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Lære Rolling | Time Series
Time Series Essentials

bookRolling

The rolling method in Python is used to create a rolling window of a certain size on a time series or other data. It takes one or more parameters: the window size, and optionally the type of window (e.g. boxcar, hamming, etc.). It returns a new object that behaves like a moving window on the original data. You can then perform various operations on this windowed data, such as calculating the mean, sum, or standard deviation.

Methods description

  • rolling(window): This method creates a Rolling object that represents a rolling window calculation over the DataFrame. The window parameter specifies the size of the rolling window;

  • mean(): This method calculates the mean value over the rolling window. It returns a new DataFrame containing the rolling mean values;

  • print(df): This function prints the rolling mean DataFrame to the console.

Opgave

Swipe to start coding

  1. Create a rolling window of size 3.
  2. Calculate the rolling_mean.
  3. Print the variable.

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bookRolling

The rolling method in Python is used to create a rolling window of a certain size on a time series or other data. It takes one or more parameters: the window size, and optionally the type of window (e.g. boxcar, hamming, etc.). It returns a new object that behaves like a moving window on the original data. You can then perform various operations on this windowed data, such as calculating the mean, sum, or standard deviation.

Methods description

  • rolling(window): This method creates a Rolling object that represents a rolling window calculation over the DataFrame. The window parameter specifies the size of the rolling window;

  • mean(): This method calculates the mean value over the rolling window. It returns a new DataFrame containing the rolling mean values;

  • print(df): This function prints the rolling mean DataFrame to the console.

Opgave

Swipe to start coding

  1. Create a rolling window of size 3.
  2. Calculate the rolling_mean.
  3. Print the variable.

Løsning

Mark tasks as Completed
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Var alt klart?

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Sektion 1. Kapitel 7
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