pd.cut()的作用，有点类似给成绩设定优良中差，比如：0-59分为差，60-70分为中，71-80分为优秀等等，在pandas中，也提供了这样一个方法来处理这些事儿。直接上代码：
import numpy as np import pandas as pd from pandas import Series, DataFrame np.random.seed(666) score_list = np.random.randint(25, 100, size=20) print(score_list) # [27 70 55 87 95 98 55 61 86 76 85 53 39 88 41 71 64 94 38 94] # 指定多个区间 bins = [0, 59, 70, 80, 100] score_cut = pd.cut(score_list, bins) print(type(score_cut)) # <class 'pandas.core.arrays.categorical.Categorical'> print(score_cut) ''' [(0, 59], (59, 70], (0, 59], (80, 100], (80, 100], ..., (70, 80], (59, 70], (80, 100], (0, 59], (80, 100]] Length: 20 Categories (4, interval[int64]): [(0, 59] < (59, 70] < (70, 80] < (80, 100]] ''' print(pd.value_counts(score_cut)) # 统计每个区间人数 ''' (80, 100] 8 (0, 59] 7 (59, 70] 3 (70, 80] 2 dtype: int64 ''' df = DataFrame() df['score'] = score_list df['student'] = [pd.util.testing.rands(3) for i in range(len(score_list))] print(df) ''' score student 0 27 1ul 1 70 yuK 2 55 WWK 3 87 EU6 4 95 Vqn 5 98 KAf 6 55 QNT 7 61 HaE 8 86 aBo 9 76 MMa 10 85 Ctc 11 53 5BI 12 39 wBp 13 88 WMB 14 41 q5t 15 71 MjZ 16 64 nTc 17 94 Kyx 18 38 Rlh 19 94 2uV ''' # 使用cut方法进行分箱 print(pd.cut(df['score'], bins)) ''' 0 (0, 59] 1 (59, 70] 2 (0, 59] 3 (80, 100] 4 (80, 100] 5 (80, 100] 6 (0, 59] 7 (59, 70] 8 (80, 100] 9 (70, 80] 10 (80, 100] 11 (0, 59] 12 (0, 59] 13 (80, 100] 14 (0, 59] 15 (70, 80] 16 (59, 70] 17 (80, 100] 18 (0, 59] 19 (80, 100] Name: score, dtype: category Categories (4, interval[int64]): [(0, 59] < (59, 70] < (70, 80] < (80, 100]] ''' df['Categories'] = pd.cut(df['score'], bins) print(df) ''' score student Categories 0 27 1ul (0, 59] 1 70 yuK (59, 70] 2 55 WWK (0, 59] 3 87 EU6 (80, 100] 4 95 Vqn (80, 100] 5 98 KAf (80, 100] 6 55 QNT (0, 59] 7 61 HaE (59, 70] 8 86 aBo (80, 100] 9 76 MMa (70, 80] 10 85 Ctc (80, 100] 11 53 5BI (0, 59] 12 39 wBp (0, 59] 13 88 WMB (80, 100] 14 41 q5t (0, 59] 15 71 MjZ (70, 80] 16 64 nTc (59, 70] 17 94 Kyx (80, 100] 18 38 Rlh (0, 59] 19 94 2uV (80, 100] ''' # 但是这样的方法不是很适合阅读，可以使用cut方法中的label参数 # 为每个区间指定一个label df['Categories'] = pd.cut(df['score'], bins, labels=['low', 'middle', 'good', 'perfect']) print(df) ''' score student Categories 0 27 1ul low 1 70 yuK middle 2 55 WWK low 3 87 EU6 perfect 4 95 Vqn perfect 5 98 KAf perfect 6 55 QNT low 7 61 HaE middle 8 86 aBo perfect 9 76 MMa good 10 85 Ctc perfect 11 53 5BI low 12 39 wBp low 13 88 WMB perfect 14 41 q5t low 15 71 MjZ good 16 64 nTc middle 17 94 Kyx perfect 18 38 Rlh low 19 94 2uV perfect '''