pandas 的交叉表与 pandas 的数据透视表有何不同? [英] How is a Pandas crosstab different from a Pandas pivot_table?

问题描述

pandas.crosstab和Pandas数据透视表似乎都提供了完全相同的功能.有什么区别吗?

Both the pandas.crosstab and the Pandas pivot table seem to provide the exact same functionality. Are there any differences?

推荐答案

两者之间的主要区别是pivot_table期望您的输入数据已经是一个DataFrame；您将DataFrame传递给pivot_table，并通过将列名作为字符串传递来指定index/columns/values.使用cross_tab，您不必插入一个DataFrame，因为您只需为index/columns/values传递类似数组的对象即可.

The main difference between the two is the pivot_table expects your input data to already be a DataFrame; you pass a DataFrame to pivot_table and specify the index/columns/values by passing the column names as strings. With cross_tab, you don't necessarily need to have a DataFrame going in, as you just pass array-like objects for index/columns/values.

查看源代码(对于crosstab)，它实际上会接收您传递的类似数组的对象，创建一个DataFrame，然后视情况调用pivot_table.

Looking at the source code for crosstab, it essentially takes the array-like objects you pass, creates a DataFrame, then calls pivot_table as appropriate.

通常，如果您已经有一个DataFrame，请使用pivot_table，这样就没有额外的开销来再次创建相同的DataFrame.如果您从类似数组的对象开始，并且仅关注数据透视表，请使用crosstab.在大多数情况下，我认为您决定使用哪种功能并不会真正起到作用.

In general, use pivot_table if you already have a DataFrame, so you don't have the additional overhead of creating the same DataFrame again. If you're starting from array-like objects and are only concerned with the pivoted data, use crosstab. In most cases, I don't think it will really make a difference which function you decide to use.

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