In [1]:
%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import datetime
In [2]:
pd.read_csv('data_dump.csv')[0:10]
Out[2]:
| read-size | lo-read | hi-read | signal-time | |
|---|---|---|---|---|
| 0 | 414 | 158 | 3 | 2016-12-02T18:48:24.551122 |
| 1 | 414 | 158 | 3 | 2016-12-02T18:48:24.551212 |
| 2 | 414 | 158 | 3 | 2016-12-02T18:48:24.551264 |
| 3 | 414 | 158 | 3 | 2016-12-02T18:48:24.551312 |
| 4 | 415 | 159 | 3 | 2016-12-02T18:48:24.551358 |
| 5 | 416 | 160 | 3 | 2016-12-02T18:48:24.551410 |
| 6 | 416 | 160 | 3 | 2016-12-02T18:48:24.551457 |
| 7 | 418 | 162 | 3 | 2016-12-02T18:48:24.551503 |
| 8 | 418 | 162 | 3 | 2016-12-02T18:48:24.551549 |
| 9 | 417 | 161 | 3 | 2016-12-02T18:48:24.551599 |
In [3]:
df = pd.read_csv('data_dump.csv')
In [4]:
df['signal-time'] = pd.to_datetime(df['signal-time'])
In [5]:
df['signal-time'][0].microsecond
Out[5]:
551122
In [6]:
time = []
for i in range(df['signal-time'].count()):
if i == 0:
t = 0
else:
t = df['signal-time'][i].microsecond - df['signal-time'][i-1].microsecond
time.append(t)
In [7]:
timedelta = pd.Series(time)
In [8]:
df['time-delta'] = timedelta
In [10]:
df[df['time-delta'] > 200]
Out[10]:
| read-size | lo-read | hi-read | signal-time | time-delta | |
|---|---|---|---|---|---|
| 130 | 415 | 159 | 3 | 2016-12-02 18:48:24.567076 | 11305 |
| 137 | 412 | 156 | 3 | 2016-12-02 18:48:24.582897 | 15490 |
| 144 | 412 | 156 | 3 | 2016-12-02 18:48:24.598936 | 15867 |
| 151 | 412 | 156 | 3 | 2016-12-02 18:48:24.615036 | 15779 |
| 158 | 412 | 156 | 3 | 2016-12-02 18:48:24.631054 | 15720 |
| 165 | 419 | 163 | 3 | 2016-12-02 18:48:24.647045 | 15651 |
| 172 | 416 | 160 | 3 | 2016-12-02 18:48:24.662966 | 15560 |
| 179 | 416 | 160 | 3 | 2016-12-02 18:48:24.678946 | 15697 |
| 186 | 420 | 164 | 3 | 2016-12-02 18:48:24.695007 | 15879 |
| 193 | 413 | 157 | 3 | 2016-12-02 18:48:24.710866 | 15707 |
| 200 | 418 | 162 | 3 | 2016-12-02 18:48:24.727018 | 15990 |
| 207 | 414 | 158 | 3 | 2016-12-02 18:48:24.742976 | 15616 |
| 214 | 417 | 161 | 3 | 2016-12-02 18:48:24.758981 | 15674 |
| 221 | 413 | 157 | 3 | 2016-12-02 18:48:24.774946 | 15631 |
| 224 | 1 | 1 | 2 | 2016-12-02 18:48:24.790967 | 15875 |
| 225 | 515 | 3 | 4 | 2016-12-02 18:48:24.806920 | 15953 |
| 226 | 404 | 148 | 3 | 2016-12-02 18:48:24.838915 | 31995 |
| 233 | 411 | 155 | 3 | 2016-12-02 18:48:24.854950 | 15701 |
| 240 | 410 | 154 | 3 | 2016-12-02 18:48:24.870902 | 15611 |
| 247 | 422 | 166 | 3 | 2016-12-02 18:48:24.886899 | 15663 |
| 254 | 423 | 167 | 3 | 2016-12-02 18:48:24.902893 | 15663 |
| 261 | 418 | 162 | 3 | 2016-12-02 18:48:24.918872 | 15646 |
| 268 | 412 | 156 | 3 | 2016-12-02 18:48:24.934837 | 15627 |
| 275 | 409 | 153 | 3 | 2016-12-02 18:48:24.950829 | 15633 |
| 282 | 412 | 156 | 3 | 2016-12-02 18:48:24.966836 | 15650 |
| 289 | 411 | 155 | 3 | 2016-12-02 18:48:24.982801 | 15632 |
| 296 | 410 | 154 | 3 | 2016-12-02 18:48:24.998792 | 15659 |
| 310 | 416 | 160 | 3 | 2016-12-02 18:48:25.030811 | 15591 |
| 317 | 418 | 162 | 3 | 2016-12-02 18:48:25.046830 | 15684 |
| 324 | 412 | 156 | 3 | 2016-12-02 18:48:25.062780 | 15482 |
| ... | ... | ... | ... | ... | ... |
| 834 | 416 | 160 | 3 | 2016-12-02 18:48:26.485994 | 15669 |
| 836 | 1 | 1 | 2 | 2016-12-02 18:48:26.501948 | 15878 |
| 837 | 515 | 3 | 4 | 2016-12-02 18:48:26.517932 | 15984 |
| 838 | 419 | 163 | 3 | 2016-12-02 18:48:26.534000 | 16068 |
| 839 | 418 | 162 | 3 | 2016-12-02 18:48:26.549882 | 15882 |
| 846 | 416 | 160 | 3 | 2016-12-02 18:48:26.565923 | 15702 |
| 853 | 418 | 162 | 3 | 2016-12-02 18:48:26.581890 | 15654 |
| 860 | 414 | 158 | 3 | 2016-12-02 18:48:26.597881 | 15681 |
| 867 | 414 | 158 | 3 | 2016-12-02 18:48:26.613931 | 15740 |
| 874 | 414 | 158 | 3 | 2016-12-02 18:48:26.629919 | 15658 |
| 881 | 406 | 150 | 3 | 2016-12-02 18:48:26.645885 | 15611 |
| 888 | 414 | 158 | 3 | 2016-12-02 18:48:26.661877 | 15681 |
| 895 | 418 | 162 | 3 | 2016-12-02 18:48:26.677901 | 15708 |
| 902 | 416 | 160 | 3 | 2016-12-02 18:48:26.693896 | 15680 |
| 909 | 416 | 160 | 3 | 2016-12-02 18:48:26.709882 | 15671 |
| 916 | 418 | 162 | 3 | 2016-12-02 18:48:26.725859 | 15465 |
| 923 | 418 | 162 | 3 | 2016-12-02 18:48:26.741864 | 15666 |
| 930 | 412 | 156 | 3 | 2016-12-02 18:48:26.757840 | 15664 |
| 937 | 404 | 148 | 3 | 2016-12-02 18:48:26.773831 | 15680 |
| 939 | 515 | 3 | 4 | 2016-12-02 18:48:26.805534 | 31624 |
| 940 | 408 | 152 | 3 | 2016-12-02 18:48:26.821823 | 16289 |
| 942 | 412 | 156 | 3 | 2016-12-02 18:48:26.837740 | 15843 |
| 949 | 416 | 160 | 3 | 2016-12-02 18:48:26.853764 | 15698 |
| 956 | 416 | 160 | 3 | 2016-12-02 18:48:26.869726 | 15636 |
| 963 | 414 | 158 | 3 | 2016-12-02 18:48:26.885715 | 15653 |
| 970 | 406 | 150 | 3 | 2016-12-02 18:48:26.901709 | 15656 |
| 977 | 406 | 150 | 3 | 2016-12-02 18:48:26.917695 | 15624 |
| 984 | 410 | 154 | 3 | 2016-12-02 18:48:26.933651 | 15624 |
| 991 | 415 | 159 | 3 | 2016-12-02 18:48:26.949642 | 15656 |
| 998 | 411 | 155 | 3 | 2016-12-02 18:48:26.965610 | 15632 |
142 rows × 5 columns
In [12]:
plt.plot(df[['read-size']], df[['signal-time']])
plt.ylabel('volume-graph')
plt.show()
In [ ]: