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Pandas | to_datetime method

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Pandas
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General Functions
schedule Jul 1, 2022
Last updated
local_offer PythonPandas
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Pandas to_datetime(~) method converts the argument into a datetime object.

Parameters

1. arglink | number or string or datetime or sequence or map

The object to convert into a datetime object.

2. errors | string | optional

How to handle cases when conversion is not successful:

Value

Description

"raise"

Raise an error.

"coerce"

NaT is returned.

"ignore"

Return the input.

By default, errors="raise".

3. dayfirst | boolean | optional

If True, then treat the first number as a day. For instance, "10/12/2020" will be parsed as December 10th, 2020. By default, dayfirst=False.

4. yearfirst | boolean | optional

If True, then treat the first number as a year. For instance, "20/12/10" will be parsed as December 10th, 2020. By default, yearfirst=False.

5. utc | boolean | optional

Whether or not to set the timezone to UTC. By default, utc=False.

6. format | string | optional

The format string for dates, which follows the standard Python syntax (e.g. "%d/%m/%Y").

7. exact | boolean | optional

Whether or not to enforce exact match for the specified format. By default, exact=True.

8. unit | string | optional

The time unit of the argument:

"D", "s", "ms", "us", "ns"

By default, unit="ns".

9. infer_datetime_format | boolean | optional

If format is not specified and this parameter is set to True, then infer the format if possible. If format can be inferred, then the dates will be parsed more efficiently.

By default, infer_datetime_format=False.

10. origin | scalar | optional

The reference date to use:

  • "unix": use 1970-01-01 as the reference date

  • "julian": use the start of the Julian Calendar as the reference date

By default, origin="unix".

11. cache | boolean | optional

Whether or not to leverage caching when parsing dates. Using cache will speed up the process of parsing duplicate dates, particularly those with timezone offsets. Note that caching will only take effect when the number of dates to be parsed is at least 50. By default, cache=False.

Return Value

The return type depends on the type of arg:

  • array-like: DatetimeIndex is returned.

  • Series: Series of type datetime64 is returned.

  • scalar: Timestamp is returned.

Examples

Timestamp

To convert a date string of format MM/DD/YYYY to Timestamp:

pd.to_datetime("10/12/2020")   # October
Timestamp('2020-10-12 00:00:00')

Notice how Pandas officially use YYYY-MM-DD for dates.

The date string can also be in the format YYYY/MM/DD as well:

pd.to_datetime("2020/12/10")   # December
Timestamp('2020-12-10 00:00:00')

It is easy to confuse the days and months, so a good practice would be to specify the format parameter:

pd.to_datetime("10/12/2020", format="%d/%m/%Y")
Timestamp('2020-12-10 00:00:00')

Datetime64

To convert a Series of date strings to a Series of dtype datetime64[ns]:

pd.to_datetime(pd.Series(["25/12/2020", "26/12/2020"]))
0 2020-12-25
1 2020-12-26
dtype: datetime64[ns]

DatetimeIndex

To convert an array of date strings to DatetimeIndex, which can be used as the index of a DataFrame, pass in an array like so:

pd.to_datetime(["25/12/2020", "26/12/2020"])
DatetimeIndex(['2020-12-25', '2020-12-26'], dtype='datetime64[ns]', freq=None)
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Published by Isshin Inada
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