15, 2018, as measured by the presence of session_start events??


Question: What is the retention rate for users two weeks after their initial quickplay event, calculated separately for each quickplay event type, within the period from July 2, 2018, to July 16, 2018? Please focus on users who started a session (session_start) during this period.


Question: Could you please help me get the weekly customer retention rate in September 2018 for new customers who first used our app (first_open event) within the first week starting from September 1st, 2018 (timezone in Shanghai)? The retention rates should cover the following weeks 1, 2, and 3 period after the initial use and display them in column format.


Question: For all users who first opened the app in September 2018 and then uninstalled within seven days, I want to know what percentage of them experienced an app crash (app_exception). The calculation should be done by converting the timestamps to dates first, and then calculating the days to uninstall based on the dates. Only users who uninstalled within 7 days and experienced a crash should be considered in the final percentage.


Question: Can you calculate the 5-day symmetric moving average of predicted toy sales for December 5 to 8, 2018, using daily sales data from January 1, 2017, to August 29, 2018, with a simple linear regression model? Finally provide the sum of those four 5-day moving averages?


Question: According to the RFM definition document, calculate the average sales per order for each customer within distinct RFM segments, considering only 'delivered' orders. Use the customer unique identifier. Clearly define how to calculate Recency based on the latest purchase timestamp and specify the criteria for classifying RFM segments. The average sales should be computed as the total spend divided by the total number of orders. Please analyze and report the differences in average sales across the RFM segments


Question: Could you tell me the number of orders, average payment per order and customer lifespan in weeks of the 3 custumers with the highest average payment per order, where the lifespan is calculated by subtracting the earliest purchase date from the latest purchase date in days, dividing by seven, and if the result is less than seven days, setting it to 1.0?


Question: Could you help me calculate the average single career span value in years for all baseball players? Please precise the result as a float number. First, calculate the difference in years, months, and days between the debut and final game dates. For each player, the career span is computed as the sum of the absolute number of years, plus the absolute number of months divided by 12, plus the absolute number of days divided by 365. Round each part to two decimal places before summing. Finally, average the career spans and round the result to a float number.


Question: I would like to know the given names of baseball players who have achieved the highest value of games played, runs, hits, and home runs, with their corresponding score values.


Question: What is the distance of the longest route where Abakan is either the departure or destination city (in kilometers)?


Question: Distribute all the unique city pairs into the distance ranges 0, 1000, 2000, 3000, 4000, 5000, and 6000+, based on their average distance of all routes between them. Then how many pairs are there in the distance range with the fewest unique city paires?


Question: Please calculate the fatality rate for motorcycle collisions, separated by helmet usage. Specifically, calculate two percentages: 1) the percentage of motorcyclist fatalities in collisions where parties (drivers or passengers) were wearing helmets, and 2) the percentage of motorcyclist fatalities in collisions where parties were not wearing helmets. For each group, compute this by dividing the total number of motorcyclist fatalities by the total number of collisions involving that group. Use the parties table to determine helmet usage (from party_safety_equipment fields).


Question: In which year were the two most common causes of traffic accidents different from those in other years?


Question: For the primary collision factor violation category that was the most common cause of traffic accidents in 2021, how many percentage points did its share of annual road incidents in 2021 decrease compared to its share in 2011?


Question: For the NXT title that had the shortest match (excluding titles with "title change"), what were the names of the two wrestlers involved?


Question: Please help me identify the top 3 bowlers who, in the overs where the maximum runs were conceded in each match, gave up the highest number of runs in a single over across all matches. For each of these bowlers, provide the match in which they conceded these maximum runs. Only consider overs that had the most runs conceded within their respective matches, and among these, determine which bowlers conceded the most runs in a single over overall.


Question: Which bowler has the lowest bowling average per wicket taken?


Question: Could you calculate the average of the total runs scored by all strikers who have scored more than 50 runs in any single match?


Question: Retrieve the names of players who scored no less than 100 runs in a match while playing for the team that lost that match.


Question: Please help me find the names of top 5 players with the highest average runs per match in season 5, along with their batting averages.


Question: Can you help me find the top 5 countries whose players have the highest average of their individual average runs per match across all seasons? Specifically, for each player, calculate their average runs per match over all matches they played, then compute the average of these player averages for each country, and include these country batting averages in the result.


Question: For each match, considering every innings, please combine runs from both batsman scored and extra runs for each over, then identify the single over with the highest total runs, retrieve the bowler for that over from the ball by ball table, and calculate the average of these highest over totals across all matches, ensuring that all runs and bowler details are accurately reflected.


Question: Could you generate a report that shows the number of delivered orders for each month in the years 2016, 2017, and 2018? Each column represents a year, and each row represents a month


Question: What is the highest monthly delivered orders volume in the year with the lowest annual delivered orders volume among 2016, 2017, and 2018?


Question: Please identify the top three customers, based on their customer_unique_id, who have the highest number of delivered orders, and provide the average payment value, city, and state for each of these customers.


Question: Among all cities with delivered orders, find the five cities whose summed payments are the lowest, then calculate the average of their total payments and the average of their total delivered order counts.


Question: Could you help me find the sellers who excel in the following categories, considering only delivered orders: the seller with the highest number of distinct customer unique IDs, the seller with the highest profit (calculated as price minus freight value), the seller with the highest number of distinct orders, and the seller with the most 5-star ratings? For each category, please provide the seller ID and the corresponding value, labeling each row with a description of the achievement.


Question: Could you help me calculate the average of the total number of payments made using the most preferred payment method for each product category, where the most preferred payment method in a category is the one with the highest number of payments?


Question: Identify the top three product categories whose most commonly used payment type has the highest number of payments across all categories, and specify the number of payments made in each category using that payment type.


Question: In the “olist_geolocation” table, please identify which two consecutive cities, when sorted by geolocation_state, geolocation_city, geolocation_zip_code_prefix, geolocation_lat, and geolocation_lng, have the greatest distance between them based on the difference in distance computed between each city and its immediate predecessor in that ordering.


Question: Could you help me determine which actor starred most frequently in English-language children's category films that were rated either G or PG, had a running time of 120 minutes or less, and were released between 2000 and 2010? Please provide the actor's full name.


Question: Please help me find the film category with the highest total rental hours in cities where the city's name either starts with "A" or contains a hyphen.


Question: In the combined dataset that unifies the trees data with the income data by ZIP code, filling missing ZIP values where necessary, which three boroughs, restricted to records with median and mean income both greater than zero and a valid borough name, contain the highest number of trees, and what is the average mean income for each of these three boroughs?


Question: What percentage of trees in the Bronx have a health status of Good?


Question: Can you help me calculate the average number of new unicorn companies per year in the top industry from 2019 to 2021?


Question: Could you tell me the first names of customers who spent less than $1 on albums by the best-selling artist, along with the amounts they spent?


Question: Identify the artist with the highest overall sales of albums (tie broken by alphabetical order) and the artist with the lowest overall sales of albums (tie broken by alphabetical order), then calculate the amount each customer spent specifically on those two artists’ albums. Next, compute the average spending for the customers who purchased from the top-selling artist and the average spending for the customers who purchased from the lowest-selling artist, and finally return the absolute difference between these two averages.


Question: Using the sales data, what is the median value of total sales made in countries where the number of customers is greater than 4?


Question: Which customer has the highest average monthly change in payment amounts? Provide the customer's full name.


Question: Can you provide a list of hardware product segments along with their unique product counts for 2020 in the output, ordered by the highest percentage increase in unique fact sales products from 2020 to 2021?


Question: For the calendar year 2021, what is the overall average quantity sold of the top three best-selling hardware products (by total quantity sold) in each division?


Question: In the United States, for Q4 2019 and Q4 2020, first select only those cities where total sales (with no promotions) rose by at least 20% from Q4 2019 to Q4 2020. Among these cities, rank products by their overall sales (still excluding promotions) in those quarters and take the top 20%. Then compute each top product’s share of total sales in Q4 2019 and Q4 2020 and calculate the difference in share from Q4 2019 to Q4 2020, returning the results in descending order of that share change.


Question: Among all products sold in the United States with promo_id=999, considering only those cities whose sales increased by at least 20% from Q4 2019 (calendar_quarter_id=1772) to Q4 2020 (calendar_quarter_id=1776), which product that ranks in the top 20% of total sales has the smallest percentage-point change in its share of total sales between these two quarters?


Question: What is the average projected monthly sales in USD for France in 2021, considering only product sales with promotions where promo_total_id = 1 and channels where channel_total_id = 1, by taking each product’s monthly sales from 2019 and 2020, calculating the growth rate from 2019 to 2020 for that same product and month, applying this growth rate to project 2021 monthly sales, converting all projected 2021 amounts to USD with the 2021 exchange rates, and finally averaging and listing them by month?


Question: What is the median of the average monthly projected sales in USD for France in 2021, calculated by using the monthly sales data from 2019 and 2020 (filtered by promo_total_id=1 and channel_total_id=1), applying the growth rate from 2019 to 2020 to project 2021, converting to USD based on the currency table, and then determining the monthly averages before finding their median?


Question: Please group all Italian customers into ten buckets for December 2021 by summing their profits from all products purchased (where profit is calculated as quantity_sold multiplied by the difference between unit_price and unit_cost), then divide the overall range of total monthly profits into ten equal intervals. For each bucket, provide the number of customers, and identify the minimum and maximum total profits within that bucket.


Question: Can you provide the highest and lowest profits for Italian customers segmented into ten evenly divided tiers based on their December 2021 sales profits?


Question: Please examine our database records for Chinese cities (country_code_2 = 'cn') during July 2021 and identify both the shortest and longest streaks of consecutive date entries. For each date in these streaks, return exactly one record per date along with the corresponding city name. In your output, please ensure the first letter of each city name is capitalized and the rest are lowercase. Display the dates and city names for both the shortest and longest consecutive date streaks, ordered by date.


Question: Could you review our records in June 2022 and identify which countries have the longest streak of consecutive inserted city dates? Please list the 2-letter length country codes of these countries.


Question: Identify the country with data inserted on nine different days in January 2022. Then, find the longest consecutive period with data insertions for this country during January 2022, and calculate the proportion of entries that are from its capital city within this longest consecutive insertion period.


Question: Calculate the number of new cities inserted in April, May, and June for each year from 2021 to 2023. For each month, compute the cumulative running total of cities added for that specific month across the years up to and including the given year (i.e., sum the counts of that month over the years). Additionally, calculate the year-over-year growth percentages for both the monthly total and the running total for each month, comparing each year to the previous year. Present the results only for 2022 and 2023, listing the year, the month, the total number of cities added in that month, the cumulative running total for that month, and the year-over-year growth percentages for both the monthly total and the running total. Use the data from 2021 solely as a baseline for calculating growth rates, and exclude it from the final output.


Question: For each pizza order, provide a single result row with the row ID, order ID, customer ID, pizza name, and final set of ingredients. The final ingredients are determined by starting with the standard toppings from the pizza’s recipe, removing any excluded toppings, and adding any extra toppings. Present the ingredients in a string starting with the pizza name followed by ': ', with ingredients listed in alphabetical order. Ingredients appearing multiple times (e.g., from standard and extra toppings) should be prefixed with '2x' and listed first, followed by single-occurrence ingredients, both in alphabetical order. Group by row ID, order ID, pizza name, and order time to ensure each order appears once. Sort results by row ID in ascending order. Assign pizza_id 1 to 'Meatlovers' pizzas and pizza_id 2 to all others.


Question: Based on our customer pizza order information, summarize the total quantity of each ingredient used in the pizzas we delivered. Output the name and quantity for each ingredient.


Question: Calculate the total income from Meat Lovers pizzas priced at $12 and Vegetarian pizzas at $10. Include any extra toppings charged at $1 each. Ensure that canceled orders are filtered out. How much money has Pizza Runner earned in total?


Question: Please generate a summary of the closing balances at the end of each month for each customer transactions, show the monthly changes and monthly cumulative bank account balances. Ensure that even if a customer has no account activity in a given month, the balance for that month is still included in the output.


Question: For each customer and each month of 2020, first calculate the month-end balance by adding all deposit amounts and subtracting all withdrawal amounts that occurred during that specific month. Then determine which month in 2020 has the highest count of customers with a positive month-end balance and which month has the lowest count. For each of these two months, compute the average month-end balance across all customers and provide the difference between these two averages


Question: For each customer, group all deposits and withdrawals by the first day of each month to obtain a monthly net amount, then calculate each month’s closing balance by cumulatively summing these monthly nets. Next, determine the most recent month’s growth rate by comparing its closing balance to the prior month’s balance, treating deposits as positive and withdrawals as negative, and if the previous month’s balance is zero, the growth rate should be the current month’s balance multiplied by 100. Finally, compute the percentage of customers whose most recent month shows a growth rate of more than 5%.


Question: For each month, calculate the total balance from all users for the previous month (measured as of the 1st of each month), replacing any negative balances with zero. Ensure that data from the first month is used only as a baseline for calculating previous total balance, and exclude it from the final output. Sort the results in ascending order by month.


Question: For a bank database with customer transactions, calculate each customer's daily running balance (where deposits add to the balance and other transaction types subtract). For each customer and each day, compute the 30-day rolling average balance (only after having 30 days of data, and treating negative averages as zero). Then group these daily averages by month and find each customer's maximum 30-day average balance within each month. Sum these maximum values across all customers for each month. Consider the first month of each customer's transaction history as the baseline period and exclude it from the final results, presenting monthly totals of these summed maximum 30-day average balances.


Question: For each customer, calculate their daily balances for every day between their earliest and latest transaction dates, including days without transactions by carrying forward the previous day's balance. Treat any negative daily balances as zero. Then, for each month, determine the highest daily balance each customer had during that month. Finally, for each month, sum these maximum daily balances across all customers to obtain a monthly total.


Question: Can you provide a breakdown of how many times each product was viewed, how many times they were added to the shopping cart, and how many times they were left in the cart without being purchased? Also, give me the count of actual purchases for each product. Ensure that products with a page id in (1, 2, 12, 13) are filtered out.


Question: Please analyze our interest data from September 2018 to August 2019. For each month, calculate the average composition for each interest by dividing the composition by the index value. Identify the interest with the highest average composition value each month and report its average composition as the max index composition for that month. Compute the three-month rolling average of these monthly max index compositions. Ensure the output includes the date, the interest name, the max index composition for that month, the rolling average, and the names and max index compositions of the top interests from one month ago and two months ago.


Question: Identify the top 10 and bottom 10 interest categories based on their highest composition values across all months. For each category, display the time(MM-YYYY), interest name, and the composition value


Question: Considering only the customers who placed orders in 1998, calculate the total amount each customer spent by summing the unit price multiplied by the quantity of all products in their orders, excluding any discounts. Assign each customer to a spending group based on the customer group thresholds, and determine how many customers are in each spending group and what percentage of the total number of customers who placed orders in 1998 each group represents.


Question: Among employees who have more than 50 total orders, which three have the highest percentage of late orders, where an order is considered late if the shipped date is on or after its required date? Please list each employee's ID, the number of late orders, and the corresponding late-order percentage.


Question: For each year, calculate the percentage of films that had exclusively female actors (meaning no male actors and no actors with unknown/unspecified gender). Consider actors with gender marked as 'Male' or 'None' as non-female. For the results, display the year, the total number of movies in that year, and the percentage of movies with exclusively female actors. Extract the year from the Movie.year field by taking the last 4 characters and converting to a number.


Question: Could you analyze our data and identify which ten-year period starting from any movie release year present in the data had the largest number of films, considering consecutive ten-year periods beginning at each unique year? Only output the start year and the total count for that specific period.


Question: From the first year each actor appeared in a film to the last, how many actors in the database never had a gap longer than three consecutive years without at least one new movie appearance, meaning there is no four-year span anywhere in their active career without at least a single film credit?


Question: I need you to look into the actor collaborations and tell me how many actors have made more films with Yash Chopra than with any other director. This will help us understand his influence on the industry better.


Question: Find out how many actors have a 'Shahrukh number' of 2? This means they acted in a film with someone who acted with Shahrukh Khan, but not directly with him.


Question: Provide a detailed web sales report for each region, including the number of orders, total sales amount, and the name and sales amount of all sales representatives who achieved the highest total sales amount in that region (include all representatives in case of a tie).


Question: List the bowlers (including their ID, first name, and last name), match number, game number, handicap score, tournament date, and location for only those bowlers who have won games with a handicap score of 190 or less at all three venues: Thunderbird Lanes, Totem Lanes, and Bolero Lanes. Only include the specific game records where they won with a handicap score of 190 or less at these three locations.


Question: Could you provide a list of last names for all students who have completed English courses (where completion is defined as having a ClassStatus of 2), along with their quintile ranks based on their individual grades in those courses? The quintile should be determined by calculating how many students have grades greater than or equal to each student's grade, then dividing this ranking by the total number of students who completed English courses. The quintiles should be labeled as "First" (top 20%), "Second" (top 21-40%), "Third" (top 41-60%), "Fourth" (top 61-80%), and "Fifth" (bottom 20%). Please sort the results from highest performing quintile to lowest (First to Fifth).


Question: Could you list each musical style with the number of times it appears as a 1st, 2nd, or 3rd preference in a single row per style?


Question: Given a database of musical styles and user preferences, where Musical_Preferences contains user rankings of musical styles (PreferenceSeq=1 for first choice, PreferenceSeq=2 for second choice, PreferenceSeq=3 for third choice): Calculate a weighted score for each musical style by assigning 3 points for each time it was ranked as first choice, 2 points for each second choice, and 1 point for each third choice ranking. Calculate the total weighted score for each musical style that has been ranked by at least one user. Then, compute the absolute difference between each style's total weighted score and the average total weighted score across all such styles.


Question: Show all pairs of entertainers and customers who each have up to three style strengths or preferences, where the first and second style preferences of the customers match the first and second style strengths of the entertainers (or in reverse order). Only return the entertainer’s stage name and the customer’s last name


Question: How did each salesperson's annual total sales compare to their annual sales quota? Provide the difference between their total sales and the quota for each year, organized by salesperson and year.


Question: Can you provide the top 9 directors by movie count, including their ID, name, number of movies, average inter-movie duration (rounded to the nearest integer), average rating (rounded to 2 decimals), total votes, minimum and maximum ratings, and total movie duration? Sort the output first by movie count in descending order and then by total movie duration in descending order.


Question: Determine the top three genres with the most movies rated above 8, and then identify the top four directors who have directed the most films rated above 8 within those genres. List these directors and their respective movie counts.


Question: Analyze the annual average purchase price per Bitcoin by region, computed as the total dollar amount spent divided by the total quantity purchased each year, excluding the first year's data for each region. Then, for each year, rank the regions based on these average purchase prices, and calculate the annual percentage change in cost for each region compared to the previous year.


Question: Using the "bitcoin_prices" table, please calculate the daily percentage change in trading volume for each ticker from August 1 to August 10, 2021, ensuring that any volume ending in "K" or "M" is accurately converted to thousands or millions, any "-" volume is treated as zero, only non-zero volumes are used to determine the previous day's volume, and the results are ordered by ticker and date.


Question: Which university faculty members' salaries are closest to the average salary for their respective ranks? Please provide the ranks, first names, last names, and salaries.university


Question: Among job postings that specifically have the Data Analyst, require a non-null annual average salary, and are remote, what is the overall average salary when considering only the top three most frequently demanded skills for these positions?


Question: What is the annual retention rate of legislators who began their first term between January 1, 1917 and December 31, 1999, measured as the proportion of this cohort still in office on December 31st for each of the first 20 years following their initial term start? The results should show all 20 periods in sequence regardless of whether any legislators were retained in a particular year.


Question: For male legislators from Louisiana, how many distinct legislators were actively serving on December 31 of each year from more than 30 years since their first term up to less than 50 years, grouping the results by the exact number of years elapsed since their first term?


Question: Based on the state each female legislator first represented, which state has the highest number of female legislators whose terms included December 31st at any point, and what is that count? Please provide the state's abbreviation.


Question: Identify the state abbreviations where, for both male and female legislators, the retention rate remains greater than zero at specific intervals of 0, 2, 4, 6, 8, and 10 years after their first term start date. A legislator is considered retained if they are serving on December 31 of the respective year. Only include states where both gender cohorts maintain non-zero retention rates at all six of these time points during the first decade of service.


Question: Could you find out the average percentage of the total lifetime sales (LTV) that occur in the first 7 and 30 days after a customer's initial purchase? Also, include the average total lifetime sales (LTV). Please exclude customers with zero lifetime sales. The 7- and 30-day periods should be based on the exact number of hours-minutes-seconds, not calendar days.


Question: Please provide a list of the top three revenue-generating films for each actor, along with the average revenue per actor in those films, calculated by dividing the total film revenue equally among the actors for each film.


Question: Please find out how widespread the appeal of our top five actors is. What percentage of our customers have rented films featuring these actors?


Question: For each rating category of the first movie rented by customers—where the first movie is identified based on the earliest payment date per customer—please provide the average total amount spent per customer and the average number of subsequent rentals (calculated as the total number of rentals minus one) for customers whose first rented movie falls into that rating category.


Question: Among our top 10 paying customers, can you identify the largest change in payment amounts from one month to the immediately following month? Specifically, please determine for which customer and during which month this maximum month-over-month difference occurred, and provide the difference rounded to two decimal places.


Question: Can you identify the year and month with the highest rental orders created by the store's staff for each store? Please list the store ID, the year, the month, and the total rentals for those dates.


Question: Identify the first 10 words, sorted alphabetically, that are 4 to 5 characters long, start with 'r', and have at least one anagram of the same length, considering case-sensitive letters. Provide the count of such anagrams for each word.


Question: For alien data, how many of the top 10 states by alien population have a higher percentage of friendly aliens than hostile aliens, with an average alien age exceeding 200?


Question: In the dataset of orders joined with store information, which store has the highest total number of orders, and among that store’s orders, what is the ratio of orders that appear in the deliveries table with a 'DELIVERED' status to the total orders for that store?


Question: Can you identify the hubs that saw more than a 20% increase in finished orders from February to March?


Question: Can you find 5 delivery drivers with the highest average number of daily deliveries?


Question: Can you calculate the median from the highest season goals of each team?


Question: In each league, considering all seasons, which single team has the fewest total match wins based on comparing home and away goals, including teams with zero wins, ensuring that if multiple teams tie for the fewest wins, only one team is returned for each league?


Question: Tell me top10 teams with the most wins across the league


Question: Which player has participated in the highest number of winning matches and which player has participated in the highest number of losing matches, considering only matches where they actually played (excluding null entries) and where their team won or lost (excluding draws)?


Question: For each IPL season, identify the top three batsmen with the highest total runs scored and the top three bowlers with the most wickets taken, excluding ‘run out’, ‘hit wicket’, and ‘retired hurt’ dismissals. In the event of ties in runs or wickets, break the tie using the smaller player ID. Then output these six players in matched positions—batsman 1 with bowler 1, batsman 2 with bowler 2, and batsman 3 with bowler 3—in ascending order of the season ID, along with each player’s total runs or wickets.


Question: Find the IDs of players who scored the highest number of partnership runs for each match. The output should include the IDs of two players, each with their individual scores and the total partnership score. For each pair, the player with the higher individual score should be listed as player 1, and the player with the lower score as player 2. In cases where both players have the same score, the player with the higher ID should be player 1, and the player with the lower ID should be player 2. There can be multiple rows for a single match.


Question: Calculate the duration of each track, classify them as short, medium, or long, output the minimum and maximum time for each kind (in minutes) and the total revenue for each category, group by the category.


Question: Using a Salary Dataset where the salary values need to be cleaned by removing non-numeric characters and converting them to a numeric type, write a detailed SQL query that identifies the top 5 companies by average salary in each of Mumbai, Pune, New Delhi, and Hyderabad, then compares each company’s average salary in those cities to the overall national average salary. The final result should display four columns: Location, Company Name, Average Salary in State, and Average Salary in Country, listing only the top 5 companies in each of the specified locations.


Question: Calculate the total number of wickets taken by each bowler (excluding run-outs and other dismissals not attributed to the bowler), their economy rate (total runs conceded divided by total overs bowled, considering only runs scored off the bat and ignoring any extra runs like wides and no-balls), their strike rate (average number of balls bowled per wicket taken), and their best bowling performance in a single match (the match with the most wickets taken by the bowler, formatted as "wickets-runs" where runs are the runs conceded excluding extras).


Question: For each player, list their ID, name, their most frequent role across all matches, batting hand, bowling skill, total runs scored, total matches played, total times they were dismissed, batting average (total runs divided by total dismissals), highest score in a single match, the number of matches in which they scored at least 30 runs, at least 50 runs, and at least 100 runs, total balls faced in their career, strike rate (total runs divided by total balls faced, multiplied by 100), total wickets taken, economy rate (average runs conceded per over), and their best bowling performance in a single match (most wickets taken in a match, formatted as "wickets taken-runs given", where the best performance is the one with the most wickets, and if tied, the fewest runs conceded). Ignore the extra runs data.


Question: Which problems exceed the total number of times they appear in the solution table when counting all occurrences, across steps 1, 2, and 3, where any non-"Stack" model's maximum test score is lower than the "Stack" model's test score for the same step and version?


Question: Identify the L1_model associated with each model (specified by name and version) that occurs most frequently for each status ('strong' or 'soft'), along with the number of times it occurs. A model has a 'strong' status if, for any of its steps, the maximum test score among non-'Stack' models is less than the 'Stack' model's test score. It has a 'soft' status if the maximum test score among non-'Stack' models equals the 'Stack' model's test score. Count how many times each L1_model is associated with a 'strong' or 'soft' status across all models, and determine which L1_model has the highest occurrence for each status.


Question: Which model category (L1_model) appears the most frequently across all steps and versions when comparing traditional models to the Stack model, and what is the total count of its occurrences?


Question: What is the average total quantity across all final packaging combinations, considering only the leaf-level items within each combination after fully expanding any nested packaging relationships?


Question: Which top-level packaging containers, meaning those not contained within any other packaging, have any item for which the total quantity accumulated across all nested levels in the hierarchy exceeds 500, and what are the names of both these containers and the corresponding items?


Question: For order 423, identify the product IDs, aisles, and positions from which to pick the exact quantities needed for each order line, ensuring that the total picked quantity for each product matches the cumulative quantities ordered without exceeding the available inventory in warehouse 1. Calculate the quantities to be picked from each location by prioritizing inventory with earlier purchased dates and smaller quantities, and ensure that picking respects the sequence and cumulative quantities of the order lines for products with multiple entries.


Question: Calculate the average pick percentage for each product name, using a first-in-first-out approach that selects from inventory locations based on the earliest purchase date and smallest available quantity, ensuring that the picked quantity reflects only the overlapping range between each order’s required quantity and the inventory’s available quantity, and then grouping and ordering the results by product name?


Question: Which products were picked for order 421, and what is the average number of units picked for each product, using FIFO (First-In, First-Out) method?


Question: Based on monthly sales data starting in January 2016 and using a centered moving average to adjust for seasonality, which products had a seasonality-adjusted sales ratio that stayed consistently above 2 for every month in the year 2017?


Question: What is the average forecasted annual sales for products 4160 and 7790 during 2018, using monthly sales data starting from January 2016 for the first 36 months, applying seasonality adjustments from time steps 7 through 30, and employing a weighted regression method to estimate sales?


Question: Using a recursive monthly inventory adjustment model starting from December 2018 inventory levels, where we restock a product if its ending inventory drops below the minimum required level, determine for each product the month in 2019 where the absolute difference between its ending inventory and the minimum required level is the smallest, and return the product_id, that month, and the absolute difference.


Question: Analyze the soccer match dataset to determine the champion team for each season across all countries and leagues, awarding 3 points for every win, 1 point for every tie, and 0 points for every loss. For each season, return the champion’s team name, the league, the country, and the total points accumulated.


Question: For veg whsle data, can you generate a summary of our items' loss rates? Include the average loss rate, and also break down the count of items that are below, above, and within one standard deviation from this average.


Question: For veg whsle data, can you analyze our financial performance over the years 2020 to 2023? I need insights into the average wholesale price, maximum wholesale price, minimum wholesale price, wholesale price difference, total wholesale price, total selling price, average loss rate, total loss, and profit for each category within each year. Round all calculated values to two decimal places.


Question: Prepare a comprehensive performance report on our sellers, focusing on total sales, average item price, average review scores, and packing times. Ensure that the report includes only those sellers who have sold a quantity of more than 100 products and highlight the product category names in English with the highest sales volume.


Question: For weekly-sales data, I need an analysis of our sales performance around mid-June for the years 2018, 2019, and 2020. Specifically, calculate the percentage change in sales between the four weeks leading up to June 15 and the four weeks following June 15 for each year.


Question: Analyze the average percentage change in sales between the 12 weeks before and after June 15, 2020, for each attribute type: region, platform, age band, demographic, and customer type. For each attribute type, calculate the average percentage change in sales across all its attribute values. Identify the attribute type with the highest negative impact on sales and provide its average percentage change in sales.


Question: How many unique sessions visited the /regist/input page and then the /regist/confirm page, in that order?


Question: Using the activity log table, compute the total number of unique user sessions where each web page appears as either a landing page (the first page visited in a session based on timestamp) or an exit page (the last page visited in a session based on timestamp), or both. Count each session only once per page even if the page serves as both landing and exit for that session.


Question: Which three distinct third-page visits are most frequently observed immediately after two consecutive visits to the '/detail' page, and how many times does each third-page visit occur?


Question: How many users are there in each age category (20s, 30s, 40s, 50s, and others)?


Question: For each user session in the activity log table, identify the number of events that occurred before the first '/detail' click or '/complete' conversion, counting only events that have a non-empty search type. Find the sessions with the minimum count of such pre-click/pre-conversion events. If multiple sessions share this minimum count, include all of them in the results. Return each qualifying session along with the corresponding path and search type.


Question: Considering all races where pit stop data is available, and focusing on instances when a driver was not behind another car on the previous lap but is behind on the current lap (accounting for retirements, pit-stop entries, pit-stop exits, and race starts), how many times has each type of overtake occurred in Formula 1?


Question: In the first five laps of the race, how many overtakes occurred in each category—retirements, pit stops, start-related overtakes, and standard on-track passes?


Question: In Formula 1 seasons since 2001, considering only drivers who scored points in a season, which five constructors have had the most seasons where their drivers scored the fewest total points among all point-scoring drivers in that season?


Question: For each year, which driver and which constructor scored the most points? I want the full name of each driver.


Question: Using only the data from the ‘results’ table, find the three years in which the sum of the highest total points earned by any driver and the highest total points earned by any constructor in that year (both calculated by summing up points from the ‘results’ table) is smallest, and list those three years in order of ascending total.


Question: Which constructors had the top 3 combined points from their best driver and team, and in which years did they achieve them?


Question: Among Formula 1 drivers who raced during the 1950s, which drivers completed a season in that decade with the same constructor in both the first and the last race they participated in, while also taking part in at least two distinct race rounds during that season?


Question: Calculate the overall average first round and average last round of races missed by Formula 1 drivers across all years. Include only drivers who missed fewer than three races in a given year and who switched teams between their appearances immediately before and after their hiatus (i.e., the constructor ID for the race right before their first missed race must be different from the constructor ID for the race right after their last missed race in that year). Do not group results by year; return just the overall averages across the entire dataset.


Question: Provide the full names of drivers who have been overtaken on track more times than they have overtaken others on track during race laps, excluding position changes due to pit stops (both at pit entry and exit), retirements, or position changes that occurred during the first lap of a race (considered as start movements).


Question: Assuming today is April 1, 2024, I would like to know the daily snowfall amounts greater than 6 inches for each U.S. postal code during the week ending after the first two full weeks of the previous year. Show the postal code, date, and snowfall amount.


Question: For each year from 2015 to 2020, which Census Zip Code Tabulation Area had the second-highest annual population growth rate? Include only areas with a population estimate of at least 25,000 people in that year (based on 5-Year American Community Survey estimates). For each year, provide the zip code, state abbreviation, and the annual growth rate percentage.


Question: As of December 31, 2022, list the top 10 active banks with assets exceeding $10 billion, ranked by the highest percentage of uninsured assets, where the percentage is calculated as one minus the value of the '% Insured (Estimated)' variable from quarterly estimates. Provide the names of these banks and their respective percentages of uninsured assets.


Question: What was the percentage change in post-market close prices for the Magnificent 7 tech companies from January 1 to June 30, 2024?


Question: For each U.S. state, find how the number of active financial branch entities has changed from March 1, 2020, to December 31, 2021. An entity is considered active on a specific date if its start date is on or before that date and its end date is either null or on or after that date. For each state, calculate the number of entities active on March 1, 2020, the number of entities active on December 31, 2021, and the percentage change in these counts


Question: Determine the percentage change in gross income inflow and the seasonally-adjusted purchase-only home price index for the Phoenix-Mesa-Scottsdale, AZ Metro Area from January 1, 2023, to December 31, 2023. Gross income inflow refers to the total adjusted gross income from all financial entities within the specified metro area


Question: What are the cumulative ratios of mortgages near default in California for each recorded date in 2023, including those that are 90 to 180 days past due, in forbearance, or undergoing foreclosure, bankruptcy, or deed-in-lieu processes?


Question: How can we find the shortest straight-line distance in miles between each 'The Home Depot' store and its nearest 'Lowe's Home Improvement' location? Using the US_REAL_ESTATE.CYBERSYN database, join the point_of_interest_index table with point_of_interest_addresses_relationships and us_addresses tables to get geographic coordinates. For each 'The Home Depot' location (identified by its poi_id), calculate its distance to all 'Lowe's Home Improvement' stores using ST_DISTANCE and ST_MAKEPOINT functions, convert the distance from meters to miles (dividing by 1609), and return only the record with the minimum distance for each 'The Home Depot' store using QUALIFY with ROW_NUMBER().


Question: Using data from the FEMA National Flood Insurance Program Claim Index, for each year from 2010 through 2019, what were the total building damage amounts and total contents damage amounts reported under the National Flood Insurance Program for the NFIP community named 'City Of New York,' grouped by each year of loss?


Question: Examine user engagement with push notifications within a specified one-hour window on June 1, 2023.


Question: How many unique users started sessions each day within each app group between June 1, 2023, and June 7, 2023? Also show the app group ID and the start day of the session.


Question: Generate a daily detailed sales report for each product under the 'Manufacturing' distributor view, covering the 30 days leading up to February 6, 2022, by joining the sales, traffic, inventory, and net PPM data on date, ASIN, program, period, and distributor_view. The report must include total ordered units, ordered revenue, average selling price, glance views, conversion rate, shipped units, shipped revenue, average net PPM, average procurable product OOS, total on-hand units and value, net received units and value, open purchase order quantities, unfilled customer ordered units, and average vendor confirmation rate, receive fill rate, sell-through rate, and vendor lead time.


Question: Find the top 10 northernmost addresses in Florida's largest zip code area. What are their address numbers, street names, and types?


Question: A real estate company needs a detailed side-by-side comparison of buildings in Amsterdam and Rotterdam. They require a report showing each building class and subclass, with the total surface area (in square meters) and the total number of buildings for each classification category in both cities. The data should be organized by building class and subclass in ascending order, with Amsterdam and Rotterdam statistics presented in parallel columns to facilitate direct comparison. Can you generate this comprehensive building classification comparison report?


Question: Compare the total road lengths in Amsterdam and Rotterdam by creating a side-by-side analysis of both cities. For each combination of road class and subclass, calculate the total length of roads (in meters) specifically for QUADKEY segments '12020210' and '12020211'. Present the results with columns for class, subclass, Amsterdam's road lengths, and Rotterdam's road lengths.


Question: Produce a report for ERCOT on October 1, 2022, that combines hourly data on day-ahead and real-time prices from node ID 10000697078, load forecasts (datatypeid 19060) and actual loads, plus wind (forecast datatypeid 9285, actual datatypeid 16) and solar (forecast datatypeid 662, actual datatypeid 650) generation forecasts and actuals from object ID 10000712973. This report should include time zone alignments, peak classifications, and net load calculations, providing insights into daily operational dynamics and efficiency.


Question: Determine the population distribution within each block group relative to its census tract in New York State using 2021 ACS data. Include block group ID, census value, state county tract ID, total tract population, and the population ratio of each block group.


Question: What is the New York State ZIP code with the highest number of commuters traveling over one hour, according to 2021 ACS data? Include the zip code, the total commuters, state benchmark for this duration, and state population.