如何阅读和写入包含额外信息的数据框并从信息中添加新列

Question

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#Timestamp中的“Date”、“UTCoffset-Time（Substraction）”和#GLOBALŠu SUMMARY中的“ZenAngle”信息向现有表中添加额外的列

我使用pd.read\u csv命令来读取它，但它只在我跳过前8行时起作用，其中包括我需要的信息。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

Answer 1

我认为您可以首先使用^{}来创建3DataFrames：

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
"""

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