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

以下代码。我是Python的新手，并不能完全理解我如何更改语法（如果它是一个语法python 3问题）。有人遇到过吗？我认为这与升序=假[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

而不是

    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),之后插入False。）

False是sort()方法参数的值，其含义为“不按递增顺序”，i。即需要降序一个。因此，您需要使用sort()终止)方法参数列表，然后从第二个中划分DataFrame构造函数的第一个参数 em> ,。

[1:6]是DataFrame构造函数的第二个参数（用于结果框架的索引）

Answer 2

是的，你不能False[1:6] - False是bool ean，这意味着它只能是两件事之一（False或{{ 1}}）

只需将其更改为True即可解决您的问题。

False构造用于处理[1:6] s。所以如果你有，例如：

list

在python中，这被称为＆＃34;切片＆＃34;，并且非常有用。

您还可以执行以下操作：

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']

我鼓励您使用Jupyter Notebook进行播放 - 当然，请阅读documentation

TypeError：'bool'对象不是可订阅的Python 3

2 个答案: