In [27]:
%matplotlib notebook
from matplotlib import pyplot as plt
In [22]:
from datetime import datetime
In [13]:
from pymongo import MongoClient
client = MongoClient("mongodb://zui:F0reverqwerty@localhost:27017/")
db = client['hkns3']
In [14]:
col = db['items']
In [17]:
import pandas
In [24]:
df = pandas.DataFrame(list(db['raw'].find({"type":"max_id"})))
In [37]:
df['time'].astype(int)
Out[37]:
0 1494944438
1 1494944588
2 1494944740
3 1494944890
4 1494945043
5 1494945193
6 1494945490
7 1494945642
8 1494945793
9 1494945944
10 1494946095
11 1494946247
12 1494946397
13 1494946549
14 1494946699
15 1494946851
16 1494947002
17 1494947152
18 1494947304
19 1494947454
20 1494947606
21 1494947756
22 1494947909
23 1494948059
24 1494948209
25 1494948361
26 1494948511
27 1494948663
28 1494948814
29 1494948965
...
3007 1495398797
3008 1495398947
3009 1495399099
3010 1495399249
3011 1495399401
3012 1495399552
3013 1495399703
3014 1495399854
3015 1495400006
3016 1495400156
3017 1495400306
3018 1495400458
3019 1495400609
3020 1495400761
3021 1495400911
3022 1495401063
3023 1495401213
3024 1495401366
3025 1495401516
3026 1495401668
3027 1495401818
3028 1495401969
3029 1495402119
3030 1495402270
3031 1495402423
3032 1495402573
3033 1495402724
3034 1495402875
3035 1495403027
3036 1495403178
Name: time, dtype: int32
In [25]:
df
Out[25]:
_id
id
time
type
0
591b0ab6421e8130778051d9
14349758
1.494944e+09
max_id
1
591b0b4c421e813077805240
14349776
1.494945e+09
max_id
2
591b0be4421e813077805253
14349795
1.494945e+09
max_id
3
591b0c7a421e813077805268
14349815
1.494945e+09
max_id
4
591b0d13421e81307780527d
14349837
1.494945e+09
max_id
5
591b0da9421e813077805295
14349850
1.494945e+09
max_id
6
591b0ed2421e8132180221b9
14349895
1.494945e+09
max_id
7
591b0f6a421e8132180221e7
14349916
1.494946e+09
max_id
8
591b1001421e8132180221ff
14349944
1.494946e+09
max_id
9
591b1098421e81321802221c
14349967
1.494946e+09
max_id
10
591b112f421e813218022236
14349994
1.494946e+09
max_id
11
591b11c7421e813218022252
14350006
1.494946e+09
max_id
12
591b125d421e813218022261
14350033
1.494946e+09
max_id
13
591b12f5421e81321802227d
14350059
1.494947e+09
max_id
14
591b138b421e81321802229a
14350071
1.494947e+09
max_id
15
591b1423421e8132180222a7
14350099
1.494947e+09
max_id
16
591b14ba421e8132180222c6
14350119
1.494947e+09
max_id
17
591b1550421e8132180222db
14350142
1.494947e+09
max_id
18
591b15e8421e8132180222f5
14350164
1.494947e+09
max_id
19
591b167e421e81321802230c
14350184
1.494947e+09
max_id
20
591b1716421e813218022323
14350197
1.494948e+09
max_id
21
591b17ac421e813218022331
14350217
1.494948e+09
max_id
22
591b1845421e813218022348
14350235
1.494948e+09
max_id
23
591b18db421e81321802235b
14350256
1.494948e+09
max_id
24
591b1971421e813218022373
14350268
1.494948e+09
max_id
25
591b1a09421e813218022380
14350292
1.494948e+09
max_id
26
591b1a9f421e81321802239b
14350306
1.494949e+09
max_id
27
591b1b37421e8132180223aa
14350321
1.494949e+09
max_id
28
591b1bce421e8132180223bc
14350354
1.494949e+09
max_id
29
591b1c65421e8132180223de
14350372
1.494949e+09
max_id
...
...
...
...
...
3007
5921f98d421e81321802d282
14389116
1.495399e+09
max_id
3008
5921fa23421e81321802d293
14389129
1.495399e+09
max_id
3009
5921fabb421e81321802d2a1
14389138
1.495399e+09
max_id
3010
5921fb51421e81321802d2ad
14389149
1.495399e+09
max_id
3011
5921fbe9421e81321802d2b9
14389154
1.495399e+09
max_id
3012
5921fc80421e81321802d2c1
14389160
1.495400e+09
max_id
3013
5921fd17421e81321802d2c8
14389174
1.495400e+09
max_id
3014
5921fdae421e81321802d2d9
14389188
1.495400e+09
max_id
3015
5921fe46421e81321802d2e8
14389199
1.495400e+09
max_id
3016
5921fedc421e81321802d2f6
14389209
1.495400e+09
max_id
3017
5921ff72421e81321802d301
14389220
1.495400e+09
max_id
3018
5922000a421e81321802d30f
14389233
1.495400e+09
max_id
3019
592200a1421e81321802d31d
14389244
1.495401e+09
max_id
3020
59220139421e81321802d32b
14389256
1.495401e+09
max_id
3021
592201cf421e81321802d338
14389264
1.495401e+09
max_id
3022
59220267421e81321802d343
14389277
1.495401e+09
max_id
3023
592202fd421e81321802d351
14389290
1.495401e+09
max_id
3024
59220396421e81321802d361
14389299
1.495401e+09
max_id
3025
5922042c421e81321802d36b
14389309
1.495402e+09
max_id
3026
592204c4421e81321802d378
14389316
1.495402e+09
max_id
3027
5922055a421e81321802d380
14389323
1.495402e+09
max_id
3028
592205f1421e81321802d38a
14389334
1.495402e+09
max_id
3029
59220687421e81321802d396
14389344
1.495402e+09
max_id
3030
5922071e421e81321802d3a3
14389356
1.495402e+09
max_id
3031
592207b7421e81321802d3b0
14389366
1.495402e+09
max_id
3032
5922084d421e81321802d3bd
14389376
1.495403e+09
max_id
3033
592208e4421e81321802d3c8
14389390
1.495403e+09
max_id
3034
5922097b421e81321802d3d9
14389396
1.495403e+09
max_id
3035
59220a13421e81321802d3e0
14389409
1.495403e+09
max_id
3036
59220aaa421e81321802d3f0
14389414
1.495403e+09
max_id
3037 rows × 4 columns
In [23]:
datetime.fromtimestamp(db['raw'].find_one({"type":"max_id"})['time'])
Out[23]:
datetime.datetime(2017, 5, 16, 10, 20, 38, 307522)
In [28]:
df.plot(x="time", y="id")
Out[28]:
<matplotlib.axes._subplots.AxesSubplot at 0x1215c341710>
In [ ]:
Content source: zuik/stuff
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