Creating date-time stamps#

Programming for Geoscientists Data Science and Machine Learning for Geoscientists

Let’s create columns with hours, minutes and seconds from utc_time column (contains strings) in format:

HH:MM:SS

by splitting the strings with a colon and creating float columns.

Let’s load New Zealand earthquake data:

import pandas as pd

nz_eqs = pd.read_csv("../../geosciences/data/nz_largest_eq_since_1970.csv")
nz_eqs.head(4)

	year	month	day	utc_time	mag	lat	lon	depth_km	region	iris_id	timestamp
0	2009	7	15	09:22:31	7.8	-45.8339	166.6363	20.9	OFF W. COAST OF S. ISLAND, N.Z.	2871698	1247649751
1	2016	11	13	11:02:59	7.8	-42.7245	173.0647	22.0	SOUTH ISLAND, NEW ZEALAND	5197722	1479034979
2	2003	8	21	12:12:47	7.2	-45.0875	167.0892	6.8	SOUTH ISLAND, NEW ZEALAND	1628007	1061467967
3	2001	8	21	06:52:06	7.1	-36.8010	-179.7230	33.5	EAST OF NORTH ISLAND, N.Z.	1169374	998376726

nz_eqs["hour"] = nz_eqs["utc_time"].str.split(':').str.get(0).astype(float)
nz_eqs["minute"] = nz_eqs["utc_time"].str.split(':').str.get(1).astype(float)
nz_eqs["second"] = nz_eqs["utc_time"].str.split(':').str.get(2).astype(float)

With to_datetime() function, we can easily create date time stamp used by pandas:

nz_eqs["datetime"] = pd.to_datetime(nz_eqs[['year', 'month', 'day', 'hour', 'minute', 'second']])
nz_eqs.head(4)

	year	month	day	utc_time	mag	lat	lon	depth_km	region	iris_id	timestamp	hour	minute	second	datetime
0	2009	7	15	09:22:31	7.8	-45.8339	166.6363	20.9	OFF W. COAST OF S. ISLAND, N.Z.	2871698	1247649751	9.0	22.0	31.0	2009-07-15 09:22:31
1	2016	11	13	11:02:59	7.8	-42.7245	173.0647	22.0	SOUTH ISLAND, NEW ZEALAND	5197722	1479034979	11.0	2.0	59.0	2016-11-13 11:02:59
2	2003	8	21	12:12:47	7.2	-45.0875	167.0892	6.8	SOUTH ISLAND, NEW ZEALAND	1628007	1061467967	12.0	12.0	47.0	2003-08-21 12:12:47
3	2001	8	21	06:52:06	7.1	-36.8010	-179.7230	33.5	EAST OF NORTH ISLAND, N.Z.	1169374	998376726	6.0	52.0	6.0	2001-08-21 06:52:06

The datetime column is not yet recognised as index so we can simply use:

nz_eqs = nz_eqs.set_index('datetime')

This timestamp will be useful for plotting data.

References#

The notebook was compiled based on:

ESE Jupyter Material

Creating date-time stamps

Contents

Creating date-time stamps#

References#