'parquet' 검색 결과

Use when the user has tabular data (pandas DataFrame, parquet, csv, Arrow, json) and wants to filter, group, aggregate, join, or speed up slow pandas. Provides chDB DataStore - same pandas API, ClickHouse engine underneath. Also handles reading from S3, MySQL, PostgreSQL, MongoDB, ClickHouse Cloud, Iceberg, Delta Lake as DataFrames and joining across sources. TRIGGER when: user mentions DataFrame, parquet, csv, "fast pandas", "speed up pandas", or cross-source DataFrame joins; user imports `chdb.datastore` or `from datastore import DataStore`. SKIP this skill for raw SQL syntax (use chdb-sql instead), ClickHouse server administration, or non-Python DataStore API work.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

DuckDB CLI specialist for SQL analysis, data processing and file conversion. Use for SQL queries, CSV/Parquet/JSON analysis, database queries, or data conversion. Triggers on "duckdb", "sql", "query", "data analysis", "parquet", "convert data".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Prepare raw credit data for pre-loan modeling by checking data quality and screening variables. Review missing values, predictive usefulness, stability, noise, and correlation, then export an Excel report with organization-level statistics and cleaning results.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Prepare credit data before building a model
• Compare missing values and bad-sample rates across organizations
• Screen variables for usefulness, stability, and redundancy

Prepare training datasets for large language models with GPU-assisted cleaning. Covers duplicate removal, quality filtering, and personal information redaction, with workflows for text, images, video, and audio using NeMo Curator.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Clean web data for model training
• Remove exact and near duplicates from large text collections
• Redact personal information from training text

Explore a table or data file before starting analysis. Summarize its structure, missing values, duplicates, and distributions, flag potential quality issues, and suggest useful measures, grouping columns, and follow-up analyses.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Understanding an unfamiliar table or data file
• Checking missing values, duplicates, and suspicious entries
• Choosing measures and grouping columns for analysis

Diagnoses and fixes Java style violations in the FE module using Checkstyle, a tool that checks coding rules. It interprets errors, applies the project's formatting and import restrictions, and reruns checks to verify fixes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking FE Java changes before committing
• Fixing build failures caused by Checkstyle violations
• Understanding project-specific style and import rules

Prepare training datasets for large language models with GPU-assisted cleaning. Covers duplicate removal, quality filtering, and personal information redaction, with workflows for text, images, video, and audio using NeMo Curator.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Clean web data for model training
• Remove exact and near duplicates from large text collections
• Redact personal information from training text

Clean Excel data, summarize groups, and examine relationships between numeric columns. Produce charts such as scatter plots with trend lines, calculate measures of fit and statistical significance, and export cleaned data and statistical summaries.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Clean missing group labels and numeric text
• Compare group averages and variation
• Analyze numeric relationships with scatter plots and trend lines

Clean Excel data and group records with similar numerical features. Produce a combined view of indicator trends, value distributions, category shares, and grouped data, then export the charts as an image and cleaned records as Excel.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing trends across multiple indicators
• Checking value distributions and category shares
• Grouping records with similar numerical features

Filter rows from a selected Excel sheet, summarize category counts and percentages, and export the required columns to a new workbook. The workflow also cleans filter values, renames columns, and provides a download link.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Extract rows matching a specific group
• Summarize category counts and percentages
• Export selected columns with clearer names

Extract keyword-matched entries from an Excel sheet, clean whitespace and empty values, and count distinct entries by category. Export separate Excel files containing summary counts and detailed lists for review.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Counting distinct facility names by category
• Extracting entries that match specific keywords
• Creating summary and detail reports from Excel data

Clean Excel columns by removing unwanted prefixes from numeric values and extracting Chinese characters from text. Add the cleaned results as new columns, then save an Excel file and provide a download link.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Cleaning numeric columns with unwanted prefixes
• Extracting Chinese characters from text fields
• Exporting cleaned data as an Excel file

Process multi-sheet Excel data by finding columns through keywords and counting specified values. Clean text fields to retain Chinese characters, then export the processed data as an Excel file with its size and download link.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding a target column by a keyword in its name
• Counting specified values across Excel sheets
• Extracting Chinese characters from mixed text

Extract coefficients associated with a chosen label across Excel sheets and check them against a standard value. Create a comparison workbook showing each value, its source sheet, and red highlighting for rows that fail the check.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check coefficients across multiple Excel sheets
• Find values that differ from a specified standard
• Compare sheets with different column layouts

Convert recorded robot workflows from HDF5 into local LeRobot datasets for training or browser inspection. Preserves actions, state, camera recordings, and task labels, then checks episode counts, metadata, videos, and data dimensions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Convert successful robot episodes into a training dataset
• Prepare recorded workflows for browser inspection
• Check converted videos, metadata, and data dimensions

Exports Amazon RDS or Aurora database snapshots to Amazon S3 in Apache Parquet format for analytics, backup, or data migration. Handles snapshot selection or creation, IAM role setup, KMS encryption, S3 bucket preparation, export task execution, progress monitoring, and data verification. Use when exporting RDS/Aurora data to S3 for Athena, Glue, or Redshift Spectrum consumption.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use BEFORE reading any data file that could be large (CSV/TSV, Parquet, HDF5, FITS, NetCDF, NDJSON, genomics FASTQ/FASTA/VCF/BAM, GRIB, ROOT, or big text/simulation logs like VASP OUTCAR). Returns a compact memory pointer — header/schema/shape/sample/key numbers — by introspection and sampling in bounded memory, so you never load a file bigger than the context window into the model. Reference data via the pointer; read specific ranges deterministically.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Read or write data files in MATLAB. Use when the task involves tables, spreadsheets, delimited text, or structured files in CSV, Excel, Parquet, JSON, or XML format — including but not limited to importing, exporting, loading, parsing, converting, validating, configuring import options, reading from URLs, handling locales or encodings, diagnosing file errors, and modernizing legacy file I/O code. MATLAB provides built-in functions for these workflows with no additional products required.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Analyze event logs, clickstreams, user paths, product funnels, retention, behavioral segments, transition graphs, step matrices, sequence patterns, and customer journeys using Retentioneering. Use when the user provides CSV, Parquet, pandas, or database event data containing user, event, and timestamp columns, or asks why users convert, churn, loop, abandon a flow, or follow particular product paths. Do not use for qualitative journey-mapping workshops or aggregate website traffic without user-level event sequences.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use this skill for Hugging Face Dataset Viewer API workflows that fetch subset/split metadata, paginate rows, search text, apply filters, download parquet URLs, and read size or statistics.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Data ingestion patterns for loading data from cloud storage, APIs, files, and streaming sources into databases. Use when importing CSV/JSON/Parquet files, pulling from S3/GCS buckets, consuming API feeds, or building ETL pipelines.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Out-of-core DataFrame for billion-row data via lazy evaluation and memory-mapped files. Use when data exceeds RAM (10 GB–TB) for fast aggregation, filtering, virtual columns, and visualization without loading. Supports HDF5, Arrow, Parquet, CSV with cloud (S3, GCS, Azure). Built-in ML transformers (scaling, PCA, K-means). In-memory: polars; distributed: dask.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Geospatial vector analysis extending pandas. Read/write spatial formats (Shapefile, GeoJSON, GeoPackage, Parquet, PostGIS), CRS handling, geometric ops (buffer, simplify, centroid, affine), spatial analysis (joins, overlays, dissolve, clipping, distance), visualization (choropleth, interactive maps, basemaps). Use for spatial joins, overlays, CRS transforms, area/distance, maps.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.