Results for “pandadoc”

Perform efficient pandas DataFrame operations for data analysis, manipulation, and transformation with production-grade patterns.

Generate Python SDK setup, CRUD, bulk, and paging snippets for Microsoft Dataverse using official patterns.

Process tabular data with Polars' expression API, lazy evaluation, and parallel execution for faster pandas-style workflows.

Process tabular data with Polars' expression API, lazy evaluation, and parallel execution for fast in-memory analysis and pandas migration.

Scales pandas/NumPy workflows beyond memory with Dask distributed computing — parallel DataFrames, arrays, delayed task graphs, and cluster execution. Use when existing pandas/NumPy code must run on larger-than-RAM data or across clusters, for parallel file processing, distributed ML, or integration with existing pandas code. For out-of-core analytics on a single machine prefer vaex; for in-memory speed prefer polars. Part of the AlterLab Academic Skills suite.

Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.

Write, review, debug, test, or optimize pandas Series, DataFrame, Index, groupby, merge, reshape, dtype, missing-value, and time-series code.

Read, write, and modify DICOM medical imaging files, including pixel data extraction, anonymization, format conversion, and compression handling.

Lark Doc 文档统一入口：处理在线 Docx/Wiki 与本地 Word/PDF 文档任务。在线文档 URL/token、读取、创建、编辑、总结等任务路由到 online-doc；本地 .docx/.doc/.pdf 文件、明确要求 Word/PDF 交付或格式保留处理的任务路由到 office-word。不处理 Sheet、Slide、Excel、PowerPoint、Base 表内操作。

Manipulates and analyzes data with pandas, including DataFrames, group operations, and time series.

DataFrame operations with pandas and polars — groupby, joins, reshaping, performance. Use when manipulating tabular data, choosing between pandas and polars, optimizing DataFrame code, or translating between the two libraries.

Expert guidance for Ibis, the Python dataframe library that provides a pandas-like API but generates SQL for execution on any backend — DuckDB, PostgreSQL, BigQuery, Snowflake, Spark, and more. Helps developers write analytics code once and run it anywhere without rewriting SQL for each database.

Creates executable Polars dataframe contracts using Pandera's Polars backend for runtime validation of schemas, columns, and checks.

Pythonic wrapper around RDKit for cheminformatics, simplifying SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, and parallel processing while returning native rdkit.Chem.Mol objects.

Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.

Process in-memory datasets with Polars' expression API, lazy evaluation, and parallel execution, including pandas migration patterns and I/O for CSV, Parquet, and JSON.