![]() You can also convert multiple string columns to DateTime in panadas DataFrame, here you have two columns Inserted and Updated that are strings holding DateTime. When you use the to_datetime() function to parse the column as DateTime, use infer_datetime_format=True where it will automatically detect the format and convert the mentioned column to DateTime. Make sure you import datatime before using it. Use the lambda expression in the place of func for simplicity. You can also use the DataFrame.apply() and lambda function to operate on the values, here I will be using datetime.strptime() function to convert. Convert String to DateTime Using Lambda Function Note that Inserted column on the DataFrame has DateTime in the format of "%m/%d/%Y, %H:%M:%S"Ĥ. Our DataFrame contains column names Courses, Fee, Duration, Discount and Inserted. Now, let’s create a DataFrame with a few rows and columns, execute the above examples and validate results. # Convert pandas multiple columns to Datetimeĭf] = df].apply(pd.to_datetime, errors='coerce') # Convert pandas column to DateTime using Series.astype() methodĭf = df.astype('datetime64') # To pandas.to_datetime() using infer_datetime_format=Trueĭf = pd.to_datetime(df, infer_datetime_format=True) # Using DataFrame.apply() and lambda functionĭf = df.apply(lambda _: datetime.strptime(_,"%m/%d/%Y, %H:%M:%S")) ![]() # Using pandas.to_datetime() to convert pandas column to DateTimeĭf = pd.to_datetime(df, format="%m/%d/%Y, %H:%M:%S")ĭf = pd.to_datetime(df)
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