Pandas read csv Custom Date Format: Difference between revisions
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A CSV column can be parsed as date with: | A CSV column can be parsed as date with: | ||
<syntaxhighlight lang='py'> | |||
df = pd.read_csv("./timeseries.csv", parse_dates=["date"]) | |||
</syntaxhighlight> | |||
This assumes a "2023-10-31" format. If the string format is different there are several options: | |||
=<tt>date_format</tt> Parameter= | =<tt>date_format</tt> Parameter= |
Revision as of 00:39, 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
date_parser Parameter
Custom Date
This syntax assumes that the "date" column is encoded in the default Panda date format ('YYYY-MM-DD'). If that is not the case, the format can be specified with the date_format
parameters, as shown below:
df = pd.read_csv("./timeseries.csv", parse_dates=["date"], date_format='%m/%Y/%d')
The common timestamp elements are '%Y-%m-%d %H:%M:%S'. For more details on date format, see ?
For more complicated formats, the parsing function can be provided as a named function or a lambda:
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: