Pandas 交叉表与 Pandas 数据透视表有何不同？
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How is a Pandas crosstab different from a Pandas pivot_table?
提问by root
Both the pandas.crosstab and the Pandas pivot table seem to provide the exact same functionality. Are there any differences?
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？
回答by root
The main difference between the two is the pivot_tableexpects your input data to already be a DataFrame; you pass a DataFrame to pivot_tableand specify the index/columns/valuesby 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.
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；你传递一个数据帧到pivot_table并指定index/ columns/values通过将列名作为字符串。有了cross_tab，你不一定需要有一个数据帧中，会因为你只是通过阵列状的对象index/ columns/ values。
Looking at the source codefor crosstab, it essentially takes the array-like objects you pass, creates a DataFrame, then calls pivot_tableas appropriate.
纵观源代码的crosstab，它基本上是采取类似数组的传递对象，创建了一个数据帧，然后调用pivot_table适当的。
In general, use pivot_tableif 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.
一般来说，pivot_table如果您已经有一个 DataFrame 就使用它，这样您就没有再次创建同一个 DataFrame 的额外开销。如果您从类似数组的对象开始并且只关心旋转的数据，请使用crosstab. 在大多数情况下，我认为您决定使用哪个功能不会真正产生影响。
回答by jezrael
Is it the same, if in pivot_tableuse aggfunc=lenand fill_value=0:
是否相同，如果正在pivot_table使用aggfunc=len和fill_value=0：
pd.crosstab(df['Col X'], df['Col Y'])
pd.pivot_table(df, index=['Col X'], columns=['Col Y'], aggfunc=len, fill_value=0)
EDIT: There is more difference:
编辑：还有更多区别：
Default aggfuncare different: pivot_table- np.mean, crosstab- len.
默认值aggfunc不同：pivot_table- np.mean，crosstab- len。
Parameter margins_nameis only in pivot_table.
参数margins_name仅在pivot_table.
In pivot_tableyou can use Grouperfor indexand columnskeywords.
在pivot_table您可以使用Grouperforindex和columns关键字。
I think if you need simply frequency table, crosstabfunction is better.
我认为如果您只需要频率表，crosstab功能会更好。
回答by yzerman
The pivot_tabledoes not have the normalizeargument, unfortunately.
不幸的pivot_table是，没有normalize论点。
In crosstab, the normalizeargument calculates percentages by dividing each cell by the sum of cells, as described below:
在 中crosstab，normalize参数通过将每个单元格除以单元格的总和来计算百分比，如下所述：
normalize = 'index'divides each cell by the sum of its rownormalize = 'columns'divides each cell by the sum of its columnnormalize = Truedivides each cell by the total of all cells in the table
normalize = 'index'将每个单元格除以其行的总和normalize = 'columns'将每个单元格除以其列的总和normalize = True将每个单元格除以表格中所有单元格的总数
