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我试图交错（不规则地）两个数据帧（每个包含两列 x1 和 x2），这取决于每个数据帧中 x1-x1 和 x2-x2 之间的条件关系。使用单个 for 循环和每个数据帧的计数器，我想将 x1/x2 对增量添加到最终列表/数据帧中，具体取决于条件（4x if-and 条件）。df_out 是一个不规则拼接的单个数据帧，包含两列 x1/x2，以及所有 x1/x2 对。也许for循环中的双计数器有问题？（我的实际 df 包含 30 多列和 1000 行...给出的示例 df）

toolz interleave 不起作用，因为拼接不规则。if-conditional 中的 if-conditional 缩进不起作用，迭代在某些时候失败，但不知道为什么。

dfDown 输入数据帧 1 dfDown 输入数据帧1

dfUp 输入数据帧 2 dfUp 输入数据帧2

df_desired_out 是所需的输出 df df_desired_out 是所需的输出 df

import pandas as pd
import numpy as np

dataDown = {'x1':(0,0,0,0,0,0,0), 'x2':(2,10,20,25,33,47,57)}
dataUp = {'x1':(2,2,2,2,2,2), 'x2':(7,13,24,30,36,39)}

dfDown = pd.DataFrame(dataDown)
dfUp = pd.DataFrame(dataUp)

totalUpDown = len(dfUp) + len(dfDown)   # total number of x1/x2 pairs
countUpDown = np.arange(totalUpDown)   # to be used in for loop
allUpDown = []   # empty list
countUp = 0   # up data counter to be used in for loop
countDown = 0   # down data counter to be used in for loop

for count in countUpDown:   # single for loop containing 4 exclusive conditionals, and two 'counters'

    #   this conditional should write a dfDown x1/x2 pair into list allUpDown, and increment down-counter by 1
    if dfDown['x1'][countDown] < dfUp['x1'][countUp] and dfDown['x2'][countDown] < dfUp['x2'][countUp]:
        combi = pd.DataFrame([[[dfDown['x1'][countDown]], dfDown['x2'][countDown]]],
                             columns = ['x1', 'x2'])
        allUpDown.append(combi)
        countDown +=1

    #   this conditional should write a dfUp x1/x2 pair into list allUpDown, and increment up-counter by 1 
    if dfDown['x1'][countDown] < dfUp['x1'][countUp] and dfDown['x2'][countDown] > dfUp['x2'][countUp]:
        combi = pd.DataFrame([[[dfUp['x1'][countUp]], dfUp['x2'][countUp]]],
                             columns = ['x1', 'x2'])
        allUpDown.append(combi)
        countUp +=1

    #   this conditional should write a dfDown x1/x2 pair into list allUpDown, and increment down-counter by 1 
    if dfDown['x1'][countDown] > dfUp['x1'][countUp] and dfDown['x2'][countDown] < dfUp['x2'][countUp]:
        combi = pd.DataFrame([[[dfDown['x1'][countDown]], dfDown['x2'][countDown]]],
                             columns = ['x1', 'x2'])
        allUpDown.append(combi)
        countDown +=1

    #   this conditional should write a dfUp x1/x2 pair into list allUpDown, and increment up-counter by 1 
    if dfDown['x1'][countDown] > dfUp['x1'][countUp] and dfDown['x2'][countDown] > dfUp['x2'][countUp]:
        combi = pd.DataFrame([[[dfUp['x1'][countUp]], dfUp['x2'][countUp]]],
                             columns = ['x1', 'x2'])
        allUpDown.append(combi)
        countUp +=1

# Build the interleaved dataframe from the list of all x1/x2 pairs
df_out = pd.concat(allUpDown, ignore_index = True)    
df_out

df_out 应该类似于此处显示的 df_desired_out：

desired_out = {'x1':(0,2,0,2,0,2,0,2,0,2,2,0,0), 'x2':(2,7,10,13,20,24,25,30,33,36,39,47,57)}
df_desired_out = pd.DataFrame(desired_out)
df_desired_out
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