'parquet' 검색 결과

Filter rows from Excel or previously converted Parquet files, a format designed for efficient data storage. Summarize matching records by group with counts, percentages, and totals, then export the summary to Excel.

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• Count records containing a specified risk level by work category
• Calculate group shares among matching records
• Summarize large datasets already converted to Parquet

Count rows across Excel sheets to assess data size, then analyze an existing Parquet file, a format for storing tabular data. Calculate category counts and percentages, and save the results as an Excel summary and a pie chart.

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• Counting rows across every sheet in an Excel workbook
• Calculating category counts and percentages from Parquet data
• Creating a pie chart of category proportions

Analyze Excel files with 10,000 or more rows using reading methods suited to their size. Convert data to Parquet, a column-based storage format, reduce memory use, and prepare summaries, charts, or filtered exports.

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• Summarizing regional sales from 100,000 rows
• Exporting high-value transactions from a million-row log
• Speeding up repeated analysis with Parquet

Analyze Excel datasets totaling at least 10,000 rows using an existing Parquet file, a column-based data format. Extract a chosen metric, identify the category with the highest value, and export an Excel summary with that row highlighted.

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• Counting rows across multiple Excel sheets
• Extracting a chosen metric from a large dataset
• Finding the category with the highest value

Analyze CSV, Parquet, and JSON files to answer questions through grouping, joins, cleaning, time series, and charts. Chooses DuckDB or Polars and a suitable processing location based on the task and available memory.

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• Summarizing and joining data files
• Exploring trends and distributions
• Cleaning data before analysis

Explore Hugging Face datasets through the Dataset Viewer API. Preview and retrieve rows, search text, filter records, inspect sizes and column statistics, and find Parquet download links for further analysis or data extraction.

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• Preview dataset contents and data splits
• Search text and filter dataset rows
• Inspect dataset sizes, statistics, and Parquet links

Analyze large Excel workbooks by totaling values by category and identifying the highest result. The workflow can convert data to Parquet, a column-based storage format, and export a styled Excel report with highlighted results.

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• Assessing row counts across workbook sheets
• Converting large datasets to Parquet
• Finding the category with the highest total

Parquet格式化器。用于加载和格式化parquet类型的文件。 本SKILL使用依赖data_juicer，请在调用前安装好python环境并安装data_juicer，你可用以下指令进行安装： pip install py-data-juicer 当用户提到Parquet格式化、Parquet数据加载、读取Parquet文件、Parquet转JSONL等需求时使用此skill。

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Extract selected ranges from Excel workbooks with multiple sheets, check missing values, and clean numeric data. Fit a straight trend line, save charts and CSV results, and convert larger tables to Parquet for subsequent analysis.

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• Checking missing values and category counts in a selected sheet
• Extracting numeric pairs from selected rows and columns
• Creating a scatter plot with a straight trend line

Inspect multi-sheet Excel files, use Parquet, a column-based file format, for larger datasets, and clean text before analysis. Produce category summaries, identify maximum values, and export formatted reports or scatter plots with fitted straight trend lines.

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• Assess data volume in multi-sheet Excel files
• Clean unwanted characters from text columns
• Summarize categories and highlight the maximum

Prepare large Excel datasets by combining sheets, using Parquet, a format for efficient table storage, and cleaning a selected text column to retain only Chinese characters. Export the cleaned data as an Excel file with a local download link.

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• Removing symbols and numbers from Chinese text columns
• Preparing large Excel datasets for analysis
• Combining sheets and exporting cleaned data

Use DuckDB from MATLAB via Database Toolbox (R2026a+) as a non-math operations engine on large tabular files (CSV/Parquet/JSON) and as a zero-config embedded database. Use when connecting to DuckDB, querying CSV, Parquet, and JSON files directly with SQL, reducing or profiling large data before MATLAB analysis, creating portable development databases, or installing DuckDB extensions. Triggers on: DuckDB, duckdb(), large CSV/Parquet/JSON, file too large for readtable, filter/aggregate at source, deduplicate, reduce before analysis, profile large file, persistent file import, analytical engine, SQL on CSV, SQL on Parquet, SQL on JSON, query CSV with SQL, query Parquet with SQL, run SQL on files, SQL queries on files, query files directly, SQL without database, in-process SQL.

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Use the GeoParquet specification and bundled inspection helpers to author, validate, distribute, or safely review geospatial Parquet data, including GeoParquet 2.0 vector metadata, external writer choices, and the alpha Parquet Raster proposal.

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Use Dask to process tables, numerical arrays, and file collections in pieces across CPU cores or machines. Helps choose data structures, size memory-friendly chunks, coordinate dependent tasks, and diagnose slow or memory-heavy workflows.

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• Aggregate CSV or Parquet files that exceed available memory
• Process large scientific arrays in chunks
• Clean JSON records and logs before analysis

Build data processing pipelines for machine learning, from preprocessing to batch predictions. Uses Ray Data to handle datasets larger than memory, distribute work across CPUs and GPUs, and feed data into training frameworks.

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• Preprocessing datasets larger than memory
• Running batch predictions across multiple GPUs
• Preparing image and tabular data for training

Build data processing pipelines for machine learning, from preprocessing to batch predictions. Uses Ray Data to handle datasets larger than memory, distribute work across CPUs and GPUs, and feed data into training frameworks.

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• Preprocessing datasets larger than memory
• Running batch predictions across multiple GPUs
• Preparing image and tabular data for training

Write and review R analysis scripts for a sewage research project using its established conventions. Estimate requested regression models, add specifications, and save LaTeX tables, figures, and model objects for use in the research paper.

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• Estimate models of sewage spills and property prices
• Add specifications to an existing regression analysis
• Create descriptive plots and maps

DuckDBを使用した大規模データ分析の専門スキル。DuckDBのアーキテクチャ、SQL構文、パフォーマンス最適化、各種ファイル形式（CSV、Parquet、JSON等）の効率的な読み込み・書き出しを熟知。メモリ効率の良い分析、複雑なクエリの最適化、データパイプライン構築を支援。Use when analyzing large datasets, querying CSV/Parquet/JSON files directly, building data pipelines, or optimizing analytical queries with DuckDB.

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Analyze large scientific tables with Vaex, including datasets that exceed available memory. Build calculated columns, filter rows, compute statistics, visualize aggregated data, and convert files while identifying operations that may still require substantial RAM.

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• Computing statistics on tables larger than available memory
• Creating calculated features without storing full columns
• Visualizing large datasets with aggregated grids

Build Python workflows for filtering, grouping, joining, and reshaping table data with Polars. Get guidance on migrating pandas code and using lazy evaluation, which optimizes a processing plan before execution, for large datasets.

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• Filtering and summarizing large CSV files
• Migrating pandas processing code to Polars
• Joining and reshaping multiple tables

Build Apache Spark pipelines for large datasets, including transformations, aggregations, and streaming analysis. Get PySpark or Scala code, partitioning strategies for dividing data across workers, and configuration advice to investigate slow jobs and resource usage.

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• Build a pipeline to transform and aggregate large datasets
• Investigate slow Spark jobs and uneven data distribution
• Tune memory settings and data partitions

Find key figures in tabular data using column names or row keywords, calculate category averages and percentages, and export the results as a structured Excel report and a pie chart.

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• Find figures using keywords in rows
• Calculate averages for a selected category
• Calculate one figure as a percentage of another

Analyze a selected entity’s time series in Excel, calculate its average, and export a formatted report showing differences from that average. Rows below the average are highlighted in green for easy comparison.

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• Highlight periods with values below the average
• Report a selected entity’s time series
• Check the total row count across Excel sheets

Organize Excel analysis from reading multiple sheets through cleaning, filtering, aggregation, and export. Choose processing methods based on row counts, and use supporting skills to produce tables, charts, and Excel or CSV reports.

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• Clean missing values and inconsistent text in Excel
• Filter and aggregate data across multiple sheets
• Analyze large spreadsheets using row-based processing strategies