89 个 · 按相关度排序
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.
中文简介正在准备中。当前显示英语简介。
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.
中文简介正在准备中。当前显示英语简介。
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.
中文简介正在准备中。当前显示英语简介。
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.
中文简介正在准备中。当前显示英语简介。
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.
中文简介正在准备中。当前显示英语简介。
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.
中文简介正在准备中。当前显示英语简介。
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.
中文简介正在准备中。当前显示英语简介。
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.
中文简介正在准备中。当前显示英语简介。
End-to-end R data analysis for the sewage project. Writes analysis scripts following project conventions (here::here, arrow/parquet, fixest, modelsummary, native pipe), runs code review, and produces publication-ready tables and figures. This skill should be used when asked to "run an analysis", "estimate the model", "add a specification", or "write an R script".
中文简介正在准备中。当前显示原文说明。
Processes and analyzes data with resident-kernel engines (DuckDB, Polars) and one-shot tools. Use for CSV/parquet/JSON analysis, group-by/join/aggregation, time series, distributions, cleaning, or plotting a dataset.
中文简介正在准备中。当前显示原文说明。
Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that do not fit in memory.
中文简介正在准备中。当前显示原文说明。
Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV/Parquet I/O, nullable semantics, and multi-GPU DataFrame workloads.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
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.
中文简介正在准备中。当前显示原文说明。
Fast in-memory DataFrame with lazy evaluation, parallel execution, Arrow backend. Use for tabular data in RAM (1–100 GB) when pandas is too slow. Expression API: select, filter, group_by, joins, pivots, window. Lazy mode enables predicate/projection pushdown. Reads CSV, Parquet, JSON, Excel, DBs, cloud. Larger-than-RAM: Dask; GPU: cuDF.
中文简介正在准备中。当前显示原文说明。
Query and download NCI Imaging Data Commons (IDC) cancer radiology and pathology datasets via the idc-index Python client. No authentication required: the parquet index ships inside the pip wheel, SQL runs locally via DuckDB, and DICOM downloads stream from public S3/GCS buckets through s5cmd. Use sql_query() for DuckDB cohort selection, get_collections/get_patients/get_dicom_studies/get_dicom_series for hierarchical browsing, download_from_selection() for downloads, and get_viewer_URL() for OHIF/Slim links. Use pydicom-medical-imaging for local DICOM reading; histolab for whole-slide pathology preprocessing.
中文简介正在准备中。当前显示原文说明。
Load and parse DDC CWICR construction cost database from multiple formats: Parquet, Excel, CSV, Qdrant snapshots. Foundation for all CWICR operations.
中文简介正在准备中。当前显示原文说明。