如何将两个不同字典中的数据帧连接到python中的一个新数据框架中？

Question

这是我的示例代码

dataset_current=dataset_seq['Motor_Current_Average']
dataset_consistency=dataset_seq['Consistency_Average']

#technique with non-overlapping the values(for current)
dataset_slide=dataset_current.tolist()
from window_slider import Slider
import numpy
list = numpy.array(dataset_slide)
bucket_size = 336
overlap_count = 0
slider = Slider(bucket_size,overlap_count)
slider.fit(list)      
empty_dictionary = {}
count = 0
while True:
  count += 1
  window_data = slider.slide()
  empty_dictionary['df_current%s'%count] = window_data
  empty_dictionary['df_current%s'%count] =pd.DataFrame(empty_dictionary['df_current%s'%count])
  empty_dictionary['df_current%s'%count]= empty_dictionary['df_current%s'%count].rename(columns={0: 'Motor_Current_Average'})
  if slider.reached_end_of_list(): break
  locals().update(empty_dictionary)


#technique with non-overlapping the values(for consistency)
dataset_slide_consistency=dataset_consistency.tolist()
list = numpy.array(dataset_slide_consistency)
slider_consistency = Slider(bucket_size,overlap_count)
slider_consistency.fit(list)      
empty_dictionary_consistency = {}
count_consistency = 0
while True:
  count_consistency += 1
  window_data_consistency = slider_consistency.slide()
  empty_dictionary_consistency['df_consistency%s'%count_consistency] = window_data_consistency
  empty_dictionary_consistency['df_consistency%s'%count_consistency] =pd.DataFrame(empty_dictionary_consistency['df_consistency%s'%count_consistency])
  empty_dictionary_consistency['df_consistency%s'%count_consistency]= empty_dictionary_consistency['df_consistency%s'%count_consistency].rename(columns={0: 'Consistency_Average'})
  if slider_consistency.reached_end_of_list(): break
  locals().update(empty_dictionary_consistency)
import pandas as pd
output_current ={}
increment = 0
while True:
   increment +=1
   output_current['dataframe%s'%increment] = pd.concat([empty_dictionary_consistency['df_consistency%s'%count_consistency],empty_dictionary['df_current%s'%count]],axis=1)

我的问题是，我有两个字典，每个字典中包含79个数据帧，即"empty_dictionary_consistency“和"empty_dictionary”。我想为其中的每一个创建一个新的数据框架，以便将empty_dictionary_consistency中的df1与来自empty_dictionary .So的df1连接起来，从从empty_dictionary_consistency到df1从empty_dictionary到df79从empty_dictionary_consistency到df79从empty_dictionary连接开始。我尝试使用while循环来增加它，但是没有显示任何输出。

output_current ={}
increment = 0
while True:
   increment +=1
   output_current['dataframe%s'%increment] = pd.concat([empty_dictionary_consistency['df_consistency%s'%count_consistency],empty_dictionary['df_current%s'%count]],axis=1) 

有人能帮我吗？我该怎么做呢。

Answer 1

我现在不在我的电脑附近，所以我不能测试代码，但问题似乎在索引中。在最后一个循环中，在每次迭代中都会增加一个名为“增量”的变量，但您仍然要将前面循环中的索引用于要连接的字典。尝试将用于索引所有字典的变量更改为“增量”。还有一件事-我看不出这个循环什么时候会结束？

我的意思是：

length = len(empty_dictionary_consistency)
increment = 0 
while increment < length: 
    increment +=1
    output_current['dataframe%s'%increment] = pd.concat([empty_dictionary_consistency['df_consistency%s'%increment],empty_dictionary['df_current%s'%increment]],axis=1) 

在遍历字典时，应该使用一个变量作为所有三个字典中的索引。一旦在循环中不使用Slider对象，就必须在第一个字典结束时停止它。