用python熊猫分箱列

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我有一个数据框列与数字值:

df['percentage'].head()
46.5
44.2
100.0
42.12

我希望将列视为bin计数:

bins = [0, 1, 5, 10, 25, 50, 100]

我怎样才能将结果作为箱子与他们一起得到value counts

[0, 1] bin amount
[1, 5] etc 
[5, 10] etc 
......
提问于
用户回答回答于

你可以使用pandas.cut

bins = [0, 1, 5, 10, 25, 50, 100]
df['binned'] = pd.cut(df['percentage'], bins)
print (df)
   percentage     binned
0       46.50   (25, 50]
1       44.20   (25, 50]
2      100.00  (50, 100]
3       42.12   (25, 50]
bins = [0, 1, 5, 10, 25, 50, 100]
labels = [1,2,3,4,5,6]
df['binned'] = pd.cut(df['percentage'], bins=bins, labels=labels)
print (df)
   percentage binned
0       46.50      5
1       44.20      5
2      100.00      6
3       42.12      5

或者numpy.searchsorted

bins = [0, 1, 5, 10, 25, 50, 100]
df['binned'] = np.searchsorted(bins, df['percentage'].values)
print (df)
   percentage  binned
0       46.50       5
1       44.20       5
2      100.00       6
3       42.12       5

......然后value_counts或者groupby聚合size

s = pd.cut(df['percentage'], bins=bins).value_counts()
print (s)
(25, 50]     3
(50, 100]    1
(10, 25]     0
(5, 10]      0
(1, 5]       0
(0, 1]       0
Name: percentage, dtype: int64
s = df.groupby(pd.cut(df['percentage'], bins=bins)).size()
print (s)
percentage
(0, 1]       0
(1, 5]       0
(5, 10]      0
(10, 25]     0
(25, 50]     3
(50, 100]    1
dtype: int64

默认cut返回categorical

Series类似的方法Series.value_counts()将使用所有类别,即使数据中不存在某些类别,也可以使用分类中的操作

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