In [35]:
#Unfortunatel, this won't work on Windows.
!head sample_data.csv
2016-01-01 00:00:09,0.0815162037037037
2016-01-01 00:00:40,0.1334837962962963
2016-01-01 00:01:09,20.388726851851853
2016-01-01 00:02:59,0.9811458333333334
2016-01-01 00:03:03,7.048576388888889
2016-01-01 00:03:03,0.1400810185185185
2016-01-01 00:03:29,0.11086805555555555
2016-01-01 00:04:06,0.016967592592592593
2016-01-01 00:04:37,0.1597222222222222
2016-01-01 00:04:56,2.996585648148148
In [36]:
data_tuples = list()
with open('sample_data.csv','r') as f:
for line in f:
data_tuples.append(line.strip().split(','))
In [37]:
data_tuples[0:10]
Out[37]:
[['2016-01-01 00:00:09', '0.0815162037037037'],
['2016-01-01 00:00:40', '0.1334837962962963'],
['2016-01-01 00:01:09', '20.388726851851853'],
['2016-01-01 00:02:59', '0.9811458333333334'],
['2016-01-01 00:03:03', '7.048576388888889'],
['2016-01-01 00:03:03', '0.1400810185185185'],
['2016-01-01 00:03:29', '0.11086805555555555'],
['2016-01-01 00:04:06', '0.016967592592592593'],
['2016-01-01 00:04:37', '0.1597222222222222'],
['2016-01-01 00:04:56', '2.996585648148148']]
In [38]:
#Figure out the format string
# http://pubs.opengroup.org/onlinepubs/009695399/functions/strptime.html
import datetime
x='2016-01-01 00:00:09'
format_str = "%Y-%m-%d %H:%M:%S"
datetime.datetime.strptime(x,format_str)
Out[38]:
datetime.datetime(2016, 1, 1, 0, 0, 9)
In [39]:
data_tuples = list()
with open('sample_data.csv','r') as f:
for line in f:
data_tuples.append(line.strip().split(','))
import datetime
for i in range(0,len(data_tuples)):
data_tuples[i][0] = datetime.datetime.strptime(data_tuples[i][0],format_str)
data_tuples[i][1] = float(data_tuples[i][1])
In [40]:
#Let's see if this worked
data_tuples[0:10]
Out[40]:
[[datetime.datetime(2016, 1, 1, 0, 0, 9), 0.0815162037037037],
[datetime.datetime(2016, 1, 1, 0, 0, 40), 0.1334837962962963],
[datetime.datetime(2016, 1, 1, 0, 1, 9), 20.388726851851853],
[datetime.datetime(2016, 1, 1, 0, 2, 59), 0.9811458333333334],
[datetime.datetime(2016, 1, 1, 0, 3, 3), 7.048576388888889],
[datetime.datetime(2016, 1, 1, 0, 3, 3), 0.1400810185185185],
[datetime.datetime(2016, 1, 1, 0, 3, 29), 0.11086805555555555],
[datetime.datetime(2016, 1, 1, 0, 4, 6), 0.016967592592592593],
[datetime.datetime(2016, 1, 1, 0, 4, 37), 0.1597222222222222],
[datetime.datetime(2016, 1, 1, 0, 4, 56), 2.996585648148148]]
In [41]:
#Extract the hour from a datetime object
x=data_tuples[0][0]
x.hour
Out[41]:
0
In [42]:
data_tuples = [(x[0].hour,x[1]) for x in data_tuples]
In [43]:
data_tuples[0:10]
Out[43]:
[(0, 0.0815162037037037),
(0, 0.1334837962962963),
(0, 20.388726851851853),
(0, 0.9811458333333334),
(0, 7.048576388888889),
(0, 0.1400810185185185),
(0, 0.11086805555555555),
(0, 0.016967592592592593),
(0, 0.1597222222222222),
(0, 2.996585648148148)]
In [44]:
data_tuples = list()
with open('sample_data.csv','r') as f:
for line in f:
data_tuples.append(line.strip().split(','))
import datetime
for i in range(0,len(data_tuples)):
data_tuples[i][0] = datetime.datetime.strptime(data_tuples[i][0],format_str)
data_tuples[i][1] = float(data_tuples[i][1])
In [22]:
def get_data(filename):
data_tuples = list()
with open(filename,'r') as f:
for line in f:
data_tuples.append(line.strip().split(','))
import datetime
format_str = "%Y-%m-%d %H:%M:%S"
data_tuples = [(datetime.datetime.strptime(x[0],format_str).hour,float(x[1])) for x in data_tuples]
return data_tuples
In [23]:
get_data('sample_data.csv')
Out[23]:
[(0, 0.0815162037037037),
(0, 0.1334837962962963),
(0, 20.388726851851853),
(0, 0.9811458333333334),
(0, 7.048576388888889),
(0, 0.1400810185185185),
(0, 0.11086805555555555),
(0, 0.016967592592592593),
(0, 0.1597222222222222),
(0, 2.996585648148148),
(0, 0.06299768518518518),
(0, 0.059479166666666666),
(0, 0.003460648148148148),
(0, 0.22096064814814814),
(0, 0.10398148148148148),
(0, 0.2975231481481482),
(0, 0.09293981481481481),
(0, 0.016446759259259258),
(0, 0.06824074074074074),
(0, 0.04800925925925926),
(0, 0.26761574074074074),
(0, 1.4127662037037036),
(0, 0.4363078703703704),
(0, 0.869375),
(0, 0.05337962962962963),
(0, 0.6558333333333334),
(0, 0.3119560185185185),
(0, 3.580636574074074),
(0, 0.1267939814814815),
(0, 5.040613425925926),
(0, 0.022662037037037036),
(0, 0.31908564814814816),
(0, 1.001412037037037),
(0, 0.3957407407407407),
(0, 0.01945601851851852),
(0, 0.1460300925925926),
(0, 0.6539351851851852),
(0, 0.027731481481481482),
(0, 0.12319444444444444),
(0, 0.05623842592592593),
(0, 0.31396990740740743),
(0, 0.01125),
(0, 0.31091435185185184),
(0, 0.03392361111111111),
(0, 0.35060185185185183),
(0, 0.574375),
(0, 0.02789351851851852),
(0, 0.270625),
(0, 0.013888888888888888),
(0, 0.08671296296296296),
(0, 0.34273148148148147),
(0, 0.10928240740740741),
(0, 0.3605902777777778),
(0, 1.4861111111111112),
(0, 0.02428240740740741),
(0, 0.1840625),
(0, 0.12356481481481481),
(0, 0.12082175925925925),
(0, 0.14607638888888888),
(0, 0.6406481481481482),
(0, 0.12211805555555555),
(0, 0.02834490740740741),
(0, 6.293506944444444),
(0, 0.6482175925925926),
(0, 0.07873842592592592),
(0, 0.11435185185185186),
(0, 0.6484606481481482),
(0, 0.07287037037037038),
(0, 3.375115740740741),
(0, 0.045787037037037036),
(0, 0.021006944444444446),
(0, 0.02380787037037037),
(0, 0.09158564814814815),
(0, 0.053668981481481484),
(0, 0.11513888888888889),
(0, 0.01951388888888889),
(0, 0.03994212962962963),
(0, 0.11658564814814815),
(0, 0.06086805555555556),
(0, 0.0940162037037037),
(0, 0.05965277777777778),
(0, 0.08891203703703704),
(0, 0.05778935185185185),
(0, 0.00542824074074074),
(0, 0.10305555555555555),
(0, 0.26667824074074076),
(0, 0.056435185185185185),
(0, 0.2379050925925926),
(0, 0.034722222222222224),
(0, 0.11533564814814815),
(0, 10.834027777777777),
(0, 0.1352199074074074),
(0, 3.5227199074074074),
(0, 3.43125),
(0, 0.09475694444444445),
(0, 0.05552083333333333),
(0, 0.03581018518518519),
(0, 0.03158564814814815),
(0, 0.05498842592592593),
(0, 5.524895833333333),
(0, 0.07480324074074074),
(0, 1.3978356481481482),
(0, 0.09047453703703703),
(0, 0.04027777777777778),
(0, 0.08449074074074074),
(0, 0.04626157407407407),
(0, 0.15074074074074073),
(0, 0.07430555555555556),
(0, 0.2509837962962963),
(0, 0.18662037037037038),
(0, 0.06695601851851851),
(0, 0.10469907407407407),
(0, 6.486203703703704),
(0, 0.5620138888888889),
(0, 0.6415046296296296),
(0, 0.39327546296296295),
(0, 0.2999537037037037),
(0, 3.4520833333333334),
(0, 0.2852199074074074),
(0, 0.0724074074074074),
(0, 0.033715277777777775),
(0, 0.5611111111111111),
(0, 0.02921296296296296),
(0, 0.28434027777777776),
(0, 0.05820601851851852),
(0, 0.6398032407407407),
(0, 0.0631712962962963),
(0, 0.1084837962962963),
(0, 0.2834259259259259),
(0, 0.00866898148148148),
(0, 0.12859953703703703),
(0, 0.033483796296296296),
(0, 0.05050925925925926),
(0, 0.3253125),
(0, 0.28261574074074075),
(0, 0.11918981481481482),
(0, 0.032060185185185185),
(0, 0.0),
(0, 0.08721064814814815),
(0, 0.12758101851851852),
(0, 2.9717939814814813),
(0, 0.20175925925925925),
(0, 0.2932060185185185),
(0, 0.10611111111111111),
(0, 0.10516203703703704),
(0, 0.2697800925925926),
(0, 0.13408564814814813),
(0, 0.04069444444444444),
(0, 0.09778935185185185),
(0, 0.15186342592592592),
(0, 0.09658564814814814),
(0, 0.20041666666666666),
(0, 0.1255324074074074),
(0, 0.23927083333333332),
(0, 0.2946412037037037),
(0, 0.11641203703703704),
(0, 0.2584375),
(0, 0.08299768518518519),
(0, 0.007997685185185186),
(0, 0.0679513888888889),
(0, 0.1264236111111111),
(0, 0.020370370370370372),
(0, 0.003275462962962963),
(0, 0.12425925925925926),
(0, 0.4534722222222222),
(0, 0.02133101851851852),
(0, 0.1449537037037037),
(0, 0.03175925925925926),
(0, 0.09855324074074075),
(0, 0.06689814814814815),
(0, 6.0151157407407405),
(0, 0.29105324074074074),
(0, 0.09331018518518519),
(0, 0.18921296296296297),
(0, 0.2907986111111111),
(0, 0.018900462962962963),
(0, 7.350219907407407),
(0, 0.3190972222222222),
(0, 0.1746875),
(0, 0.0869212962962963),
(0, 0.09215277777777778),
(0, 0.07538194444444445),
(0, 0.034965277777777776),
(0, 0.269849537037037),
(0, 0.1404513888888889),
(0, 0.2877662037037037),
(0, 0.6313541666666667),
(0, 0.016006944444444445),
(0, 5.4186805555555555),
(0, 0.15917824074074075),
(0, 0.3180324074074074),
(0, 0.2349189814814815),
(0, 0.19148148148148147),
(0, 0.09983796296296296),
(0, 0.07717592592592593),
(0, 0.1524074074074074),
(0, 0.38230324074074074),
(0, 0.25211805555555555),
(0, 0.014097222222222223),
(0, 0.09979166666666667),
(0, 0.09635416666666667),
(0, 0.036238425925925924),
(0, 0.0228125),
(0, 0.12427083333333333),
(0, 0.23729166666666668),
(0, 0.6286226851851852),
(0, 0.05758101851851852),
(0, 0.23189814814814816),
(0, 0.12540509259259258),
(0, 0.45188657407407407),
(0, 0.3801851851851852),
(0, 0.21715277777777778),
(0, 0.08715277777777777),
(0, 0.2314351851851852),
(0, 0.305),
(0, 0.014872685185185185),
(0, 0.5487268518518519),
(0, 0.0346412037037037),
(0, 4.587893518518518),
(0, 0.011458333333333333),
(0, 0.08106481481481481),
(0, 0.30517361111111113),
(0, 0.1631712962962963),
(0, 0.11505787037037037),
(0, 0.03980324074074074),
(0, 0.09520833333333334),
(0, 0.025706018518518517),
(0, 0.17506944444444444),
(0, 0.0876736111111111),
(0, 0.26028935185185187),
(0, 0.009733796296296296),
(0, 0.02519675925925926),
(0, 2.7027083333333333),
(0, 2.7027083333333333),
(0, 0.03243055555555555),
(0, 2.642662037037037),
(0, 0.3372453703703704),
(0, 0.024074074074074074),
(0, 0.04697916666666667),
(0, 0.28702546296296294),
(0, 0.11546296296296296),
(1, 0.008645833333333333),
(1, 0.0077314814814814815),
(1, 0.12260416666666667),
(1, 0.18751157407407407),
(1, 0.05607638888888889),
(1, 0.03173611111111111),
(1, 0.21184027777777778),
(1, 0.11371527777777778),
(1, 0.014884259259259259),
(1, 0.13283564814814816),
(1, 2.639826388888889),
(1, 0.07567129629629629),
(1, 0.0068865740740740745),
(1, 1.375949074074074),
(1, 0.22591435185185185),
(1, 0.01636574074074074),
(1, 0.30422453703703706),
(1, 0.23064814814814816),
(1, 0.08771990740740741),
(1, 0.08447916666666666),
(1, 0.4452662037037037),
(1, 0.045092592592592594),
(1, 0.29788194444444444),
(1, 0.12480324074074074),
(1, 2.6976851851851853),
(1, 6.007511574074074),
(1, 0.310625),
(1, 0.23364583333333333),
(1, 0.21171296296296296),
(1, 0.00400462962962963),
(1, 0.1800462962962963),
(1, 0.35811342592592593),
(1, 0.026284722222222223),
(1, 0.22322916666666667),
(1, 0.02775462962962963),
(1, 0.02834490740740741),
(1, 0.051041666666666666),
(1, 0.2560648148148148),
(1, 0.07827546296296296),
(1, 0.0030439814814814813),
(1, 0.014375),
(1, 0.18381944444444445),
(1, 0.3706365740740741),
(1, 0.020844907407407406),
(1, 0.3703240740740741),
(1, 0.07829861111111111),
(1, 0.013275462962962963),
(1, 0.11875),
(1, 4.471018518518519),
(1, 0.025798611111111112),
(1, 0.9344560185185186),
(1, 0.17678240740740742),
(1, 0.01761574074074074),
(1, 0.015752314814814816),
(1, 0.25506944444444446),
(1, 0.2693287037037037),
(1, 0.2962268518518518),
(1, 0.027314814814814816),
(1, 0.03515046296296296),
(1, 0.18133101851851852),
(1, 0.04200231481481481),
(1, 0.22340277777777778),
(1, 0.06408564814814814),
(1, 0.03925925925925926),
(1, 0.023668981481481482),
(1, 2.6313310185185186),
(1, 0.22269675925925925),
(1, 0.020520833333333332),
(1, 0.2338425925925926),
(1, 0.17561342592592594),
(1, 0.10863425925925926),
(1, 0.038622685185185184),
(1, 5.537476851851852),
(1, 5.537465277777778),
(1, 5.537442129629629),
(1, 5.537430555555556),
(1, 0.05315972222222222),
(1, 0.034074074074074076),
(1, 0.5500578703703703),
(1, 5.536701388888889),
(1, 0.022881944444444444),
(1, 5.536655092592593),
(1, 5.53662037037037),
(1, 5.536574074074074),
(1, 0.06733796296296296),
(1, 0.1794675925925926),
(1, 0.023275462962962963),
(1, 0.0346412037037037),
(1, 0.37047453703703703),
(1, 0.022268518518518517),
(1, 3.4188541666666667),
(1, 5.535844907407407),
(1, 5.535821759259259),
(1, 5.535821759259259),
(1, 5.535798611111111),
(1, 5.535775462962963),
(1, 5.535775462962963),
(1, 5.53574074074074),
(1, 5.535717592592593),
(1, 5.535694444444444),
(1, 5.535659722222222),
(1, 5.535648148148148),
(1, 0.10561342592592593),
(1, 0.009270833333333334),
(1, 1.4357407407407408),
(1, 0.11370370370370371),
(1, 0.04259259259259259),
(1, 0.26501157407407405),
(1, 0.2896412037037037),
(1, 0.5327314814814815),
(1, 0.05893518518518519),
(1, 0.10252314814814815),
(1, 5.534907407407408),
(1, 5.534895833333334),
(1, 5.534872685185185),
(1, 5.534849537037037),
(1, 5.534826388888889),
(1, 5.53480324074074),
(1, 5.534780092592593),
(1, 0.009270833333333334),
(1, 0.020856481481481483),
(1, 0.01912037037037037),
(1, 0.3539699074074074),
(1, 5.534050925925926),
(1, 5.534027777777778),
(1, 5.228356481481481),
(1, 5.228356481481481),
(1, 5.228356481481481),
(1, 0.10972222222222222),
(1, 1.4444444444444444),
(1, 0.2724421296296296),
(1, 0.17671296296296296),
(1, 3.5166666666666666),
(1, 0.019305555555555555),
(1, 0.27336805555555554),
(1, 0.00912037037037037),
(1, 0.07657407407407407),
(1, 0.5217939814814815),
(1, 0.28738425925925926),
(1, 0.04532407407407407),
(1, 0.17570601851851853),
(1, 0.1904398148148148),
(1, 0.018113425925925925),
(1, 0.06284722222222222),
(1, 0.0061574074074074074),
(1, 0.09725694444444444),
(1, 0.06871527777777778),
(1, 0.3154282407407407),
(1, 0.01758101851851852),
(1, 0.0840625),
(1, 0.24689814814814814),
(1, 1.4416666666666667),
(1, 0.612037037037037),
(1, 0.11572916666666666),
(1, 0.03630787037037037),
(1, 0.055081018518518515),
(1, 0.03252314814814815),
(1, 0.04465277777777778),
(1, 0.5423842592592593),
(1, 0.2625462962962963),
(1, 0.002800925925925926),
(1, 0.17319444444444446),
(1, 48.419375),
(1, 5.223310185185185),
(1, 0.21864583333333334),
(1, 0.2831828703703704),
(1, 0.22372685185185184),
(1, 0.28791666666666665),
(1, 0.1509375),
(1, 0.034861111111111114),
(1, 0.2829513888888889),
(1, 0.2595486111111111),
(1, 0.022048611111111113),
(1, 0.0591087962962963),
(1, 0.22239583333333332),
(1, 0.02267361111111111),
(1, 0.2417476851851852),
(1, 0.05712962962962963),
(1, 0.01318287037037037),
(1, 1.1759953703703703),
(1, 0.14092592592592593),
(1, 0.05917824074074074),
(1, 0.024305555555555556),
(1, 0.1688888888888889),
(1, 0.33587962962962964),
(1, 0.08981481481481482),
(1, 0.28149305555555554),
(1, 68.60969907407407),
(1, 0.012037037037037037),
(1, 0.16962962962962963),
(1, 0.2154976851851852),
(1, 5.378715277777777),
(1, 1.4196180555555555),
(1, 0.02837962962962963),
(1, 0.28050925925925924),
(1, 0.6023032407407407),
(1, 0.2788541666666667),
(1, 3.591666666666667),
(1, 0.010960648148148148),
(1, 0.11039351851851852),
(1, 0.19077546296296297),
(1, 59.326909722222226),
(1, 0.1321875),
(1, 0.04549768518518518),
(1, 0.05421296296296296),
(1, 0.13712962962962963),
(1, 0.13784722222222223),
(1, 0.20434027777777777),
(1, 0.013888888888888888),
(1, 0.19517361111111112),
(1, 0.010416666666666666),
(1, 68.60802083333333),
(1, 2.6177199074074076),
(1, 0.17012731481481483),
(1, 0.6046064814814814),
(1, 0.21744212962962964),
(1, 55.524537037037035),
(1, 0.24275462962962963),
(1, 3.9792708333333335),
(1, 0.0),
(1, 0.3404513888888889),
(1, 0.23671296296296296),
(1, 0.008784722222222222),
(1, 0.16887731481481483),
(1, 0.2390625),
(1, 0.2413773148148148),
(1, 0.04982638888888889),
(1, 2.615787037037037),
(1, 0.04414351851851852),
(1, 87.51457175925925),
(1, 18.35710648148148),
(1, 18.35710648148148),
(1, 5.366898148148148),
(1, 0.25416666666666665),
(1, 0.013703703703703704),
(1, 0.0580787037037037),
(1, 0.4797106481481481),
(1, 0.27587962962962964),
(1, 0.24028935185185185),
(1, 0.25341435185185185),
(1, 0.09081018518518519),
(1, 0.2736689814814815),
(1, 0.00949074074074074),
(1, 0.27578703703703705),
(1, 0.12849537037037037),
(1, 0.0),
(1, 0.08425925925925926),
(1, 0.23917824074074073),
(1, 0.010046296296296296),
(1, 0.20065972222222223),
(1, 0.352962962962963),
(1, 1.4291666666666667),
(1, 0.12827546296296297),
(1, 0.1699074074074074),
(1, 0.07219907407407407),
(1, 0.10920138888888889),
(1, 0.5851041666666666),
(1, 0.2374074074074074),
(1, 0.5068287037037037),
(1, 2.976747685185185),
(1, 0.008414351851851852),
(1, 0.20884259259259258),
(1, 0.2507638888888889),
(1, 0.35739583333333336),
(1, 0.36605324074074075),
(1, 68.60498842592592),
(1, 0.2361574074074074),
(1, 6.430983796296296),
(1, 55.39054398148148),
(1, 0.006701388888888889),
(1, 0.05575231481481482),
(1, 0.12337962962962963),
(1, 0.23501157407407408),
(1, 0.05053240740740741),
(1, 0.06914351851851852),
(1, 0.20688657407407407),
(1, 0.08793981481481482),
(1, 0.01601851851851852),
(1, 69.51873842592593),
(1, 0.05730324074074074),
(1, 0.0500462962962963),
(1, 0.005775462962962963),
(1, 0.23381944444444444),
(1, 0.09854166666666667),
(1, 19.496631944444445),
(1, 2.6100810185185184),
(1, 0.20274305555555555),
(1, 0.2199074074074074),
(1, 0.043912037037037034),
(1, 0.12083333333333333),
(1, 0.007268518518518519),
(1, 0.0772800925925926),
(1, 0.5264930555555556),
(1, 0.05466435185185185),
(1, 0.027511574074074074),
(1, 0.1946875),
(1, 0.08146990740740741),
(1, 0.04524305555555556),
(1, 0.245625),
(1, 0.01556712962962963),
(1, 0.28630787037037037),
(1, 0.23092592592592592),
(1, 0.2270949074074074),
(1, 0.01724537037037037),
(1, 0.5781828703703704),
(1, 69.5161574074074),
(1, 0.014166666666666666),
(1, 0.05350694444444445),
(1, 0.5081712962962963),
(1, 0.20099537037037038),
(1, 0.24232638888888888),
(1, 0.01888888888888889),
(1, 0.185),
(1, 0.1332523148148148),
(1, 0.053287037037037036),
(1, 0.22686342592592593),
(1, 0.05959490740740741),
(1, 0.12964120370370372),
(1, 0.2416087962962963),
(1, 0.06989583333333334),
(1, 0.05552083333333333),
(1, 0.03076388888888889),
(1, 2.605324074074074),
(1, 0.153125),
(1, 0.011400462962962963),
(1, 0.541724537037037),
(1, 0.40875),
(1, 0.13319444444444445),
(1, 0.07288194444444444),
(1, 0.07697916666666667),
(1, 0.2630439814814815),
(1, 2.6604976851851854),
(1, 5.633043981481482),
(1, 0.026469907407407407),
(1, 0.0528125),
(1, 0.07224537037037038),
(1, 0.23971064814814816),
(1, 0.26255787037037037),
(1, 0.009282407407407408),
(1, 0.07688657407407408),
(2, 0.040844907407407406),
(2, 0.06746527777777778),
(2, 1.3357407407407407),
(2, 0.13982638888888888),
(2, 0.11550925925925926),
(2, 0.2205787037037037),
(2, 4.6722222222222225),
(2, 0.1709375),
(2, 0.22224537037037037),
(2, 0.07493055555555556),
(2, 0.1526736111111111),
(2, 0.189375),
(2, 0.06673611111111111),
(2, 0.19559027777777777),
(2, 0.02199074074074074),
(2, 0.07450231481481481),
(2, 0.2349074074074074),
(2, 0.25805555555555554),
(2, 2.597627314814815),
(2, 0.21976851851851853),
(2, 0.315),
(2, 0.14849537037037036),
(2, 0.256400462962963),
(2, 0.008067129629629629),
(2, 0.018935185185185187),
(2, 0.026863425925925926),
(2, 68.5777662037037),
(2, 0.4469675925925926),
(2, 0.1953125),
(2, 0.0344212962962963),
(2, 0.1578125),
(2, 0.024479166666666666),
(2, 0.029872685185185186),
(2, 0.48746527777777776),
(2, 2.5935185185185183),
(2, 0.2303587962962963),
(2, 0.5789583333333334),
(2, 0.1869212962962963),
(2, 0.04925925925925926),
(2, 0.041875),
(2, 0.22902777777777777),
(2, 0.00798611111111111),
(2, 2.5917013888888887),
(2, 0.2279050925925926),
(2, 0.13148148148148148),
(2, 0.021076388888888888),
(2, 0.2508680555555556),
(2, 0.01861111111111111),
(2, 0.007916666666666667),
(2, 0.03347222222222222),
(2, 0.05818287037037037),
(2, 0.18171296296296297),
(2, 0.034826388888888886),
(2, 0.006342592592592592),
(2, 56.30033564814815),
(2, 0.22483796296296296),
(2, 2.588599537037037),
(2, 0.39241898148148147),
(2, 0.0006365740740740741),
(2, 0.048587962962962965),
(2, 0.25078703703703703),
(2, 0.015497685185185186),
(2, 0.05288194444444445),
(2, 0.02638888888888889),
(2, 0.02767361111111111),
(2, 1.4006944444444445),
(2, 0.07605324074074074),
(2, 0.48113425925925923),
(2, 0.1323263888888889),
(2, 0.1715509259259259),
(2, 3.948449074074074),
(2, 0.24959490740740742),
(2, 0.03888888888888889),
(2, 0.10967592592592593),
(2, 0.003599537037037037),
(2, 0.03203703703703704),
(2, 0.04618055555555556),
(2, 0.10894675925925926),
(2, 0.337349537037037),
(2, 0.1331712962962963),
(2, 0.05987268518518519),
(2, 0.1403125),
(2, 0.023599537037037037),
(2, 0.21996527777777777),
(2, 0.000462962962962963),
(2, 0.05137731481481481),
(2, 0.4229513888888889),
(2, 0.03246527777777778),
(2, 0.27546296296296297),
(2, 0.1003125),
(2, 11.89730324074074),
(2, 0.06104166666666667),
(2, 0.03179398148148148),
(2, 0.18252314814814816),
(2, 0.005162037037037037),
(2, 0.272349537037037),
(2, 0.07179398148148149),
(2, 0.21876157407407407),
(2, 0.17947916666666666),
(2, 9.328113425925926),
(2, 0.24217592592592593),
(2, 0.005798611111111111),
(2, 0.04949074074074074),
(2, 0.04417824074074074),
(2, 0.13555555555555557),
(2, 0.178125),
(2, 0.2440162037037037),
(2, 0.13820601851851852),
(2, 0.03082175925925926),
(2, 68.56353009259259),
(2, 0.16494212962962962),
(2, 0.010300925925925925),
(2, 2.5793981481481483),
(2, 0.0),
(2, 0.17315972222222223),
(2, 0.17842592592592593),
(2, 0.029166666666666667),
(2, 0.5637615740740741),
(2, 0.12914351851851852),
(2, 0.21519675925925927),
(2, 0.049965277777777775),
(2, 0.21460648148148148),
(2, 0.06515046296296297),
(2, 0.32969907407407406),
(2, 90.46327546296297),
(2, 0.046377314814814816),
(2, 83.35862268518518),
(2, 0.04832175925925926),
(2, 0.21306712962962962),
(2, 0.1736574074074074),
(2, 0.03690972222222222),
(2, 0.29516203703703703),
(2, 0.10208333333333333),
(2, 0.005),
(2, 0.005636574074074074),
(2, 0.05449074074074074),
(2, 0.030162037037037036),
(2, 0.0003587962962962963),
(2, 0.04351851851851852),
(2, 0.04170138888888889),
(2, 0.10864583333333333),
(2, 0.4752777777777778),
(2, 0.1594675925925926),
(2, 0.01994212962962963),
(2, 0.0024421296296296296),
(2, 2.9336574074074075),
(2, 0.4890625),
(2, 3.9358217592592593),
(2, 0.5301967592592592),
(2, 0.1557060185185185),
(2, 0.019976851851851853),
(2, 0.23145833333333332),
(2, 0.09592592592592593),
(2, 0.030960648148148147),
(2, 0.2517013888888889),
(2, 0.019328703703703702),
(2, 0.19344907407407408),
(2, 0.02290509259259259),
(2, 0.1096412037037037),
(2, 0.017824074074074076),
(2, 0.039074074074074074),
(2, 0.09384259259259259),
(2, 0.915162037037037),
(2, 0.017118055555555556),
(2, 0.004212962962962963),
(2, 0.04236111111111111),
(2, 0.19934027777777777),
(2, 0.16777777777777778),
(2, 0.06722222222222222),
(2, 0.08005787037037038),
(2, 0.15319444444444444),
(2, 0.12376157407407408),
(2, 0.09378472222222223),
(2, 0.014814814814814815),
(2, 2.5686574074074073),
(2, 4.2909143518518515),
(2, 0.04356481481481481),
(2, 0.16028935185185186),
(2, 0.043912037037037034),
(2, 0.3265972222222222),
(2, 0.042604166666666665),
(2, 1.9290162037037037),
(2, 5.791631944444444),
(2, 0.17292824074074073),
(2, 0.08665509259259259),
(2, 0.11993055555555555),
(2, 0.5507175925925926),
(2, 0.04667824074074074),
(2, 0.19934027777777777),
(2, 0.3232523148148148),
(2, 0.14752314814814815),
(2, 0.07835648148148149),
(2, 0.1343287037037037),
(2, 0.045162037037037035),
(2, 0.19300925925925927),
(2, 0.030555555555555555),
(2, 0.2044675925925926),
(2, 0.081875),
(2, 5.923090277777778),
(3, 0.4115740740740741),
(3, 0.19508101851851853),
(3, 0.021423611111111112),
(3, 0.022256944444444444),
(3, 0.038148148148148146),
(3, 0.15813657407407408),
(3, 0.1953587962962963),
(3, 0.3164814814814815),
(3, 0.020787037037037038),
(3, 0.16685185185185186),
(3, 3.289363425925926),
(3, 0.19486111111111112),
(3, 0.0850925925925926),
(3, 1.5627199074074074),
(3, 0.015069444444444444),
(3, 0.10355324074074074),
(3, 0.21893518518518518),
(3, 0.23576388888888888),
(3, 0.19381944444444443),
(3, 0.04064814814814815),
(3, 0.02644675925925926),
(3, 0.19199074074074074),
(3, 0.4515972222222222),
(3, 0.23798611111111112),
(3, 0.09693287037037036),
(3, 68.54539351851852),
(3, 0.14238425925925927),
(3, 0.191875),
(3, 0.18648148148148147),
(3, 3.5190972222222223),
(3, 0.065),
(3, 0.06717592592592593),
(3, 0.18997685185185184),
(3, 0.18487268518518518),
(3, 0.032407407407407406),
(3, 68.53947916666667),
(3, 0.2146412037037037),
(3, 0.08688657407407407),
(3, 0.4902546296296296),
(3, 0.18890046296296295),
(3, 0.09189814814814815),
(3, 0.08917824074074074),
(3, 0.21355324074074075),
(3, 4.911493055555556),
(3, 0.18871527777777777),
(3, 1.2863078703703703),
(3, 0.0493287037037037),
(3, 0.29834490740740743),
(3, 83.26734953703703),
(3, 3.7636458333333334),
(3, 28.290983796296295),
(3, 0.016805555555555556),
(3, 4.407268518518518),
(3, 4.407268518518518),
(3, 0.17002314814814815),
(3, 0.19349537037037037),
(3, 4.911747685185185),
(3, 0.0115625),
(3, 0.09986111111111111),
(3, 0.011458333333333333),
(3, 0.040462962962962964),
(3, 0.10170138888888888),
(3, 0.01826388888888889),
(3, 0.05810185185185185),
(3, 0.38733796296296297),
(3, 0.14243055555555556),
(3, 0.013935185185185186),
(3, 0.013090277777777777),
(3, 0.09791666666666667),
(3, 0.022152777777777778),
(3, 57.31135416666667),
(3, 0.10548611111111111),
(3, 0.09739583333333333),
(3, 0.4426851851851852),
(3, 0.06356481481481481),
(3, 0.10217592592592592),
(3, 0.5173958333333334),
(3, 0.5347800925925926),
(3, 0.0972800925925926),
(3, 0.029953703703703705),
(3, 0.13613425925925926),
(3, 3.2930555555555556),
(3, 0.3790277777777778),
(3, 0.2256712962962963),
(3, 0.14954861111111112),
(3, 0.11707175925925926),
(3, 0.009537037037037037),
(3, 0.30520833333333336),
(3, 0.051724537037037034),
(3, 5.7640625),
(3, 0.2345949074074074),
(3, 0.035798611111111114),
(3, 0.17487268518518517),
(3, 0.5326620370370371),
(3, 0.1803935185185185),
(3, 0.10387731481481481),
(3, 0.03888888888888889),
(3, 0.31090277777777775),
(3, 0.11872685185185185),
(3, 0.05216435185185185),
(3, 0.16802083333333334),
(3, 88.5587037037037),
(3, 0.008541666666666666),
(3, 0.30452546296296296),
(3, 0.01778935185185185),
(3, 0.0909375),
(3, 0.17425925925925925),
(3, 1.3569444444444445),
(3, 0.16260416666666666),
(3, 0.004976851851851852),
(3, 0.17282407407407407),
(3, 0.09824074074074074),
(3, 0.17230324074074074),
(3, 0.05068287037037037),
(3, 0.20575231481481482),
(3, 0.005162037037037037),
(3, 0.5309837962962963),
(3, 4.90457175925926),
(3, 0.3034837962962963),
(3, 0.08675925925925926),
(3, 0.44212962962962965),
(3, 0.22008101851851852),
(3, 0.12730324074074073),
(3, 0.051666666666666666),
(3, 0.17710648148148148),
(3, 0.18334490740740741),
(3, 3.414953703703704),
(3, 0.12621527777777777),
(3, 0.03625),
(3, 0.07423611111111111),
(3, 94.3489236111111),
(3, 0.020694444444444446),
(3, 0.1752314814814815),
(3, 0.19953703703703704),
(3, 0.3020601851851852),
(3, 0.1990625),
(3, 0.03491898148148148),
(3, 0.43287037037037035),
(3, 0.09111111111111111),
(3, 0.17239583333333333),
(3, 0.01193287037037037),
(3, 0.08747685185185185),
(3, 0.0840625),
(3, 0.2545601851851852),
(3, 0.3341550925925926),
(3, 0.04456018518518518),
(3, 55.32193287037037),
(3, 0.07489583333333333),
(3, 0.13519675925925925),
(3, 0.19605324074074074),
(3, 0.021956018518518517),
(3, 0.01292824074074074),
(3, 0.01380787037037037),
(3, 1.7757175925925925),
(3, 0.018738425925925926),
(3, 0.15986111111111112),
(3, 0.08038194444444445),
(3, 0.2525),
(3, 0.12413194444444445),
(3, 0.06079861111111111),
(3, 0.16921296296296295),
(3, 2.5355902777777777),
(3, 0.011261574074074075),
(3, 19.434444444444445),
(3, 0.027962962962962964),
(3, 0.3652546296296296),
(3, 0.0334375),
(3, 0.12555555555555556),
(3, 0.10034722222222223),
(3, 0.07094907407407407),
(3, 0.07390046296296296),
(3, 4.387118055555556),
(3, 0.5190625),
(3, 0.06814814814814815),
(3, 0.5234027777777778),
(3, 0.16574074074074074),
(3, 0.007326388888888889),
(3, 0.1489699074074074),
(3, 0.1645949074074074),
(3, 0.17087962962962963),
(3, 0.06935185185185185),
(3, 0.29847222222222225),
(3, 0.06680555555555556),
(3, 0.21975694444444444),
(3, 0.16280092592592593),
(3, 0.12641203703703704),
(3, 0.009780092592592592),
(3, 0.01810185185185185),
(3, 4.88880787037037),
(3, 2.528287037037037),
(3, 0.16157407407407406),
(3, 0.005300925925925926),
(3, 0.11314814814814815),
(3, 2.5281712962962963),
(3, 0.1870601851851852),
(3, 5.122685185185185),
(3, 0.16045138888888888),
(3, 0.004027777777777778),
(3, 0.03005787037037037),
(3, 0.029814814814814815),
(3, 0.0840625),
(3, 0.1602199074074074),
(3, 6.23199074074074),
(3, 0.4184953703703704),
(3, 0.032789351851851854),
(3, 0.07689814814814815),
(3, 0.060856481481481484),
(3, 0.18447916666666667),
(3, 0.06355324074074074),
(3, 5.11962962962963),
(3, 0.04383101851851852),
(3, 0.0032523148148148147),
(3, 4.377465277777778),
(3, 0.24363425925925927),
(3, 0.2849305555555556),
(3, 0.05835648148148148),
(3, 0.15751157407407407),
(3, 0.03068287037037037),
(3, 0.10040509259259259),
(3, 0.05800925925925926),
...]
In [24]:
buckets = dict()
for item in get_data('sample_data.csv'):
if item[0] in buckets:
buckets[item[0]][0] += 1
buckets[item[0]][1] += item[1]
else:
buckets[item[0]] = [1,item[1]]
In [25]:
buckets
Out[25]:
{0: [241, 158.34932870370375],
1: [340, 1006.8582291666668],
2: [199, 464.6581249999997],
3: [221, 681.5493865740739],
4: [157, 732.1197337962964],
5: [112, 285.60615740740764],
6: [80, 427.54798611111124],
7: [71, 183.4966435185184],
8: [99, 601.1727546296297],
9: [132, 1130.5627199074067],
10: [137, 1735.9673726851845],
11: [182, 1074.1009490740735],
12: [168, 2295.5562731481473],
13: [195, 1675.7310300925922],
14: [185, 1498.5249999999999],
15: [193, 2465.890451388889],
16: [204, 2232.515092592593],
17: [211, 1399.851180555556],
18: [182, 1333.1421180555558],
19: [165, 1501.3013541666667],
20: [158, 821.5105439814813],
21: [161, 763.653865740741],
22: [218, 1841.9319444444443],
23: [210, 1088.8371064814814]}
In [26]:
for key,value in buckets.items():
print("Hour:",key,"\tAverage:",value[1]/value[0])
Hour: 0 Average: 0.6570511564469035
Hour: 1 Average: 2.9613477328431377
Hour: 2 Average: 2.334965452261305
Hour: 3 Average: 3.0839338759007866
Hour: 4 Average: 4.663183017810805
Hour: 5 Average: 2.550054976851854
Hour: 6 Average: 5.344349826388891
Hour: 7 Average: 2.5844597678664565
Hour: 8 Average: 6.0724520669659565
Hour: 9 Average: 8.564869090207626
Hour: 10 Average: 12.671294691132733
Hour: 11 Average: 5.901653566341063
Hour: 12 Average: 13.66402543540564
Hour: 13 Average: 8.593492462013293
Hour: 14 Average: 8.100135135135135
Hour: 15 Average: 12.776634463154863
Hour: 16 Average: 10.943701434277418
Hour: 17 Average: 6.634365784623489
Hour: 18 Average: 7.324956692612944
Hour: 19 Average: 9.098796085858586
Hour: 20 Average: 5.199433822667603
Hour: 21 Average: 4.74319171267541
Hour: 22 Average: 8.449229102956167
Hour: 23 Average: 5.184938602292768
In [27]:
def get_hour_bucket_averages(filename):
def get_data(filename):
data_tuples = list()
with open(filename,'r') as f:
for line in f:
data_tuples.append(line.strip().split(','))
import datetime
format_str = "%Y-%m-%d %H:%M:%S"
data_tuples = [(datetime.datetime.strptime(x[0],format_str).hour,float(x[1])) for x in data_tuples]
return data_tuples
buckets = dict()
for item in get_data(filename):
if item[0] in buckets:
buckets[item[0]][0] += 1
buckets[item[0]][1] += item[1]
else:
buckets[item[0]] = [1,item[1]]
return [(key,value[1]/value[0]) for key,value in buckets.items()]
In [28]:
get_hour_bucket_averages('sample_data.csv')
Out[28]:
[(0, 0.6570511564469035),
(1, 2.9613477328431377),
(2, 2.334965452261305),
(3, 3.0839338759007866),
(4, 4.663183017810805),
(5, 2.550054976851854),
(6, 5.344349826388891),
(7, 2.5844597678664565),
(8, 6.0724520669659565),
(9, 8.564869090207626),
(10, 12.671294691132733),
(11, 5.901653566341063),
(12, 13.66402543540564),
(13, 8.593492462013293),
(14, 8.100135135135135),
(15, 12.776634463154863),
(16, 10.943701434277418),
(17, 6.634365784623489),
(18, 7.324956692612944),
(19, 9.098796085858586),
(20, 5.199433822667603),
(21, 4.74319171267541),
(22, 8.449229102956167),
(23, 5.184938602292768)]
In [29]:
get_hour_bucket_averages('all_data.csv')
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
<ipython-input-29-ef70bf40816e> in <module>()
----> 1 get_hour_bucket_averages('all_data.csv')
<ipython-input-27-a3649d614e2d> in get_hour_bucket_averages(filename)
10 return data_tuples
11 buckets = dict()
---> 12 for item in get_data(filename):
13 if item[0] in buckets:
14 buckets[item[0]][0] += 1
<ipython-input-27-a3649d614e2d> in get_data(filename)
2 def get_data(filename):
3 data_tuples = list()
----> 4 with open(filename,'r') as f:
5 for line in f:
6 data_tuples.append(line.strip().split(','))
FileNotFoundError: [Errno 2] No such file or directory: 'all_data.csv'
In [46]:
def remove_punctuation(word):
punctuations = ['.', '!', '?', ',', '(', ')']
for punctuation in punctuations:
if punctuation in word:
print(punctuation)
word.replace(punctuation, '')
return word
remove_punctuation("sis!")
!
Out[46]:
'sis!'
In [ ]:
Content source: KECB/learn
Similar notebooks: