Why write SQL queries when you can get an LLM to write the code for you? Query NFL data using querychat, a new chatbot ...

随着数据规模的不断扩大，传统的数据处理工具难以应对大规模数据的挑战。Pandas 作为 Python 数据分析领域的核心工具，因其直观的 API 和丰富的功能而备受欢迎。然而，Pandas 受限于单机内存的限制，难以处理超过内存大小的数据集。为了解决这一问题，Dask 应运而生。Dask 以其灵活的调度系统和与 ...

Python, a versatile programming language, has established itself as a staple in the data analysis landscape, primarily due to its powerful libraries: Pandas, NumPy, and Matplotlib. These libraries ...

Pandas 基于 NumPy 开发，它提供了快速、灵活、明确的数据结构，旨在简单、直观地处理数据。 Pandas 适用于处理以下类型的数据： 有序和无序的时间序列数据 带行列标签的矩阵数据，包括同构或异构型数据 与 SQL 或 Excel 表类似 ...

Pandas makes it easy to quickly load, manipulate, align, merge, and even visualize data tables directly in Python.

What you could do is to convert pandas df to a pyarrow.Table adding a Schema specifying the object column to be a string dtype and then pyarrow.feather.write_feather should work fine.

So to update an existing parquet file you have to read the existing data into memory, add the new data and write that to disk as a new file what if the dataset is too large to load in memory?