Rolling standard deviation in Pandas - Mini Project: Build & Apply

Rolling Standard Deviation with pandas

📖 Scenario: You work in a weather station. You have daily temperature data for a week. You want to understand how much the temperature changes over a short period.

🎯 Goal: Calculate the rolling standard deviation of daily temperatures over a 3-day window using pandas.

📋 What You'll Learn

Create a pandas DataFrame with daily temperatures for 7 days.

Set a window size variable for rolling calculation.

Calculate the rolling standard deviation using the window size.

Print the resulting rolling standard deviation values.

💡 Why This Matters

🌍 Real World

Rolling standard deviation helps weather analysts understand short-term temperature fluctuations, which is useful for forecasting and detecting unusual weather patterns.

💼 Career

Data scientists and analysts use rolling statistics to analyze trends and variability in time series data across many fields like finance, weather, and manufacturing.

1

Create the temperature DataFrame

Create a pandas DataFrame called df with a column 'Temperature' containing these exact values: [22, 21, 23, 24, 22, 20, 19].

Pandas

# Create the DataFrame df with the Temperature column
# Your code here

Hint

Use pd.DataFrame with a dictionary where the key is 'Temperature' and the value is the list of temperatures.

2

Set the rolling window size

Create a variable called window_size and set it to 3.

Pandas

import pandas as pd

df = pd.DataFrame({'Temperature': [22, 21, 23, 24, 22, 20, 19]})

# Set the rolling window size to 3
# Your code here

Hint

Just create a variable named window_size and assign the number 3 to it.

3

Calculate rolling standard deviation

Create a new column in df called 'RollingStd' that contains the rolling standard deviation of the 'Temperature' column using the window size stored in window_size. Use pandas rolling() and std() methods.

Pandas

import pandas as pd

df = pd.DataFrame({'Temperature': [22, 21, 23, 24, 22, 20, 19]})

window_size = 3

# Calculate rolling standard deviation and store in 'RollingStd' column
# Your code here

Hint

Use df['Temperature'].rolling(window=window_size).std() to calculate the rolling standard deviation.

4

Print the rolling standard deviation

Print the df DataFrame to display the 'Temperature' and 'RollingStd' columns.

Pandas

import pandas as pd

df = pd.DataFrame({'Temperature': [22, 21, 23, 24, 22, 20, 19]})

window_size = 3

df['RollingStd'] = df['Temperature'].rolling(window=window_size).std()

# Print the DataFrame df
# Your code here

Hint

Use print(df) to show the DataFrame with the new rolling standard deviation column.

Practice

1. What does the rolling(window).std() function calculate in pandas?

easy

A. The standard deviation of values within a moving window

B. The sum of values within a moving window

C. The average of values within a moving window

D. The maximum value within a moving window

Solution

1. Step 1: Understand rolling window concept

   The rolling function creates a moving window over data points.

2. Step 2: Apply std() to rolling window

   The std() function calculates the standard deviation of values inside each window.

3. Final Answer:

   The standard deviation of values within a moving window -> Option A

4. Quick Check:

   rolling(window).std() = standard deviation [OK]

Hint: Remember std() means standard deviation, not sum or mean [OK]

Common Mistakes:

• Confusing std() with sum() or mean()
• Thinking rolling() alone calculates std
• Assuming it returns a single value, not a series

2. Which of the following is the correct syntax to calculate a rolling standard deviation with a window size of 3 on a pandas Series named data?

easy

A. data.std().rolling(3)

B. data.rolling_std(3)

C. rolling(data, 3).std()

D. data.rolling(3).std()

Solution

1. Step 1: Identify correct method chaining

   The rolling() method is called on the Series, then std() is applied.

2. Step 2: Check syntax correctness

   data.rolling(3).std() uses correct pandas syntax: data.rolling(3).std()

3. Final Answer:

   data.rolling(3).std() -> Option D

4. Quick Check:

   Correct method chaining = data.rolling(3).std() [OK]

Hint: Call rolling() first, then std() to get rolling std dev [OK]

Common Mistakes:

• Calling std() before rolling()
• Using non-existent rolling_std() method
• Passing window size inside std()

3. Given the pandas Series data = pd.Series([2, 4, 6, 8, 10]), what is the output of data.rolling(3).std()?

medium

A. [NaN, 1.0, 2.0, 3.0, 4.0]

B. [NaN, NaN, 2.0, 2.0, 2.0]

C. [2.0, 2.0, 2.0, 2.0, 2.0]

D. [NaN, NaN, NaN, NaN, NaN]

Solution

1. Step 1: Understand rolling std with window=3

   For the first two values, window size 3 is not met, so result is NaN.

2. Step 2: Calculate std for windows

   Windows: [2,4,6], [4,6,8], [6,8,10] each have std deviation 2.0.

3. Final Answer:

   [NaN, NaN, 2.0, 2.0, 2.0] -> Option B

4. Quick Check:

   Rolling std with window 3 = [NaN, NaN, 2.0, 2.0, 2.0] [OK]

Hint: First (window-1) results are NaN, then std calculated [OK]

Common Mistakes:

• Expecting values for first two positions
• Confusing std with mean or sum
• Miscomputing standard deviation values

4. What is wrong with this code snippet to calculate rolling standard deviation on a DataFrame column df['values'] with window size 4?

df['rolling_std'] = df['values'].rolling.std(4)

medium

A. The code is correct and will run without error

B. The std() function should be called before rolling()

C. The rolling method is missing parentheses before std()

D. Window size should be passed to std(), not rolling()

Solution

1. Step 1: Check method chaining syntax

   rolling() requires parentheses with window size: rolling(4)

2. Step 2: Identify error in code

   Code uses rolling.std(4) which is invalid; should be rolling(4).std()

3. Final Answer:

   The rolling method is missing parentheses before std() -> Option C

4. Quick Check:

   rolling() needs parentheses before std() [OK]

Hint: Always use rolling(window).std(), not rolling.std(window) [OK]

Common Mistakes:

• Calling std() before rolling()
• Passing window size to std() instead of rolling()
• Omitting parentheses after rolling

5. You have daily sales data in a pandas DataFrame df with a column 'sales'. You want to detect days where sales variability over the last 5 days was unusually high. Which code correctly adds a column 'high_volatility' that is true when the rolling standard deviation exceeds 100?

hard

A. df['high_volatility'] = df['sales'].rolling(5).std() > 100

B. df['high_volatility'] = df['sales'].std().rolling(5) > 100

C. df['high_volatility'] = df['sales'].rolling(window=5).mean() > 100

D. df['high_volatility'] = df['sales'].rolling(5).sum() > 100

Solution

1. Step 1: Calculate rolling standard deviation over 5 days

   Use rolling(5).std() on 'sales' to get variability in last 5 days.

2. Step 2: Compare rolling std to threshold 100

   Create boolean column where rolling std is greater than 100.

3. Final Answer:

   df['high_volatility'] = df['sales'].rolling(5).std() > 100 -> Option A

4. Quick Check:

   rolling(5).std() > 100 detects high variability [OK]

Hint: Use rolling(5).std() then compare to threshold for volatility [OK]

Common Mistakes:

• Using std() before rolling()
• Using mean() or sum() instead of std()
• Comparing rolling sum or mean to threshold