(Python Example for Beginners)
Write a Pandas program to convert integer or float epoch times to Timestamp and DatetimeIndex.
Python Code :
import pandas as pd dates1 = pd.to_datetime([1329806505, 129806505, 1249892905, 1249979305, 1250065705], unit='s') print("Convert integer or float epoch times to Timestamp and DatetimeIndex upto second:") print(dates1) print("nConvert integer or float epoch times to Timestamp and DatetimeIndex upto milisecond:") dates2 = pd.to_datetime([1249720105100, 1249720105200, 1249720105300, 1249720105400, 1249720105500], unit='ms') print(dates2)
Convert integer or float epoch times to Timestamp and DatetimeIndex upto second: DatetimeIndex(['2012-02-21 06:41:45', '1974-02-11 09:21:45', '2009-08-10 08:28:25', '2009-08-11 08:28:25', '2009-08-12 08:28:25'], dtype='datetime64[ns]', freq=None) Convert integer or float epoch times to Timestamp and DatetimeIndex upto milisecond: DatetimeIndex(['2009-08-08 08:28:25.100000', '2009-08-08 08:28:25.200000', '2009-08-08 08:28:25.300000', '2009-08-08 08:28:25.400000', '2009-08-08 08:28:25.500000'], dtype='datetime64[ns]', freq=None)
Python Example for Beginners
Two Machine Learning Fields
There are two sides to machine learning:
- Practical Machine Learning:This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.
- Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.
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