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“林深见鹿（三十二）：统计学(5)”。
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Today, the editor brings the "Deep in the Woods, the Deer Appears (Part 32): Statistics（5）".
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思维导图
Mind mapping
数据处理是统计分析的前期准备，其核心是将原始数据转化为可供分析的整洁格式。Python拥有丰富的数据处理工具，其中最基础的是NumPy与Pandas两大库。NumPy提供多维数组对象ndarray，支持高效的向量化运算，是数值计算的基础。Pandas则在此基础上构建了Series（序列）和DataFrame（数据框）两种核心数据结构。Series是一维带标签的数组，DataFrame是二维表格型数据结构，每列可以是不同类型，类似于Excel表格或数据库表。掌握这些数据结构是进行任何数据操作的前提。
Data processing is the preparatory phase of statistical analysis, with the core task of transforming raw data into a clean format ready for analysis. Python boasts a wealth of data processing tools, among which NumPy and Pandas are the most fundamental. NumPy provides the multidimensional array object ndarray, supporting efficient vectorized operations and serving as the foundation for numerical computing. Built on top of NumPy, Pandas introduces two core data structures: Series (one-dimensional labeled array) and DataFrame (two-dimensional tabular data structure). A DataFrame resembles an Excel spreadsheet or a database table, with each column potentially containing different data types. Mastering these data structures is a prerequisite for any data manipulation task.
在数据分析中，经常需要从数据中抽取子集或进行随机抽样。Pandas提供了灵活的筛选与抽样功能。条件筛选可通过布尔索引实现，例如df[df['age'] > 30]选取年龄大于30的行。随机抽样使用sample方法，可指定抽样数量或比例，并可设置是否放回。此外，iloc和loc分别按位置和标签进行索引，query方法支持字符串表达式筛选。这些操作为后续的描述性统计和推断分析提供了灵活的样本选取方式。
In data analysis, it is often necessary to extract subsets from data or perform random sampling. Pandas offers flexible filtering and sampling capabilities. Conditional filtering can be achieved through Boolean indexing, e.g., df[df['age'] > 30] selects rows where age exceeds 30. Random sampling uses the sample method, allowing specification of sample size or proportion, and can be set with or without replacement. Additionally, iloc and loc index by position and label respectively, while the query method supports filtering with string expressions. These operations provide flexible sample selection for subsequent descriptive and inferential analysis.
频数分布表是描述分类数据的基础工具。对于分类数据，使用value_counts方法可生成各类别频数表，并可设置normalize=True获得相对频率。对于数值数据，需先通过cut或qcut函数进行分组。cut根据指定的分组边界将数据划分为若干区间，qcut则根据分位数等频划分。分组后，再结合value_counts或groupby操作得到分组频数表。频数分布表不仅揭示数据的分布特征，也是绘制直方图、条形图等图形的基础。
A frequency distribution table is a fundamental tool for describing categorical data. For categorical data, the value_counts method generates a frequency table for each category, and setting normalize=True yields relative frequencies. For numerical data, the data must first be grouped using the cut or qcut function. cut partitions the data into intervals based on specified bin edges, while qcut divides into equal-frequency bins based on quantiles. After grouping, the grouped frequency table can be obtained using value_counts or groupby operations. Frequency tables not only reveal distributional characteristics but also serve as the basis for plotting histograms and bar charts.
数据处理是连接原始数据与统计分析的桥梁。通过掌握Python的基本数据结构、灵活的数据筛选与抽样方法，以及频数分布表的生成技术，研究者能够高效地将杂乱数据转化为结构化信息，为后续的图表展示和统计推断奠定坚实基础。
Data processing serves as the bridge between raw data and statistical analysis. By mastering Python's basic data structures, flexible data filtering and sampling techniques, and methods for generating frequency distribution tables, researchers can efficiently transform messy data into structured information, laying a solid foundation for subsequent graphical displays and statistical inference.
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翻译：文心一言
参考资料：百度百科
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