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

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:

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.

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:

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

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

CodePudding user response：

Use:

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

m1 = df.shift(fill_value=0).isna().all(axis=1)
m2 = df.notna().all(axis=1)
df = df[(m1 & m2).cummax()]

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