您应该使用len(edus==1)而不是np.sum，并且与其他类似

df = pd.read_csv("assests/NISPUF17.csv", index_col=0)你必须用下面的行替换这一行，因为你写的资产拼写错误。
df = pd.read_csv("assets/NISPUF17.csv", index_col=0)

正确的代码是：

import pandas as pd
import numpy as np

def proportion_of_education():
    
    df = pd.read_csv("assets/NISPUF17.csv", index_col = 0)
    
    EDUS = df['EDUC1']
    edus = np.sort(EDUS.values)
    
    poe = {"less than high school": 0,
        "high school": 0,
        "more than high school but not college": 0,
        "college": 0}
    n = len(edus)
    
    poe["less than high school"] = np.sum(edus == 1)/n
    poe["high school"] = np.sum(edus == 2)/n
    poe["more than high school but not college"] = np.sum(edus == 3)/n
    poe["college"] = np.sum(edus == 4)/n
    
    return poe
    raise NotImplementedError()

import pandas as pd import numpy as np
def proportion_of_education（）：

# your code goes here
# YOUR CODE HERE

df = pd.read_csv(r'assets/NISPUF17.csv',index_col=0)
EDUC1 = df['EDUC1']
educ1 = np.sort(EDUC1.values)
poe = {"less than high school": 0,
    "high school": 0,
    "more than high school but not college": 0,
    "college": 0}
total = len(educ1)
poe['less than high school'] = len(df[df['EDUC1'] == 1])/total
poe['high school'] = len(df[df['EDUC1'] == 2])/total
poe['more than high school but not college'] = len(df[df['EDUC1'] == 3])/total
poe['college'] = len(df[df['EDUC1'] == 4])/total
return poe
raise NotImplementedError()

proportion_of_education（）

在问题下面的第一个单元格中键入此内容以读取数据

import pandas as pd
df=pd.read_csv('assets/NISPUF17.csv',index_col=0)
df

在下一个单元格

def proportion_of_education():
    # your code goes here
    cat=pd.value_counts(df['EDUC1'])
    total=sum(cat)
    a_dict=dict(cat)
    s=pd.Series(a_dict)
    f=lambda x: x/total
    s=s.apply(f)
    s_dict=dict(s)
    s_dict['less than high school'] = s_dict.pop(1)
    s_dict['high school'] = s_dict.pop(2)
    s_dict['more than high school but not college'] = s_dict.pop(3)
    s_dict['college'] = s_dict.pop(4)
    return s_dict

    raise NotImplementedError()

要检查，请在下一个单元格中键入以下内容

proportion_of_education()

这个问题的最后一个单元格在下面

assert type(proportion_of_education())==type({}), "You must return a dictionary."
assert len(proportion_of_education()) == 4, "You have not returned a dictionary with four items in it."
assert "less than high school" in proportion_of_education().keys(), "You have not returned a dictionary with the correct keys."
assert "high school" in proportion_of_education().keys(), "You have not returned a dictionary with the correct keys."
assert "more than high school but not college" in proportion_of_education().keys(), "You have not returned a dictionary with the correct keys."
assert "college" in proportion_of_education().keys(), "You have not returned a dictionary with the correct keys."

在“EDUC 1”列中，将有1 2 3 4
=〉1 -小于高中
=〉高中
=〉3 -超过高中但不是大学
=〉4 -学院
lambda函数用于计算所有类别的比例
.pop（）方法用于将1重命名为小于高中，其他类别也是如此

import pandas as pd
    import numpy as np
    def proportion_of_education():
    # your code goes here
    # YOUR CODE HERE
        df=pd.read_csv('assets/NISPUF17.csv',index_col=0)
        poe = {"less than high school": len(df[df['EDUC1'] == 1])/len(df),
"high school": len(df[df['EDUC1'] == 2])/len(df),
"more than high school but not college": len(df[df['EDUC1'] == 3])/len(df),
"college": len(df[df['EDUC1'] == 4])/len(df)}
      return poe

    proportion_of_education()

简单的解决方案

def proportion_of_education():
    import pandas as pd

    df = pd.read_csv("NISPUF17.csv")

    df = df["EDUC1"]
    lessThanHighSchool = (df[df == 1].count()) / df.count()
    highScool = (df[df == 2].count()) / df.count()
    moreThanHighSchoolNotCollage = (df[df == 3].count()) / df.count()
    collage = (df[df == 4].count()) / df.count()

    dictionary = {"less than high school ":lessThanHighSchool ,
        "high school":highScool,
        "more than high school but not college":moreThanHighSchoolNotCollage,
        "college":collage
        }

    return dictionary

简单的解决方法是：
df=pd.read_csv（“assets/NISPUF17.csv”，index_col=0）

df['EDUC1 Label'] =df["EDUC1"].map({
    1:"less than high school",
    2:"high school",
    3:"more than high school but not college",
    4:"college"})
total=df["EDUC1 Label"].value_counts(normalize=True).to_dict()
return total

def proportion_of_education():
  import pandas as pd
  df = pd.read_csv("assets/NISPUF17.csv", index_col= 0)
  EDUC1 = df['EDUC1']
  less = len(df[df['EDUC1']== 1]['EDUC1'])/len(EDUC1)
  high = len(df[df['EDUC1']== 2]['EDUC1'])/len(EDUC1)
  more = len(df[df['EDUC1']== 3]['EDUC1'])/len(EDUC1)
  college = len(df[df['EDUC1']== 4]['EDUC1'])/len(EDUC1)
  poe = {"less than high school":less,
  "high school":high,
  "more than high school but not college":more,
  "college":college}
  return poe
  raise NotImplementedError()
proportion_of_education()