Develop Reference

Cannot read PDF Data into Sheets with Gspread-DataFrame

I want to read data from a PDF I downloaded using Tabula into Google Sheets, and when I transfer the data as it was read into Google Sheets, I get an error. I know the data I downloaded is dirty, but I wanted to clean it up in Google Sheets.
Downloading Data from Pdf Portion of Full Portion of Code
import tabula
import pandas as pd
file_path = 'TnPresidentbyCountyNov2016.pdf'
df = tabula.read_pdf(file_path, pages='all', multiple_tables='FALSE', stream='TRUE')
print (df)
[ Anderson 19,212 9,013 74 1,034 42 174 189 28 0 0.1
0 Bedford 11,486 3,395 25 306 8 47 75 5 0 0
1 Benton 4,716 1,474 12 83 13 11 14 2 0 0
2 Bledsoe 3,622 897 7 95 4 9 18 2 0 0
3 Blount 37,443 12,100 83 1,666 72 250 313 51 1 1
4 Bradley 29,768 7,070 66 1,098 44 143 210 29 1 1
5 Campbell 9,870 2,248 32 251 25 43 45 5 0 0
6 Cannon 4,007 1,127 8 106 7 18 29 3 0 0
7 Carroll 7,756 2,327 22 181 20 18 39 2 0 0
8 Carter 16,898 3,453 30 409 20 54 130 26 0 0
9 Cheatham 11,297 3,878 26 463 13 50 99 8 0 0
10 Chester 5,081 1,243 5 115 4 12 10 4 0 0
11 Claiborne 8,602 1,832 16 192 24 27 29 2 0 0
12 Clay 2,141 707 2 47 2 10 11 0 0 0
13 Cocke 9,791 1,981 21 211 19 27 59 2 0 2
14 Coffee 14,417 4,743 32 517 23 62 113 9 0 1
15 Crockett 3,982 1,303 7 76 3 8 13 1 0 0
16 Cumberland 20,413 5,202 37 471 26 53 99 17 0 1
17 Davidson 84,550 148,864 412 9,603 304 619 2,459 106 0 6
18 Decatur 3,588 894 5 70 4 8 16 2 0 0
19 DeKalb 5,171 1,569 10 117 6 29 49 0 0 0
20 Dickson 13,233 4,722 32 489 18 58 94 9 0 3
21 Dyer 10,180 2,816 19 193 13 27 48 3 0 0
22 Fayette 13,055 5,874 19 261 16 37 62 21 0 0
23 Fentress 6,038 1,100 10 107 14 11 37 1 0 0
24 Franklin 11,532 4,374 28 319 16 36 66 7 0 0
25 Gibson 13,786 5,258 26 305 18 36 66 8 0 0
26 Giles 7,970 2,917 16 162 11 11 41 1 0 0
27 Grainger 6,626 1,154 17 130 12 28 26 4 0 0
28 Greene 18,562 4,216 28 481 29 56 152 14 0 0
29 Grundy 3,636 999 11 80 3 13 19 0 0 0
30 Hamblen 15,857 4,075 30 443 27 73 93 8 0 0
31 Hamilton 78,733 55,316 147 5,443 138 349 1,098 121 0 0
32 Hancock 1,843 322 4 42 1 5 13 0 0 0
33 Hardeman 4,919 4,185 18 84 11 13 30 9 0 0
34 Hardin 8,012 1,622 15 134 22 48 96 0 0 0
35 Hawkins 16,648 3,507 31 397 12 52 91 7 0 3
36 Haywood 3,013 3,711 11 60 10 10 19 0 0 0
37 Henderson 8,138 1,800 13 172 9 27 39 1 0 0
38 Henry 9,508 3,063 18 223 15 27 60 4 0 0
39 Hickman 5,695 1,824 20 161 19 15 39 18 0 0
40 Houston 2,182 866 9 88 4 7 12 0 0 0
41 Humphreys 4,930 1,967 17 166 12 23 26 5 0 0
42 Jackson 3,236 1,129 2 62 1 7 17 1 0 0
43 Jefferson 14,776 3,494 34 497 22 76 115 8 0 1
44 Johnson 5,410 988 11 102 7 9 39 6 0 0
45 Knox 105,767 62,878 382 7,458 227 986 1,634 122 0 9
46 Lake 1,357 577 5 18 1 6 6 0 0 0, Lauderdale 4,884 3,056 14 87 13 10 14.1 \
0 Lawrence 12,420 2,821 21 271 13 36 77
1 Lewis 3,585 890 14 59 8 9 42
2 Lincoln 10,398 2,554 19 231 13 39 46
3 Loudon 17,610 4,919 41 573 22 77 87
Just a sample of the data I pulled. Again, not what I completely envisioned, but as a beginner coder, I wanted to clean it up in Sheets
HERE is an image of the PDF I was downloading data from.
Here is the link to download the PDF I am downloading data from
Now I want to import gspread and gpsread_dataframe to upload into a Google Sheet tab and here is where I am having problems.
EDIT: Whereas neither section included all of my coding, now the top and bottom portions include all of my coding done so far.
from oauth2client.service_account import ServiceAccountCredentials
import json
import gspread
SHEET_ID = '18xad0TbNGMPh8gUSIsEr6wNsFzcpKGbyUIQ-A4GQ1bo'
SHEET_NAME = '2016'
gc = gspread.service_account('waynetennesseedems.json')
spreadsheet = gc.open_by_key(SHEET_ID)
worksheet = spreadsheet.worksheet(SHEET_NAME)
from gspread_dataframe import set_with_dataframe
set_with_dataframe(worksheet, df, include_column_header='False')
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
/var/folders/zc/x2w76_4121g3gzfxybkz2q480000gn/T/ipykernel_44678/2784595029.py in <module>
----> 1 set_with_dataframe(worksheet, df, include_column_header='False')
/opt/anaconda3/lib/python3.9/site-packages/gspread_dataframe.py in set_with_dataframe(worksheet, dataframe, row, col, include_index, include_column_header, resize, allow_formulas, string_escaping)
260 # If header-related params are True, the values are adjusted
261 # to allow space for the headers.
--> 262 y, x = dataframe.shape
263 index_col_size = 0
264 column_header_size = 0
AttributeError: 'list' object has no attribute 'shape'
Does it have to do with how my Data was pulled from my PDF?

It seems that df is a list, first be sure to have downloaded the tabula-py module, secondly try to pass the parameter output_format='dataframe' to the tabula.read_pdf() function, like so:
import pandas as pd
import json
import gspread
from tabula.io import read_pdf
from oauth2client.service_account import ServiceAccountCredentials
from gspread_dataframe import set_with_dataframe
file_path = 'TnPresidentbyCountyNov2016.pdf'
df = read_pdf(file_path, output_format='dataframe', pages='all', multiple_tables='FALSE', stream='TRUE')
# print (df)
SHEET_ID = '18xad0TbNGMPh8gUSIsEr6wNsFzcpKGbyUIQ-A4GQ1bo'
SHEET_NAME = '2016'
gc = gspread.service_account('waynetennesseedems.json')
spreadsheet = gc.open_by_key(SHEET_ID)
worksheet = spreadsheet.worksheet(SHEET_NAME)
set_with_dataframe(worksheet, df, include_column_header='False')
Moreover I suggest you to take a look at the PEP8 style guide, to have a better idea on how to write a well formatted script.

How to group and sum rows by ID and subtract from group of rows with same ID? [python]

I have the following dataframe:
ID_A ID_B ID_C Value ID_B_Value_Sum
-----------------------------------------------
0 22 5 1 54 208
1 23 5 2 34 208
2 24 6 1 44 268
3 25 6 1 64 268
4 26 5 2 35 208
5 27 7 3 45 229
6 28 7 2 66 229
7 29 8 1 76 161
8 30 8 2 25 161
9 31 6 2 27 268
10 32 5 3 14 208
11 33 5 3 17 208
12 34 6 2 43 268
13 35 6 2 53 268
14 36 8 1 22 161
15 37 7 3 65 229
16 38 7 1 53 229
17 39 8 2 23 161
18 40 8 3 15 161
19 41 6 3 37 268
20 42 5 2 54 208
Each row contains a unique "ID_A", while different rows can have the same "ID_B" and "ID_C". Each row corresponds to its own "Value", where this "Value" can be the same between different rows. The "ID_B_Value_Sum" column contains the sums of all values from the "Value" column for all rows containing the same "ID_B". Calculating this sum is straightforward with python and pandas.
What I want to do is, for each row, take the "ID_B_Value_Sum" column, but subtract all values corresponding to rows containing the same "ID_C", exclusive of the target row. For example, taking "ID_B" = 6, we see the sum of all the "Value" values from this "ID_B" = 6 group = 268, as shown in all corresponding rows in the "ID_B_Value_Sum" column. Now, two of the rows in this group contain "ID_C" = 1, three rows in this group contain "ID_C" = 2, and one row in this group contain "ID_C" = 3. Starting with row 2, with "ID_C" = 1, this means taking the corresponding "ID_B_Value_Sum" value and subtracting the "Value" values from all other rows containing both "ID_B" = 6 and "ID_C = 1", exclusive of the target row. And so for row 2 I take 268 - 64 = 204. And for another example, for row 4, this means 208 - 34 - 54 = 120. And another example, for row 7, this means 161 - 22 = 139. These new values will go in a new "Value_Sum_New" column for each row.
And so I want to produce the following output dataframe:
ID_A ID_B ID_C Value ID_B_Value_Sum Value_Sum_New
---------------------------------------------------------------
0 22 5 1 54 208 XX
1 23 5 2 34 208 XX
2 24 6 1 44 268 204
3 25 6 1 64 268 XX
4 26 5 2 35 208 120
5 27 7 3 45 229 XX
6 28 7 2 66 229 XX
7 29 8 1 76 161 139
8 30 8 2 25 161 XX
9 31 6 2 27 268 XX
10 32 5 3 14 208 XX
11 33 5 3 17 208 XX
12 34 6 2 43 268 XX
13 35 6 2 53 268 XX
14 36 8 1 22 161 XX
15 37 7 3 65 229 XX
16 38 7 1 53 229 XX
17 39 8 2 23 161 XX
18 40 8 3 15 161 XX
19 41 6 3 37 268 XX
20 42 5 2 54 208 XX
What I am having trouble with conceptualizing is how to, for each row, group together all columns with the same "ID_B" and then group together all of those rows and sub-group all rows with the same "ID_C" and subtract their sum from the "Value" of the target row, but still including the "Value" from the target row, to create the final "Value_Sum_New". It seems like so many actions and sub-actions to take and I am confused with how to approach this in a simple and streamlined manner, as I am confused with how to organize and order the workflow. How might I approach calculating this sum in python?

IIUC, you need:
df['Value_Sum_New'] = (df['ID_B_Value_Sum']
- df.groupby(['ID_B', 'ID_C'])['Value'].transform('sum')
+ df['Value']
)
output:
ID_A ID_B ID_C Value ID_B_Value_Sum Value_Sum_New
0 22 5 1 54 208 208
1 23 5 2 34 208 119
2 24 6 1 44 268 204
3 25 6 1 64 268 224
4 26 5 2 35 208 120
5 27 7 3 45 229 164
6 28 7 2 66 229 229
7 29 8 1 76 161 139
8 30 8 2 25 161 138
9 31 6 2 27 268 172
10 32 5 3 14 208 191
11 33 5 3 17 208 194
12 34 6 2 43 268 188
13 35 6 2 53 268 198
14 36 8 1 22 161 85
15 37 7 3 65 229 184
16 38 7 1 53 229 229
17 39 8 2 23 161 136
18 40 8 3 15 161 161
19 41 6 3 37 268 268
20 42 5 2 54 208 139
explanation
As you said, computing a sum per group is easy in pandas. You can actually compute ID_B_Value_Sum with:
df['ID_B_Value_Sum'] = df.groupby('ID_B')['Value'].transform('sum')
Now we do the same for groups of ID_B + ID_C, we subtract it from ID_B_Value_Sum, and as we want to exclude only the other rows in the group, we add back the row Value itself.

how to split an integer value from one column to two columns in text file using pandas or numpy (python)

I have a text file which has a number of integer values like this.
20180701 20180707 52 11 1 2 4 1 0 0 10 7 1 3 1 0 4 5 2
20180708 20180714 266 8 19 3 2 9 7 25 20 17 12 9 9 27 34 54 11
20180715 20180721 654 52 34 31 20 16 12 25 84 31 38 37 38 69 66 87 14
20180722 201807281017 110 72 46 52 29 29 22 204 41 46 51 57 67 82 92 17
20180729 201808041106 276 37 11 87 20 10 8 284 54 54 72 38 49 41 53 12
20180805 20180811 624 78 19 15 55 16 8 9 172 15 31 35 38 47 29 36 21
20180812 20180818 488 63 17 7 26 10 9 7 116 17 14 39 31 34 27 64 7
20180819 20180825 91 4 7 0 4 5 1 3 16 3 4 5 10 10 7 11 1
20180826 20180901 49 2 2 1 0 4 0 1 2 0 1 4 8 2 6 6 10
I have to make a file by merging several files like this but you guys can see a problem with this data.
In 4 and 5 lines, the first values, 1017 and 1106, right next to period index make a problem.
When I try to read these two lines, I always have had this result.
It came out that first values in first column next to index columns couldn't recognized as first values themselves.
In [14]: fw.iloc[80,:]
Out[14]:
3 72.0
4 46.0
5 52.0
6 29.0
7 29.0
8 22.0
9 204.0
10 41.0
11 46.0
12 51.0
13 57.0
14 67.0
15 82.0
16 92.0
17 17.0
18 NaN
Name: (20180722, 201807281017), dtype: float64
I tried to make it correct with indexing but failed.
The desirable result is,
In [14]: fw.iloc[80,:]
Out[14]:
2 1017.0
3 110.0
4 72.0
5 46.0
6 52.0
7 29.0
8 29.0
9 22.0
10 204.0
11 41.0
12 46.0
13 51.0
14 57.0
15 67.0
16 82.0
17 92.0
18 17.0
Name: (20180722, 201807281017), dtype: float64
How can I solve this problem?
+
I used this code to read this file.
fw = pd.read_csv('warm_patient.txt', index_col=[0,1], header=None, delim_whitespace=True)

A better fit for this would be pandas.read_fwf. For your example:
df = pd.read_fwf(filename, index_col=[0,1], header=None, widths=2*[10]+17*[4])
I don't know if the column widths can be inferred for all your data or need to be hardcoded.

One possibility would be to manually construct the dataframe, this way we can parse the text by splitting the values every 4 characters.
from textwrap import wrap
import pandas as pd
def read_file(f_name):
data = []
with open(f_name) as f:
for line in f.readlines():
idx1 = line[0:8]
idx2 = line[10:18]
points = map(lambda x: int(x.replace(" ", "")), wrap(line.rstrip()[18:], 4))
data.append([idx1, idx2, *points])
return pd.DataFrame(data).set_index([0, 1])

It could be made somewhat more efficient (in particular if this is a particularly long text file), but here's one solution.
fw = pd.read_csv('test.txt', header=None, delim_whitespace=True)
for i in fw[pd.isna(fw.iloc[:,-1])].index:
num_str = str(fw.iat[i,1])
a,b = map(int,[num_str[:-4],num_str[-4:]])
fw.iloc[i,3:] = fw.iloc[i,2:-1]
fw.iloc[i,:3] = [fw.iat[i,0],a,b]
fw = fw.set_index([0,1])
The result of print(fw) from there is
2 3 4 5 6 7 8 9 10 11 12 13 14 15 \
0 1
20180701 20180707 52 11 1 2 4 1 0 0 10 7 1 3 1 0
20180708 20180714 266 8 19 3 2 9 7 25 20 17 12 9 9 27
20180715 20180721 654 52 34 31 20 16 12 25 84 31 38 37 38 69
20180722 20180728 1017 110 72 46 52 29 29 22 204 41 46 51 57 67
20180729 20180804 1106 276 37 11 87 20 10 8 284 54 54 72 38 49
20180805 20180811 624 78 19 15 55 16 8 9 172 15 31 35 38 47
20180812 20180818 488 63 17 7 26 10 9 7 116 17 14 39 31 34
20180819 20180825 91 4 7 0 4 5 1 3 16 3 4 5 10 10
20180826 20180901 49 2 2 1 0 4 0 1 2 0 1 4 8 2
16 17 18
0 1
20180701 20180707 4 5 2.0
20180708 20180714 34 54 11.0
20180715 20180721 66 87 14.0
20180722 20180728 82 92 17.0
20180729 20180804 41 53 12.0
20180805 20180811 29 36 21.0
20180812 20180818 27 64 7.0
20180819 20180825 7 11 1.0
20180826 20180901 6 6 10.0
Here's the result of the print after applying your initial solution of fw = pd.read_csv('test.txt', index_col=[0,1], header=None, delim_whitespace=True) for comparison.
2 3 4 5 6 7 8 9 10 11 12 13 14 \
0 1
20180701 20180707 52 11 1 2 4 1 0 0 10 7 1 3 1
20180708 20180714 266 8 19 3 2 9 7 25 20 17 12 9 9
20180715 20180721 654 52 34 31 20 16 12 25 84 31 38 37 38
20180722 201807281017 110 72 46 52 29 29 22 204 41 46 51 57 67
20180729 201808041106 276 37 11 87 20 10 8 284 54 54 72 38 49
20180805 20180811 624 78 19 15 55 16 8 9 172 15 31 35 38
20180812 20180818 488 63 17 7 26 10 9 7 116 17 14 39 31
20180819 20180825 91 4 7 0 4 5 1 3 16 3 4 5 10
20180826 20180901 49 2 2 1 0 4 0 1 2 0 1 4 8
15 16 17 18
0 1
20180701 20180707 0 4 5 2.0
20180708 20180714 27 34 54 11.0
20180715 20180721 69 66 87 14.0
20180722 201807281017 82 92 17 NaN
20180729 201808041106 41 53 12 NaN
20180805 20180811 47 29 36 21.0
20180812 20180818 34 27 64 7.0
20180819 20180825 10 7 11 1.0
20180826 20180901 2 6 6 10.0

Reading XML (NIST .n42 file) and data extraction

I have a xml file that I need to extract data from 'channelData' in the below xml.
from xml.dom import minidom
xmldoc = minidom.parse('Annex_B_n42.xml')
itemlist = xmldoc.getElementsByTagName('ChannelData')
print(len(itemlist))
print(itemlist[0].attributes['compressionCode'].value)
for s in itemlist:
print(s.attributes['compressionCode'].value)
Which doesn't return the data, just the value 'None'.
I also tried another approach from an another example:
import xml.etree.ElementTree as ET
root = ET.parse('Annex_B_n42.xml').getroot()
#value=[]
for type_tag in root.findall('Spectrum'):
value = type_tag.get('id')
print(value)
print("data from file " +str(value))
This did not work at all and value is not being populated. I really don't understand how to parse xml.
Here is the xml file
<?xml version="1.0"?>
<?xml-model href="http://physics.nist.gov/N42/2011/N42/schematron/n42.sch" type="application/xml" schematypens="http://purl.oclc.org/dsdl/schematron"?>
<RadInstrumentData xmlns="http://physics.nist.gov/N42/2011/N42" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://physics.nist.gov/N42/2011/N42 file:///d:/Data%20Files/ANSI%20N42%2042/V2/Schema/n42.xsd" n42DocUUID="d72b7fa7-4a20-43d4-b1b2-7e3b8c6620c1">
<RadInstrumentInformation id="RadInstrumentInformation-1">
<RadInstrumentManufacturerName>RIIDs R Us</RadInstrumentManufacturerName>
<RadInstrumentModelName>iRIID</RadInstrumentModelName>
<RadInstrumentClassCode>Radionuclide Identifier</RadInstrumentClassCode>
<RadInstrumentVersion>
<RadInstrumentComponentName>Software</RadInstrumentComponentName>
<RadInstrumentComponentVersion>1.1</RadInstrumentComponentVersion>
</RadInstrumentVersion>
</RadInstrumentInformation>
<RadDetectorInformation id="RadDetectorInformation-1">
<RadDetectorCategoryCode>Gamma</RadDetectorCategoryCode>
<RadDetectorKindCode>NaI</RadDetectorKindCode>
</RadDetectorInformation>
<EnergyCalibration id="EnergyCalibration-1">
<CoefficientValues>-21.8 12.1 6.55e-03</CoefficientValues>
</EnergyCalibration>
<RadMeasurement id="RadMeasurement-1">
<MeasurementClassCode>Foreground</MeasurementClassCode>
<StartDateTime>2003-11-22T23:45:19-07:00</StartDateTime>
<RealTimeDuration>PT60S</RealTimeDuration>
<Spectrum id="RadMeasurement-1Spectrum-1" radDetectorInformationReference="RadDetectorInformation-1" energyCalibrationReference="EnergyCalibration-1">
<LiveTimeDuration>PT59.61S</LiveTimeDuration>
<ChannelData compressionCode="None">
0 0 0 22 421 847 1295 1982 2127 2222 2302 2276
2234 1921 1939 1715 1586 1469 1296 1178 1127 1047 928 760
679 641 542 529 443 423 397 393 322 272 294 227
216 224 208 191 189 163 167 173 150 137 136 129
150 142 160 159 140 103 90 82 83 85 67 76
73 84 63 74 70 69 76 61 49 61 63 65
58 62 48 75 56 61 46 56 43 37 55 47
50 40 38 54 43 41 45 51 32 35 29 33
40 44 33 35 20 26 27 17 19 20 16 19
18 19 18 20 17 45 55 70 62 59 32 30
21 23 10 9 5 13 11 11 6 7 7 9
11 4 8 8 14 14 11 9 13 5 5 6
10 9 3 4 3 7 5 5 4 5 3 6
5 0 5 6 3 1 4 4 3 10 11 4
1 4 2 11 9 6 3 5 5 1 4 2
6 6 2 3 0 2 2 2 2 0 1 3
1 1 2 3 2 4 5 2 6 4 1 0
3 1 2 1 1 0 1 0 0 2 0 1
0 0 0 1 0 0 0 0 0 0 0 2
0 0 0 1 0 1 0 0 2 1 0 0
0 0 1 3 0 0 0 1 0 1 0 0
0 0 0 0
</ChannelData>
</Spectrum>
</RadMeasurement>
</RadInstrumentData>

You can use BeautifulSoup to get the channeldata tag value like following
from bs4 import BeautifulSoup
with open('Annex_B_n42.xml') as f:
xml = f.read()
bs_obj = BeautifulSoup(xml)
print(bs_obj.find_all("channeldata")[0].text)
That will print you
' 0 0 0 22 421 847 1295 1982 2127 2222 2302 2276 2234 1921 1939 1715 1586 1469 1296 1178 1127 1047 928 760 679 641 542 529 443 423 397 393 322 272 294 227 216 224 208 191 189 163 167 173 150 137 136 129 150 142 160 159 140 103 90 82 83 85 67 76 73 84 63 74 70 69 76 61 49 61 63 65 58 62 48 75 56 61 46 56 43 37 55 47 50 40 38 54 43 41 45 51 32 35 29 33 40 44 33 35 20 26 27 17 19 20 16 19
18 19 18 20 17 45 55 70 62 59 32 30 21 23 10 9 5 13 11 11 6 7 7 9 11 4 8 8 14 14 11 9 13 5 5 6 10 9 3 4 3 7 5 5 4 5 3 6 5 0 5 6 3 1 4 4 3 10 11 4 1 4 2 11 9 6 3 5 5 1 4 2 6 6 2 3 0 2 2 2 2 0 1 3 1 1 2 3 2 4 5 2 6 4 1 0 3 1 2 1 1 0 1 0 0 2 0 1 0 0 0 1 0 0 0 0 0 0 0 2 0 0 0 1 0 1 0 0 2 1 0 0 0 0 1 3 0 0 0 1 0 1 0 0 0 0 0 0 '

Try this:
import xml.etree.ElementTree as ET
root = ET.parse('Annex_B_n42.xml').getroot()
elems = root.findall(".//*[#compressionCode='None']")
print(elems[0].text)

Issue combining columns in Dataframe?

I have the following dataframe:
Obj BIT BIT BIT GAS GAS GAS OIL OIL OIL
Date
2007-01-03 18 7 0 184 35 2 52 14 0
2007-01-09 43 3 0 249 35 2 68 11 1
2007-01-16 60 6 0 254 35 5 72 13 1
2007-01-23 69 11 1 255 43 2 81 6 0
2007-01-30 74 8 0 263 29 4 69 9 0
2007-02-06 78 6 1 259 34 2 79 6 0
2007-02-14 76 9 1 263 24 2 70 10 1
2007-02-20 85 7 0 241 20 6 72 4 0
2007-02-27 79 6 0 242 35 3 68 7 0
2007-03-06 68 14 0 225 26 2 57 10 1
How can I sum each of the 9 columns into 3 columns. "BIT","GAS" and "OIL"
This is the code for the dataframe which basically just gets me a cross section from a larger df I want:
ABrigsA = ndfAB.xs(['BIT','GAS','OIL'],axis=1)
Any suggestions?

Assuming that you want to sum similarly-named columns, you can use groupby [tutorial docs]:
>>> df.groupby(level=0, axis='columns').sum()
Obj BIT GAS OIL
Date
2007-01-03 25 221 66
2007-01-09 46 286 80
2007-01-16 66 294 86
2007-01-23 81 300 87
2007-01-30 82 296 78
2007-02-06 85 295 85
2007-02-14 86 289 81
2007-02-20 92 267 76
2007-02-27 85 280 75
2007-03-06 82 253 68