数据清洗和整理是数据处理过程中至关重要的环节，旨在确保数据质量高、准确性强，并为后续分析和应用提供可靠基础。最佳实践涉及数据去重、缺失值处理、异常值处理、数据转换和标准化等步骤，以确保数据集合适用于各种分析和应用场景。
- 数据去重：删除数据集中的重复记录，避免对结果产生不必要影响。
- 缺失值处理：识别和填补数据中的缺失值，采用插值或删除策略，避免影响分析结果准确性。
- 异常值处理：检测和处理异常值，防止其对模型和统计结果产生误导性影响。
- 数据转换：对数据进行格式转换、归一化或标准化，以便于不同数据源之间的比较和分析。
- 数据标准化：将数据按照规范进行整理，确保数据格式一致性，减少后续处理中的问题。
通过遵循这些最佳实践，数据清洗和整理的过程能够有效提高数据质量和可靠性，从而为决策制定和业务分析提供有力支持。
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