How to create a timeseries from a Pandas series of date-strings in Python?

I was working on a dataset that contained a column in which all dates were stored and I ran into an error that said that the column was a string object column and not a datetime object column. I’m facing difficulties in converting this column or series of date strings into a proper time-series object that can be used for my analysis. I’m not sure about the necessary data conversion steps, the correct date-time format to use, and the most efficient way to perform the conversion. I would greatly appreciate it if anyone could provide some methods or techniques for doing this.

Hi @mubashir_rizvi!

  • Pandas has a method pd.to_datetime() which can be used to convert a given argument to a Pandas DateTime object.

  • This method returns a Pandas DatetimeIndex object or a Series object of datetime64[ns] dtype, depending on the input argument.

@mubashir_rizvi, As we all know, NumPy allows the creation of an array of datetime64 elements by specifying the datatype in the np.array() constructor. You can use this method to first convert our date-strings series into a NumPy array of type datetime64[ns]. Then, you can convert this NumPy array back into a series object using pd.Series() constructor.

Hi @mubashir_rizvi ,to convert a date string in a variety of formats to a datetime64[ns] object, you can use the parse() method from the dateutil.parser library. This method can be applied to each element in a Pandas series using the apply() method of the series. By doing this, you can convert a whole series of date strings to a corresponding series of datetime64[ns] objects, which can be very useful for data analysis and visualization tasks.

I hope this helps! Let me know if you have any further questions.