更好地在 pandas 中进行分箱 [英] Better binning in pandas

问题描述

我有一个数据框，想要按一定范围的值进行过滤或合并，然后获取每个合并中的值计数.

I've got a data frame and want to filter or bin by a range of values and then get the counts of values in each bin.

当前，我正在这样做:

x = 5
y = 17
z = 33
filter_values = [x, y, z]
filtered_a = df[df.filtercol <= x]
a_count = filtered_a.filtercol.count()

filtered_b = df[df.filtercol > x]
filtered_b = filtered_b[filtered_b <= y]
b_count = filtered_b.filtercol.count()

filtered_c = df[df.filtercol > y]
c_count = filtered_c.filtercol.count()

但是有没有更简洁的方法来完成相同的事情?

But is there a more concise way to accomplish the same thing?

推荐答案

也许您正在寻找收益

(17, 33]    16
(5, 17]     12
(0, 5]       5

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要对结果重新排序以使bin范围按顺序显示，您可以使用

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To reorder the result so the bin ranges appear in order, you could use

counts.sort_index()

产生

(0, 5]       5
(5, 17]     12
(17, 33]    16

感谢 nivniv 和InLaw 进行了此项改进.

Thanks to nivniv and InLaw for this improvement.

另请参见离散化和量化

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