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所在平台: Udemy |
课程主页: https://www.udemy.com/course/pandas-interview-questions-practice-test-series/
课程评论：没有评论
课程名称：Pandas面试问题练习测试系列 课程概述：本课程提供一个结构化且深入的练习测试系列，旨在让学习者掌握Pandas，Python中最广泛使用的数据分析库。内容分为六个关键部分，每个部分都旨在增强您对Pandas的实际数据处理理解。通过180个精心挑选的选择题和详细的解释，学习者将提升在数据操作中的掌握能力，从基本结构到复杂转换。 1. **Pandas基础和数据结构**：学习Series、DataFrames及支撑Pandas的基础概念。理解数据类型、索引，并学习如何创建和探索基本数据结构。 2. **数据索引和选择**：深入了解基于标签和基于位置的索引，使用.loc[]、.iloc[]和.at[]。学习如何切片、过滤和查询数据集，实现精确的数据选择。 3. **数据清洗和转换**：处理缺失数据、重复项，并使用内置函数应用转换。重点在于数据的重塑、重命名、类型转换和数据标准化。 4. **分组、聚合和透视表**：掌握使用groupby()、聚合函数和透视表的方法。了解如何通过分割、应用和组合操作从分组数据中获取洞察。 5. **合并、连接和拼接**：探索使用merge()、join()和concat()结合多个数据集的方法。学习如何以结构化方式对齐来自不同来源的数据。 6. **时间序列、可视化和性能优化**：学习处理日期时间对象、重采样数据和可视化趋势。此外，介绍高效的Pandas技术，以优化大规模数据处理。 每个部分都聚焦于实际场景，通过问答形式加深理解。本系列课程确保学习者在各种商业和科学域的数据显示工作流中，扎实掌握Pandas。
This course offers a structured and in-depth practice test series for mastering Pandas, one of the most widely-used libraries in data analysis with Python. The content is divided into six key sections, each designed to strengthen your practical understanding of real-world data manipulation with Pandas. Through 180 handpicked multiple-choice questions with detailed explanations, learners will refine their command over data operations, from basic structures to complex transformations.1. Pandas Basics and Data StructuresLearn about Series, DataFrames, and the foundational concepts that power Pandas. Understand data types, indexing, and how to create and explore basic data structures.2. Data Indexing and SelectionDive into label-based and position-based indexing using.loc[],.iloc[], and.at[]. Learn how to slice, filter, and query datasets for precise data selection.3. Data Cleaning and TransformationHandle missing data, duplicates, and apply transformations using built-in functions. This section focuses on reshaping, renaming, type conversions, and data normalization.4. Grouping, Aggregation, and PivotingMaster the use of groupby(), aggregation functions, and pivot tables. Understand how to derive insights from grouped data through splitting, applying, and combining operations.5. Merging, Joining, and ConcatenationExplore methods for combining multiple datasets using merge(), join(), and concat(). Learn how to align data from different sources in structured ways.6. Time Series, Visualization, and Performance OptimizationLearn to handle datetime objects, resample data, and visualize trends. Also, get introduced to efficient Pandas techniques to optimize large-scale data processing.Each section focuses on practical scenarios with a question-answer format to reinforce your understanding. This series ensures a solid grasp of Pandas in data analysis workflows across a variety of business and scientific domains.
