如何在pandas中的Excel枢轴表函数中使用分组功能？

发布于 2025-01-23 07:36:07 字数 2224 浏览 0 评论 0原文

我有熊猫数据框架。

id	Score	Action_flag
S11	0.585366	Not Traded
P555	0.457778	Not Traded
B28	0.636154	Not Traded
A859	0.000000	Traded
P556	0.761905	Not Traded
Y461	0.333333	Not Traded
S121	0.444444	Not Traded
K481	0.000000	Traded
S122	1.000000	Not Traded
R556	0.000000	Traded
R627	0.602778	Traded

In excel using pivot table and grouping我能够做到这一点。

如何使用pandas

和pivot Table

原文

I have a pandas dataframe.

id	Score	Action_flag
S11	0.585366	Not Traded
P555	0.457778	Not Traded
B28	0.636154	Not Traded
A859	0.000000	Traded
P556	0.761905	Not Traded
Y461	0.333333	Not Traded
S121	0.444444	Not Traded
K481	0.000000	Traded
S122	1.000000	Not Traded
R556	0.000000	Traded
R627	0.602778	Traded

In excel using pivot table and grouping I was able to do this.

How to achieve this using pandas

And pivot table

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給妳壹絲溫柔 2025-01-30 07:36:07

iiuc，您可以使用pd.cut创建垃圾箱，然后使用crosstab获取每个操作标志的计数。最后，分配对新列“ Grand_total”的行总计：

out = pd.crosstab(pd.cut(df['Score'], np.linspace(0,1,21), include_lowest=True), df['Action_flag']).assign(Grand_total=lambda x: x.sum(axis=1))

输出：

Action_flag     Not Traded  Traded  Grand_total
Score                                          
(-0.001, 0.05]           0       3            3
(0.3, 0.35]              1       0            1
(0.4, 0.45]              1       0            1
(0.45, 0.5]              1       0            1
(0.55, 0.6]              1       0            1
(0.6, 0.65]              1       1            2
(0.75, 0.8]              1       0            1
(0.95, 1.0]              1       0            1

IIUC, you could create bins using pd.cut, then use crosstab to get the counts each each action flag. Finally, assign the row-wise totals to a new column "Grand_total":

out = pd.crosstab(pd.cut(df['Score'], np.linspace(0,1,21), include_lowest=True), df['Action_flag']).assign(Grand_total=lambda x: x.sum(axis=1))

Output:

Action_flag     Not Traded  Traded  Grand_total
Score                                          
(-0.001, 0.05]           0       3            3
(0.3, 0.35]              1       0            1
(0.4, 0.45]              1       0            1
(0.45, 0.5]              1       0            1
(0.55, 0.6]              1       0            1
(0.6, 0.65]              1       1            2
(0.75, 0.8]              1       0            1
(0.95, 1.0]              1       0            1

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