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Convert monthly to weekly data

You have learned in the video how to use .reindex() to conform an existing time series to a DateTimeIndex at a different frequency.

Let's practice this method by creating monthly data and then converting this data to weekly frequency while applying various fill logic options.

This is a part of the course

“Manipulating Time Series Data in Python”

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Exercise instructions

We have already imported pandas as pd for you. We have also defined start and end dates.

  • Create monthly_dates using pd.date_range with start, end and frequency alias 'M'.
  • Create and print the pd.Series monthly, passing the list [1, 2] as the data argument, and using monthly_dates as index.
  • Create weekly_dates using pd.date_range with start, end and frequency alias 'W'.
  • Apply .reindex() to monthly three times: first without additional options, then with bfill and then with ffill, print()-ing each result.

Hands-on interactive exercise

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

# Set start and end dates
start = '2016-1-1'
end = '2016-2-29'

# Create monthly_dates here
monthly_dates = ____

# Create and print monthly here
monthly = ____
print(____)

# Create weekly_dates here
weekly_dates = ____

# Print monthly, reindexed using weekly_dates
print(____)
print(____)
print(____)
Edit and Run Code

This exercise is part of the course

Manipulating Time Series Data in Python

IntermediateSkill Level
4.3+
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In this course you'll learn the basics of working with time series data.

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This chapter dives deeper into the essential time series functionality made available through the pandas DataTimeIndex. It introduces resampling and how to compare different time series by normalizing their start points.

Exercise 1: Compare time series growth ratesExercise 2: Compare the performance of several asset classesExercise 3: Comparing stock prices with a benchmarkExercise 4: Plot performance difference vs benchmark indexExercise 5: Changing the time series frequency: resamplingExercise 6: Convert monthly to weekly data
Exercise 7: Create weekly from monthly unemployment dataExercise 8: Upsampling & interpolation with .resample()Exercise 9: Use interpolation to create weekly employment dataExercise 10: Interpolate debt/GDP and compare to unemploymentExercise 11: Downsampling & aggregationExercise 12: Compare weekly, monthly and annual ozone trends for NYC & LAExercise 13: Compare monthly average stock prices for Facebook and GoogleExercise 14: Compare quarterly GDP growth rate and stock returnsExercise 15: Visualize monthly mean, median and standard deviation of S&P500 returns

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