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

Need a 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

Need a 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

Need a 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

Need a hint?

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