importpandasaspdd=['2025-06-21','2025-06-22']res=pd.to_datetime(d)print(res)

DatetimeIndex(['2025-06-21', '2025-06-22'], dtype='datetime64[ns]', freq=None)

Explanation: date strings are in ISO format (YYYY-MM-DD), whichpandas.to_datetime()parses by default, returning a DatetimeIndex.

Syntax

pandas.to_datetime(arg, errors='raise', format=None, dayfirst=False, ...)

Parameters:

Returns: A datetime64 dtype object, which can be a single Timestamp, DatetimeIndex or Series, depending on the input.

Examples

Example 1: In this example, we convert date strings written inDD/MM/YYYY format to datetime objects using the format parameter.

importpandasaspdd=['21/06/2025','22/06/2025']res=pd.to_datetime(d,format='%d/%m/%Y')print(res)

DatetimeIndex(['2025-06-21', '2025-06-22'], dtype='datetime64[ns]', freq=None)

Explanation: pandas.to_datetime()expects dates in ISO format (YYYY-MM-DD), so we use 'format='%d/%m/%Y' to correctly parse day-first strings. It converts them into datetime objects and returns a DatetimeIndex holding datetime64 values.

Example 2: In this example, we convert a list of date strings and handle invalid entries usingerrors='coerce', which replaces the invalid value withNaT.

importpandasaspdd=['2025-06-21','invalid']res=pd.to_datetime(d,errors='coerce')print(res)

DatetimeIndex(['2025-06-21', 'NaT'], dtype='datetime64[ns]', freq=None)

Explanation: pandas.to_datetime()attempts to parse each string, but since 'invalid' is not a valid date, usingerrors='coerce' replaces it with NaT instead of raising an error, ensuring safe and error-free conversion.

Example 3: In this example, we convert a column of string dates in a DataFrame to datetime format usingpd.to_datetime() for further time-based operations.

importpandasaspddf=pd.DataFrame({'date':['2025-06-21','2025-06-22']})df['date']=pd.to_datetime(df['date'])print(df)

        date0 2025-06-211 2025-06-22

Explanation: pd.to_datetime() is applied to the 'date' column to convert string values into datetime objects.

Example 4: In this example, we parse dates where the day comes before the month by settingdayfirst=True.

importpandasaspdd=['21/06/2025','22/06/2025']res=pd.to_datetime(d,dayfirst=True)print(res)

DatetimeIndex(['2025-06-21', '2025-06-22'], dtype='datetime64[ns]', freq=None)

Explanation: By settingdayfirst=True,pandas.to_datetime() correctly interprets the first number as the day, not the month, ensuring accurate conversion to datetime objects.

Example 5: In this example, we convert a list of integers into dates by treating them as day offsets from the origin date '2025-06-01'.

importpandasaspdd=[0,1,2]res=pd.to_datetime(d,unit='D',origin='2025-06-01')print(res)

DatetimeIndex(['2025-06-01', '2025-06-02', '2025-06-03'], dtype='datetime64[ns]', freq=None)

Explanation: By setting unit='D' and specifying the origin, pandas calculates and returns the corresponding datetime values as a DatetimeIndex.

Example 6: In this example, we convert a datetime string into UTC timezone-aware datetime usingutc=True.

importpandasaspdd=['2025-06-21 12:00']res=pd.to_datetime(d,utc=True)print(res)

DatetimeIndex(['2025-06-21 12:00:00+00:00'], dtype='datetime64[ns, UTC]', freq=None)

Explanation:By passingutc=True,pandas.to_datetime()converts the datetime into a timezone-aware datetime in UTC (Coordinated Universal Time).