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 '!='.

Practice

1. What does the ewm() function in pandas primarily do?

easy

A. Sorts the data in ascending order

B. Calculates the simple moving average with equal weights

C. Calculates the exponential moving average giving more weight to recent data points

D. Removes missing values from the dataset

Solution

1. Step 1: Understand the purpose of ewm()

   The ewm() function is used to calculate exponential weighted functions, mainly the exponential moving average (EMA), which gives more importance to recent data points.

2. Step 2: Compare with other options

   Simple moving average uses equal weights, sorting and removing missing values are unrelated to ewm().

3. Final Answer:

   Calculates the exponential moving average giving more weight to recent data points -> Option C

4. Quick Check:

   ewm() = exponential moving average [OK]

Hint: Remember: ewm() weights recent data more [OK]

Common Mistakes:

• Confusing ewm() with simple moving average
• Thinking ewm() sorts data
• Assuming ewm() removes missing values

2. Which of the following is the correct syntax to calculate the exponential moving average with a span of 3 on a pandas Series data?

easy

A. data.ewm(span=3).mean()

B. data.ewm(3).mean()

C. data.ewm(span=3).sum()

D. data.ewm(window=3).mean()

Solution

1. Step 1: Identify correct parameter usage in ewm()

   The span parameter sets the decay in ewm(). It must be named explicitly as span=3.

2. Step 2: Confirm correct method to get EMA

   Calling mean() after ewm() computes the exponential moving average. Using sum() or unnamed parameters is incorrect.

3. Final Answer:

   data.ewm(span=3).mean() -> Option A

4. Quick Check:

   Use span=3 and mean() [OK]

Hint: Use span= for decay, then call mean() [OK]

Common Mistakes:

• Omitting parameter name for span
• Using sum() instead of mean()
• Using window parameter instead of span

3. Given the pandas Series data = pd.Series([10, 20, 30, 40]), what is the output of data.ewm(span=2).mean()?

medium

A. [10.0, 20.0, 30.0, 40.0]

B. [10.0, 15.0, 25.0, 35.0]

C. [10.0, 18.0, 26.4, 36.16]

D. [10.0, 16.67, 25.56, 35.19]

Solution

1. Step 1: Calculate alpha from span

   Alpha = 2 / (span + 1) = 2 / (2 + 1) = 0.6667 approximately.

2. Step 2: Compute EMA step-by-step

   EMA_0 = 10.0
   EMA_1 = 0.6667*20 + 0.3333*10 ≈ 16.67
   EMA_2 = 0.6667*30 + 0.3333*16.67 ≈ 25.56
   EMA_3 = 0.6667*40 + 0.3333*25.56 ≈ 35.19

3. Final Answer:

   [10.0, 16.67, 25.56, 35.19] -> Option D

4. Quick Check:

   EMA with span=2 = [10.0, 16.67, 25.56, 35.19] [OK]

Hint: Calculate alpha = 2/(span+1), then apply EMA formula [OK]

Common Mistakes:

• Using simple average instead of EMA
• Wrong alpha calculation
• Not applying weights correctly

4. What is wrong with this code snippet to calculate EMA?

import pandas as pd
data = pd.Series([1, 2, 3, 4])
ema = data.ewm(span=3).mean

medium

A. span parameter should be an integer, not 3

B. Missing parentheses after mean to call the function

C. ewm() does not accept span parameter

D. Series data must be converted to DataFrame first

Solution

1. Step 1: Check method call syntax

   The code assigns mean without parentheses, so it assigns the method itself, not the result.

2. Step 2: Confirm correct usage of mean()

   To get EMA values, mean() must be called with parentheses: mean().

3. Final Answer:

   Missing parentheses after mean to call the function -> Option B

4. Quick Check:

   Call mean() with () to get EMA [OK]

Hint: Always call mean() with parentheses to compute EMA [OK]

Common Mistakes:

• Forgetting parentheses after mean
• Misunderstanding span parameter type
• Thinking ewm() needs DataFrame

5. You have daily sales data in a pandas DataFrame df with a column 'sales'. You want to smooth the sales data giving more importance to recent days but also want to limit the influence of very old data. Which ewm() parameter should you adjust to control how fast the weights decrease?

hard

A. span

B. min_periods

C. adjust

D. ignore_na

Solution

1. Step 1: Understand the role of span in ewm()

   The span parameter controls the decay rate of weights, determining how quickly older data's influence decreases.

2. Step 2: Differentiate from other parameters

   min_periods sets minimum data points for calculation, adjust changes calculation method, and ignore_na handles missing data, none control weight decay speed.

3. Final Answer:

   span -> Option A

4. Quick Check:

   Weight decay speed controlled by span [OK]

Hint: Adjust span to control weight decay speed [OK]

Common Mistakes:

• Confusing span with min_periods
• Using adjust to control weight decay
• Ignoring role of ignore_na