熊猫cut（）创建更少的垃圾箱

Question

我正在尝试使用pd.cut（）通过以下代码创建3个垃圾箱：

cut_bins= [0,139.99,199.99,250]
cut_labels = ['Set1', 'Set2', 'Set3']
pima1['G_set'] = pd.cut(pima1['G'],bins=cut_bins,labels=cut_labels)
print(pima1['G_set'].unique())

但是输出只给了我2个垃圾箱：

[Set1, Set2, NaN]
Categories (2, object): [Set1 < Set2]

代码是否有问题？

谢谢！

Answer 1

我认为这取决于数据，NaN表示容器外的数据：

cut_bins= [0,139.99,199.99,250]
cut_labels = ['Set1', 'Set2', 'Set3']

pima1 = pd.DataFrame({'G':[150, 200, 300]})

pima1['G_set'] = pd.cut(pima1['G'],bins=cut_bins,labels=cut_labels)
print(pima1['G_set'])
0    Set2
1    Set3
2     NaN
Name: G_set, dtype: category
Categories (3, object): [Set1 < Set2 < Set3]

或者：

pima1 = pd.DataFrame({'G':[0, 150, 200]})

pima1['G_set'] = pd.cut(pima1['G'],bins=cut_bins,labels=cut_labels)
print(pima1['G_set'])
0     NaN
1    Set2
2    Set3
Name: G_set, dtype: category
Categories (3, object): [Set1 < Set2 < Set3]

如果需要在箱中使用0，请添加include_lowest=True参数：

pima1['G_set'] = pd.cut(pima1['G'],bins=cut_bins,labels=cut_labels, include_lowest=True)
print(pima1['G_set'])
0    Set1
1    Set2
2    Set3
Name: G_set, dtype: category
Categories (3, object): [Set1 < Set2 < Set3]