split dataframe and write new csv's based on loop

Question

I have a df like this:

Allotment   Year    NDVI     A_Annex    Bachelor
A_Annex     1984    1.0      0.40       0.60
A_Annex     1984    1.5      0.56       0.89
A_Annex     1984    2.0      0.78       0.76
A_Annex     1985    3.4      0.89       0.54
A_Annex     1985    1.6      0.98       0.66
A_Annex     1986    2.5      1.10       0.44
A_Annex     1986    1.7      0.87       0.65

and I want to write each column to a new dataframe, and a subsequent csv based on the column name. So I want a new csv for the contents of each column.

so far I have done this:

outpath='C:\'
for column in df:
    x=pd.DataFrame(df[column])
    outpath=outpath + column + '.csv'
    x.to_csv(outpath)

but column contains every single column name, when I only want to write to separate ones. My desired file names would be something like Allotment.csv, Year.csv, NDVI.csv, A_Annex.csv, Bachelor.csv

I won't add an answer since you already accepted one, but you could do this in one line: for column in df: df[column].to_csv( 'C:\' + column + '.csv') — JohnE
– JohnE, Commented Feb 22, 2016 at 1:42
ah, good to know and much simpler, thank you. Just curious, if I wanted two send to columns at once to a new csv how would I do this? e.g. If I wanted to send Year and A_Annex together? — Stefano Potter
– Stefano Potter, Commented Feb 22, 2016 at 1:46

Paul · Accepted Answer · 2016-02-22 01:35:47Z

1

This line is collecting all the column names:

outpath = outpath + column + '.csv'

because it is run on every loop of the for loop

This works:

outpath='C:\'
for column in df:
     x=pd.DataFrame(df[column])
     outfile=outpath + column + '.csv'
     x.to_csv(outfile)

Notice the loop variable name is changed from outpath to a new var outfile which will prevent the filenames from being aggregated and mangled.

I noted the .csv files have two columns, the row number and the value. If you don't want the row numbers, use x.to_csv(outfile, index=False)

answered Feb 22, 2016 at 1:35

Paul

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