Working with DateTime columns
pandas loads datetime columns as an object data type by default. Having text dates aren't much use for time series analysis, so you must be able to convert those columns into a datetime datatype. This data type allows you to work more flexibly with pandas dates and extract useful features from them.
Another stocks dataset, this time for Apple, has been loaded as apple.
This exercise is part of the course
Anomaly Detection in Python
Exercise instructions
- Convert the
Datecolumn ofappleto adatetimecolumn. - Extract the day of the week and store it in a new column called
day_of_week. - Extract the month number and store it in a new column called
month. - Extract the day of the month and store it in a new column called
day_of_month.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Convert the Date column to DateTime
apple['Date'] = ____
# Create a column for the day of the week
apple['day_of_week'] = ____
# Create a column for the month
apple['month'] = ____
# Create a column for the day of the month
apple['day_of_month'] = ____
print(apple[['day_of_week', 'month', 'day_of_month']])