Pandas read csv Custom Date Format: Difference between revisions
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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') | ||
</syntaxhighlight> | </syntaxhighlight> | ||
More details on format: {{Internal|Time,_Date,_Timestamp_in_Python#Format|<tt>datetime</tt> Format}} | More details on format: {{Internal|Time,_Date,_Timestamp_in_Python#Format|<tt>datetime</tt> Format}} | ||
<font color=darkkhaki>The problem with that is that I don't get a series of <code>datetime</code>s, but a series of <code>object</code>s. Why?</code> | |||
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=<tt>date_parser</tt> Parameter= | =<tt>date_parser</tt> Parameter= |
Revision as of 01:34, 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
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 datetime
s, but a series of object
s. Why?