Output different precision by column with pandas.DataFrame.to_csv()?

Change the type of column "vals" prior to exporting the data frame to a CSV file

df_data['vals'] = df_data['vals'].map(lambda x: '%2.1f' % x)

df_data.to_csv(outfile, index=False, header=False, float_format='%11.6f')

The more current version of hknust's first line would be:

df_data['vals'] = df_data['vals'].map(lambda x: '{0:.1}'.format(x))

To print without scientific notation:

df_data['vals'] = df_data['vals'].map(lambda x: '{0:.1f}'.format(x)) 

You can use round method for dataframe before saving the dataframe to the file.

df_data = df_data.round(6)
df_data.to_csv('myfile.dat')

This question is a bit old, but I'd like to contribute with a better answer, I think so:

formats = {'lats': '{:10.5f}', 'lons': '{:.3E}', 'vals': '{:2.1f}'}

for col, f in formats.items():
    df_data[col] = df_data[col].map(lambda x: f.format(x))

I tried with the solution here, but it didn't work for me, I decided to experiment with previus solutions given here combined with that from the link above.

You can do this with to_string. There is a formatters argument where you can provide a dict of columns names to formatters. Then you can use some regexp to replace the default column separators with your delimiter of choice.