TreeLedger

TreeLedger cleans and analyses an urban tree census. It takes a raw municipal dataset, tidies it into a consistent form, and runs basic analysis on how trees are distributed across the city.

I built this solo while learning data cleaning and analysis with Pandas.

What it does

The code sits in two files in src/:

• data_cleaning.py cleans the raw CSV: it standardises column names, trims text fields, forces numeric columns to be numeric, and drops columns that are not needed.

• analysis.py runs the analysis: tree count per ward, the most common species, the height distribution, and a function that lists wards with a below-average tree count.

Dataset

A municipal urban tree census with 9,623 trees and 25 attributes per tree (e.g. height, girth, canopy diameter, ward, botanical name, and common name). It covers 71 wards and 155 species.

What the analysis produces

• Tree count per ward, and the top 10 wards by count.
• The most common species across the dataset.
• The height distribution. The median height is 6 m, with a range of 2 m to 24 m.
• A list of wards that sit below the average tree count.

How to use

1. Install dependencies:

   pip install -r requirements.txt
   

2. Clean the raw data:

   from src.data_cleaning import clean_tree_data
   clean_tree_data(
       "data/raw/urban_tree_census.csv",
       "data/processed/urban_tree_census_cleaned.csv",
   )

3. Run the analysis:

   from src.analysis import analyze_tree_data
   results = analyze_tree_data(
       "data/processed/urban_tree_census_cleaned.csv",
       "outputs/plots",
       mode="real",
   )

4. List the below-average wards:

   from src.analysis import identify_low_density_areas
   import pandas as pd
   df = pd.read_csv("data/processed/urban_tree_census_cleaned.csv")
   print(identify_low_density_areas(df))

   Plots are saved in outputs/plots/.

Tech stack

Python 3, Pandas, NumPy, Matplotlib, Jupyter.

Files

treeledger/
  data/
    raw/         original datasets
    processed/   cleaned datasets
  notebooks/
    exploration.ipynb
  src/
    data_cleaning.py
    analysis.py
  outputs/plots/  generated plots
  requirements.txt