Day 6: 🙌 Coding Colab

Introduction

In this coding colab, you’ll analyze global temperature anomalies and CO2 concentration data. You’ll practice data manipulation, joining datasets, time series analysis, and visualization techniques.

Learning Objectives

By the end of this colab, you will be able to:

1. Load and preprocess time series data
2. Join datasets based on datetime indices
3. Perform basic time series analysis and resampling
4. Apply data manipulation techniques to extract insights from environmental datasets

Setup

Let’s start by importing necessary libraries and loading our datasets:

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Load the temperature anomaly dataset
temp_url = "https://bit.ly/monthly_temp"
temp_df = pd.read_csv(temp_url, parse_dates=['Date'])

# Load the CO2 concentration dataset
co2_url = "https://bit.ly/monthly_CO2"
co2_df = pd.read_csv(co2_url, parse_dates=['Date'])

print("Temperature data:")
print(temp_df.head())
print("\nCO2 data:")
print(co2_df.head())

Temperature data:
        Date  MonthlyAnomaly
0 1880-01-01           -0.20
1 1880-02-01           -0.25
2 1880-03-01           -0.09
3 1880-04-01           -0.16
4 1880-05-01           -0.09

CO2 data:
        Date  CO2Concentration
0 1958-04-01            317.45
1 1958-05-01            317.51
2 1958-06-01            317.27
3 1958-07-01            315.87
4 1958-08-01            314.93

Task 1: Data Preparation

1. Set the ‘Date’ column as the index for both dataframes.
2. Ensure that there are no missing values in either dataset.

Task 2: Joining Datasets

1. Merge the temperature and CO2 datasets based on their date index.
2. Handle any missing values that may have been introduced by the merge.
3. Create some plots showing temperature anomalies and CO2 concentrations over time using pandas built-in plotting functions.

Task 3: Time Series Analysis

1. Resample the data to annual averages.
2. Calculate the year-over-year change in temperature anomalies and CO2 concentrations.
3. Create a scatter plot (use the plt.scatter() function) of annual temperature anomalies vs CO2 concentrations.

Task 4: Seasonal Analysis

1. Create a function to extract the season from a given date (hint: use the date.month attribute and if-elif-else to assign the season in your function).
2. Use the function to create a new column called Season
3. Calculate the average temperature anomaly and CO2 concentration for each season.
4. Create a box plot (use sns.boxplot) showing the distribution of temperature anomalies for each season.