Resampling time series data in Pandas - Mini Project: Build & Apply

Resampling Time Series Data

📖 Scenario: You work as a data analyst for a weather station. You have collected temperature data every hour for a few days. Now, you want to analyze the data by looking at daily average temperatures instead of hourly values.

🎯 Goal: Learn how to resample hourly time series data to daily averages using pandas.

📋 What You'll Learn

Create a pandas DataFrame with hourly temperature data and datetime index

Create a variable for the resampling frequency

Use pandas resample method to calculate daily average temperatures

Print the resulting daily average temperature DataFrame

💡 Why This Matters

🌍 Real World

Weather stations and many industries collect data frequently. Resampling helps summarize and analyze data at different time scales.

💼 Career

Data analysts and scientists often resample time series data to find trends and patterns over days, weeks, or months.

1

Create hourly temperature data

Create a pandas DataFrame called df with a datetime index from '2024-01-01 00:00' to '2024-01-03 23:00' hourly. Add a column 'temperature' with these exact values: [22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, -1, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, -1].

Pandas

import pandas as pd
# Create the DataFrame df with datetime index and temperature column
# Your code here

Need a hint?

Use pd.date_range to create the datetime index with hourly frequency. Then create the DataFrame with the temperature column.

2

Set resampling frequency

Create a variable called freq and set it to the string 'D' to represent daily frequency for resampling.

Pandas

import pandas as pd

dates = pd.date_range(start='2024-01-01 00:00', end='2024-01-03 23:00', freq='H')
temperatures = [22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, -1, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, -1]
df = pd.DataFrame({'temperature': temperatures}, index=dates)
# Create a variable freq and set it to 'D'
# Your code here

Need a hint?

Set freq to the string 'D' which means daily frequency in pandas.

3

Resample to daily average temperature

Create a new DataFrame called daily_avg by resampling df using the freq variable and calculating the mean temperature for each day.

Pandas

import pandas as pd

dates = pd.date_range(start='2024-01-01 00:00', end='2024-01-03 23:00', freq='H')
temperatures = [22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, -1, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, -1]
df = pd.DataFrame({'temperature': temperatures}, index=dates)
freq = 'D'
# Resample df using freq and calculate mean temperature
# Your code here

Need a hint?

Use df.resample(freq).mean() to get daily average temperatures.

4

Print daily average temperatures

Print the daily_avg DataFrame to display the daily average temperatures.

Pandas

import pandas as pd

dates = pd.date_range(start='2024-01-01 00:00', end='2024-01-03 23:00', freq='H')
temperatures = [22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, -1, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, -1]
df = pd.DataFrame({'temperature': temperatures}, index=dates)
freq = 'D'
daily_avg = df.resample(freq).mean()
# Print daily_avg
# Your code here

Need a hint?

Use print(daily_avg) to show the daily average temperatures.