I have a data file from columns A-G like below but when I am reading it with pd.read_csv(‘data.csv’) it prints an extra unnamed column at the end for no reason. colA ColB colC colD colE colF colG Unnamed: 7 44 45 26 26 40 26 46 NaN 47 16 38 47 48 22 37 NaN … Read more
How can I pick out the difference between to columns of the same name in two dataframes?
I mean I have dataframe A with a column named X and dataframe B with column named X, if i do pd.merge(A, B, on=['X']), i’ll get the common X values of A and B, but how can i get the “non-common” ones?
I have the following for loop:
Without using groupby how would I filter out data without NaN?
I am trying to extract strings from a DF in pandas dataframe and the source strings are in a list from which I have to match. I tried using a df.str.extract(list1) but i got an error of unhashable types i guess i the way I compare the list to the DF is not correct
consider the pd.Series s
I would like to use pd.write_csv to write “filename” (with headers) if “filename” doesn’t exist, otherwise to append to “filename” if it exists. If I simply use command:
I’m currently using pandas to read an Excel file and present its sheet names to the user, so he can select which sheet he would like to use. The problem is that the files are really big (70 columns x 65k rows), taking up to 14s to load on a notebook (the same data in a CSV file is taking 3s).
I have a dataframe, something like:
I have a dataframe containing a single column of IDs and all other columns are numerical values for which I want to compute z-scores. Here’s a subsection of it: