Table对象VS. Pandas DataFrame
- 每个Table必有一个字符串的name属性;
- 每个Table有一个字符串可选的description属性，用于描述Table自身
- 每个Table的行索引总是为默认的RangeIndex;
- 每个Table的列索引将总是字符串且独一无二且单层,且不为缺失值;
- 每个Table的列索引将具有额外标签column_labels用于描述列索引;
- 每个Table的类型转换系统将是简化后的Nullable数据类型;
- 每个Table的HTML显示，会显示列的简化类型
MultiTable VS. DiskCache
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每个MultiTable的值总是Table对象
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每个MultiTable的size_limit将为当前磁盘的free disk_usage*0.95 以及cull_limit将为0;
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每个MultiTable的with语句退出后不仅close cache数据库还删除数据库;
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增加concat, reshape, eval, aggregate, format操作五个数据pipeline方法:
- concat: 横向/纵向合并 （不新增值但改变维度）
- reshape: 长转宽/宽转长 （改变形状和行列值）
- mutate: 基于现有列计算新列; （根据已有列修改列但不改变维度）
- aggregate: 加总列信息为更小行数的列; (根据已有列，创建新Table) （比如从个体加总成家庭层面）
- format: 列排序/行排序/表名/表描述/列值范围/列值替换/列数据类型/列名重命名/列名标签 （不改变行列值和形状）
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区分add和update方法: add仅仅在key不存在时使用, update则将可以更新存在的key; 使用选项来设置是否lock source
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通过构造表达式来
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增加IO：
- from_csv
- from_tsv
- from_pickle
- from_stata
- to_csv
- to_tsv
- to_pickle
- to_stata
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Release files for tidydata 0.1.19
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| tidydata-0.1.19-py3-none-any.whl | Python 3 | none | any | Details |
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