how to figure out correct headers of an excel file programmatically while reading it from pandas?

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I have a list of excel files (.xlsx,.xls), I'm trying to get headers of each of these files after loaded.

Here I have taken a one excel file and loaded into pandas as.

pd.read_excel("sample.xlsx")

output is:

enter image description here

Here we would like to get an header information as per our requirement, here in the attached image the required headers are existed at index 8 as you can see in red color coded.

pd.read_excel('sample.xlsx',skiprows=9)

as we know now we have a correct header at 8 i can go back and specify in read_excel as skip_rows at 8 so that it reads from this index and headers will be appeared as.

enter image description here

How to handle this type of cases programmatically among a list of excel files where we don't know where the header is existed? in this case we have known that header is at 8. but what if we don't know this in other files.

Sample file can be downloaded for your ref: https://github.com/myamullaciencia/pg_diploma_ai_ml_uohyd/blob/main/sample_file.xlsx

openpyxl xlsxwriter pandas excel python
2 Answers

Use:

df = pd.read_excel('sample_file.xlsx')

#test all rows if previous row is only NaNs
m1 = df.shift(fill_value=0).isna().all(axis=1)
#test all rows if no NaNs
m2 = df.notna().all(axis=1)
#chain together and filter all next rows after first match
df = df[(m1 & m2).cummax()]

#set first row to columns names
df = df.set_axis(df.iloc[0].rename(None), axis=1).iloc[1:].reset_index(drop=True)

print (df)
   LN  FN          SSN        DOB        DOH Gender  Comp_2011 Comp_2010  \
0  Ax  Bx  000-00-0000   8/3/1800   1/1/1800   Male  384025.56    396317   
1  Er  Ds  000-00-0000   5/7/1800   7/1/1800   Male  382263.86    392474   
2  Po  Ch  000-00-0000   9/9/1800   1/1/1800   Male  406799.34    395677   
3  Rt  Da  000-00-0000  6/24/1800   7/1/1800   Male  395767.12    424093   
4  Yh  St  000-00-0000  3/15/1800   7/1/1800   Male  376936.58    373754   
5  Ws  Ra  000-00-0000  6/12/1800  7/10/1800   Male  425720.06    420927   

  Comp_2009 Allocation Group                  NRD  
0    360000             0.05  2022-09-01 00:00:00  
1    360000             0.05  2015-06-01 00:00:00  
2    360000             0.05  2013-01-01 00:00:00  
3    360000             0.05  2020-07-01 00:00:00  
4    360000                0  2013-01-01 00:00:00  
5    306960                0  2034-07-01 00:00:00
user_29010020

Use:

df = pd.read_excel('sample_file.xlsx', header = None)
d = df[df[5] == 'Gender'].index[0]
ndf = df[d+1:]
ndf.columns = df.loc[d].values
ndf.reset_index(drop = True)

Output:

Please note that the idea is that the Gender is always in the headers.

enter image description here

Based on your comment, you can use the following condition:

df[df.notna().sum(axis = 1)==11].index[0]
user_72816750
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