Monthly Rainfall in Brisbane for the last 10 years

Python

26Summer

Author

Rion S. Salman

Published

February 1, 2026

1. Introduction

This report presents a Python-based analysis and visualisation of monthly rainfall in Brisbane over the last ten years. The analysis focuses on exploring temporal rainfall patterns and highlighting months with relatively high rainfall accumulation. Python was used to manage the dataset efficiently and to generate clear, reproducible visualisations that support exploratory climate analysis.

Monthly rainfall data were obtained in tabular format, with rainfall totals (mm) recorded for each month and year. The dataset was initially organised in a wide format, where months were represented as separate columns. To enable flexible plotting and comparison across time, the data were restructured into a long format using Python, allowing each observation to be defined by year, month, and rainfall value.

For visual emphasis on significant rainfall events, a threshold of 50 mm per month was applied, and only values exceeding this threshold were highlighted in the final visualisation. Data visualisation was performed using the Seaborn and Matplotlib libraries, combining full rainfall records as background context with highlighted high-rainfall months. This approach ensures clarity, reproducibility, and effective interpretation of monthly rainfall variability.

2. Data

Show code:

Code

import pandas as pd
rainfall = pd.read_excel("Data/Rainfall_Brisbane_AERO.xlsx")
rainfall.head()

3. Method

3.1. Coverting the actual data to long data format

Show Code:

Code

month_order = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']
rainfall_long = rainfall.melt(id_vars='Year', value_vars=month_order,
               var_name='Month', value_name='Rainfall_mm')
rainfall_long.head()

3.2. Selecting Rainfall more than 50 mm per Month

Show code:

Code

rainfall_longmore50 = rainfall_long[rainfall_long["Rainfall_mm"] > 50]
rainfall_longmore50.head()

3.3. Season selecting

Code

months = rainfall_long["Month"].unique()
season = ["Summer", "Summer", "Summer", "Autumn", "Autumn", "Autumn", "Winter", "Winter", "Winter", "Spring", "Spring", "Spring"]
rainfall_long["Season"] = rainfall_long["Month"].replace(months, season)
grouping_season = rainfall_long.groupby("Season")
grouped_mean = grouping_season["Rainfall_mm"].mean()
grouped_max = grouping_season["Rainfall_mm"].max()
grouped_min = grouping_season["Rainfall_mm"].min()
rainfall_long.head()

4. Result and Discussion

4.1. Data Visualization

Show code:

Code

import seaborn as sns
import matplotlib.pyplot as plt
sns.relplot(rainfall_long, x = "Month", y = "Rainfall_mm", color = "grey")
sns.lineplot(rainfall_longmore50, x = "Month", y = "Rainfall_mm", hue = "Year")

plt.xlabel("Month")
plt.ylabel("Rainfall (mm)")
plt.legend(title = "Year")
plt.title("Monthly Rainfall Data > 50mm per Month")
plt.show()

Show Code:

Code

import numpy as np
years = rainfall['Year'].values
n_years = len(years)

x = np.arange(len(month_order))
bar_width = 0.8 / n_years   # total width = 0.8

plt.figure(figsize=(14,6))

for i, year in enumerate(years):
    rainfall_new = rainfall.loc[rainfall['Year'] == year, months].values.flatten()
    plt.bar(
        x + i * bar_width,
        rainfall_new,
        width=bar_width,
        label=str(year)
    )

plt.xticks(x + bar_width * (n_years-1)/2, months)
plt.ylabel('Rainfall (mm)')
plt.xlabel('Month')
plt.title('Monthly Rainfall in Brisbane (2016–2025)')
plt.legend(ncol=5, fontsize=9)
plt.tight_layout()
plt.show()

Show Code:

Code

rainfall_long['Month'] = pd.Categorical(
    rainfall_long['Month'],
    categories=month_order,
    ordered=True)

pivot = rainfall_long.pivot(index="Year", columns="Month", values="Rainfall_mm")

plt.figure(figsize=(12,5))
sns.heatmap(pivot, cmap="coolwarm", annot=False)

plt.title("Monthly Rainfall Heatmap (mm)")
plt.xlabel("Month")
plt.ylabel("Year")
plt.tight_layout()
plt.show()

4.2. Statistical Analysis

Average Rainfall per Sesason

Code

grouped_mean.head()
grouped_mean.plot(kind = "bar", title = "average rainfall")
plt.xlabel("Season")
plt.ylabel("Rainfall (mm)")

Maximum Rainfall per Season

Code

grouped_max.head()
grouped_max.plot(kind = "bar", title = "Maximum Rainfall")
plt.xlabel("Season")
plt.ylabel("Rainfall (mm)")

Minimum Rainfall per Season

Code

grouped_min.head()
grouped_min.plot(kind = "bar", title = "Minimum Rainfall")
plt.xlabel("Season")
plt.ylabel("Rainfall (mm)")