python psycopg2 changed dateformat - python-3.x

When i select in psql this
select ts_changed, to_char(ts_changed, 'YYYY-MM-DD HH24:MI:SS.MS')
from ...
i got this format.
2022-01-05 14:51:11.334
in a python script with psycopg i got this
2022-01-05 14:49:05.777000
psycopg has added three more digits i dont want, because i provides trouble with elasticsearch.
Here runs my query
pg_init_connect = 'host={} dbname={} user={}'.format(db_host, db_db, db_user)
pg_connect = psycopg2.connect(pg_init_connect)
do_sql = pg_connect.cursor()
do_sql.execute(db_query)
result = do_sql.fetchall()
pg_connect.commit()
return(result)
How to say psycopg to return the right date?

Related

Sorting dictionary by keys that are dates

When developing a telegram bot, you need to sort the dictionary by date, to output news in chronological order. The problem is the difference in key formats (dates). There is the format %d.%m at %H:M%, and there is %d.%m.%Y at %H:M%.
for k,v in sorted(Dnews_dict.items(), key=lambda x: DT.strptime(x[1].get("time"),'%d.%m.%Y at %H:%M')):
news = f"<b>{v['time']}</b>\n"\
f"{hlink(v['title'],v['url'])}"
await message.answer(news)
This code works fine, but only with 1 date type. As an option, i tried to add a string length condition (length is constant).
if len(round_data) == 18:
for k,v in sorted(Dnews_dict.items(), key=lambda x:DT.strptime(x[1].get("time"),'%d.%m.%Y в %H:%M')):
news = f"<b>{v['time']}</b>\n"\
f"{hlink(v['title'],v['url'])}"
await message.answer(news)
else:
for k,v in sorted(Dnews_dict.items(), key=lambda x:DT.strptime(x[1].get("time"),'%d.%m at %H:%M')):
news = f"<b>{v['time']}</b>\n"\
f"{hlink(v['title'],v['url'])}"
await message.answer(news)
But the condition doesn’t work. But that condition doesn’t work. How can this dilemma be resolved?
enter image description here

While I am unfamiliar with the telegraph bot, the following is a way to deal with datetime data having mixed formats e you have isolated the specific text containing the date time data:
So given a list of mixed datetime string data of the following formats:
datelist = ['08.02.2022 at 23:53',
'13.07 at 18:13',
'23.11.2022 at 19:55',
'15.02 at 01:06',
'09.07.2022 at 14:57',
'09.08 at 04:06',
'19.04.2022 at 07:19',
'28.10 at 21:56',
'19.10.2022 at 02:18',
'23.04 at 18:15']
from dateutil import parser #Utility for handling all the messy dates you encounter along the way
from dateutil.relativedelta import *
for dt in datelist:
print(parser.parse(dt))
Yields:
2022-08-02 23:53:00
2023-01-13 18:13:00
2022-11-23 19:55:00
2023-01-15 01:06:00
2022-09-07 14:57:00
2023-01-09 04:06:00
2022-04-19 07:19:00
2023-01-28 21:56:00
2022-10-19 02:18:00
2023-01-23 18:15:00
If the year 2023 is of concern, you can do the following:
for dt in datelist:
dtx = parser.parse(dt)
if dtx.year == 2023:
dtx = dtx + relativedelta(year=2022)
print(dtx)
Which produces a result keyed to the year 2022:
2022-08-02 23:53:00
2022-01-13 18:13:00
2022-11-23 19:55:00
2022-01-15 01:06:00
2022-09-07 14:57:00
2022-01-09 04:06:00
2022-04-19 07:19:00
2022-01-28 21:56:00
2022-10-19 02:18:00
2022-01-23 18:15:00

How do I convert numpy array to days, hours, mins?

Running with this series
X = number_of_logons_all.values
split = round(len(X) / 2)
X1, X2 = X[0:split], X[split:]
mean1, mean2 = X1.mean(), X2.mean()
var1, var2 = X1.var(), X2.var()
print('mean1=%f, mean2=%f' % (mean1, mean2))
print('variance1=%f, variance2=%f' % (var1, var2))
I get:
mean1=60785.792548, mean2=61291.266868
variance1=7483553053.651829, variance2=7603208729.348722
But I wanted something like this in my PyCharm console (pulled from another result):
>>> -103 days +04:37:13.802435724...
Tried to place the np.array in a pd.Dataframe() to get the expected value by adding
.apply(pd.to_timedelta, unit='s')
...this didn't work, so I tried
new = pd.DataFrame([mean1]).to_numpy(dtype='timedelta64[ns]')
...and (still) got something like this:
>>>> [[63394]]
Anyone out there who could assist me converting to an easily comprehended datetime result from my means calculation above?
Thx, in advance for your kind support.

You can use f-strings:
mean1, mean2 = 60785.792548, 61291.266868
variance1, variance2=7603208729.348722,7483553053.651829
print(f'mean1={pd.Timedelta(mean1, unit="s")}, mean2={pd.Timedelta(mean2, unit="s")}')
print(f'variance1={pd.Timedelta(variance1, unit="s")}, variance2={pd.Timedelta(variance2, unit="s")}')
mean1=0 days 16:53:05.792548, mean2=0 days 17:01:31.266868
variance1=88000 days 02:25:29.348722458, variance2=86615 days 04:44:13.651828766

Date of 7 days before today

I give current day in a string in MATLAB. For example if today is '20180703', I need 7 strings containing:
'20180702'
'20180701'
'20180630'
'20180629'
'20180628'
'20180627'
'20180626'

Simple:
t = datetime('20180703', 'InputFormat', 'yyyyMMdd')
t = t - days(1:7)
datestr(t, 'yyyymmdd')
Edit.
As excaza pointed out, datetime and datestr use different input format. Hence, 'MM' in the first function, and 'mm' in the second one.

I would go with something like:
lastSevenDays = arrayfun(#(offset) datestr(now-offset, 'yyyymmdd'), 1:7, 'UniformOutput', false)
or more matlaby:
datestr(now - days(1:7), 'yyyymmdd')

pd.to_datetime to solve '2010/1/1' rather than '2010/01/01'

I have a dataframe which contain a column 'trade_dt' like this
2009/12/1
2009/12/2
2009/12/3
2009/12/4
I got this problem
benchmark['trade_dt'] = pd.to_datetime(benchmark['trade_dt'], format='%Y-&m-%d')
ValueError: time data '2009/12/1' does not match format '%Y-&m-%d' (match)
how to solve it? Thanks~

Need change format for match - replace & and - to % and /:
benchmark['trade_dt'] = pd.to_datetime(benchmark['trade_dt'], format='%Y/%m/%d')
Also working with sample data removing format (but not sure with real data):
benchmark['trade_dt'] = pd.to_datetime(benchmark['trade_dt'])
print (benchmark)
trade_dt
0 2009-12-01
1 2009-12-02
2 2009-12-03
3 2009-12-04

How to convert a milliseconds into the normal time inside the list?

I'm parcing a web-site on Python 3.6.2
I got a long list (it has about 17000 symbols):
[[1194570000000, 1806.22], [1194829200000, 1792.02], [1194915600000, 1777.35], [1195002000000, 1783.27], [1195088400000, 1782.55], [1195174800000, 1760.89], [1195434000000, 1751.49], [1195520400000, 1747.99], [1195606800000, 1741.47], [1195693200000, 1726.78], [1195779600000, 1730.69], [1196038800000, 1750.07], [1196125200000, 1738.24], [1196211600000, 1730.1], [1196298000000, 1744.72], [1196384400000, 1759.95], [1196643600000, 1759.74], [1196730000000, 1754.45], [1196816400000, 1772.8], [1196902800000, 1786.09], [1196989200000, 1790.25], [1197248400000, 1796.63], [1197334800000, 1815.07], [1197421200000, 1818.52], [1197507600000, 1816.8], [1197594000000, 1796.78], [1197853200000, 1772.76], [1197939600000, 1789.9], [1198026000000, 1781.98], [1198112400000, 1794.38], [1198198800000, 1797.66], [1198458000000, 1807.15], [1198544400000, 1801.53], [1198630800000, 1799.07], [1198717200000, 1800.78], [1198803600000, 1800.3], [1198890000000, 1800.23], [1199926800000, 1825.09], [1200013200000, 1827.88], [1200272400000, 1828.87], [1200358800000, 1832.82], [1200445200000, 1788.15], [1200531600000, 1746.4], [1200618000000, 1729.26], [1200877200000, 1657.58], [1200963600000, 1606.25], [1201050000000, 1603.64], [1201136400000, 1621.03], [1201222800000, 1657.67], [1201482000000, 1628.03], [1201568400000, 1629.02], [1201654800000, 1617.45], [1201741200000, 1585.1], [1201827600000, 1615.29], [1202086800000, 1666.69], [1202173200000, 1673.41], [1202259600000, 1633.71], [1202346000000, 1625.94], [1202432400000, 1624.1], [1202691600000, 1638.08], [1202778000000, 1701.16], [1202864400000, 1721.58], [1202950800000, 1756.86], [1203037200000, 1732.13], [1203296400000, 1751.02], [1203382800000, 1760.49], [1203469200000, 1755.08], [1203555600000, 1780.1], [1203642000000, 1778.15], [1203987600000, 1786.42], [1204074000000, 1777.88], [1204160400000, 1767.45], [1204246800000, 1764.72], [1204506000000, 1734.83], [1204592400000, 1739.02], [1204678800000, 1730.82], [1204765200000, 1738.13], [1204851600000, 1710.22], [1205197200000, 1714.67], [1205283600000, 1748.51], [1205370000000, 1727.69], [1205456400000, 1735.35], [1205715600000, 1681.17], [1205802000000, 1680.49], [1205888400000, 1687.95], [1205974800000, 1658.55], [1206061200000, 1669.67], [1206320400000, 1666.83], [1206406800000, 1682.01], [1206493200000, 1673.85], [1206579600000, 1692.85], [1206666000000, 1711.2], [1206921600000, 1732.77], [1207008000000, 1747.96], [1207094400000, 1757.32], [1207180800000, 1754.46], [1207267200000, 1745.45], [1207526400000, 1764.22], [1207612800000, 1758.4], [1207699200000, 1770.59], [1207785600000, 1776.8], [1207872000000, 1775.61], [1208131200000, 1759.18], [1208217600000, 1782.33], [1208304000000, 1803.16], [1208390400000, 1819.44], [1208476800000, 1825.26], [1208736000000, 1834.73], [1208822400000, 1819.78], [1208908800000, 1818.49], [1208995200000, 1804.03], [1209081600000, 1797.37], [1209340800000, 1819.62], [1209427200000, 1816.22], [1209513600000, 1788.91], [1209859200000, 1818.08], [1209945600000, 1825.41], [1210032000000, 1835.86], [1210118400000, 1860.42], [1210204800000, 1890.98], [1210550400000, 1912.79], [1210636800000, 1921.4], [1210723200000, 1930.14], [1210809600000, 1961.11], [1210896000000, 1976.18], [1211155200000, 1982.32], [1211241600000, 1962.19], [1211328000000, 1971.47], [1211414400000, 1958.48], [1211500800000, 1937.04], [1211760000000, 1924.79], [1211846400000, 1920.34], [1211932800000, 1910.95], [1212019200000, 1937.63], [1212105600000, 1939.91], [1212364800000, 1954.14], [1212451200000, 1943.88], [1212537600000, 1917.49], [1212624000000, 1908.67], [1212710400000, 1920.71], [1212796800000, 1888.57], [1212969600000, 1902.74], [1213056000000, 1893.78], [1213142400000, 1910.03], [1213574400000, 1934.58], [1213660800000, 1954.97], [1213747200000, 1971.5], [1213833600000, 1967.82], [1213920000000, 1958.94], [1214179200000, 1925.44], [1214265600000, 1897.82], [1214352000000, 1898.14], [1214438400000, 1888.13], [1214524800000, 1853.59], [1214784000000, 1874.87], [1214870400000, 1843.88], [1214956800000, 1836.45], [1215043200000, 1801.29], [1215129600000, 1799.02], [1215388800000, 1801.73], [1215475200000, 1778.26], [1215561600000, 1782.45], [1215648000000, 1781.25], [1215734400000, 1768.2], [1215993600000, 1764.23], [1216080000000, 1737.92], [1216166400000, 1725.59], [1216252800000, 1752.41], [1216339200000, 1738.93], [1216598400000, 1718.19], [1216684800000, 1712.45], [1216771200000, 1718.95], [1216857600000, 1686.71], [1216944000000, 1587.53], [1217203200000, 1581.52], [1217289600000, 1524.75], [1217376000000, 1557.89], [1217462400000, 1581.82], [1217548800000, 1580.59], [1217808000000, 1559.64], [1217894400000, 1513.67], [1217980800000, 1503.23], [1218067200000, 1527.85], [1218153600000, 1482.64], [1218412800000, 1445.45], [1218499200000, 1508.53], [1218585600000, 1514.26], [1218672000000, 1539.56], [1218758400000, 1535.05], [1219017600000, 1535.95], [1219104000000, 1477.17], [1219276800000, 1483.31], [1219363200000, 1476.09], [1219622400000, 1442.8], [1219708800000, 1364.55], [1219795200000, 1367.89], [1219881600000, 1391.75], [1219968000000, 1404.44], [1220227200000, 1426.43], [1220313600000, 1418.14], [1220400000000, 1389.37], [1220486400000, 1360.89], [1220572800000, 1255.55], [1220832000000, 1293.47], [1220918400000, 1239.64], [1221004800000, 1132.71], [1221091200000, 1138.64], [1221177600000, 1157.26], [1221436800000, 1125.8], [1221523200000, 1023.4], [1221609600000, 964.38], [1221696000000, 963.58], [1221782400000, 1096.9], [1222041600000, 1158.72], [1222128000000, 1126.96], [1222214400000, 1138.86], [1222300800000, 1107.07], [1222387200000, 1093.35], [1222646400000, 1056.35], [1222732800000, 994.01], [1222819200000, 1025.51], [1222905600000, 997.97], [1222992000000, 916.35], [1223251200000, 808.1], [1223337600000, 757.5], [1223424000000, 682.82], [1223510400000, 694.25], [1223596800000, 694.19], [1223856000000, 672.24], [1223942400000, 718.12], [1224028800000, 682.73], [1224115200000, 614.08], [1224201600000, 573.74], [1224460800000, 578.35], [1224547200000, 602.25], [1224633600000, 584.62], [1224720000000, 563.86], [1224806400000, 514.53], [1225069200000, 514.39], [1225155600000, 488.76], [1225242000000, 529.27], [1225328400000, 611.05], [1225414800000, 625.17], [1225501200000, 689.32], [1225846800000, 732.17], [1225933200000, 650.25], [1226019600000, 640.83], [1226278800000, 679.3], [1226365200000, 589.93], [1226451600000, 589.87], [1226538000000, 517.05], [1226624400000, 542.3], [1226883600000, 501.9], [1226970000000, 479.59], [1227056400000, 478.47], [1227142800000, 450.07], [1227229200000, 457.15], [1227488400000, 478.68], [1227574800000, 517.51], [1227661200000, 505.31], [1227747600000, 528.05], [1227834000000, 534.75], [1228093200000, 536.86], [1228179600000, 510.96], [1228266000000, 516.41], [1228352400000, 519.82], [1228438800000, 509.61], [1228698000000, 542.01], [1228784400000, 559.07], [1228870800000, 571.89], [1228957200000, 575.07], [1229043600000, 547.36], [1229302800000, 575.82], [1229389200000, 571.53], [1229475600000, 574.57], [1229562000000, 552.97], [1229648400000, 534.69], [1229907600000, 568.61], [1229994000000, 580.45], [1230080400000, 586.15], [1230166800000, 566.55], [1230253200000, 557.38], [1230512400000, 555.76], [1230598800000, 547.45], [1230685200000, 548.16], [1231635600000, 563.58], [1231722000000, 575.88], [1231808400000, 572.75], [1231894800000, 576.31], [1231981200000, 549.98], [1232067600000, 553.97], [1232326800000, 530.95], [1232413200000, 512.12], [1232499600000, 508.34], [1232586000000, 514.01], [1232672400000, 489.04], [1232931600000, 509.06], [1233018000000, 528.42], [1233104400000, 539.64], [1233190800000, 546.1], [1233277200000, 556.09], [1233536400000, 555.23], [1233622800000, 565.5], [1233709200000, 576.94], [1233795600000, 583.89], [1233882000000, 606.37], [1234141200000, 650.84], [1234227600000, 670.48], [1234314000000, 665.39], [1234400400000, 657.63], [1234486800000, 660.24], [1234746000000, 663.05], [1234832400000, 637.9], [1234918800000, 600.87], [1235005200000, 621.45], [1235091600000, 607.74], [1235437200000, 597.27], [1235523600000, 620.61], [1235610000000, 631.12], [1235696400000, 632.53], [1235955600000, 616.06], [1236042000000, 614.71], [1236128400000, 634.31], [1236214800000, 633.7], [1236301200000, 636.42], [1236646800000, 676.8], [1236733200000, 687.33], [1236819600000, 664.34], [1236906000000, 677.91], [1237165200000, 673.76], [1237251600000, 680.96], [1237338000000, 678.9], [1237424400000, 690.34], [1237510800000, 713.94], [1237770000000, 765.98], [1237856400000, 761.21], [1237942800000, 792.88], [1238029200000, 815.54], [1238115600000, 817.43], [1238371200000, 757.88], [1238457600000, 768.05], [1238544000000, 771.56], [1238630400000, 799.54], [1238716800000, 806.71], [1238976000000, 826.06], [1239062400000, 805.96], [1239148800000, 816.81], [1239235200000, 867.51], [1239321600000, 897.57], [1239580800000, 905.51], [1239667200000, 943.68], [1239753600000, 947.12], [1239840000000, 952.44], [1239926400000, 974.06], [1240185600000, 950.03], [1240272000000, 921.33], [1240358400000, 938.02], [1240444800000, 972.55], [1240531200000, 987.71], [1240790400000, 969.15], [1240876800000, 929.82], [1240963200000, 975.75], [1241049600000, 1009.64], [1241395200000, 1042.23], [1241481600000, 1069.43], [1241568000000, 1084.97], [1241654400000, 1163.66], [1241740800000, 1161.41], [1242086400000, 1181.97], [1242172800000, 1176.05], [1242259200000, 1108.11], [1242345600000, 1137.12], [1242604800000, 1121.17], [1242691200000, 1151.36], [1242777600000, 1171.26], [1242864000000, 1164.11], [1242950400000, 1162.57], [1243209600000, 1165.35], [1243296000000, 1142.74], [1243382400000, 1171.37], [1243468800000, 1172.33], [1243555200000, 1203.54], [1243814400000, 1262.5], [1243900800000, 1289.68], [1243987200000, 1268.4], [1244073600000, 1250.81], [1244160000000, 1285.42], [1244419200000, 1262.42], [1244505600000, 1277.5], [1244592000000, 1304.35], [1244678400000, 1300.95], [1245024000000, 1264.32], [1245110400000, 1255.02], [1245196800000, 1233], [1245283200000, 1183.33], [1245369600000, 1192.41], [1245628800000, 1142.32], [1245715200000, 1069.01], [1245801600000, 1118.55], [1245888000000, 1127.34], [1245974400000, 1141.99], [1246233600000, 1124.32], [1246320000000, 1144.29], [1246406400000, 1128.87], [1246492800000, 1122.6], [1246579200000, 1106.46], [1246838400000, 1076.98], [1246924800000, 1076.09], [1247011200000, 1042.85], [1247097600000, 1041.93], [1247184000000, 1010.26], [1247443200000, 997.8], [1247529600000, 1037.49], [1247616000000, 1044.34], [1247702400000, 1050.26], [1247788800000, 1063.75], [1248048000000, 1089.15], [1248134400000, 1101.04], [1248220800000, 1093.33], [1248307200000, 1107.34], [1248393600000, 1145.76], [1248652800000, 1183.04], [1248739200000, 1172.51], [1248825600000, 1152.45], [1248912000000, 1185.01], [1248998400000, 1203.98], [1249257600000, 1249.48], [1249344000000, 1235.59], [1249430400000, 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[1254182400000, 1690.57], [1254268800000, 1711.31], [1254355200000, 1726.62], [1254441600000, 1668.86], [1254700800000, 1698.15], [1254787200000, 1723.63], [1254873600000, 1796.48], [1254960000000, 1855.47], [1255046400000, 1866.12], [1255305600000, 1909.47], [1255392000000, 1909.81], [1255478400000, 1947.76], [1255564800000, 1947.59], [1255651200000, 1929.36], [1255910400000, 1947.13], [1255996800000, 1977.64], [1256083200000, 1975.57], [1256169600000, 1962.69], [1256256000000, 2009.06], [1256518800000, 2038.76], [1256605200000, 1973.44], [1256691600000, 1900.16], [1256778000000, 1865.5], [1256864400000, 1901.15], [1257123600000, 1867.59], [1257210000000, 1840.98], [1257382800000, 1864.77], [1257469200000, 1880.08], [1257728400000, 1920.11], [1257814800000, 1948.4], [1257901200000, 1962.72], [1257987600000, 1922.58], [1258074000000, 1902.03], [1258333200000, 1949.41], [1258419600000, 1943.86], [1258506000000, 1972.27], [1258592400000, 1945.69], [1258678800000, 1930.92], 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2272.35], [1317254400000, 2279.69], [1317340800000, 2257.6], [1317600000000, 2204.21], [1317686400000, 2127.62], [1317772800000, 2097.34], [1317859200000, 2132.02]]
It has following structure:
[Date, price]
How can I convert all milliseconds to normal date (DD/MM/YYYY) inside the list?

Those look like milliseconds since the epoch. You can use the Python datetime library.
import datetime
def timestamp(ms):
return datetime.datetime.fromtimestamp(ms / 1000.0).strftime('%d-%m-%Y %H:%M')
def use_timestamps(lst):
return [[timestamp(ms), price] for ms, price in lst]
Here is the output using the list you provided:
https://pastebin.com/zJx94EpQ

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