在python中根据时间对两个日志数据数组进行排序

我想创建一个基于两个数据帧的新矩阵。第一个阵列df1每秒收集数据，第二个阵列df2每隔30分钟收集数据。理想情况下，来自df2的数据将添加到df1中，以表示正确的时间序列。在实践中，数据是完全不规则的，如果某些传感器被激活，数据会随机出现。例如：

df1 = [['10-11', '14:21:01', '65'],
       ['10-11', '14:21:02', '55'],
       ['10-11', '14:21:03', '26'],
       ['12-11', '17:29:58', '89'],
       ['12-11', '17:29:59', '12'],
       ['12-11', '17:30:00', '65'],
       ['12-11', '17:30:01', '3'],
       ['12-11', '17:30:02', '66'],
       ['12-11', '17:30:03', '971']]

df2 = [['10-11', '14:30', '9.9','112'],
       ['10-11', '15:00', '7.8','165'],
       ['12-11', '17:00', '6.1','154'],
       ['12-11', '17:30', '6.2','165'],
       ['12-11', '18:00', '6.5','170']]

我希望以一种方式对数据进行排序，例如，df1中数据在14:00:00-14:29:59之间的行将在每行中添加“9.9”和“112”的值，这与df2中的相关值相对应。其想法是，生成的数据帧将显示如下数组：

finaldf = [['10-11', '14:21:01', '65', '9.9','112'],
           ['10-11', '14:21:02', '55', '9.9','112'],
           ['10-11', '14:21:03', '26', '9.9','112'],
           ['12-11', '17:29:58', '89', '6.2','165'],
           ['12-11', '17:29:59', '12', '6.2','165'],
           ['12-11', '17:30:00', '65', '6.5','170'],
           ['12-11', '17:30:01', '3', '6.5','170'],
           ['12-11', '17:30:02', '66', '6.5','170'],
           ['12-11', '17:30:03', '971', '6.5','170']]

---

如果这让人觉得很复杂，我很抱歉，如果您能帮我解决这个问题或给我指出正确的方向，我将不胜感激。

您可以在

df1

中创建新列，并通过迭代

df2

中的行（对于大型数据帧可能非常慢）和使用

datetime

过滤时间来填充它们。从你的例子

import pandas as pd
import datetime as dt

df1 = [['10-11', '14:21:01', '65'],
       ['10-11', '14:21:02', '55'],
       ['10-11', '14:21:03', '26'],
       ['12-11', '17:29:58', '89'],
       ['12-11', '17:29:59', '12'],
       ['12-11', '17:30:00', '65'],
       ['12-11', '17:30:01', '3'],
       ['12-11', '17:30:02', '66'],
       ['12-11', '17:30:03', '971']]

df2 = [['10-11', '14:30', '9.9','112'],
       ['10-11', '15:00', '7.8','165'],
       ['12-11', '17:00', '6.1','154'],
       ['12-11', '17:30', '6.2','165'],
       ['12-11', '18:00', '6.5','170']]

# convert to pandas DataFrame and name columns
df1 = pd.DataFrame(df1, columns=['date', 'time', 'val1'])
df2 = pd.DataFrame(df2, columns=['date', 'time', 'val2', 'val3'])

finaldf = df1
finaldf['val2'] = -1  # initialize to -1
finaldf['val3'] = -1  # initialize to -1
for i, d, t, v2, v3 in df2.itertuples():
    # get the starting time by subtracting 30 minutes
    tmin = (dt.datetime.strptime(t, '%H:%M') + dt.timedelta(minutes=-30)).time().strftime("%H:%M:%S")
    tmax = t + ":00"  # add seconds to end of string

    # filter df1 by matching date and time range
    index = (finaldf['date'] == d) & (finaldf['time'] >= tmin) & (finaldf['time'] < tmax)
    finaldf.loc[index, 'val2'] = v2
    finaldf.loc[index, 'val3'] = v3

---

请注意，在这段代码中，我将时间字符串转换为

datetime

，并调用

time（）

函数来获取时间。更好的方法可能是将整个日期和时间转换为

datetime.datetime

，并将

timedelta

应用于整个事件。（我无法从您的数据推断它是MM-DD还是DD-MM。）

您可以在创建日期时间索引后使用

pd.merge\u asof

：

df_1 = pd.DataFrame(df1)

df_2 = pd.DataFrame(df2)

df_1 = df_1.set_index(pd.to_datetime(df_1[0]+' ' +df_1[1],format='%m-%d %H:%M:%S'))
df_2 = df_2.set_index(pd.to_datetime(df_2[0]+ ' ' +df_2[1],format='%m-%d %H:%M'))

arr_out = pd.merge_asof(df_1, df_2, 
                        right_index=True, left_index=True, 
                        direction='forward', suffixes=('','_r'))\
            .drop(['0_r','1_r'], 1).values.tolist()

arr_out

输出：

[['10-11', '14:21:01', '65', '9.9', '112'],
 ['10-11', '14:21:02', '55', '9.9', '112'],
 ['10-11', '14:21:03', '26', '9.9', '112'],
 ['12-11', '17:29:58', '89', '6.2', '165'],
 ['12-11', '17:29:59', '12', '6.2', '165'],
 ['12-11', '17:30:00', '65', '6.2', '165'],
 ['12-11', '17:30:01', '3', '6.5', '170'],
 ['12-11', '17:30:02', '66', '6.5', '170'],
 ['12-11', '17:30:03', '971', '6.5', '170']]

[['10-11', '14:21:01', '65', '9.9', '112'],
 ['10-11', '14:21:02', '55', '9.9', '112'],
 ['10-11', '14:21:03', '26', '9.9', '112'],
 ['12-11', '17:29:58', '89', '6.2', '165'],
 ['12-11', '17:29:59', '12', '6.2', '165'],
 ['12-11', '17:30:00', '65', '6.2', '165'],
 ['12-11', '17:30:01', '3', '6.5', '170'],
 ['12-11', '17:30:02', '66', '6.5', '170'],
 ['12-11', '17:30:03', '971', '6.5', '170']]