更好地装箱大熊猫

Question

我有一个数据框,想要按一系列值过滤或分组,然后获取每个bin中的值计数.

目前,我这样做:

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()

但有没有更简洁的方法来完成同样的事情？

Answer 1

也许你正在寻找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)

产量

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

要重新排序结果,以便按顺序显示bin范围,您可以使用

counts.sort_index()

产量

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

感谢nivniv和InLaw的改进.

另请参阅离散化和量化.