Revision as of 01:34, 9 October 2023

Internal

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:

df = pd.read_csv("./timeseries.csv", parse_dates=["date"], date_format='%m/%Y/%d')

More details on format:

datetime Format

The problem with that is that I don't get a series of datetimes, but a series of objects. Why?

@@ Line 16: / Line 16: @@
 df = pd.read_csv("./timeseries.csv", parse_dates=["date"], date_format='%m/%Y/%d')
 </syntaxhighlight>
 More details on format: {{Internal|Time,_Date,_Timestamp_in_Python#Format|<tt>datetime</tt> Format}}
-For more complicated formats, the parsing function can be provided as a named function or a lambda:
+<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>
-<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}}
 =<tt>date_parser</tt> Parameter=