Python 动态附加到数据帧
我一直在玩Pandas，将HTTP日志输入Pandas进行分析，因为它是大量数据的良好来源，可以让我学习Pandas 我一次一行地获取日志，因此无法从CSV导入，需要将这些日志“泵送”到Pandas数据帧中，然后将其保存到HDFStore文件中 我现在写的代码确实是从GZIP读取的，只是为了让这个过程继续进行，但是一旦我完成了Panda的部分，我将修改它，使之成为一个事件驱动的pubsub风格的联合例程 以下是我目前的代码：Python 动态附加到数据帧,python,numpy,pandas,hdfstore,Python,Numpy,Pandas,Hdfstore,我一直在玩Pandas，将HTTP日志输入Pandas进行分析，因为它是大量数据的良好来源，可以让我学习Pandas 我一次一行地获取日志，因此无法从CSV导入，需要将这些日志“泵送”到Pandas数据帧中，然后将其保存到HDFStore文件中 我现在写的代码确实是从GZIP读取的，只是为了让这个过程继续进行，但是一旦我完成了Panda的部分，我将修改它，使之成为一个事件驱动的pubsub风格的联合例程 以下是我目前的代码： import os import gzip, io
import os
import gzip, io
import pandas as pd
import numpy as np
def read_gzip(file):
if os.path.isdir(os.path.dirname(file)):
if os.path.isfile(file):
while True:
for line in io.BufferedReader(gzip.open(file)):
yield line
class ElbParser(object):
def __init__(self, file):
self.file = file
def get_log_lines(self):
return read_gzip(self.file)
def build_series(self, log_line):
def normalise_ip(input_string):
try:
text = input_string.split(':')[0]
except:
text = np.NaN
finally:
return text
def normalise_time(input_string):
try:
text = float(input_string)
except:
text = np.NaN
finally:
return text
def normalise_ints(input_string):
try:
text = int(input_string)
except:
text = np.NaN
finally:
return text
log_list = log_line.split()
elb_series = pd.Series({
'timestamp' : np.datetime64(log_list[0]),
'elb_name' : log_list[1],
'client_ip' : normalise_ip(log_list[2]),
'backend_ip' : normalise_ip(log_list[3]),
'request_processing_time' : normalise_time(log_list[4]),
'backend_processing_time' : normalise_time(log_list[5]),
'response_processing_time' : normalise_time(log_list[6]),
'elb_status_code' : normalise_ints(log_list[7]),
'backend_status_code' : normalise_ints(log_list[8]),
'received_bytes' : normalise_ints(log_list[9]),
'sent_bytes' : normalise_ints(log_list[10]),
'http_method' : log_list[11].strip("'").strip('"'),
'request' : log_list[13].strip("'").strip('"')
})
return elb_series
import os
import sys
import unittest
import pandas as pd
from lbreader import ElbParser
test_elb_log_file = 'tests/resources/lb_log.gz'
class ElbTest(unittest.TestCase):
#
# Fixture Framework
#
def setUp(self):
print '\nTest Fixture setUp'
self.elbparser = ElbParser(test_elb_log_file)
def tearDown(self):
print '\nTest Fixture tearDown'
del self.elbparser
#
# Tests
#
def test_01_get_aws_elb_log_line(self):
print '\n running get aws elb log line'
log_lines = self.elbparser.get_log_lines()
self.failUnlessEqual('2014-05-15T23:00:04.696671Z poundlb 192.168.0.10:53345 - -1 -1 -1 408 408 55 0 "PUT https://localhost:443/private/urls HTTP/1.1"\n'
, log_lines.next())
def test_02_build_series_with_aws_elb_log_line(self):
print '\n running build series from elb log'
log_lines = self.elbparser.get_log_lines()
test = self.elbparser.build_series(log_lines.next())
test_line_dict = {'backend_ip': '-',
'backend_processing_time': -1.0,
'backend_status_code': 408,
'client_ip': '192.168.0.10',
'lb_name': 'poundlb',
'elb_status_code': 408,
'http_method': 'PUT',
'received_bytes': 55,
'request': 'HTTP/1.1',
'request_processing_time': -1.0,
'response_processing_time': -1.0,
'sent_bytes': 0,
'timestamp': numpy.datetime64('2014-05-16T00:00:04.696671+0100')}
self.failUnlessEqual(test_line_dict, test.to_dict())
if __name__ == '__main__':
unittest.main()
test.values
>>> array(['-', -1.0, 408, '192.168.0.10', 'poundlb', 408, 'PUT', 55,
'HTTP/1.1', -1.0, -1.0, 0,
numpy.datetime64('2014-05-16T00:00:04.696671+0100')], dtype=object)
df = pd.DataFrame(test.values.tolist(), column_list)
>>> df
Out[35]:
0
backend_ip -
backend_processing_time -1
backend_status_code 408
client_ip 192.168.0.10
elb_name poundlb
elb_status_code 408
http_method PUT
received_bytes 55
request HTTP/1.1
request_processing_time -1
response_processing_time -1
sent_bytes 0
timestamp 2014-05-16T00:00:04.696671+0100
[13 rows x 1 columns]
backend_ip backend_processing_time backend_status_code backend_status_code client_ip elb_name elb_status_code http_method received_bytes request request_processing_time response_processing_time sent_bytes timestamp
0 - -1 408 408 192.168.0.10 poundlb 408 PUT 55 HTTP/1.1 -1 -1 0 2014-05-16T00:00:04
1 - -1 408 408 192.168.0.11 poundlb 408 PUT 55 HTTP/1.1 -1 -1 0 2014-05-16T00:00:05
2 - -1 408 408 192.168.0.12 poundlb 408 PUT 55 HTTP/1.1 -1 -1 0 2014-05-16T00:00:06
>>> test0 = elbparser.build_series(log_lines.next())
>>> test1 = elbparser.build_series(log_lines.next())
>>> test2 = elbparser.build_series(log_lines.next())
>>> test3 = elbparser.build_series(log_lines.next())
>>> test4 = elbparser.build_series(log_lines.next())
>>> test5 = elbparser.build_series(log_lines.next())
>>> test6 = elbparser.build_series(log_lines.next())
>>> test7 = elbparser.build_series(log_lines.next())
>>> test8 = elbparser.build_series(log_lines.next())
>>> test9 = elbparser.build_series(log_lines.next())
>>> test10 = elbparser.build_series(log_lines.next())
>>> test_list = [test.to_dict(), test1.to_dict(), test2.to_dict(), test3.to_dict(), test4.to_dict(), test5.to_dict(), test6.to_dict(), test7.to_dict(), test8.to_dict(), test9.to_dict(), test10.to_dict()]
>>> test_list
[{'backend_ip': '-',
'backend_processing_time': -1.0,
'backend_status_code': 408,
'client_ip': '192.168.0.1',
'elb_name': 'poundlb',
'elb_status_code': 408,
'http_method': 'PUT',
'received_bytes': 55,
'request': 'HTTP/1.1',
'request_processing_time': -1.0,
'response_processing_time': -1.0,
'sent_bytes': 0,
'timestamp': numpy.datetime64('2014-05-16T00:00:04.696671+0100')},
{'backend_ip': '10.0.0.241',
'backend_processing_time': 59.246736,
'backend_status_code': 403,
'client_ip': '192.168.0.2',
'elb_name': 'poundlb',
'elb_status_code': 403,
'http_method': 'PUT',
'received_bytes': 55,
'request': 'HTTP/1.1',
'request_processing_time': 3.4e-05,
'response_processing_time': 2.9e-05,
'sent_bytes': 64,
'timestamp': numpy.datetime64('2014-05-16T00:00:30.980494+0100')},
{'backend_ip': '10.0.0.242',
'backend_processing_time': 59.42053,
'backend_status_code': 200,
'client_ip': '192.168.0.3',
'elb_name': 'poundlb',
'elb_status_code': 200,
'http_method': 'PUT',
'received_bytes': 173,
'request': 'HTTP/1.1',
'request_processing_time': 8.4e-05,
'response_processing_time': 6.9e-05,
'sent_bytes': 149,
'timestamp': numpy.datetime64('2014-05-16T00:00:32.687835+0100')},
{'backend_ip': '10.0.0.39',
'backend_processing_time': 0.016443,
'backend_status_code': 200,
'client_ip': '192.168.0.4',
'elb_name': 'poundlb',
'elb_status_code': 200,
'http_method': 'GET',
'received_bytes': 0,
'request': 'HTTP/1.1',
'request_processing_time': 7.9e-05,
'response_processing_time': 4e-05,
'sent_bytes': 289,
'timestamp': numpy.datetime64('2014-05-16T00:00:38.247760+0100')},
{'backend_ip': '10.0.0.41',
'backend_processing_time': 0.008624,
'backend_status_code': 200,
'client_ip': '192.168.0.5',
'elb_name': 'poundlb',
'elb_status_code': 200,
'http_method': 'GET',
'received_bytes': 0,
'request': 'HTTP/1.1',
'request_processing_time': 5.4e-05,
'response_processing_time': 3.2e-05,
'sent_bytes': 200,
'timestamp': numpy.datetime64('2014-05-16T00:00:38.432535+0100')},
{'backend_ip': '10.0.0.43',
'backend_processing_time': 0.138925,
'backend_status_code': 200,
'client_ip': '192.168.0.6',
'elb_name': 'poundlb',
'elb_status_code': 200,
'http_method': 'GET',
'received_bytes': 0,
'request': 'HTTP/1.1',
'request_processing_time': 4.5e-05,
'response_processing_time': 4.3e-05,
'sent_bytes': 268,
'timestamp': numpy.datetime64('2014-05-16T00:00:38.509598+0100')},
{'backend_ip': '10.0.0.38',
'backend_processing_time': 0.013578,
'backend_status_code': 200,
'client_ip': '192.168.0.7',
'elb_name': 'poundlb',
'elb_status_code': 200,
'http_method': 'POST',
'received_bytes': 291,
'request': 'HTTP/1.1',
'request_processing_time': 4.2e-05,
'response_processing_time': 2.7e-05,
'sent_bytes': 36,
'timestamp': numpy.datetime64('2014-05-16T00:00:38.667479+0100')},
{'backend_ip': '10.0.0.42',
'backend_processing_time': 0.017493,
'backend_status_code': 200,
'client_ip': '192.168.0.8',
'elb_name': 'poundlb',
'elb_status_code': 200,
'http_method': 'GET',
'received_bytes': 0,
'request': 'HTTP/1.1',
'request_processing_time': 3.7e-05,
'response_processing_time': 2.7e-05,
'sent_bytes': 290,
'timestamp': numpy.datetime64('2014-05-16T00:00:38.708697+0100')},
{'backend_ip': '10.0.0.40',
'backend_processing_time': 0.014167,
'backend_status_code': 200,
'client_ip': '192.168.0.9',
'elb_name': 'poundlb',
'elb_status_code': 200,
'http_method': 'POST',
'received_bytes': 297,
'request': 'HTTP/1.1',
'request_processing_time': 3.5e-05,
'response_processing_time': 2.7e-05,
'sent_bytes': 36,
'timestamp': numpy.datetime64('2014-05-16T00:00:38.746867+0100')},
{'backend_ip': '10.0.0.40',
'backend_processing_time': 0.094383,
'backend_status_code': 200,
'client_ip': '192.168.0.10',
'elb_name': 'poundlb',
'elb_status_code': 200,
'http_method': 'PUT',
'received_bytes': 79,
'request': 'HTTP/1.1',
'request_processing_time': 3.4e-05,
'response_processing_time': 3.6e-05,
'sent_bytes': 148,
'timestamp': numpy.datetime64('2014-05-16T00:00:39.333482+0100')},
{'backend_ip': '10.0.0.42',
'backend_processing_time': 0.061355,
'backend_status_code': 200,
'client_ip': '192.168.0,10',
'elb_name': 'poundlb',
'elb_status_code': 200,
'http_method': 'PUT',
'received_bytes': 79,
'request': 'HTTP/1.1',
'request_processing_time': 9.6e-05,
'response_processing_time': 5.8e-05,
'sent_bytes': 148,
'timestamp': numpy.datetime64('2014-05-16T00:00:39.345097+0100')}]
>>> df = pd.DataFrame(test.to_dict(), index_list)
>>> df
Out[45]:
backend_ip backend_processing_time
backend_ip - -1
backend_processing_time - -1
backend_status_code - -1
client_ip - -1
elb_name - -1
elb_status_code - -1
http_method - -1
received_bytes - -1
request - -1
request_processing_time - -1
response_processing_time - -1
sent_bytes - -1
timestamp - -1
backend_status_code client_ip
backend_ip 408 192.168.0.10
backend_processing_time 408 192.168.0.10
backend_status_code 408 192.168.0.10
client_ip 408 192.168.0.10
elb_name 408 192.168.0.10
elb_status_code 408 192.168.0.10
http_method 408 192.168.0.10
received_bytes 408 192.168.0.10
request 408 192.168.0.10
request_processing_time 408 192.168.0.10
response_processing_time 408 192.168.0.10
sent_bytes 408 192.168.0.10
timestamp 408 192.168.0.10
elb_name elb_status_code http_method
backend_ip poundlb 408 PUT
backend_processing_time poundlb 408 PUT
backend_status_code poundlb 408 PUT
client_ip poundlb 408 PUT
elb_name poundlb 408 PUT
elb_status_code poundlb 408 PUT
http_method poundlb 408 PUT
received_bytes poundlb 408 PUT
request poundlb 408 PUT
request_processing_time poundlb 408 PUT
response_processing_time poundlb 408 PUT
sent_bytes poundlb 408 PUT
timestamp poundlb 408 PUT
received_bytes request request_processing_time
backend_ip 55 HTTP/1.1 -1
backend_processing_time 55 HTTP/1.1 -1
backend_status_code 55 HTTP/1.1 -1
client_ip 55 HTTP/1.1 -1
elb_name 55 HTTP/1.1 -1
elb_status_code 55 HTTP/1.1 -1
http_method 55 HTTP/1.1 -1
received_bytes 55 HTTP/1.1 -1
request 55 HTTP/1.1 -1
request_processing_time 55 HTTP/1.1 -1
response_processing_time 55 HTTP/1.1 -1
sent_bytes 55 HTTP/1.1 -1
timestamp 55 HTTP/1.1 -1
response_processing_time sent_bytes
backend_ip -1 0
backend_processing_time -1 0
backend_status_code -1 0
client_ip -1 0
elb_name -1 0
elb_status_code -1 0
http_method -1 0
received_bytes -1 0
request -1 0
request_processing_time -1 0
response_processing_time -1 0
sent_bytes -1 0
timestamp -1 0
timestamp
backend_ip 2014-05-15 23:00:04.696671
backend_processing_time 2014-05-15 23:00:04.696671
backend_status_code 2014-05-15 23:00:04.696671
client_ip 2014-05-15 23:00:04.696671
elb_name 2014-05-15 23:00:04.696671
elb_status_code 2014-05-15 23:00:04.696671
http_method 2014-05-15 23:00:04.696671
received_bytes 2014-05-15 23:00:04.696671
request 2014-05-15 23:00:04.696671
request_processing_time 2014-05-15 23:00:04.696671
response_processing_time 2014-05-15 23:00:04.696671
sent_bytes 2014-05-15 23:00:04.696671
timestamp 2014-05-15 23:00:04.696671
[13 rows x 13 columns]
我将从嗨，纪尧姆那里调查解决方案，感谢您的回复，我非常感谢您的快速回复。我将试一试，看看是否能按照您的建议实现连接。我是否应该关心连接的速度，因为我将每天增加大约12亿个请求。。。。我应该看看附件吗？注意：我将经常将数据封送到HDFStore和磁盘，可能每50000个请求一次。。我担心这会花多少时间。每天都会存储日志并创建一个新的HDFStore文件。系统将跨越午夜。我猜您必须在ipython中使用%timeit来查看在[39]中执行操作所需的时间：%timeit pd.concat（[df，df，df]）1000个循环，最好是每个循环3:554µs。需要注意的是，无论您找到什么样的解决方案来附加到数据帧，您将无法避免数据帧的完整副本，除非您通过定义一个足够大的空帧（按顺序填充）来预先分配内存。每个人，请纠正我，如果我在这个问题上是错误的，以及在这种情况下，没有预分配的情况下，如何可以进行追加。
import pandas as pd
import numpy
# Create a data dict
test_line_dict = {'backend_ip': '-',
'backend_processing_time': -1.0,
'backend_status_code': 408,
'client_ip': '192.168.0.10',
'lb_name': 'poundlb',
'elb_status_code': 408,
'http_method': 'PUT',
'received_bytes': 55,
'request': 'HTTP/1.1',
'request_processing_time': -1.0,
'response_processing_time': -1.0,
'sent_bytes': 0,
'timestamp': numpy.datetime64('2014-05-16T00:00:04.696671+0100')}
# Create the corresponding dataframe
idOfTheFirstElement = '1st'
df=pd.DataFrame.from_dict({idOfTheFirstElement:test_line_dict})
# Transpose the result
df = df.T
# One can dynamically append/add new rows with pd.concat
dfNew = pd.concat([df,df,df])