将can消息对象转换为pandas数据帧

Question

1. 我的日志按以下格式存储在列表中：

log = [can.Message（timestamp = 1551734911.0096931，arbitration_id = 0x14ff0065，extended_id = True，channel = 2，dlc = 8，data = [0xf4,0x7c，0x89,0x35,0x28,0xf，0xea，0xe]），可以。消息（timestamp = 1551734911.0102572，arbitration_id = 0x14ff0165，extended_id = True，channel = 2，dlc = 8，data = [0x40,0x14,0x0,0x36,0xd0,0x39,0x60,0x22]），can.Message（timestamp = 1551734911.0108252 ，arbitration_id = 0x14ff0265，extended_id = True，channel = 2，dlc = 8，data = [0x80,0x35,0x9,0xf，0x8c，0x0,0x0,0x0]），can.Message（timestamp = 1551734911.0114133，arbitration_id = 0x14fef100， extended_id = True，channel = 2，dlc = 8，data = [0xff，0xff，0xff，0xff，0xff，0xff，0xff，0xff]）]

目标：将日志转换为如下所示的pandas数据框：

timestamp   arbitration_id  extended_id channel dlc data

0 1551734911.00969 0x14ff0065 TRUE 2 8 [0xf4,0x7c，0x89,0x35,0x28,0xf，0xea，0xe] 1 1551734911.01025 = 0x14ff0165 TRUE 2 8 [0x40,0x14,0x0,0x36,0xd0,0x39,0x60,0x22] 2 1551734911.01082 0x14ff0265 TRUE 2 8 [0x80,0x35,0x9,0xf，0x8c，0x0,0x0,0x0]

我尝试了以下代码：

log = can.BLFReader(filename)
log = list(log)
df = pd.DataFrame(log)
print(df.head(5))

输出：

                                               0

0时间戳：1551734911.009693 ID：14ff0065 ... 1时间戳：1551734911.010257 ID：14ff0165 ... 2时间戳：1551734911.010825 ID：14ff0265 ... 3时间戳：1551734911.011413 ID：14fef100 ... 4时间戳：1551734911.011973 ID：14ff0068 ...

Answer 1

import pandas as pd
import can

print(pd.__version__)

log = [can.Message(timestamp=1551734911.0096931, arbitration_id=0x14ff0065, extended_id=True, channel=2, dlc=8, data=[0xf4, 0x7c, 0x89, 0x35, 0x28, 0xf, 0xea, 0xe]), can.Message(timestamp=1551734911.0102572, arbitration_id=0x14ff0165, extended_id=True, channel=2, dlc=8, data=[0x40, 0x14, 0x0, 0x36, 0xd0, 0x39, 0x60, 0x22]), can.Message(timestamp=1551734911.0108252, arbitration_id=0x14ff0265, extended_id=True, channel=2, dlc=8, data=[0x80, 0x35, 0x9, 0xf, 0x8c, 0x0, 0x0, 0x0]), can.Message(timestamp=1551734911.0114133, arbitration_id=0x14fef100, extended_id=True, channel=2, dlc=8, data=[0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff])]

df_data = [{'timestamp':m.timestamp, 'arbitration_id':m.arbitration_id, 'extended_id':m.is_extended_id, 'channel':m.channel, 'dlc': m.dlc, 'data':m.data} for m in log]
df = pd.DataFrame(df_data)