Lets say I imported a really messy data from a PFD and I´m cleaning it. I have something like this: Name Type Date other1 other2 other3 Name1 '' '' Type1 '' Date1 Name2 '' '' '' Type2 Date2 Name3 '' '' Type3 Date3 '' Name4 '' Type4 '' '' Date4 Name5 Type5 '' Date5 '' '' And so on. As

How to delete empty spaces from pandas DataFrame rows until first populated field?

Lets say I imported a really messy data from a PFD and I´m cleaning it. I have something like this:

Name	Type	Date	other1	other2	other3
Name1	”	”	Type1	”	Date1
Name2	”	”	”	Type2	Date2
Name3	”	”	Type3	Date3	”
Name4	”	Type4	”	”	Date4
Name5	Type5	”	Date5	”	”

And so on. As you can see, Type is always before date on each row, but I basically need to delete all ” (currently empty strings on the DataFrame) while moving everything to the left so they align with their respective Type and Date columns. Additionally, there’s more columns to the right with the same problem, but for structural reasons I cant remove ALL ”, the solution I´m looking for would just move ‘everything to the left’ so to speak (as it happens with pd.shift).

I appreciate your help.

Answer

What worked for me was:

while '' in df['Type'].unique():
    for i,row in df.iterrows():
        if row['Type'] == '':
            df.iloc[i, 1:] = df.iloc[i, 1:].shift(-1, fill_value='')

And the same for next column

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Answer