通过python数组循环以匹配第二个数组中的多个条件，快速方法？

Question

我是一个初学Python的人，想知道是否有更快的方法来做这个代码，所以请原谅我的无知。我有2个Excel工作表：其中一个（结果）拥有大约30,000行唯一用户标识，然后我提出了30个问题列，下面的单元格为空。我的第二张（回答），有大约400,000行和3列。第一列有用户ID，第二栏有问题，第三栏有用户对应的每个问题的答案。我想要做的事实质上是一个索引匹配数组excel函数，我可以通过匹配用户标识和问题来填充表单1中的空白单元格以及来自表单2的答案。

现在我写了一段代码，但花了大约2个小时只处理从表1，我试图找出4列，如果我做这件事的方式是不采取完整的Numpy功能优势。

import pandas as pd 
import numpy as np 

# Need to take in data from 'answers' and merge it into the 'results' data 
# Will requiring matching the data based on 'id' in column 1 of 'answers' and the 
# 'question' in column 2 of 'answers' 
results = pd.read_excel("/Users/data.xlsx", 'Results') 
answers = pd.read_excel("/Users/data.xlsx", 'Answers') 

answers_array = np.array(answers) ######### 

# Create a list of questions being asked that will be matched to column 2 in answers. 
# Just getting all the questions I want 
column_headers = list(results.columns) 
formula_headers = []    ######### 
for header in column_headers: 
    formula_headers.append(header) 
del formula_headers[0:13] 

# Create an empty array with ids in which the 'merged' data will be fed into 
pre_ids = np.array(results['Id']) 
ids = np.reshape(pre_ids, (pre_ids.shape[0], 1)) 
ids = ids.astype(str) 

zero_array = np.zeros((ids.shape[0], len(formula_headers))) 
ids_array = np.hstack((ids, zero_array)) ########## 


for header in range(len(formula_headers)): 
    question_index = formula_headers[header] 
    for user in range(ids_array.shape[0]): 
     user_index = ids_array[user, 0] 
     location = answers_array[(answers_array[:, 0] == int(user_index)) & (answers_array[:, 1] == question_index)] 
     # This location formula is what I feel is messing everything up, 
     # or could be because of the nested loops 
     # If can't find the user id and question in the answers array 
     if location.size == 0: 
      ids_array[user][header + 1] = '' 
     else: 
      row_location_1 = np.where(np.all(answers_array == location[0], axis=1)) 
      row_location = int(row_location_1[0][0]) 
      ids_array[user][header + 1] = answers_array[row_location][2] 

print ids_array 

Answer 1

不是用第二个数据填充第一个数据帧，我们可以转向第二个数据帧。

answers.set_index(['id', 'question']).answer.unstack() 

如果你所需要的行和列是相同的results数据框，你可以，如果你有添加reindex_like方法

answers.set_index(['id', 'question']).answer.unstack().reindex_like(results) 

复制

cols = ['id', 'question'] 
answers.drop_duplicates(cols).set_index(cols).answer.unstack()