Window functions (expanding, ewm) in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the expanding sum of the 'sales' column.

Data Analysis Python

import pandas as pd

data = {'sales': [10, 20, 30, 40, 50]}
df = pd.DataFrame(data)
df['expanding_sum'] = df['sales'].[1]().sum()
print(df)

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

Arolling

Bewm

Cexpanding

Dcumsum

Attempts:

3 left

💡 Hint

Common Mistakes

Using rolling() instead of expanding()

Using cumsum() which is similar but not a window function

Using ewm() which is for exponential weighted calculations

The expanding() function creates an expanding window that includes all previous rows. Using sum() on it calculates the cumulative sum up to each row.

2fill in blank

medium

Complete the code to calculate the exponentially weighted mean with a span of 3.

Data Analysis Python

import pandas as pd

data = {'values': [1, 2, 3, 4, 5]}
df = pd.DataFrame(data)
df['ewm_mean'] = df['values'].[1](span=3).mean()
print(df)

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

Arolling

Bewm

Cexpanding

Dcumsum

Attempts:

3 left

💡 Hint

Common Mistakes

Using rolling() which uses fixed window size

Using expanding() which uses all previous data equally

Using cumsum() which is a simple cumulative sum

The ewm() function calculates exponentially weighted statistics. Setting span=3 controls the decay rate for weighting.

3fill in blank

hard

Fix the error in the code to calculate the expanding mean of the 'temperature' column.

Data Analysis Python

import pandas as pd

data = {'temperature': [22, 21, 23, 24, 25]}
df = pd.DataFrame(data)
df['expanding_mean'] = df['temperature'].[1]().mean
print(df)

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

Aexpanding

Brolling

Cewm

Dcumsum

Attempts:

3 left

💡 Hint

Common Mistakes

Forgetting parentheses after mean

Using rolling() instead of expanding()

Using cumsum() which is not a window function

The mean is a method and needs parentheses () to be called. Using expanding() creates the expanding window.

4fill in blank

hard

Fill both blanks to create a dictionary of word lengths for words longer than 3 characters.

Data Analysis Python

words = ['data', 'science', 'is', 'fun']
lengths = {word: [1] for word in words if len(word) [2] 3}
print(lengths)

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

Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using word instead of len(word) for values

Using < instead of > in the condition

Not filtering words by length

The dictionary comprehension maps each word to its length using len(word). The condition filters words with length greater than 3 using >.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase keys and values greater than 0.

Data Analysis Python

data = {'a': 1, 'b': -2, 'c': 3}
result = {{ [1]: [2] for k, v in data.items() if v [3] 0 }}
print(result)

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

Ak.upper()

Bv

C>

Dk

Attempts:

3 left

💡 Hint

Common Mistakes

Using k instead of k.upper() for keys

Filtering with < instead of >

Using k instead of v for values

The dictionary comprehension uses k.upper() for keys, keeps values v, and filters for values greater than zero with >.

Practice

1. What does the expanding() window function do in data analysis?

easy

A. Calculates statistics using a fixed-size sliding window

B. Calculates statistics using only the last 3 data points

C. Calculates statistics by giving more weight to recent data points

D. Calculates statistics using all data points up to the current one

Solution

1. Step 1: Understand expanding() function

   The expanding() function includes all data points from the start up to the current point for calculations.

2. Step 2: Compare with other window types

   Unlike rolling windows or ewm, expanding uses all past data, not a fixed window or weighted recent data.

3. Final Answer:

   Calculates statistics using all data points up to the current one -> Option D

4. Quick Check:

   expanding() = all data so far [OK]

Hint: expanding() uses all data up to now, no fixed window [OK]

Common Mistakes:

• Confusing expanding() with rolling() fixed window
• Thinking ewm() is the same as expanding()
• Assuming expanding() only uses recent data

2. Which of the following is the correct syntax to calculate the exponentially weighted mean of a pandas Series data with a span of 3?

easy

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

B. data.expanding(span=3).mean()

C. data.rolling(window=3).ewm().mean()

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

Solution

1. Step 1: Recall ewm() syntax

   The ewm() function uses parameters like span to control decay; span=3 is valid.

2. Step 2: Check each option

   data.ewm(span=3).mean() uses correct syntax: data.ewm(span=3).mean(). Others misuse parameters or combine methods incorrectly.

3. Final Answer:

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

4. Quick Check:

   ewm(span=3) is correct syntax [OK]

Hint: Use ewm(span=3).mean() for weighted mean with span 3 [OK]

Common Mistakes:

• Using expanding() instead of ewm() for weighted mean
• Passing window parameter to ewm() which is invalid
• Chaining rolling() before ewm() incorrectly

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

medium

A. [10.0, 15.0, 20.0, 25.0]

B. [10.0, 20.0, 30.0, 40.0]

C. [10.0, 15.0, 25.0, 35.0]

D. [10.0, 20.0, 25.0, 30.0]

Solution

1. Step 1: Calculate expanding mean step-by-step

   Expanding mean at each point is average of all values up to that index:
   Index 0: 10 / 1 = 10.0
   Index 1: (10+20)/2 = 15.0
   Index 2: (10+20+30)/3 = 20.0
   Index 3: (10+20+30+40)/4 = 25.0

2. Step 2: Match calculated list with options

   The list is [10.0, 15.0, 20.0, 25.0], which matches [10.0, 15.0, 20.0, 25.0] exactly.

3. Final Answer:

   [10.0, 15.0, 20.0, 25.0] -> Option A

4. Quick Check:

   Expanding mean averages all so far [OK]

Hint: Expanding mean averages all data up to current index [OK]

Common Mistakes:

• Calculating rolling mean instead of expanding mean
• Using last value only instead of average
• Miscomputing cumulative sums

4. You run the code data.ewm(span=2).mean() but get an error. Which fix will solve the problem?

medium

A. Import pandas as pd before using data

B. Replace span with window parameter

C. Use data.ewm(span=2).mean() on a pandas Series, not a list

D. Call mean() before ewm()

Solution

1. Step 1: Identify cause of error

   The ewm() method is a pandas Series method. If data is a plain list, it causes an error.

2. Step 2: Correct usage context

   Convert data to a pandas Series before calling ewm(), e.g., pd.Series(data).ewm(span=2).mean().

3. Final Answer:

   Use data.ewm(span=2).mean() on a pandas Series, not a list -> Option C

4. Quick Check:

   ewm() needs pandas Series [OK]

Hint: ewm() works only on pandas Series, not plain lists [OK]

Common Mistakes:

• Trying to use ewm() on Python lists
• Using wrong parameter names like window in ewm()
• Calling mean() before ewm()

5. You have daily sales data with some missing days. You want to smooth the sales trend giving more weight to recent days but still include all past data. Which pandas window function and parameter combination is best?

hard

A. data.expanding(min_periods=5).mean() to average all data after 5 days

B. data.ewm(span=5, adjust=False).mean() to weight recent days more

C. data.rolling(window=5).mean() to use last 5 days only

D. data.expanding().mean() to average all data equally

Solution

1. Step 1: Understand smoothing with weights

   To give more weight to recent days, ewm() is appropriate because it applies exponential decay.

2. Step 2: Consider missing days and all past data

   ewm() includes all past data but weights recent points more; adjust=False gives a smoother result.

3. Step 3: Compare options

   data.ewm(span=5, adjust=False).mean() to weight recent days more uses ewm(span=5, adjust=False) which fits the requirement best. Others either use equal weights or fixed windows.

4. Final Answer:

   data.ewm(span=5, adjust=False).mean() -> Option B

5. Quick Check:

   ewm() weights recent data more [OK]

Hint: Use ewm(span=5, adjust=False) for weighted smoothing with all past data [OK]

Common Mistakes:

• Using expanding() which weights all data equally
• Using rolling() which ignores older data beyond window
• Not setting adjust=False for smoother ewm