Moving averages in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the simple moving average of a list of numbers.

ML Python

def simple_moving_average(data, window_size):
    moving_averages = []
    for i in range(len(data) - window_size + 1):
        window = data[i:i + window_size]
        average = sum(window) / [1]
        moving_averages.append(average)
    return moving_averages

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

Ai

Blen(data)

Cwindow_size

Dlen(window)

Attempts:

3 left

💡 Hint

Common Mistakes

Using the length of the whole data list instead of the window size.

Dividing by the index variable instead of the window size.

The simple moving average divides the sum of the window by the window size to get the average.

2fill in blank

medium

Complete the code to calculate the exponential moving average (EMA) smoothing factor.

ML Python

def calculate_ema_smoothing_factor(window_size):
    smoothing_factor = 2 / ([1] + 1)
    return smoothing_factor

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

Awindow_size * 2

Bwindow_size - 1

Cwindow_size + 1

Dwindow_size

Attempts:

3 left

💡 Hint

Common Mistakes

Subtracting 1 instead of adding 1 to the window size.

Multiplying the window size instead of adding.

The smoothing factor for EMA is calculated as 2 divided by (window size plus 1).

3fill in blank

hard

Fix the error in the code to update the EMA value correctly.

ML Python

def update_ema(current_value, previous_ema, smoothing_factor):
    ema = [1] * current_value + (1 - smoothing_factor) * previous_ema
    return ema

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

Asmoothing_factor

Bprevious_ema

Ccurrent_value

D1 - smoothing_factor

Attempts:

3 left

💡 Hint

Common Mistakes

Using previous EMA instead of smoothing factor for the current value.

Using current value without multiplying by smoothing factor.

The EMA update formula multiplies the current value by the smoothing factor.

4fill in blank

hard

Fill both blanks to create a dictionary of moving averages for each window size.

ML Python

def moving_averages_dict(data, window_sizes):
    averages = {size: simple_moving_average(data, [1]) for size in [2]
    return averages

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

Asize

Bdata

Cwindow_sizes

Daverages

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'data' instead of 'window_sizes' in the loop.

Using 'averages' inside the comprehension which is not defined yet.

The dictionary comprehension uses 'size' as the window size for each key and iterates over 'window_sizes'.

5fill in blank

hard

Fill all three blanks to filter data points and compute their moving averages.

ML Python

filtered_data = [x for x in data if x [1] threshold]
moving_avg = simple_moving_average(filtered_data, [2])
result = {i: moving_avg[i] for i in range(len(moving_avg)) if moving_avg[i] [3] 0}

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

A>

Bwindow_size

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' for filtering data points.

Using wrong variable for window size.

Filtering moving averages less than zero instead of greater.

The code filters data points greater than the threshold, uses the window size to compute moving averages, and selects positive averages.

Practice

1. What is the main purpose of using a moving average in data analysis?

easy

A. To smooth out short-term fluctuations and highlight longer-term trends

B. To increase the number of data points in a dataset

C. To remove all noise from the data completely

D. To predict exact future values without error

Solution

1. Step 1: Understand the role of moving averages

   Moving averages smooth data by averaging nearby points, reducing short-term ups and downs.

2. Step 2: Identify the main goal

   The goal is to reveal longer-term trends by reducing noise, not to remove noise completely or predict exact values.

3. Final Answer:

   To smooth out short-term fluctuations and highlight longer-term trends -> Option A

4. Quick Check:

   Moving average = smoothing trends [OK]

Hint: Moving averages smooth data to show trends clearly [OK]

Common Mistakes:

• Thinking moving averages increase data points
• Believing moving averages remove all noise
• Assuming moving averages predict exact future values

2. Which of the following Python code snippets correctly computes a simple moving average with window size 3 for a list data?

easy

A. [data[i] / 3 for i in range(len(data))]

B. [sum(data[i:i+3]) for i in range(len(data)-3)]

C. [sum(data[i:i+3]) / 3 for i in range(len(data)-3)]

D. [(data[i] + data[i+1] + data[i+2]) / 3 for i in range(len(data)-2)]

Solution

1. Step 1: Understand moving average calculation

   A simple moving average with window 3 averages each group of 3 consecutive elements.

2. Step 2: Check each option's correctness

   [(data[i] + data[i+1] + data[i+2]) / 3 for i in range(len(data)-2)] correctly sums three consecutive elements and divides by 3, iterating till len(data)-2.
   [sum(data[i:i+3]) for i in range(len(data)-3)] sums but does not divide by 3.
   [sum(data[i:i+3]) / 3 for i in range(len(data)-3)] divides but uses range(len(data)-3), which is too short.
   [data[i] / 3 for i in range(len(data))] divides single elements by 3, not averaging groups.

3. Final Answer:

   [(data[i] + data[i+1] + data[i+2]) / 3 for i in range(len(data)-2)] -> Option D

4. Quick Check:

   Sum 3 elements / 3, range correct = [(data[i] + data[i+1] + data[i+2]) / 3 for i in range(len(data)-2)] [OK]

Hint: Sum 3 elements and divide by 3, loop till len-2 [OK]

Common Mistakes:

• Forgetting to divide by window size
• Using wrong range length causing index errors
• Averaging single elements instead of groups

3. Given the code below, what is the output?

data = [2, 4, 6, 8, 10]
window = 2
moving_avg = [sum(data[i:i+window]) / window for i in range(len(data) - window + 1)]
print(moving_avg)

medium

A. [2.0, 4.0, 6.0, 8.0, 10.0]

B. [3.0, 5.0, 7.0]

C. [3.0, 5.0, 7.0, 9.0]

D. [6.0, 8.0, 10.0]

Solution

1. Step 1: Calculate moving averages manually

   Window size is 2, so average pairs:
   (2+4)/2=3.0
   (4+6)/2=5.0
   (6+8)/2=7.0
   (8+10)/2=9.0

2. Step 2: Confirm output list length and values

   Length is len(data)-window+1 = 5-2+1=4, matching 4 values above.

3. Final Answer:

   [3.0, 5.0, 7.0, 9.0] -> Option C

4. Quick Check:

   Pairs averaged = [3.0, 5.0, 7.0, 9.0] [OK]

Hint: Average pairs sliding by one, length = len - window + 1 [OK]

Common Mistakes:

• Confusing window size with output length
• Calculating sums but forgetting to divide
• Off-by-one errors in range length

4. The following code is intended to compute a moving average with window size 3, but it misses the last window. What is the problem?

data = [1, 2, 3, 4, 5]
window = 3
moving_avg = [sum(data[i:i+window]) / window for i in range(len(data)-window)]
print(moving_avg)

medium

A. The range should be len(data) - window + 1 to include the last window

B. The window size is too large for the data list

C. sum() cannot be used on list slices

D. Division by window size should be outside the list comprehension

Solution

1. Step 1: Analyze the range length

   Range is len(data)-window = 5-3=2, but to cover all windows it should be len(data)-window+1 = 3.

2. Step 2: Understand impact of incorrect range

   Using len(data)-window misses the last valid window slice, causing incomplete results.

3. Final Answer:

   The range should be len(data) - window + 1 to include the last window -> Option A

4. Quick Check:

   Range length = len - window + 1 [OK]

Hint: Use range(len(data) - window + 1) for full coverage [OK]

Common Mistakes:

• Using len(data) - window instead of +1
• Thinking sum() can't handle slices
• Misplacing division outside comprehension

5. You have daily sales data for 10 days: [10, 12, 11, 14, 13, 15, 16, 14, 13, 12]. You want to smooth this data using a moving average with window size 4 but only want to keep averages where the window's average is greater than 13. Which Python code correctly computes this filtered moving average?

hard

A. [sum(data[i:i+4])/4 for i in range(len(data)-4) if sum(data[i:i+4])/4 > 13]

B. [avg for i in range(len(data)-3) if (avg := sum(data[i:i+4])/4) > 13]

C. [sum(data[i:i+4])/4 for i in range(len(data)-3) if sum(data[i:i+4]) > 13]

D. [sum(data[i:i+4])/4 for i in range(len(data)-3) if sum(data[i:i+4])/4 < 13]

Solution

1. Step 1: Understand window size and range

   Window size 4 means averaging groups of 4 elements, so range is len(data)-3 = 10-3=7.

2. Step 2: Filter averages greater than 13

   [avg for i in range(len(data)-3) if (avg := sum(data[i:i+4])/4) > 13] uses assignment expression to compute average once and filter if > 13.
   [sum(data[i:i+4])/4 for i in range(len(data)-4) if sum(data[i:i+4])/4 > 13] uses wrong range (len(data)-4=6), missing last window.
   [sum(data[i:i+4])/4 for i in range(len(data)-3) if sum(data[i:i+4]) > 13] filters sum > 13, not average > 13.
   [sum(data[i:i+4])/4 for i in range(len(data)-3) if sum(data[i:i+4])/4 < 13] filters averages less than 13, opposite condition.

3. Final Answer:

   [avg for i in range(len(data)-3) if (avg := sum(data[i:i+4])/4) > 13] -> Option B

4. Quick Check:

   Use assignment expression to filter averages > 13 [OK]

Hint: Use assignment expression (walrus) to filter averages [OK]

Common Mistakes:

• Using wrong range length missing last windows
• Filtering sum instead of average
• Using wrong comparison operator