类型错误：“bool”对象不可下标 Python 3

Question

我收到以下错误：

TypeError                                 Traceback (most recent call last)
C:\Users\levanim\Desktop\Levani Predictive\cosinesimilarity1.py in <module>()
     39 
     40 for i in meowmix_nearest_neighbors.index:
---> 41     top_ten = pd.DataFrame(similarity_matrix.ix[i,]).sort([i], 
ascending=False[1:6]).index.values
     42     meowmix_nearest_neighbors.ix[i,:] = top_ten
     43 
TypeError: 'bool' object is not subscriptable 

对于以下代码。我是 Python 新手，不太清楚如何更改语法（如果它是 python 3 语法问题）。有人遇到这种情况吗？我认为这与 ascending=False[1:6] 部分有关，并且花了一些时间将我的头撞在墙上。希望这是一个简单的修复，但还不够了解

import numpy as np
import pandas as pd
from scipy.spatial.distance import cosine


enrollments = pd.read_csv(r'C:\Users\levanim\Desktop\Levani 
Predictive\smallsample.csv')

meowmix = enrollments.fillna(0)

meowmix.ix[0:5,0:5]

def getCosine(x,y) :
    cosine = np.sum(x*y) / (np.sqrt(np.sum(x*x)) * np.sqrt(np.sum(y*y)))
    return cosine

print("done creating cosine function")

similarity_matrix = pd.DataFrame(index=meowmix.columns, 
columns=meowmix.columns)
similarity_matrix = similarity_matrix.fillna(np.nan)

similarity_matrix.ix[0:5,0:5]
print("done creating a matrix placeholder")


for i in similarity_matrix.columns:
    for j in similarity_matrix.columns:
        similarity_matrix.ix[i,j] = getCosine(meowmix[i].values, 
meowmix[j].values)

print("done looping through each column and filling in placeholder with 
cosine similarities")


meowmix_nearest_neighbors = pd.DataFrame(index=meowmix.columns,
                                        columns=['top_'+str(i+1) for i in 
range(5)])

meowmix_nearest_neighbors = meowmix_nearest_neighbors.fillna(np.nan)

print("done creating a nearest neighbor placeholder for each item")


for i in meowmix_nearest_neighbors.index:
    top_ten = pd.DataFrame(similarity_matrix.ix[i,]).sort([i], 
ascending=False[1:6]).index.values
    meowmix_nearest_neighbors.ix[i,:] = top_ten

print("done creating the top 5 neighbors for each item")

meowmix_nearest_neighbors.head()

Answer 1

代替

    top_ten = pd.DataFrame(similarity_matrix.ix[i,]).sort([i], 
ascending=False[1:6]).index.values

使用

    top_ten = pd.DataFrame(similarity_matrix.ix[i,]).sort([i], 
ascending=False), [1:6]).index.values

),（即在 后面插入False。）

False是方法参数的值，sort()含义为“不按升序排列”，即需要降序排列。因此，您需要使用 终止sort()方法参数列表)，然后使用 分隔构造函数的第一个参数DataFrame和第二个,参数。

[1:6]是DataFrame 构造函数的第二个参数（用于结果帧的索引）

Answer 2

是的，你不能这样做False[1:6]-False是一个boolean，意味着它只能是两件事之一 (False或True)

只需将其更改为False即可解决您的问题。

该[1:6]构造用于与lists 一起使用。因此，如果您有，例如：

theList = [ "a","b","c","d","e","f","g","h","i","j","k","l" ] 

print theList      # (prints the whole list)
print theList[1]   # "b"    
print theList[1:6] # ['b', 'c', 'd', 'e', 'f']

在 python 中，这称为“切片”，并且非常有用。

您还可以执行以下操作：

print theList[6:] # everything in the list after "f" 
print theList[:6] # everything in the list before "f", but including f

我鼓励您使用Jupyter Notebook来玩这个- 当然，请阅读文档