ewm() for exponential moving average in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the exponential moving average of the 'price' column with a span of 3.

Pandas

df['ema'] = df['price'].[1](span=3).mean()

Drag options to blanks, or click blank then click option'

Aewm

Brolling

Cexpanding

Dcumsum

Attempts:

3 left

💡 Hint

Common Mistakes

Using rolling() instead of ewm() which calculates simple moving average.

Forgetting to call mean() after ewm().

The ewm() method creates an exponential weighted object to calculate the exponential moving average. Using mean() on it computes the EMA.

2fill in blank

medium

Complete the code to calculate the exponential moving average with a halflife of 2.

Pandas

df['ema'] = df['price'].ewm([1]=2).mean()

Drag options to blanks, or click blank then click option'

Acom

Bspan

Calpha

Dhalflife

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'span' instead of 'halflife' when the question asks specifically for half-life.

Confusing 'alpha' with halflife.

The halflife parameter specifies the period over which the weights decrease by half in the exponential moving average calculation.

3fill in blank

hard

Fix the error in the code to correctly calculate the EMA with center of mass 1.5.

Pandas

df['ema'] = df['price'].ewm(com=[1]).mean()

Drag options to blanks, or click blank then click option'

A1.5

B'1.5'

Ccom=1.5

D1,5

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the value as a string with quotes.

Including the parameter name inside the parentheses again.

Using comma instead of dot for decimal.

The com parameter expects a numeric value, not a string or incorrect syntax. Use 1.5 as a float number.

4fill in blank

hard

Fill both blanks to create an EMA with alpha 0.3 and ignore NaN values in the calculation.

Pandas

df['ema'] = df['price'].ewm([1]=0.3, [2]=True).mean()

Drag options to blanks, or click blank then click option'

Aalpha

Badjust

Cignore_na

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'adjust' instead of 'ignore_na'.

Confusing 'adjust' with 'ignore_na'.

The alpha parameter sets the smoothing factor. The ignore_na=True parameter makes the calculation ignore NaN values properly.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that calculates EMA for each column in df with span 4 and stores results in a new dict.

Pandas

ema_dict = [1]: df[col].ewm([2]=4).mean() for col in df.columns if col [3] 'price'

Drag options to blanks, or click blank then click option'

Acol

Bspan

C!=

D==

Attempts:

3 left

💡 Hint

Common Mistakes

Using '==' instead of '!=' to exclude the 'price' column.

Using a wrong key in the dictionary comprehension.

Using 'com' instead of 'span' for the parameter.

This comprehension creates a dictionary with keys as column names and values as their EMA with span 4. It excludes the 'price' column using '!='.