import pandas as pd from numpy import nan as NA columns = ['chinese','nath','english'] data = pd.DataFrame([[81.5,76.5,73.5],[71.,68,NA],[71.,68,NA],[NA,NA,NA],[NA,96.5,93.5]],columns=columns) data data_new = data.drop_duplicates().dropna(thresh=2) data_new data_new['chinese'].fillna(mean_chinese, inplace=True) data_new['english'].fillna(mean_english, inplace=True) data_new['mean'] = data_new.mean(axis=1) data_new bins = [0,60,75,85,100] labels = ['failed','median','great','excellent'] data_new['mean'] = pd.cut(data_new['mean'],bins=bins,labels=labels) print(data_new['mean'])这段代码有错吗 需要怎么改

这段代码没有语法错误，但是其中的变量 mean_chinese 和 mean_english 没有定义，会导致 fillna() 方法执行错误。另外，最后一行的 print() 方法输出只有等级标签，没有对应的学生信息，需要进一步修改。以下是修改后的代码：

import pandas as pd
from numpy import nan as NA

columns = ['chinese', 'math', 'english'] # 将 nath 改为 math
data = pd.DataFrame([[81.5, 76.5, 73.5], [71., 68, NA], [71., 68, NA], [NA, NA, NA], [NA, 96.5, 93.5]], columns=columns)

data_new = data.drop_duplicates().dropna(thresh=2)

mean_chinese = data_new['chinese'].mean() # 计算中文成绩平均值
mean_english = data_new['english'].mean() # 计算英语成绩平均值
data_new['chinese'].fillna(mean_chinese, inplace=True)
data_new['english'].fillna(mean_english, inplace=True)

data_new['mean'] = data_new.mean(axis=1)

bins = [0, 60, 75, 85, 100]
labels = ['failed', 'median', 'great', 'excellent']
data_new['mean_label'] = pd.cut(data_new['mean'], bins=bins, labels=labels)

print(data_new) # 输出所有学生的成绩和等级标签

修改后的代码中，先将 nath 改为 math，然后计算出中文和英语成绩的平均值，将缺失值填充为平均值。接着计算每个学生的平均成绩，使用 pd.cut() 方法转化为等级标签，并将结果存储在 mean_label 列中。最后使用 print() 方法输出所有学生的成绩和等级标签。