Python 更好的 Pandas 分箱

Better binning in pandas

提问by monkut

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.

我有一个数据框，想按一系列值进行过滤或分箱，然后获取每个分箱中的值计数。

Currently, I'm doing this:

目前，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()

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

但是有没有更简洁的方法来完成同样的事情？

采纳答案by unutbu

Perhaps you are looking for pandas.cut:

也许您正在寻找pandas.cut：

import pandas as pd
import numpy as np

df = pd.DataFrame(np.arange(50), columns=['filtercol'])
filter_values = [0, 5, 17, 33]   
out = pd.cut(df.filtercol, bins=filter_values)
counts = pd.value_counts(out)
# counts is a Series
print(counts)

yields

产量

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

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

要对结果重新排序以使 bin 范围按顺序显示，您可以使用

counts.sort_index()

which yields

这产生

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

Thanks to nivnivand InLawfor this improvement.

感谢nivniv和InLaw的改进。

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See also Discretization and quantiling.

另请参阅离散化和量化。