ewm() for exponential moving average in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the ewm() function in pandas do?

The ewm() function calculates the exponential weighted moving average of data, giving more weight to recent values and less to older ones.

beginner

What parameter in ewm() controls how much weight recent data points get?

The span parameter controls the decay, determining how quickly older data points' influence decreases.

beginner

How do you apply the exponential moving average after calling ewm()?

You call a method like .mean() on the result of ewm() to get the exponential moving average values.

intermediate

Why use exponential moving average instead of simple moving average?

Exponential moving average reacts faster to recent changes because it gives more weight to recent data, unlike simple moving average which weighs all points equally.

intermediate

What does the adjust parameter do in ewm()?

The adjust parameter controls whether the calculation uses weighted averages with adjustment (True) or a recursive formula (False).

What does ewm(span=5).mean() compute on a pandas Series?

A Sum of last 5 points

B Simple moving average of last 5 points

C Exponential moving average with more weight on recent 5 points

D Median of last 5 points

ewm(span=5).mean() calculates the exponential moving average giving more weight to recent points, controlled by span=5.

Which parameter in ewm() changes how quickly older data loses influence?

A ignore_na

B min_periods

C axis

D span

The span parameter controls the decay rate of weights for older data points.

If adjust=False in ewm(), what happens?

A Uses recursive calculation without bias correction

B Adjusts weights to sum to 1

C Ignores missing values

D Calculates simple moving average

adjust=False uses a recursive formula for calculation without bias correction.

What is the main benefit of exponential moving average over simple moving average?

A More sensitive to recent changes

B Easier to calculate

C Ignores outliers

D Uses equal weights

Exponential moving average reacts faster to recent data by weighting recent points more.

Which pandas method do you call after ewm() to get the average values?

A sum()

B mean()

C median()

D max()

You call mean() to compute the exponential weighted moving average values.

Explain how the ewm() function works and why it is useful in analyzing time series data.

Think about how recent data points should influence your analysis more than older points.

Describe the role of the span and adjust parameters in the ewm() function.

Consider how these parameters affect the weight distribution and calculation style.