Python：迭代不同长度的数据帧，并使用重复值计算新值

编辑： 我意识到我的示例设置不正确，更正的版本如下：

我有两个数据帧：

df1 = pd.DataFrame({'x values': [11, 12, 13], 'time':[1,2.2,3.5})
df2 = pd.DataFrame({'x values': [11, 21, 12, 43], 'time':[1,2.1,2.6,3.1})

我需要做的是迭代这两个数据帧，并计算一个新值，即df1和df2中x值的比率。困难在于这些数据帧的长度不同

如果我只想计算这两个函数中的值，我知道我可以使用zip，甚至map之类的东西。不幸的是，我不想删除任何值。相反，我需要能够比较两个帧之间的时间列，以确定是否将上一时间的值复制到下一时间段的计算

例如，我会计算第一个比率：

df1["x values"][0]/df2["x values"][0]

然后在第二次更新中，我检查下一次发生的更新，在本例中是对df2的更新，因此df1[“time”] 对于第三个，我将看到df1[“time”]>df2[“time”]，因此第三个计算将是：

df1["x values"][1]/df2["x values"][1]

如果两个数据帧中的时间相等，则应使用这两个值从同一“位置”计算比率

---

等等。我很困惑是否可以使用lambda函数或itertools之类的东西来执行。我做了一些尝试，但大多数都出现了错误。任何帮助都将不胜感激。

您可以及时合并两个数据帧，然后计算比率

new_df = df1.merge(df2, on = 'time', how = 'outer')
new_df['ratio'] = new_df['x values_x'] / new_df['x values_y']

你得到

    time    x values_x  x values_y  ratio
0   1       11          11          1.000000
1   2       12          21          0.571429
2   2       12          12          1.000000
3   3       13          43          0.302326

---

这就是我最后要做的。希望这有助于澄清我的问题是什么。此外，如果有人能想出一个更具蟒蛇风格的方法来做这件事，我将非常感谢您的反馈

 #add a column indicating which 'type' of dataframe it is
 df1['type']=pd.Series('type1',index=df1.index)
 df2['type']=pd.Series('type2',index=df2.index)

 #concatenate the dataframes
 df = pd.concat((df1, df2),axis=0, ignore_index=True)

 #sort by time
 df = df.sort_values(by='time').reset_index()

 #we create empty arrays in order to track records
 #in a way that will let us compute ratios
 x1 = []
 x2 = []

 #we will iterate through the dataframe line by line
 for i in range(0,len(df)):

     #if the row contains data from df1
     if df["type"][i] == "type1":

         #we append the x value for that type
         x1.append(df[df["type"]=="type1"]["x values"][i])

         #if the x2 array contains exactly 1 value
         if len(x2) == 1:
              #we add it to match the number of x1
              #that we have recorded up to that point
              #this is useful if one time starts before the other
              for j in range(1, len(x1)-1):  
                  x2.append(x2[0])

         #if the x2 array contains more than 1 value
         #add a copy of the previous x2 record to correspond
         #to the new x1 record
         if len(x2) > 0:
              x2.append(x2[len(x2)-1])

    #if the row contains data from df2

    if df["type"][i] == "type2":

         #we append the x value for that type
         x2.append(df[df["type"]=="type2"]["x values"][i])

         #if the x1 array contains exactly 1 value
         if len(x1) == 1:
              #we add it to match the number of x2
              #that we have recorded up to that point
              #this is useful if one time starts before the other
              for j in range(1, len(x2)-1):  
                  x1.append(x2[0])

         #if the x1 array contains more than 1 value
         #add a copy of the previous x1 record to correspond
         #to the new x2 record
         if len(x1) > 0:
              x1.append(x1[len(x1)-1])

 #combine the records
 new__df = pd.DataFrame({'Type 1':x1, 'Type 2': x2})
 #compute the ratio
 new_df['Ratio'] = new_df['x1']/f_df['x2']

---

问题是我简化了我的示例，所以时间实际上并不在两个帧之间对齐-它们有不同的日期。那么，找到比率的基础是什么？我基本上只需要得到任何“更新”值的比率。所以如果你认为时间是线性的，任何时候我有一个与新时间段相关联的x值，我需要更新比率计算。所以我在跟踪比率随时间的变化。我现在意识到我的示例设置有点不正确。@mildlyillogical你应该提供一个可复制的示例我可能已经找到了解决方案，我将在稍后发布。

 #add a column indicating which 'type' of dataframe it is
 df1['type']=pd.Series('type1',index=df1.index)
 df2['type']=pd.Series('type2',index=df2.index)

 #concatenate the dataframes
 df = pd.concat((df1, df2),axis=0, ignore_index=True)

 #sort by time
 df = df.sort_values(by='time').reset_index()

 #we create empty arrays in order to track records
 #in a way that will let us compute ratios
 x1 = []
 x2 = []

 #we will iterate through the dataframe line by line
 for i in range(0,len(df)):

     #if the row contains data from df1
     if df["type"][i] == "type1":

         #we append the x value for that type
         x1.append(df[df["type"]=="type1"]["x values"][i])

         #if the x2 array contains exactly 1 value
         if len(x2) == 1:
              #we add it to match the number of x1
              #that we have recorded up to that point
              #this is useful if one time starts before the other
              for j in range(1, len(x1)-1):  
                  x2.append(x2[0])

         #if the x2 array contains more than 1 value
         #add a copy of the previous x2 record to correspond
         #to the new x1 record
         if len(x2) > 0:
              x2.append(x2[len(x2)-1])

    #if the row contains data from df2

    if df["type"][i] == "type2":

         #we append the x value for that type
         x2.append(df[df["type"]=="type2"]["x values"][i])

         #if the x1 array contains exactly 1 value
         if len(x1) == 1:
              #we add it to match the number of x2
              #that we have recorded up to that point
              #this is useful if one time starts before the other
              for j in range(1, len(x2)-1):  
                  x1.append(x2[0])

         #if the x1 array contains more than 1 value
         #add a copy of the previous x1 record to correspond
         #to the new x2 record
         if len(x1) > 0:
              x1.append(x1[len(x1)-1])

 #combine the records
 new__df = pd.DataFrame({'Type 1':x1, 'Type 2': x2})
 #compute the ratio
 new_df['Ratio'] = new_df['x1']/f_df['x2']