Pandas read csv Custom Date Format

Internal

• Time Series Processing with Pandas

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_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:

Time, Date, Timestamp in Python