Python Tutorial: Creating a DatetimeIndex

Want to learn more? Take the full course at https://learn.datacamp.com/courses/analyzing-police-activity-with-pandas at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- In the last exercise, you fixed the data type of the is_arrested column. Now, we're going to build a DatetimeIndex for our DataFrame. Let's take a look at the head of the dataset again. As you can see, the date and time of each traffic stop are stored in separate columns, both of which are object columns. Because we'll be using stop_date and stop_time in our analysis, we're going to combine these two columns into a single column and then convert it to pandas' datetime format. This will be beneficial because unlike object columns, datetime columns provide date-based attributes that will make our analysis easier. Let's see an example of this using the apple stock price DataFrame from the previous video. Date and time are stored in separate columns, so the first task is to combine these two columns using a string method. As you might remember from previous courses, string methods, such as replace(), are Series methods available via the str accessor. In this example, we're replacing the forward slash in the date column with a dash. It outputs a new Series in which the string replacement has been made, though this change is temporary since we haven't saved the new Series. Anyway, to combine the columns, we're going to use the str dot cat() method, which is short for concatenate. We'll concatenate the date column with the time column, and tell pandas to separate them with a space, storing the result in a Series object named combined. You can see that the combined Series contains both the date and time. It's still an object column, but it's now ready for conversion to datetime format. To convert the combined Series to datetime format, you simply pass it to the to_datetime() function, and store the result in a new column. We didn't even