In [1]:
import os; os.sys.path.append(os.path.dirname(os.path.abspath('.'))) # for relative imports
from utils.nab_data import NABData
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
In [2]:
data = NABData()
In [7]:
data.plot(6)
In [5]:
zip(range(len(data.data.keys())), data.data.keys())
Out[5]:
[(0, 'art_daily_small_noise'),
(1, 'art_daily_perfect_square_wave'),
(2, 'art_daily_no_noise'),
(3, 'art_noisy'),
(4, 'art_flatline'),
(5, 'TravelTime_387'),
(6, 'occupancy_t4013'),
(7, 'speed_7578'),
(8, 'TravelTime_451'),
(9, 'speed_t4013'),
(10, 'occupancy_6005'),
(11, 'speed_6005'),
(12, 'Twitter_volume_UPS'),
(13, 'Twitter_volume_AAPL'),
(14, 'Twitter_volume_IBM'),
(15, 'Twitter_volume_CVS'),
(16, 'Twitter_volume_PFE'),
(17, 'Twitter_volume_GOOG'),
(18, 'Twitter_volume_FB'),
(19, 'Twitter_volume_CRM'),
(20, 'Twitter_volume_KO'),
(21, 'Twitter_volume_AMZN'),
(22, 'nyc_taxi'),
(23, 'cpu_utilization_asg_misconfiguration'),
(24, 'machine_temperature_system_failure'),
(25, 'rogue_agent_key_updown'),
(26, 'ambient_temperature_system_failure'),
(27, 'ec2_request_latency_system_failure'),
(28, 'rogue_agent_key_hold'),
(29, 'art_load_balancer_spikes'),
(30, 'art_daily_flatmiddle'),
(31, 'art_increase_spike_density'),
(32, 'art_daily_nojump'),
(33, 'art_daily_jumpsdown'),
(34, 'art_daily_jumpsup'),
(35, 'exchange-2_cpc_results'),
(36, 'exchange-3_cpc_results'),
(37, 'exchange-4_cpc_results'),
(38, 'exchange-4_cpm_results'),
(39, 'exchange-3_cpm_results'),
(40, 'exchange-2_cpm_results'),
(41, 'ec2_disk_write_bytes_c0d644'),
(42, 'ec2_network_in_5abac7'),
(43, 'ec2_cpu_utilization_fe7f93'),
(44, 'elb_request_count_8c0756'),
(45, 'ec2_cpu_utilization_77c1ca'),
(46, 'grok_asg_anomaly'),
(47, 'ec2_cpu_utilization_24ae8d'),
(48, 'ec2_network_in_257a54'),
(49, 'rds_cpu_utilization_cc0c53'),
(50, 'ec2_cpu_utilization_825cc2'),
(51, 'ec2_cpu_utilization_5f5533'),
(52, 'rds_cpu_utilization_e47b3b'),
(53, 'iio_us-east-1_i-a2eb1cd9_NetworkIn'),
(54, 'ec2_cpu_utilization_53ea38'),
(55, 'ec2_cpu_utilization_ac20cd'),
(56, 'ec2_cpu_utilization_c6585a'),
(57, 'ec2_disk_write_bytes_1ef3de')]
In [4]:
data['speed_t4013']
Out[4]:
value
timestamp
2015-09-01 11:25:00
58
2015-09-01 11:30:00
63
2015-09-01 11:35:00
63
2015-09-01 11:40:00
64
2015-09-01 11:55:00
58
2015-09-01 12:00:00
63
2015-09-01 12:05:00
60
2015-09-01 12:10:00
60
2015-09-01 12:15:00
62
2015-09-01 12:20:00
62
2015-09-01 12:25:00
59
2015-09-01 12:30:00
65
2015-09-01 12:35:00
60
2015-09-01 12:45:00
63
2015-09-01 12:50:00
53
2015-09-01 12:55:00
62
2015-09-01 13:05:00
60
2015-09-01 13:10:00
60
2015-09-01 13:15:00
61
2015-09-01 13:20:00
61
2015-09-01 13:25:00
59
2015-09-01 13:35:00
63
2015-09-01 13:45:00
65
2015-09-01 13:50:00
59
2015-09-01 14:00:00
62
2015-09-01 14:05:00
58
2015-09-01 14:10:00
57
2015-09-01 14:15:00
64
2015-09-01 14:30:00
58
2015-09-01 14:35:00
62
...
...
2015-09-17 14:00:00
66
2015-09-17 14:05:00
62
2015-09-17 14:10:00
60
2015-09-17 14:15:00
63
2015-09-17 14:20:00
68
2015-09-17 14:25:00
63
2015-09-17 14:30:00
65
2015-09-17 14:34:00
57
2015-09-17 14:39:00
65
2015-09-17 14:44:00
60
2015-09-17 14:49:00
62
2015-09-17 14:54:00
65
2015-09-17 14:58:00
67
2015-09-17 15:04:00
62
2015-09-17 15:08:00
65
2015-09-17 15:13:00
61
2015-09-17 15:18:00
66
2015-09-17 15:23:00
63
2015-09-17 15:28:00
69
2015-09-17 15:34:00
64
2015-09-17 15:38:00
64
2015-09-17 15:43:00
62
2015-09-17 15:48:00
68
2015-09-17 15:49:00
65
2015-09-17 15:54:00
69
2015-09-17 15:59:00
61
2015-09-17 16:04:00
66
2015-09-17 16:09:00
65
2015-09-17 16:14:00
65
2015-09-17 16:19:00
60
2495 rows × 1 columns
In [4]:
data.labels.keys()
Out[4]:
[u'rogue_agent_key_hold',
u'nyc_taxi',
u'art_daily_no_noise',
u'rds_cpu_utilization_e47b3b',
u'Twitter_volume_AAPL',
u'elb_request_count_8c0756',
u'ec2_cpu_utilization_53ea38',
u'art_daily_nojump',
u'machine_temperature_system_failure',
u'rds_cpu_utilization_cc0c53',
u'art_daily_small_noise',
u'ec2_cpu_utilization_fe7f93',
u'Twitter_volume_AMZN',
u'iio_us-east-1_i-a2eb1cd9_NetworkIn',
u'art_load_balancer_spikes',
u'Twitter_volume_FB',
u'exchange-2_cpm_results',
u'occupancy_t4013',
u'Twitter_volume_CRM',
u'exchange-4_cpc_results',
u'grok_asg_anomaly',
u'exchange-4_cpm_results',
u'TravelTime_387',
u'ec2_network_in_5abac7',
u'art_daily_flatmiddle',
u'ec2_request_latency_system_failure',
u'art_daily_perfect_square_wave',
u'ec2_network_in_257a54',
u'ec2_disk_write_bytes_c0d644',
u'rogue_agent_key_updown',
u'ec2_disk_write_bytes_1ef3de',
u'art_flatline',
u'TravelTime_451',
u'exchange-3_cpc_results',
u'speed_6005',
u'art_increase_spike_density',
u'speed_7578',
u'exchange-3_cpm_results',
u'ec2_cpu_utilization_24ae8d',
u'ec2_cpu_utilization_c6585a',
u'Twitter_volume_IBM',
u'ambient_temperature_system_failure',
u'art_noisy',
u'art_daily_jumpsup',
u'occupancy_6005',
u'Twitter_volume_PFE',
u'exchange-2_cpc_results',
u'cpu_utilization_asg_misconfiguration',
u'ec2_cpu_utilization_5f5533',
u'Twitter_volume_GOOG',
u'Twitter_volume_KO',
u'ec2_cpu_utilization_77c1ca',
u'Twitter_volume_CVS',
u'art_daily_jumpsdown',
u'ec2_cpu_utilization_ac20cd',
u'Twitter_volume_UPS',
u'ec2_cpu_utilization_825cc2',
u'speed_t4013']
In [ ]:
Content source: jonathanstrong/NAB
Similar notebooks: