Structure the output - python-2.7

Question

I am trying to get a proper structured output into a csv.
Input:
00022d9064bc,1073260801,1073260803,819251,440006
00022d9064bc,1073260803,1073260810,819213,439954
00904b4557d3,1073260803,1073261920,817526,439458
00022de73863,1073260804,1073265410,817558,439525
00904b14b494,1073260804,1073262625,817558,439525
00022d1406df,1073260807,1073260809,820428,438735
00022d9064bc,1073260801,1073260803,819251,440006
00022dba8f51,1073260801,1073260803,819251,440006
00022de1c6c1,1073260801,1073260803,819251,440006
003065f30f37,1073260801,1073260803,819251,440006
00904b48a3b6,1073260801,1073260803,819251,440006
00904b83a0ea,1073260803,1073260810,819213,439954
00904b85d3cf,1073260803,1073261920,817526,439458
00904b14b494,1073260804,1073265410,817558,439525
00904b99499c,1073260804,1073262625,817558,439525
00904bb96e83,1073260804,1073265163,817558,439525
00904bf91b75,1073260804,1073263786,817558,439525
Code:
import pandas as pd
from datetime import datetime,time
import numpy as np
fn = r'00_Dart.csv'
cols = ['UserID','StartTime','StopTime', 'gps1', 'gps2']
df = pd.read_csv(fn, header=None, names=cols)
df['m'] = df.StopTime + df.StartTime
df['d'] = df.StopTime - df.StartTime
# 'start' and 'end' for the reporting DF: `r`
# which will contain equal intervals (1 hour in this case)
start = pd.to_datetime(df.StartTime.min(), unit='s').date()
end = pd.to_datetime(df.StopTime.max(), unit='s').date() + pd.Timedelta(days=1)
# building reporting DF: `r`
freq = '1H' # 1 Hour frequency
idx = pd.date_range(start, end, freq=freq)
r = pd.DataFrame(index=idx)
r['start'] = (r.index - pd.datetime(1970,1,1)).total_seconds().astype(np.int64)
# 1 hour in seconds, minus one second (so that we will not count it twice)
interval = 60*60 - 1
r['LogCount'] = 0
r['UniqueIDCount'] = 0
for i, row in r.iterrows():
# intervals overlap test
# https://en.wikipedia.org/wiki/Interval_tree#Overlap_test
# i've slightly simplified the calculations of m and d
# by getting rid of division by 2,
# because it can be done eliminating common terms
u = df[np.abs(df.m - 2*row.start - interval) < df.d + interval].UserID
r.ix[i, ['LogCount', 'UniqueIDCount']] = [len(u), u.nunique()]
r['Day'] = pd.to_datetime(r.start, unit='s').dt.weekday_name.str[:3]
r['StartTime'] = pd.to_datetime(r.start, unit='s').dt.time
r['EndTime'] = pd.to_datetime(r.start + interval + 1, unit='s').dt.time
#df.to_csv((r[r.LogCount > 0])'example.csv')
#print(r[r.LogCount > 0]) -- This gives the correct count and unique count but I want to write the output in a structure.
print (r['StartTime'], ['EndTime'], ['Day'], ['LogCount'], ['UniqueIDCount'])
Output: This is the output that I am getting which is not what I am looking for.
(2004-01-05 00:00:00 00:00:00
2004-01-05 01:00:00 01:00:00
2004-01-05 02:00:00 02:00:00
2004-01-05 03:00:00 03:00:00
2004-01-05 04:00:00 04:00:00
2004-01-05 05:00:00 05:00:00
2004-01-05 06:00:00 06:00:00
2004-01-05 07:00:00 07:00:00
2004-01-05 08:00:00 08:00:00
2004-01-05 09:00:00 09:00:00
And the Expected output headers are
StartTime, EndTime, Day, Count, UniqueIDCount
How do I structure the Write statement in code to have the above mentioned columns in my output csv.

Answer 1

Try This:
rout = r[['StartTime', 'EndTime', 'Day', 'LogCount', 'UniqueIDCount'] ]
print rout
rout.to_csv('results.csv', index=False)