df.to_csv структурирование вывода

попробуй это:

 cols = ['date', 'startTime', 'endTime', 'day', 'count', 'unique'] df = pd.read_csv(fn, header=None, names=cols) df.groupby(['date'])[['count','unique']].agg({'count':'median','unique':'median'}).round().to_csv('d:/temp/out.csv', header=None) 

out.csv:

 2004-01-05,764,17044.0 2004-01-06,757,17262.0 

Тебе нужно:

 import pandas as pd import io temp=u"""2004-01-05,21:00:00,22:00:00,Mon,16553,783 2004-01-05,22:00:00,23:00:00,Mon,18944,790 2004-01-05,23:00:00,00:00:00,Mon,17534,750 2004-01-06,00:00:00,01:00:00,Tue,17262,747 2004-01-06,01:00:00,02:00:00,Tue,19072,777 2004-01-06,02:00:00,03:00:00,Tue,18275,785 2004-01-06,03:00:00,04:00:00,Tue,13589,757 2004-01-06,04:00:00,05:00:00,Tue,16053,735 2004-01-06,05:00:00,06:00:00,Tue,11440,636""" #after testing replace io.StringIO(temp) to filename df = pd.read_csv(io.StringIO(temp), parse_dates=[0], names=['date', 'startTime', 'endTime', 'day', 'count', 'unique']) print (df) outf = df.groupby('date')['count', 'unique'].median().round().astype(int) print (outf) count unique date 2004-01-05 17534 783 2004-01-06 16658 752 outf.to_csv('date_med.csv', header=False) 

Сроки :

 In [20]: %timeit df.groupby('date')['count', 'unique'].median().round().astype(int) The slowest run took 4.47 times longer than the fastest. This could mean that an intermediate result is being cached. 100 loops, best of 3: 2.67 ms per loop In [21]: %timeit df.groupby(['date'])[['count','unique']].agg({'count':'median','unique':'median'}).round().astype(int) The slowest run took 4.44 times longer than the fastest. This could mean that an intermediate result is being cached. 100 loops, best of 3: 3.64 ms per loop