How to convert a milliseconds into the normal time inside the list? - python-3.x
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], 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[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], 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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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2199.22], [1264381200000, 2193.58], [1264467600000, 2144.89], [1264554000000, 2149.68], [1264640400000, 2218.12], [1264726800000, 2237.17], [1264986000000, 2229.98], [1265072400000, 2270.82], [1265158800000, 2334.71], [1265245200000, 2293.67], [1265331600000, 2170.48], [1265590800000, 2154.39], [1265677200000, 2140.96], [1265763600000, 2160.78], [1265850000000, 2157.81], [1265936400000, 2152.04], [1266195600000, 2166.07], [1266282000000, 2208.66], [1266368400000, 2241.27], [1266454800000, 2204.89], [1266541200000, 2194.01], [1266973200000, 2205.58], [1267059600000, 2195.12], [1267146000000, 2206.47], [1267232400000, 2241.65], [1267405200000, 2254.96], [1267491600000, 2259.17], [1267578000000, 2269.09], [1267664400000, 2290.47], [1267750800000, 2316.19], [1268096400000, 2312.49], [1268182800000, 2316.02], [1268269200000, 2300.43], [1268355600000, 2323.75], [1268614800000, 2333.29], [1268701200000, 2339.15], [1268787600000, 2366.15], [1268874000000, 2373.97], [1268960400000, 2367.54], [1269219600000, 2352.68], [1269306000000, 2371.24], [1269392400000, 2359.14], [1269478800000, 2365.72], [1269565200000, 2416.75], [1269820800000, 2421.75], [1269907200000, 2453.62], [1269993600000, 2485.89], [1270080000000, 2520.2], [1270166400000, 2509.26], [1270425600000, 2527.51], [1270512000000, 2549.75], [1270598400000, 2510.39], [1270684800000, 2495.96], [1270771200000, 2537.35], [1271030400000, 2556.07], [1271116800000, 2599.57], [1271203200000, 2646.47], [1271289600000, 2646.61], [1271376000000, 2592.88], [1271635200000, 2500.75], [1271721600000, 2540.25], [1271808000000, 2538.07], [1271894400000, 2509.43], [1271980800000, 2500.58], [1272240000000, 2562.76], [1272326400000, 2519.97], [1272412800000, 2457.82], [1272499200000, 2491.67], [1272585600000, 2501.05], [1272931200000, 2429.29], [1273017600000, 2362.58], [1273104000000, 2322.6], [1273190400000, 2204.22], [1273536000000, 2208.73], [1273622400000, 2243.5], [1273708800000, 2275.73], [1273795200000, 2209.73], [1274054400000, 2196.03], [1274140800000, 2239.5], [1274227200000, 2172.53], [1274313600000, 2113.25], [1274400000000, 2041.37], [1274659200000, 2096.79], [1274745600000, 2019.49], [1274832000000, 2045.77], [1274918400000, 2103.37], [1275004800000, 2117.9], [1275264000000, 2138.24], [1275350400000, 2120.55], [1275436800000, 2137.98], [1275523200000, 2200.41], [1275609600000, 2183.65], [1275868800000, 2120.63], [1275955200000, 2132.57], [1276041600000, 2135.47], [1276128000000, 2138.14], [1276214400000, 2139.16], [1276560000000, 2154.37], [1276646400000, 2166.77], [1276732800000, 2170.14], [1276819200000, 2157], [1277078400000, 2203.37], [1277164800000, 2188.95], [1277251200000, 2177.6], [1277337600000, 2152.36], [1277424000000, 2127.99], [1277683200000, 2136.44], [1277769600000, 2079.88], [1277856000000, 2041.62], [1277942400000, 1996.26], [1278028800000, 1983.89], [1278288000000, 1996.61], [1278374400000, 2029.39], [1278460800000, 2033.7], [1278547200000, 2090.78], [1278633600000, 2071.53], [1278892800000, 2088.88], [1278979200000, 2116.48], [1279065600000, 2115.64], [1279152000000, 2114.95], [1279238400000, 2091.12], [1279497600000, 2087.68], [1279584000000, 2079.61], [1279670400000, 2105.69], [1279756800000, 2113.61], [1279843200000, 2146.42], [1280102400000, 2167.61], [1280188800000, 2175.95], [1280275200000, 2181.87], [1280361600000, 2206.88], [1280448000000, 2199.06], [1280707200000, 2249.88], [1280793600000, 2238.43], [1280880000000, 2244.51], [1280966400000, 2247.07], [1281052800000, 2238.67], [1281312000000, 2260.88], [1281398400000, 2235.64], [1281484800000, 2206.03], [1281571200000, 2183.63], [1281657600000, 2182.92], [1281916800000, 2174.21], [1282003200000, 2209.7], [1282089600000, 2213.49], [1282176000000, 2219.71], [1282262400000, 2203.89], [1282521600000, 2211.16], [1282608000000, 2183.58], [1282694400000, 2185.76], [1282780800000, 2200.34], [1282867200000, 2201.8], [1283126400000, 2229.02], [1283212800000, 2205.47], [1283299200000, 2235.49], [1283385600000, 2265.73], [1283472000000, 2272.88], [1283731200000, 2307.1], [1283817600000, 2289.12], [1283904000000, 2316], [1283990400000, 2336.63], [1284076800000, 2353.65], [1284336000000, 2364.86], [1284422400000, 2345.28], [1284508800000, 2330.23], [1284595200000, 2332.73], [1284681600000, 2329.07], [1284940800000, 2311.08], [1285027200000, 2340.75], [1285113600000, 2322.33], [1285200000000, 2318.23], [1285286400000, 2314.83], [1285545600000, 2322.81], [1285632000000, 2273.99], [1285718400000, 2291.21], [1285804800000, 2299.94], [1285891200000, 2324.88], [1286150400000, 2345.1], [1286236800000, 2349.47], [1286323200000, 2362.85], [1286409600000, 2343.36], [1286496000000, 2324.29], [1286755200000, 2350.54], [1286841600000, 2356.69], [1286928000000, 2379.54], [1287014400000, 2385.66], [1287100800000, 2391.12], [1287360000000, 2391.33], [1287446400000, 2388.31], [1287532800000, 2383.03], [1287619200000, 2368.92], [1287705600000, 2365.76], [1287964800000, 2377.83], [1288051200000, 2374.57], [1288137600000, 2371.17], [1288224000000, 2368.49], [1288310400000, 2362.03], [1288573200000, 2394.35], [1288659600000, 2388.84], [1288746000000, 2391.23], [1289178000000, 2419.32], [1289264400000, 2434.15], [1289350800000, 2421.7], [1289437200000, 2436.95], [1289523600000, 2420.74], [1289610000000, 2420.22], [1289782800000, 2430.49], [1289869200000, 2436.24], [1289955600000, 2452.63], [1290042000000, 2502.14], [1290128400000, 2509.4], [1290387600000, 2521.59], [1290474000000, 2492.79], [1290560400000, 2499.77], [1290646800000, 2539.31], [1290733200000, 2541.65], [1290992400000, 2535.83], [1291078800000, 2548.12], [1291165200000, 2579.55], [1291251600000, 2630.43], [1291338000000, 2684.82], [1291597200000, 2706.76], [1291683600000, 2722.66], [1291770000000, 2699.41], [1291856400000, 2704.61], [1291942800000, 2720.08], [1292202000000, 2755.7], [1292288400000, 2775.37], [1292374800000, 2787.9], [1292461200000, 2782.11], [1292547600000, 2785.73], [1292806800000, 2822.49], [1292893200000, 2824.16], [1292979600000, 2831.9], [1293066000000, 2842.81], [1293152400000, 2842.59], [1293411600000, 2809.56], [1293498000000, 2811.79], [1293584400000, 2807.98], [1293670800000, 2810.86], [1293757200000, 2810.44], [1294707600000, 2916.55], [1294794000000, 2970.84], [1294880400000, 2985.02], [1294966800000, 2970.21], [1295226000000, 3013.12], [1295312400000, 3023.03], [1295398800000, 3034.26], [1295485200000, 2994.76], [1295571600000, 3030.34], [1295830800000, 3004.56], [1295917200000, 3007.85], [1296003600000, 3033.62], [1296090000000, 3054.33], [1296176400000, 3034.96], [1296435600000, 3001.76], [1296522000000, 3047.94], [1296608400000, 3085.91], [1296694800000, 3079.5], [1296781200000, 3081.59], [1297040400000, 3065.24], [1297126800000, 3057.8], [1297213200000, 3037.93], [1297299600000, 2994.59], [1297386000000, 3019.97], [1297645200000, 3066.61], [1297731600000, 3077.68], [1297818000000, 3075.74], [1297904400000, 3098.59], [1297990800000, 3110.37], [1298250000000, 3145.66], [1298336400000, 3114.48], [1298509200000, 3101.39], [1298595600000, 3140.65], [1298854800000, 3181.54], [1298941200000, 3187.78], [1299027600000, 3160.24], [1299114000000, 3185.79], [1299200400000, 3174.66], [1299286800000, 3178.22], [1299632400000, 3205.61], [1299718800000, 3142.39], [1299805200000, 3093.05], [1300064400000, 3095.52], [1300150800000, 2986.98], [1300237200000, 3010.72], [1300323600000, 3017.64], [1300410000000, 3040.04], [1300669200000, 3047.59], [1300755600000, 3023.41], [1300842000000, 3038.04], [1300928400000, 3068.87], [1301014800000, 3083.17], [1301270400000, 3087.51], [1301356800000, 3067.37], [1301443200000, 3098.34], [1301529600000, 3113.9], [1301616000000, 3141.69], [1301875200000, 3174.5], [1301961600000, 3172.26], [1302048000000, 3188.25], [1302134400000, 3176.8], [1302220800000, 3191.89], [1302480000000, 3185.84], [1302566400000, 3127.59], [1302652800000, 3108.23], [1302739200000, 3095.78], [1302825600000, 3089.41], [1303084800000, 3015.84], [1303171200000, 2981.92], [1303257600000, 3033.67], [1303344000000, 3053.12], [1303430400000, 3054.46], [1303689600000, 3063.89], [1303776000000, 3014.28], [1303862400000, 3014.37], [1303948800000, 3007.57], [1304035200000, 2967.66], [1304380800000, 2945.87], [1304467200000, 2901.66], [1304553600000, 2855.25], [1304640000000, 2850.45], [1304985600000, 2878.71], [1305072000000, 2866.19], [1305158400000, 2802.41], [1305244800000, 2802.41], [1305417600000, 2790.78], [1305504000000, 2790.78], [1305590400000, 2802.58], [1305676800000, 2805.54], [1305763200000, 2819.33], [1305849600000, 2795.36], [1306022400000, 2720.91], [1306108800000, 2720.91], [1306195200000, 2738.45], [1306281600000, 2742.91], [1306368000000, 2759.38], [1306454400000, 2774], [1306713600000, 2801.78], [1306800000000, 2829.22], [1306886400000, 2818.64], [1306972800000, 2799.42], [1307059200000, 2806.33], [1307318400000, 2790.76], [1307404800000, 2818.34], [1307491200000, 2836.65], [1307577600000, 2857.04], [1307664000000, 2874.5], [1308009600000, 2879.79], [1308096000000, 2905.71], [1308182400000, 2858.4], [1308268800000, 2849.95], [1308528000000, 2822.09], [1308614400000, 2846.21], [1308700800000, 2845.19], [1308787200000, 2815.21], [1308873600000, 2831.43], [1309132800000, 2814.01], [1309219200000, 2825.49], [1309305600000, 2852.74], [1309392000000, 2869.25], [1309478400000, 2909.79], [1309737600000, 2924.97], [1309824000000, 2926.85], [1309910400000, 2918.67], [1309996800000, 2961.3], [1310083200000, 2967.96], [1310342400000, 2930.53], [1310428800000, 2910.43], [1310515200000, 2927.7], [1310601600000, 2943.07], [1310688000000, 2947.03], [1310947200000, 2943.98], [1311033600000, 2925.17], [1311120000000, 2938.49], [1311206400000, 2941.49], [1311292800000, 2969.48], [1311552000000, 2955.88], [1311638400000, 2970.93], [1311724800000, 2974.8], [1311811200000, 2964.83], [1311897600000, 2959.33], [1312156800000, 2984.21], [1312243200000, 2951.08], [1312329600000, 2908.34], [1312416000000, 2840.94], [1312502400000, 2689.22], [1312761600000, 2599.58], [1312848000000, 2449.5], [1312934400000, 2534.46], [1313020800000, 2435.75], [1313107200000, 2509.19], [1313366400000, 2617.46], [1313452800000, 2607.63], [1313539200000, 2648.05], [1313625600000, 2615.57], [1313712000000, 2531.74], [1313971200000, 2553.75], [1314057600000, 2548.08], [1314144000000, 2538.26], [1314230400000, 2543.95], [1314316800000, 2515.13], [1314576000000, 2603.59], [1314662400000, 2605.23], [1314748800000, 2637.56], [1314835200000, 2630.24], [1314921600000, 2610.72], [1315180800000, 2579.43], [1315267200000, 2579.33], [1315353600000, 2621.89], [1315440000000, 2626.82], [1315526400000, 2598.39], [1315785600000, 2513.64], [1315872000000, 2525.82], [1315958400000, 2527.06], [1316044800000, 2546.5], [1316131200000, 2517.3], [1316390400000, 2486.41], [1316476800000, 2518.97], [1316563200000, 2531.8], [1316649600000, 2408.69], [1316736000000, 2247.31], [1316995200000, 2223.62], [1317081600000, 2291.87], [1317168000000, 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
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
how to perform calculation for all elements of list parellely or simultaneously to reduce the run time?
I am extracting a dataframe from yahoo API for a list of companies but I want to do it paralelly(i.e, the dataframes for all the companies should be extracted simultaneously).So, it reduces my run time....
My code:
import pandas_datareader.data as web
import pandas as pd
import datetime
end_date = datetime.datetime.now().strftime('%d/%m/%Y')
temp = datetime.datetime.now() - datetime.timedelta(6*365/12)
start_date = temp.strftime('%d/%m/%Y')
f = web.DataReader('ACC.NS', 'yahoo', start_date, end_date)
print(f)
The output of this code is a dataframe shown below:
This i have done for single company....
I want to do it for a list of companies which is:
Company_Names = ['ACC', 'ADANIENT', 'ADANIPORTS', 'ADANIPOWER', 'AJANTPHARM', 'ALBK', 'AMARAJABAT', 'AMBUJACEM', 'APOLLOHOSP', 'APOLLOTYRE', 'ARVIND', 'ASHOKLEY', 'ASIANPAINT', 'AUROPHARMA', 'AXISBANK',
'BAJAJ-AUTO', 'BAJFINANCE', 'BAJAJFINSV', 'BALKRISIND', 'BANKBARODA', 'BANKINDIA', 'BATAINDIA', 'BEML', 'BERGEPAINT', 'BEL', 'BHARATFIN', 'BHARATFORG', 'BPCL', 'BHARTIARTL', 'INFRATEL', 'BHEL', 'BIOCON', 'BOSCHLTD', 'BRITANNIA',
'CADILAHC', 'CANFINHOME', 'CANBK', 'CAPF', 'CASTROLIND', 'CEATLTD', 'CENTURYTEX', 'CESC', 'CGPOWER', 'CHENNPETRO', 'CHOLAFIN', 'CIPLA', 'COALINDIA', 'COLPAL', 'CONCOR', 'CUMMINSIND', 'DABUR', 'DCBBANK',
'DHFL', 'DISHTV', 'DIVISLAB', 'DLF', 'DRREDDY', 'EICHERMOT', 'ENGINERSIN', 'EQUITAS', 'ESCORTS', 'EXIDEIND',
'FEDERALBNK', 'GAIL', 'GLENMARK', 'GMRINFRA', 'GODFRYPHLP', 'GODREJCP', 'GODREJIND', 'GRANULES', 'GRASIM', 'GSFC', 'HAVELLS', 'HCLTECH', 'HDFCBANK', 'HDFC', 'HEROMOTOCO', 'HEXAWARE', 'HINDALCO', 'HCC', 'HINDPETRO', 'HINDUNILVR',
'HINDZINC', 'ICICIBANK', 'ICICIPRULI', 'IDBI', 'IDEA', 'IDFCBANK', 'IDFC', 'IFCI', 'IBULHSGFIN', 'INDIANB', 'IOC', 'IGL', 'INDUSINDBK', 'INFIBEAM', 'INFY', 'INDIGO', 'IRB', 'ITC', 'JISLJALEQS', 'JPASSOCIAT', 'JETAIRWAYS', 'JINDALSTEL',
'JSWSTEEL', 'JUBLFOOD', 'JUSTDIAL', 'KAJARIACER', 'KTKBANK', 'KSCL', 'KOTAKBANK', 'KPIT', 'L&TFH', 'LT', 'LICHSGFIN', 'LUPIN', 'M&MFIN', 'MGL', 'M&M', 'MANAPPURAM', 'MRPL', 'MARICO', 'MARUTI', 'MFSL', 'MINDTREE', 'MOTHERSUMI', 'MRF', 'MCX',
'MUTHOOTFIN', 'NATIONALUM', 'NBCC', 'NCC', 'NESTLEIND', 'NHPC', 'NIITTECH', 'NMDC', 'NTPC', 'ONGC', 'OIL', 'OFSS', 'ORIENTBANK', 'PAGEIND', 'PCJEWELLER', 'PETRONET', 'PIDILITIND', 'PEL', 'PFC', 'POWERGRID', 'PTC', 'PNB', 'PVR', 'RAYMOND',
'RBLBANK', 'RELCAPITAL', 'RCOM', 'RELIANCE', 'RELINFRA', 'RPOWER', 'REPCOHOME', 'RECLTD', 'SHREECEM', 'SRTRANSFIN', 'SIEMENS', 'SREINFRA', 'SRF', 'SBIN', 'SAIL', 'STAR', 'SUNPHARMA', 'SUNTV', 'SUZLON', 'SYNDIBANK', 'TATACHEM', 'TATACOMM', 'TCS',
'TATAELXSI', 'TATAGLOBAL', 'TATAMTRDVR', 'TATAMOTORS', 'TATAPOWER', 'TATASTEEL', 'TECHM', 'INDIACEM', 'RAMCOCEM', 'SOUTHBANK', 'TITAN', 'TORNTPHARM', 'TORNTPOWER', 'TV18BRDCST', 'TVSMOTOR', 'UJJIVAN', 'ULTRACEMCO', 'UNIONBANK', 'UBL', 'UPL',
'VEDL', 'VGUARD', 'VOLTAS', 'WIPRO', 'WOCKPHARMA', 'YESBANK', 'ZEEL']
For all this companies the dataframe 'f' should be extracted parallely in order to save run time. Can anyone help me to solve this?
How to create a time array in python for seasonal data
I am working with paleoclimate data (536-550 CE) in NetCDF format, which I imported with xarray. The time format is a bit strange:
import xarray as xr
ds_tas_01 = xr.open_dataset('ue536a01_temp2_seasmean.nc')
ds_tas_01['time']
<xarray.DataArray 'time' (time: 61)>
array([15360215.25, 15360430.75, 15360731.75, 15361031.75, 15370131.75,
15370430.75, 15370731.75, 15371031.75, 15380131.75, 15380430.75,
15380731.75, 15381031.75, 15390131.75, 15390430.75, 15390731.75,
15391031.75, 15400131.75, 15400430.75, 15400731.75, 15401031.75,
15410131.75, 15410430.75, 15410731.75, 15411031.75, 15420131.75,
15420430.75, 15420731.75, 15421031.75, 15430131.75, 15430430.75,
15430731.75, 15431031.75, 15440131.75, 15440430.75, 15440731.75,
15441031.75, 15450131.75, 15450430.75, 15450731.75, 15451031.75,
15460131.75, 15460430.75, 15460731.75, 15461031.75, 15470131.75,
15470430.75, 15470731.75, 15471031.75, 15480131.75, 15480430.75,
15480731.75, 15481031.75, 15490131.75, 15490430.75, 15490731.75,
15491031.75, 15500131.75, 15500430.75, 15500731.75, 15501031.75,
15501231.75])
Coordinates:
* time (time) float64 1.536e+07 1.536e+07 1.536e+07 ... 1.55e+07 1.55e+07
Attributes:
standard_name: time
bounds: time_bnds
units: day as %Y%m%d.%f
calendar: proleptic_gregorian
axis: T
So I want to make my own time array that I can use to plot the climate data. For monthly data I used:
import numpy as np
time = np.arange('0536-01-31', '0551-01-31', dtype='datetime64[M]')
which gives me an array with the years and months between those two dates.
now I grouped my data by season using cdo seasmean ('djf', 'mam', jja, 'son') and got 61 values instead of 180. Is there a way to regroup the 'time' array to seasonal values, or create a new time array that corresponds to the seasonal data?
I made it work by setting the number of steps in np.arange:
time = np.arange('0536-01-31', '0551-01-31', steps=3, dtype='datetime64[M]')
This gives a time step every three months, so essentially every 'season'.
Remove certain dates in list. Python 3.4
I have a list that has several days in it. Each day have several timestamps. What I want to do is to make a new list that only takes the start time and the end time in the list for each date.
I also want to delete the Character between the date and the time on each one, the char is always the same type of letter.
the time stamps can vary in how many they are on each date.
Since I'm new to python it would be preferred to use a lot of simple to understand codes. I've been using a lot of regex so pleas if there is a way with this one.
the list has been sorted with the command list.sort() so it's in the correct order.
code used to extract the information was the following.
file1 = open("test.txt", "r")
for f in file1:
list1 += re.findall('20\d\d-\d\d-\d\dA\d\d\:\d\d', f)
listX = (len(list1))
list2 = list1[0:listX - 2]
list2.sort()
here is a list of how it looks:
2015-12-28A09:30
2015-12-28A09:30
2015-12-28A09:35
2015-12-28A09:35
2015-12-28A12:00
2015-12-28A12:00
2015-12-28A12:15
2015-12-28A12:15
2015-12-28A14:30
2015-12-28A14:30
2015-12-28A15:15
2015-12-28A15:15
2015-12-28A16:45
2015-12-28A16:45
2015-12-28A17:00
2015-12-28A17:00
2015-12-28A18:15
2015-12-28A18:15
2015-12-29A08:30
2015-12-29A08:30
2015-12-29A08:35
2015-12-29A08:35
2015-12-29A10:45
2015-12-29A10:45
2015-12-29A11:00
2015-12-29A11:00
2015-12-29A13:15
2015-12-29A13:15
2015-12-29A14:00
2015-12-29A14:00
2015-12-29A15:30
2015-12-29A15:30
2015-12-29A15:45
2015-12-29A15:45
2015-12-29A17:15
2015-12-29A17:15
2015-12-30A08:30
2015-12-30A08:30
2015-12-30A08:35
2015-12-30A08:35
2015-12-30A10:45
2015-12-30A10:45
2015-12-30A11:00
2015-12-30A11:00
2015-12-30A13:00
2015-12-30A13:00
2015-12-30A13:45
2015-12-30A13:45
2015-12-30A15:15
2015-12-30A15:15
2015-12-30A15:30
2015-12-30A15:30
2015-12-30A17:15
2015-12-30A17:15
And this is how I want it to look like:
2015-12-28 09:30
2015-12-28 18:15
2015-12-29 08:30
2015-12-29 17:15
2015-12-30 08:30
2015-12-30 17:15
First of all, you should convert all your strings into proper dates, Python can work with. That way, you have a lot more control on it, also to change the formatting later. So let’s parse your dates using datetime.strptime in list2:
from datetime import datetime
dates = [datetime.strptime(item, '%Y-%m-%dA%H:%M') for item in list2]
This creates a new list dates that contains all your dates from list2 but as parsed datetime object.
Now, since you want to get the first and the last date of each day, we somehow have to group your dates by the date component. There are various ways to do that. I’ll be using itertools.groupby for it, with a key function that just looks at the date component of each entry:
from itertools import groupby
for day, times in groupby(dates, lambda x: x.date()):
first, *mid, last = times
print(first)
print(last)
If we run this, we already get your output (without date formatting):
2015-12-28 09:30:00
2015-12-28 18:15:00
2015-12-29 08:30:00
2015-12-29 17:15:00
2015-12-30 08:30:00
2015-12-30 17:15:00
Of course, you can also collect that first and last date in a list first to process the dates later:
filteredDates = []
for day, times in groupby(dates, lambda x: x.date()):
first, *mid, last = times
filteredDates.append(first)
filteredDates.append(last)
And you can also output your dates with a different format using datetime.strftime:
for date in filteredDates:
print(date.strftime('%Y-%m-%d %H:%M'))
That would give us the following output:
2015-12-28 09:30
2015-12-28 18:15
2015-12-29 08:30
2015-12-29 17:15
2015-12-30 08:30
2015-12-30 17:15
If you don’t want to go the route through parsing those dates, of course you could also do this simply by working on the strings. Since they are nicely formatted (i.e. they can be easily compared), you can do that as well. It would look like this then:
for day, times in groupby(list2, lambda x: x[:10]):
first, *mid, last = times
print(first)
print(last)
Producing the following output:
2015-12-28A09:30
2015-12-28A18:15
2015-12-29A08:30
2015-12-29A17:15
2015-12-30A08:30
2015-12-30A17:15
Because your data is ordered you just need to pull the first and last value from each group, you can use re.sub to remove the single letter replacing it with a space then split each date string just comparing the dates:
from re import sub
def grp(l):
it = iter(l)
prev = start = next(it).replace("A"," ")
for dte in it:
dte = dte.replace("A"," ")
# if we have a new date, yield that start and end
if dte.split(None, 1)[0] != prev.split(None,1)[0]:
yield start
yield prev
start = dte
prev = dte
yield start, prev
l=["2015-12-28A09:30", "2015-12-28A09:30", .....................
l[:] = grp(l)
This could also certainly be done as your process the file without sorting by using a dict to group:
from re import findall
from collections import OrderedDict
with open("dates.txt") as f:
od = defaultdict(lambda: {"min": "null", "max": ""})
for line in f:
for dte in findall('20\d\d-\d\d-\d\dA\d\d\:\d\d', line):
dte, tme = dte.split("A")
_dte = "{} {}".format(dte, tme)
if od[dte]["min"] > _dte:
od[dte]["min"] = _dte
if od[dte]["max"] < _dte:
od[dte]["max"] = _dt
print(list(od.values()))
Which will give you the start and end time for each date.
[{'min': '2016-01-03 23:59', 'max': '2016-01-03 23:59'},
{'min': '2015-12-28 00:00', 'max': '2015-12-28 18:15'},
{'min': '2015-12-30 08:30', 'max': '2015-12-30 17:15'},
{'min': '2015-12-29 08:30', 'max': '2015-12-29 17:15'},
{'min': '2015-12-15 08:41', 'max': '2015-12-15 08:41'}]
The start for 2015-12-28 is also 00:00 not 9:30.
if you dates are actually as posted one per line you don't need a regex either:
from collections import defaultdict
with open("dates.txt") as f:
od = defaultdict(lambda: {"min": "null", "max": ""})
for line in f:
dte, tme = line.rstrip().split("A")
_dte = "{} {}".format(dte, tme)
if od[dte]["min"] > _dte:
od[dte]["min"] = _dte
if od[dte]["max"] < _dte:
od[dte]["max"] = _dte
print(list(od.values()
Which would give you the same output.