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Using "date" information

It's easy to think of timestamps as pure numbers, but don't forget they generally correspond to things that happen in the real world. That means there's often extra information encoded in the data such as "is it a weekday?" or "is it a holiday?". This information is often useful in predicting timeseries data.

In this exercise, you'll extract these date/time based features. A single time series has been loaded in a variable called prices.

This exercise is part of the course

Machine Learning for Time Series Data in Python

View Course

Exercise instructions

  • Calculate the day of the week, week number in a year, and month number in a year.
  • Add each one as a column to the prices_perc DataFrame, under the names day_of_week, week_of_year and month_of_year, respectively.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Extract date features from the data, add them as columns
prices_perc['day_of_week'] = prices_perc.____.____
prices_perc['week_of_year'] = prices_perc.____.____
prices_perc['month_of_year'] = prices_perc.____.____

# Print prices_perc
print(prices_perc)
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