Pandas read csv Custom Date Format: Difference between revisions
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=<tt>date_format</tt> Parameter= | =<tt>date_format</tt> Parameter= | ||
For more complicated formats, the parsing function can be provided as a named function or a lambda, and that function can be passed to <code>read_csv</code> with the <code> date_parser</code> parameter. | |||
<syntaxhighlight lang='py'> | |||
def parse_timestamp(s: str): | |||
??? | |||
df = pd.read_csv("./timeseries.csv", parse_dates=["date"], date_format='%m/%Y/%d') | |||
</syntaxhighlight> | |||
For more details on timestamp parsing see: {{Internal|Time,_Date,_Timestamp_in_Python#Time.2C_Date_and_Timestamp_Parsing|Time, Date, Timestamp in Python}} | |||
<syntaxhighlight lang='py'> | <syntaxhighlight lang='py'> | ||
df = pd.read_csv("./timeseries.csv", parse_dates=["date"], date_format='%m/%Y/%d') | df = pd.read_csv("./timeseries.csv", parse_dates=["date"], date_format='%m/%Y/%d') | ||
Revision as of 01:35, 9 October 2023
Internal
Overview
A CSV column can be parsed as date with:
df = pd.read_csv("./timeseries.csv", parse_dates=["date"])
This assumes a "2023-10-31" format. If the string format is different there are several options:
date_format Parameter
For more complicated formats, the parsing function can be provided as a named function or a lambda, and that function can be passed to read_csv with the date_parser parameter.
def parse_timestamp(s: str):
???
df = pd.read_csv("./timeseries.csv", parse_dates=["date"], date_format='%m/%Y/%d')
For more details on timestamp parsing see:
df = pd.read_csv("./timeseries.csv", parse_dates=["date"], date_format='%m/%Y/%d')
More details on format:
The problem with that is that I don't get a series of datetimes, but a series of objects. Why?