Pandas read csv Custom Date Format: Difference between revisions

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This assumes a "2023-10-31" format. If the string format is different there are several options:
This assumes a <code>YYYY-MM-DD</code> "2023-12-31" format. If the string format is different there are several options:


=<tt>date_format</tt> Parameter=
=<tt>date_format</tt> Parameter=
For more complicated formats, the parsing function can be provided as a named function or a lambda, and that function can be passed to <code>read_csv</code> with the <code> date_parser</code> parameter.
<syntaxhighlight lang='py'>
<syntaxhighlight lang='py'>
def parse_timestamp(s: str):
  ???
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')
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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 datetimes, but a series of objects. Why?</font>


=<tt>date_parser</tt> Parameter=


For more details on timestamp parsing see: {{Internal|Time,_Date,_Timestamp_in_Python#Time.2C_Date_and_Timestamp_Parsing|Time, Date, Timestamp in Python}}
For more complicated formats, the parsing function can be provided as a named function or a lambda, and that function can be passed to <code>read_csv</code> with the <code> date_parser</code> parameter. Do not use it, it will be deprecated for [https://github.com/pandas-dev/pandas/issues/50601 performance reasons].


<syntaxhighlight lang='py'>
<syntaxhighlight lang='py'>
df = pd.read_csv("./timeseries.csv", parse_dates=["date"], date_format='%m/%Y/%d')
def parse_timestamp(s: str):
    from datetime import datetime
    return datetime.strptime(s, "%m/%d/%Y")
 
df = pd.read_csv("./timeseries.csv", parse_dates=["date"], date_parser=parse_timestamp)
</syntaxhighlight>
</syntaxhighlight>
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>
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=

Latest revision as of 02:19, 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 YYYY-MM-DD "2023-12-31" 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:

datetime Format

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

date_parser Parameter

For more complicated formats, the parsing function can be provided as a named function or a lambda, and that function can be passed to read_csv with the date_parser parameter. Do not use it, it will be deprecated for performance reasons. 

def parse_timestamp(s: str): 
    from datetime import datetime
    return datetime.strptime(s, "%m/%d/%Y")

df = pd.read_csv("./timeseries.csv", parse_dates=["date"], date_parser=parse_timestamp)

For more details on timestamp parsing see:

Time, Date, Timestamp in Python
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