ewm() for exponential moving average in Pandas - Mini Project: Build & Apply

Calculate Exponential Moving Average with pandas ewm()

📖 Scenario: You work as a data analyst for a small retail company. You have daily sales data for a product over 10 days. Your manager wants to see the trend of sales but wants recent days to have more importance than older days. This is called an exponential moving average (EMA).EMA helps smooth out the sales data and highlights recent changes more clearly.

🎯 Goal: Build a small program that calculates the exponential moving average of daily sales using pandas ewm() method.

📋 What You'll Learn

Create a pandas DataFrame with daily sales data for 10 days.

Set a span value to control the smoothing factor for EMA.

Use pandas ewm() method with the span to calculate the EMA.

Print the original sales and the EMA values.

💡 Why This Matters

🌍 Real World

EMA is widely used in sales analysis, stock market trends, and any time series data to smooth out noise and highlight recent changes.

💼 Career

Data analysts and data scientists use EMA to help businesses make decisions based on recent trends rather than raw noisy data.

1

Create the sales data DataFrame

Create a pandas DataFrame called sales_data with two columns: 'Day' and 'Sales'. Use these exact values for 'Day': 1 to 10, and for 'Sales': 200, 220, 215, 230, 240, 235, 250, 245, 260, 270.

Pandas

import pandas as pd
# Create the sales_data DataFrame with 'Day' and 'Sales' columns
# Your code here

Hint

Use pd.DataFrame with a dictionary containing the exact lists for 'Day' and 'Sales'.

2

Set the span for EMA calculation

Create a variable called span and set it to 3. This controls how much weight recent sales get in the exponential moving average.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Day': list(range(1, 11)),
    'Sales': [200, 220, 215, 230, 240, 235, 250, 245, 260, 270]
})
# Set the span variable to 3
# Your code here

Hint

Just create a variable named span and assign it the value 3.

3

Calculate the exponential moving average

Use the ewm() method on the 'Sales' column of sales_data with the span variable, then call mean() to calculate the EMA. Store the result in a new column called 'EMA' in sales_data.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Day': list(range(1, 11)),
    'Sales': [200, 220, 215, 230, 240, 235, 250, 245, 260, 270]
})
span = 3
# Calculate EMA and add it as 'EMA' column in sales_data
# Your code here

Hint

Use sales_data['Sales'].ewm(span=span, adjust=False).mean() to calculate EMA and assign it to sales_data['EMA'].

4

Print the sales and EMA columns

Print the sales_data DataFrame showing only the 'Day', 'Sales', and 'EMA' columns.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Day': list(range(1, 11)),
    'Sales': [200, 220, 215, 230, 240, 235, 250, 245, 260, 270]
})
span = 3
sales_data['EMA'] = sales_data['Sales'].ewm(span=span, adjust=False).mean()
# Print the 'Day', 'Sales', and 'EMA' columns of sales_data
# Your code here

Hint

Use print(sales_data[['Day', 'Sales', 'EMA']]) to show the data.

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