dynamically skip top blank rows of excel in python pandas

Question

I am reading multiple sheets of an excel file using pandas in python. I have three cases

1. some sheet has data from row 1

o         


        

Answer 1

I would propose the following algorithm:

1. Read the whole table
2. Consider the first row that contains no missing values as a header
3. Drop all the rows above the header

This code works okay for me:

import pandas as pd
for sheet in range(3):
    raw_data = pd.read_excel('blank_rows.xlsx', sheetname=sheet, header=None)
    print(raw_data)
    # looking for the header row
    for i, row in raw_data.iterrows():
        if row.notnull().all():
            data = raw_data.iloc[(i+1):].reset_index(drop=True)
            data.columns = list(raw_data.iloc[i])
            break
    # transforming columns to numeric where possible
    for c in data.columns:
        data[c] = pd.to_numeric(data[c], errors='ignore')
    print(data)

It uses this toy data sample, based on your examples. From the raw dataframes

         0        1        2
0  Country  Company  Product
1       US      ABC      XYZ
2       US      ABD      XYY

         0        1        2
0      NaN      NaN      NaN
1      NaN      NaN      NaN
2      NaN      NaN      NaN
3  Country  Company  Product
4       US      ABC      XYZ
5       US      ABD      XYY

                                       0        1        2
0  Product summary table for East region      NaN      NaN
1                    Date: 1st Sep, 2016      NaN      NaN
2                                    NaN      NaN      NaN
3                                Country  Company  Product
4                                     US      ABC      XYZ
5                                     US      ABD      XYY

the script produces the same table

  Country Company Product
0      US     ABC     XYZ
1      US     ABD     XYY