“dataframes”的搜索结果

Generate Python code to clean, transform, analyze, visualize, and model BigQuery data with BigQuery DataFrames. The skill keeps processing in the cloud and uses familiar table operations, with guidance for previews, schema checks, and machine learning.

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• Clean and transform BigQuery data with Python
• Preview data and check column types
• Visualize aggregated or sampled data

Clean, combine, summarize, and reshape tabular data with pandas DataFrames, Python's table-like data structures. Produces transformation code and validation checks, including time series processing and memory-conscious approaches for large datasets.

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• Fill missing values and correct data types
• Combine tables using multiple matching columns
• Create grouped summaries and pivot tables

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

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.

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Explore data and develop Python code in a live Jupyter session that keeps variables between runs. Inspect objects, edit notebook cells, and optionally restart and run the whole notebook to check execution from top to bottom.

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• Exploring data while keeping variables between runs
• Inspecting DataFrames and live Python objects
• Trying API usage and refining complex code

Work with genomic regions in Polars tables to find overlaps, nearby regions, covered bases, and gaps. Read and write bioinformatics files, query data with SQL, and check coordinate conventions when joining datasets or migrating from bioframe.

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• Finding variants that overlap genomic regions
• Merging regions and measuring covered bases
• Summarizing sequencing read depth

Retrieve financial data through Tushare using Python. Find relevant APIs for Chinese stocks, ETFs, funds, bonds, economic indicators, and news, then use documented parameters to obtain pandas DataFrames, tables ready for further analysis.

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• Retrieving lists of listed Chinese stocks
• Collecting ETF prices and fund size data
• Checking fund holdings and manager information

Examine Zeek connection logs for regular callbacks that may indicate communication with an attacker’s control server. Use ZAT and Pandas to group connections, measure timing variation, and identify source and destination pairs for further investigation.

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• Hunting for periodic callbacks in Zeek connection logs
• Comparing connection timing across source and destination pairs
• Developing beaconing detection rules and hunting queries

Parallel/distributed computing. Scale pandas/NumPy beyond memory, parallel DataFrames/Arrays, multi-file processing, task graphs, for larger-than-RAM datasets and parallel workflows.

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Create and validate Earth2Studio data source wrappers (DataSource, ForecastSource, DataFrameSource, ForecastFrameSource) from remote stores. Do NOT use for fetching data with existing sources, model inference, or installation tasks.

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ALWAYS use this skill instead of the Read tool for PDF files. The Read tool cannot extract PDF tables properly. Use this skill when: (1) Reading ANY PDF file, (2) Extracting tables from PDFs, (3) Converting PDF tables to pandas DataFrames, (4) Processing multi-page PDFs

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Parses, queries, converts, and extracts from GTF and GFF3 gene-model annotation files - walking the gene/transcript/exon/CDS hierarchy with gffutils (queryable SQLite DB), converting formats and extracting transcript/CDS/protein FASTA with gffread, slurping to dataframes with gtfparse/pyranges, and sanitizing malformed files with AGAT. Covers the 1-based-inclusive vs 0-based BED coordinate conversion (start-1 only), deriving implicit features (introns/UTRs/TSS), phase-not-frame, the stop-codon-in-or-out-of-CDS convention, and the chr1-vs-1 seqid and gene-ID-version mismatches that silently produce all-zero count matrices and dropped joins. Use when extracting features or sequences from an annotation, converting GTF<->GFF3 or GTF->BED, traversing the gene tree, or diagnosing a coordinate/provenance mismatch upstream of counting or DE.

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Bioinformatics-specific data operations using Polars DataFrames

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Parallel/distributed computing for larger-than-RAM data. Components: DataFrames (parallel pandas), Arrays (parallel NumPy), Bags, Futures, Schedulers. Scales laptop to HPC cluster. For single-machine speed use polars; for out-of-core without cluster use vaex.

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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.

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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.

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Parse and validate JSON data from construction APIs, IoT sensors, and BIM exports. Transform nested JSON to flat DataFrames.

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Read and parse XML from construction systems - P6 schedules, BSDD exports, IFC-XML, COBie-XML. Convert to pandas DataFrames.

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Merge pandas DataFrames from multiple construction sources. Handle different schemas, keys, and data quality issues.

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Use ArcGIS API for Python for GIS administration, spatial dataframes, feature/raster analysis, mapping, location services, geospatial deep learning, app automation, and Knowledge Graph workflows.

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Use for writing, reviewing, debugging, or testing Pandera schemas and runtime validation for Polars DataFrame or LazyFrame pipelines installed with pandera[polars]. Trigger on pandera.polars DataFrameSchema, DataFrameModel, Column, Field, Check, PolarsData, decorators, coercion, strictness, lazy error collection, and validation-depth decisions. Do not use for pandas-backed Pandera, Pydantic object models, Polars transformations without Pandera, static dataframe typing alone, or generic data-quality platforms.

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Attributed C-Sets as algebraic databases. Category-theoretic data structures generalizing graphs and dataframes with Gay.jl color integration.

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Specialist skill for Python data engineering — pandas, polars, DuckDB, numpy, ETL pipelines, tabular data ingestion, and notebook-to-module extraction. Use when working with dataframes, data validation at ingress boundaries, merge/join operations, typed column contracts, or choosing between pandas vs polars vs DuckDB for a data task.

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Load files, object storage, dataframes, or external databases into MotherDuck using an appropriate bulk ingestion path.

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