Q:

Is it possible to Skip Blank Lines in a Dataframe? If Yes then how I can do this

I am trying to run this code
num = df_out.drop_duplicates(subset=['Name', 'No.']).groupby.(['Name']).size()

But when I do I get this error:
ValueError: not enough values to unpack (expected 2, got 0)

If we think about my dataframe(df_out) as an excel file I do have blank cells but no full column or full row is blank. I needed to skip the blank lines to run the code without changing the dataframe's structure. 
Is this possible?
Thank you

A:

Consider using df.dropna(). It is uses to remove rows that contains NA. See https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.dropna.html for more information.
At first, you probably want your "blank cells" to be converted to NA value, so they can be dropped by dropna(). This can be done using various methods, notably df.replace(r'\s+', pandas.np.nan, regex=True). If your "blank cells" are all empty strings, or fixed strings equal to some value s, you can directly use (first case) df.replace('', pandas.np.nan) or (second case) df.replace(s, pandas.np.nan).

