This thread will cover different methods of an important technique used in data science and analysis projects i.e., converting a series of dates that are of type string
or object
into a series of dates where each date is of type date
or datetime
. There are several methods of achieving this and few are discussed below with example code for each method:
1. Using "pd.to_datetime()" method:
- Pandas has a method
pd.to_datetime()
which can be used to convert a given argument to a PandasDateTime
object. - This method returns a Pandas
DatetimeIndex
object or aSeries
object ofdatetime64[ns]
dtype, depending on the input argument.
2. Using "strptime()" method:
- The Datetime library has a method
strptime()
which allows you to convert a date string with a specified format to adatetime64[ns]
object. - To use this method, we first define a lambda function using
strptime()
and then we apply this lambda function to all elements in the series usingapply()
method.
3. Using "parse" method:
- The
parse()
method in thedateutil.parser
library allows you to convert a date string in a variety of formats to adatetime64[ns]
object. - This method is applied to each element in the series using
apply()
method of series.
4. Using NumPy library:
- NumPy allows creating an array of
datetime64
elements by specifying the datatype in thenp.array()
constructor. - We can use this method to first convert our date-strings series into a NumPy array of type
datetime64[ns]
. - Then, we can convert this NumPy array back into a series object using
pd.Series()
constructor.