Reputation: 313
I have the below DataFrame
As you can see, ItemNo 1 is duplicated three times, and each column has a value corresponding to it.
I am looking for a method to check against all columns, and if they match then put Price, Sales, and Stock as one entry, not three.
Any help will be greatly appreciated.
Upvotes: 0
Views: 908
Reputation: 825
Simply remove all the NaN instances and redefine the column names
df = df1.apply(lambda x: pd.Series(x.dropna().values), axis=1)
df.columns = ['ItemNo','Category','SIZE','Model','Customer','Week Date','<New col name>']
For converging to one row, you can use groupby
like this
df.groupby('ItemNo', as_index=False).first()
Upvotes: 1