Time series analysis patterns in Pandas - Mini Project: Build & Apply

Time Series Analysis Patterns

📖 Scenario: You work for a small weather station that collects daily temperature data. You want to analyze the temperature changes over a week to find patterns like rising or falling trends.

🎯 Goal: Build a simple program to store daily temperatures, set a threshold for significant change, find days where temperature changes exceed this threshold, and print those days with their changes.

📋 What You'll Learn

Create a dictionary with exact daily temperatures for 7 days

Create a variable for the temperature change threshold

Use a loop to find days where the temperature change from the previous day is greater than the threshold

Print the days and their temperature changes that meet the condition

💡 Why This Matters

🌍 Real World

Weather stations and climate researchers analyze temperature changes over time to detect trends and unusual events.

💼 Career

Data analysts and scientists often work with time series data to find patterns, anomalies, and insights for decision making.

1

Create the daily temperature data

Create a dictionary called daily_temps with these exact entries: 'Monday': 20, 'Tuesday': 22, 'Wednesday': 19, 'Thursday': 23, 'Friday': 21, 'Saturday': 24, 'Sunday': 20.

Pandas

# Create the daily_temps dictionary with exact values
# Your code here

Need a hint?

Use curly braces to create a dictionary with day names as keys and temperatures as values.

2

Set the temperature change threshold

Create a variable called threshold and set it to 2 to represent the minimum temperature change to consider significant.

Pandas

daily_temps = {'Monday': 20, 'Tuesday': 22, 'Wednesday': 19, 'Thursday': 23, 'Friday': 21, 'Saturday': 24, 'Sunday': 20}
# Create the threshold variable and set it to 2
# Your code here

Need a hint?

Just assign the number 2 to a variable named threshold.

3

Find days with significant temperature changes

Create an empty dictionary called significant_changes and a list days_order with days in order: ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']. Use a for loop over range(1, len(days_order)). Inside the loop, get day = days_order[i] and prev_day = days_order[i-1], calculate the temperature change. If the absolute change is greater than threshold, add the day and change to significant_changes.

Pandas

daily_temps = {'Monday': 20, 'Tuesday': 22, 'Wednesday': 19, 'Thursday': 23, 'Friday': 21, 'Saturday': 24, 'Sunday': 20}
threshold = 2
# Find days with temperature changes greater than threshold
# Your code here

Need a hint?

Use a loop from 1 to 6 to compare each day with the previous day. Use abs() to get absolute change.

4

Print the significant temperature changes

Write a print statement to display the significant_changes dictionary.

Pandas

daily_temps = {'Monday': 20, 'Tuesday': 22, 'Wednesday': 19, 'Thursday': 23, 'Friday': 21, 'Saturday': 24, 'Sunday': 20}
threshold = 2
significant_changes = {}
days_order = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
for i in range(1, len(days_order)):
    day = days_order[i]
    prev_day = days_order[i - 1]
    change = daily_temps[day] - daily_temps[prev_day]
    if abs(change) > threshold:
        significant_changes[day] = change
# Print the significant_changes dictionary
# Your code here

Need a hint?

Use print(significant_changes) to show the result.