print(type(dflaunath'date'. df'date' df'date'.astype('datetime64ns') or use datetime64D if you want Day precision and not nanoseconds. Name: dates, dtype: object Using the pandas astype() Series methodĪnother option is to use the astype() function: data = data. If your date column is a string of the format '' you can use pandas astype to convert it to datetime. This return the following values – note that the data type of the column is now an object which is used by pandas to represent strings (among other objects). To cast the column values use the following snippet data = data.dt.strftime('%Y%m%d') data.dtypesĭtype: object Using the strftime formatterĬonverting to string is once again, very easy thanks to the pandas dt accessor function that you can use on a pandas column (Series object). Let’s look into the DataFrame first rows:Īnd if we look at the pandas data types we see that the dates column type is a datetime64. Create a DataFrame # Initialize DataFrameĭates = pd.date_range(start='', periods = 5, freq = 'B' )ĭata = pd.DataFrame(dict(dates=dates, revenue=revenue)) Next case that we’ll see is how to cast a pandas DataFrame column that contains date values to strings. type(my_date_str ) Convert a pandas column containing dates to string Obviously, if w check the the Python object type, we’ll see it is a string. Returns the a string in yyyymmdd format: 20231101 Make sure to specify the required time units that you would like to see. We can easily transform this variable to string using the strftime formatter, which casts dates to string using the format you specify. If we look at its type we’ll see that is datetime.datetime. We will get the following date object: 00:00:00 We will Let’s create a datetime object: import datetime In this data analysis tutorial we will learn how to cast datetime objects to strings in general Python programming tasks and during Data Analysis.
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