Python 如何以数据帧的形式读写包含额外信息的表，以及如何从这些信息中添加新列

我有一个从StringIO生成的类似文件的对象，它是一个表，表的前面有几行信息（请参见下面从#TIMESTAMP开始的内容）

我想使用来自#Timestamp的信息“Date”、“UTCoffset-Time（Substraction）”和来自#GLOBAL#u SUMMARY的信息“ZenAngle”向现有表中添加额外的列

我使用pd.read_csv命令来读取它，但它只在我跳过前8行（包括我需要的信息）时起作用。当我试图将下面的对象作为dataframe导入时，还报告了错误“TypeError:data参数不能是迭代器”

#TIMESTAMP
UTCOffset,Date,Time
+00:30:32,2011-09-05,08:32:21

#GLOBAL_SUMMARY
Time,IntACGIH,IntCIE,ZenAngle,MuValue,AzimAngle,Flag,TempC,O3,Err_O3,SO2,Err_SO2,F324
08:32:21,7.3576,52.758,59.109,1.929,114.427,000000,24,291,1,,,91.9

#GLOBAL
Wavelength,S-Irradiance,Time
290.0,0.000e+00
290.5,0.000e+00
291.0,4.380e-06
291.5,2.234e-05
292.0,2.102e-05
292.5,2.204e-05
293.0,2.453e-05
293.5,2.256e-05
294.0,3.088e-05
294.5,4.676e-05
295.0,3.384e-05
295.5,3.582e-05
296.0,4.298e-05
296.5,3.774e-05
297.0,4.779e-05
297.5,7.399e-05
298.0,9.214e-05
298.5,1.080e-04
299.0,2.143e-04
299.5,3.180e-04
300.0,3.337e-04
300.5,4.990e-04
301.0,8.688e-04
301.5,1.210e-03
302.0,1.133e-03

我认为您可以首先使用创建3个数据帧：

import pandas as pd
import io

temp=u"""#TIMESTAMP
UTCOffset,Date,Time
+00:30:32,2011-09-05,08:32:21

#GLOBAL_SUMMARY
Time,IntACGIH,IntCIE,ZenAngle,MuValue,AzimAngle,Flag,TempC,O3,Err_O3,SO2,Err_SO2,F324
08:32:21,7.3576,52.758,59.109,1.929,114.427,000000,24,291,1,,,91.9

#GLOBAL
Wavelength,S-Irradiance,Time
290.0,0.000e+00
290.5,0.000e+00
291.0,4.380e-06
291.5,2.234e-05
292.0,2.102e-05
292.5,2.204e-05
293.0,2.453e-05
293.5,2.256e-05
294.0,3.088e-05
294.5,4.676e-05
295.0,3.384e-05
295.5,3.582e-05
296.0,4.298e-05
296.5,3.774e-05
297.0,4.779e-05
297.5,7.399e-05
298.0,9.214e-05
298.5,1.080e-04
299.0,2.143e-04
299.5,3.180e-04
300.0,3.337e-04
300.5,4.990e-04
301.0,8.688e-04
301.5,1.210e-03
302.0,1.133e-03
"""

---

我添加了解决方案的第一步，但我不清楚如何将

df2

和

df3

中的列连接到

df1

。你能再解释一下吗？也是三,。缺少

df1

列，是否正确？

df1 = pd.read_csv(io.StringIO(temp),
                 skiprows=9)

print (df1)
    Wavelength  S-Irradiance  Time
0        290.0      0.000000   NaN
1        290.5      0.000000   NaN
2        291.0      0.000004   NaN
3        291.5      0.000022   NaN
4        292.0      0.000021   NaN
5        292.5      0.000022   NaN
6        293.0      0.000025   NaN
7        293.5      0.000023   NaN
8        294.0      0.000031   NaN
9        294.5      0.000047   NaN
10       295.0      0.000034   NaN
11       295.5      0.000036   NaN
12       296.0      0.000043   NaN
13       296.5      0.000038   NaN
14       297.0      0.000048   NaN
15       297.5      0.000074   NaN
16       298.0      0.000092   NaN
17       298.5      0.000108   NaN
18       299.0      0.000214   NaN
19       299.5      0.000318   NaN
20       300.0      0.000334   NaN
21       300.5      0.000499   NaN
22       301.0      0.000869   NaN
23       301.5      0.001210   NaN
24       302.0      0.001133   NaN

df2 = pd.read_csv(io.StringIO(temp),
                  skiprows=1,
                  nrows=1)

print (df2)
   UTCOffset        Date      Time
0  +00:30:32  2011-09-05  08:32:21

df3 = pd.read_csv(io.StringIO(temp),
                  skiprows=5,
                  nrows=1)

print (df3)
       Time  IntACGIH  IntCIE  ZenAngle  MuValue  AzimAngle  Flag  TempC   O3  \
0  08:32:21    7.3576  52.758    59.109    1.929    114.427     0     24  291   

   Err_O3  SO2  Err_SO2  F324  
0       1  NaN      NaN  91.9