Revision as of 01:30, 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: {{Internal| 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:

Time, Date, Timestamp in Python

@@ Line 18: / Line 18: @@
 ==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 <code>date_format</code> parameters, as shown below:
 <syntaxhighlight lang='py'>
 df = pd.read_csv("./timeseries.csv", parse_dates=["date"], date_format='%m/%Y/%d')
 </syntaxhighlight>
+More details on format: {{Internal|
 The common timestamp elements are '%Y-%m-%d %H:%M:%S'. <font color=darkkhaki>For more details on date format, see ?</font>