Python提取特定时间段内数据的方法实例

python提取特定时间段内的数据

尝试一下：

data['Date'] = pd.to_datetime(data['Date'])
data = data[(data['Date'] >=pd.to_datetime('20120701')) & (data['Date'] <= pd.to_datetime('20120831'))]

实际测试

'''
Created on 2019年1月3日
@author: hcl
'''
import pandas as pd
import matplotlib.pyplot as plt
data_path = 'one_20axyz.csv'
if __name__ == '__main__': 
  msg = pd.read_csv(data_path)
#   ID_set = set(msg['Time'].tolist())
#   ID_list = list(ID_set)
#   print(len(msg['Time'].tolist()),len(ID_list),len(msg['Time'].tolist())/len(ID_list))#打印数据量  多少秒  平均每秒多少个
#   print(msg.head(10))
#   left_a = msg[msg['leg'] == 1]['az']
#   right_a = msg[msg['leg'] == 2]['az']
#   plt.plot(left_a,label = 'left_a')
#   plt.plot(right_a,label = 'right_a')
#   plt.legend(loc = 'best')
#   plt.show()
  left_msg = msg[msg['leg'] == 1] #DataFrame
  data = left_msg[(pd.to_datetime(left_msg['Time'] ,format = '%H:%M:%S')>= pd.to_datetime('16:23:42',format = '%H:%M:%S')) & (pd.to_datetime(left_msg['Time'] ,format = '%H:%M:%S') <= pd.to_datetime('16:23:52',format = '%H:%M:%S'))]
#   print(msg.head())
  print(data)

输出：

Time ID leg   ax   ay   az     a Rssi
1  16:23:42  5  1 0.6855 -0.6915 0.1120 0.980116  -34
3  16:23:42  5  1 0.6800 -0.6440 0.1365 0.946450  -31
5  16:23:42  5  1 0.7145 -0.7240 0.1095 1.023072  -34
7  16:23:42  5  1 0.7050 -0.6910 0.1080 0.993061  -30
9  16:23:42  5  1 0.7120 -0.6400 0.0920 0.961773  -31
10  16:23:42  5  1 0.7150 -0.6810 0.1290 0.995805  -34
12  16:23:42  5  1 0.7250 -0.6655 0.1890 1.002116  -32
13  16:23:42  5  1 0.7160 -0.7065 0.1000 1.010840  -31
15  16:23:42  5  1 0.7545 -0.6990 0.1715 1.042729  -30
17  16:23:42  5  1 0.7250 -0.6910 0.1325 1.010278  -31
19  16:23:42  5  1 0.7520 -0.7260 0.1820 1.060992  -33
21  16:23:42  5  1 0.7005 -0.7150 0.0605 1.002789  -33
23  16:23:42  5  1 0.7185 -0.6630 0.1430 0.988059  -30
25  16:23:42  5  1 0.7170 -0.7040 0.0920 1.009044  -34
27  16:23:42  5  1 0.7230 -0.6810 0.1060 0.998862  -31
29  16:23:42  5  1 0.7230 -0.6720 0.0940 0.991539  -31
31  16:23:42  5  1 0.6955 -0.6975 0.0720 0.987629  -33
32  16:23:42  5  1 0.7430 -0.6895 0.1495 1.024602  -34
34  16:23:43  5  1 0.7360 -0.6855 0.1200 1.012920  -32
36  16:23:43  5  1 0.7160 -0.7000 0.1330 1.010121  -30
38  16:23:43  5  1 0.7095 -0.7165 0.1090 1.014221  -31
40  16:23:43  5  1 0.7195 -0.6895 0.1270 1.004599  -34
44  16:23:43  5  1 0.7315 -0.6855 0.1000 1.007473  -34
46  16:23:43  5  1 0.7240 -0.7020 0.0960 1.013013  -31
48  16:23:43  5  1 0.7240 -0.7010 0.0970 1.012416  -32
50  16:23:43  5  1 0.7380 -0.6820 0.1480 1.015713  -34
52  16:23:43  5  1 0.7285 -0.6990 0.0990 1.014453  -33
53  16:23:43  5  1 0.7160 -0.7005 0.1630 1.014852  -30
55  16:23:43  5  1 0.7175 -0.6940 0.0735 1.000922  -29
57  16:23:43  5  1 0.7140 -0.7170 0.0960 1.016416  -28
..    ... .. ...   ...   ...   ...    ...  ...
285 16:23:51  5  1 0.0550 -1.0205 0.0955 1.026433  -35
287 16:23:51  5  1 0.0670 -1.0175 0.0915 1.023801  -22
289 16:23:51  5  1 0.0595 -1.0090 0.1025 1.015937  -24
291 16:23:51  5  1 0.0605 -0.9970 0.0905 1.002925  -32
293 16:23:51  5  1 0.0650 -1.0185 0.0740 1.023251  -31
295 16:23:51  5  1 0.0595 -0.9915 0.0945 0.997769  -35
298 16:23:51  5  1 0.0420 -1.0105 0.0970 1.016013  -18
300 16:23:51  5  1 0.0545 -1.0440 0.0795 1.048440  -21
302 16:23:51  5  1 0.0460 -0.9915 0.0765 0.995510  -30
304 16:23:51  5  1 0.0650 -1.0100 0.0810 1.015326  -30
306 16:23:51  5  1 0.0530 -1.0240 0.0765 1.028220  -34
308 16:23:51  5  1 0.0490 -1.0060 0.0785 1.010247  -21
310 16:23:52  5  1 0.0490 -1.0155 0.0760 1.019518  -24
312 16:23:52  5  1 0.0370 -0.9870 0.0660 0.989896  -30
313 16:23:52  5  1 0.0400 -1.0185 0.0435 1.020213  -30
314 16:23:52  5  1 0.0450 -1.0070 0.0540 1.009450  -34
316 16:23:52  5  1 0.0420 -0.9800 0.0595 0.982703  -34
318 16:23:52  5  1 0.0400 -1.0000 0.0595 1.002567  -20
320 16:23:52  5  1 0.0355 -1.0025 0.0635 1.005136  -20
322 16:23:52  5  1 0.0430 -0.9940 0.0735 0.997641  -30
324 16:23:52  5  1 0.0480 -1.0135 0.0640 1.016652  -33
326 16:23:52  5  1 0.0440 -1.0035 0.0670 1.006696  -33
328 16:23:52  5  1 0.0455 -1.0090 0.0600 1.011806  -21
330 16:23:52  5  1 0.0420 -1.0005 0.0605 1.003207  -15
332 16:23:52  5  1 0.0510 -1.0165 0.0670 1.019981  -29
334 16:23:52  5  1 0.0300 -1.0040 0.0460 1.005501  -30
336 16:23:52  5  1 0.0370 -1.0130 0.0500 1.014908  -34
338 16:23:52  5  1 0.0500 -1.0010 0.0530 1.003648  -20
341 16:23:52  5  1 0.0400 -0.9630 0.0615 0.965790  -21
343 16:23:52  5  1 0.0365 -1.0295 0.0410 1.030962  -30
[176 rows x 8 columns]