Weather Data (Day 25)

Reading CSV weather data in Python, first with the built-in csv library and then with pandas. It is the day-25 assignment of Udemy's 100 Days of Code: The Complete Python Pro Bootcamp. The lectures read the weather of one week from weather_data.csv, collect the temperatures as whole numbers, and then show how pandas does the same in one line: a table is a DataFrame and a column of it is a Series. They convert both to a dictionary and a list, compute the average and the maximum temperature, select rows by a condition (the row of Monday, the row of the hottest day, Monday's temperature in Fahrenheit), create a DataFrame from a dictionary, and export it to a CSV file.

The csv part has no runtime dependencies; pandas is an optional extra. The code keeps the names of the lectures (data, temperatures, temp_list, data_dict, average_temp, monday, to_dict, to_list, mean, max, to_csv) so that you can compare it with your own solution.

python -m weather in a terminal: the csv part first, then the pandas part.

Status: finished. The csv reader, the pandas analysis, the creation and export of a DataFrame and the program that prints the results are done. A short description in English, Danish and Bahasa Melayu is on the wiki of the repository (the pages are in wiki/).

Requirements

• Python 3.13 or newer.
• git, to clone the repository.
• Optional: Doxygen to build the source documentation.
• No runtime dependencies. pandas (the optional extra pandas) is needed for the second half of the lectures and is installed by the dev extra, together with the development tools (pytest, ruff, mypy).

Set up

Clone the repository, then create a local virtual environment .venv in its root, upgrade pip and install the project with its development tools. The .venv folder is ignored by git.

git clone https://git.tirsystem.com/Tirsvad-Udemy-100-days-of-code/025-weather.git
cd 025-weather

Windows powershell

python -m venv .venv
.\.venv\Scripts\Activate.ps1
python -m pip install --upgrade pip
python -m pip install -e ".[dev]"

If PowerShell refuses to run the activation script, allow scripts for this window only with Set-ExecutionPolicy -Scope Process -ExecutionPolicy RemoteSigned and activate again.

Linux debian

Debian 13 or newer ships Python 3.13. On older releases install Python 3.13 separately.

sudo apt install python3 python3-venv
python3 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install -e ".[dev]"

MacOS

brew install python@3.13
python3.13 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install -e ".[dev]"

To install only what the csv part needs, leave out the extras: python -m pip install -e .. To add pandas without the development tools use python -m pip install -e ".[pandas]".

Run

With the virtual environment active, print the results of the lectures:

python -m weather

The program prints, in the order of the lectures:

1. the rows of weather_data.csv and the temperatures as whole numbers, read with the csv library (no pandas needed);
2. the same file read with pandas as a DataFrame, converted to a dictionary and a list;
3. the average temperature, computed by hand and with mean, and the maximum;
4. the row of Monday, the row of the hottest day, and Monday in Fahrenheit;
5. a DataFrame created from a dictionary of students and scores.

Nothing is written to disk unless you ask for it. To also export the scores table to a CSV file, give a path:

python -m weather --export new_data.csv

If pandas is not installed the csv part is still printed, a message names the extra to install, and the exit code is 1.

The functions can also be used from your own code:

from weather.csv_reader import read_temperatures
from weather.analysis import read_data, average_temp, max_temp

temperatures = read_temperatures()   # [12, 14, 15, 14, 21, 22, 24]
data = read_data()                   # a pandas DataFrame
print(average_temp(data), max_temp(data))

The default data file is the weather_data.csv inside the package, so the functions work from any folder; pass a path to read another file.

Run the tests

The tests write only into temporary folders.

pytest

The same checks that continuous integration runs:

python -m pytest
python -m ruff check src tests
python -m ruff format --check src tests
python -m mypy

Continuous integration

.gitea/workflows/ci.yml runs on Gitea Actions on every push and on every pull request. It sets up Python 3.13, upgrades pip, installs .[dev], then runs pytest, ruff (lint and format check) and mypy. It does not build the source documentation: run doxygen Doxyfile yourself, as the next section shows.

The workflow lives in .gitea/workflows and not in .github/workflows, so GitHub does not run it and pushing to a GitHub mirror needs no workflow permission.

Build the source documentation

The source is documented with Doxygen comments and the Doxyfile in the root. Install Doxygen (winget install DimitriVanHeesch.Doxygen on Windows, sudo apt install doxygen on Debian, brew install doxygen on macOS), then:

doxygen Doxyfile

The HTML is written to build/doxygen/index.html. A warning fails the build.

Project layout

.
├── .gitea/workflows/ci.yml    continuous integration (Gitea Actions)
├── docs/                      business case, plan, milestones, reviews, screenshot
├── src/weather/               the package
│   ├── __init__.py
│   ├── __main__.py            prints the results: python -m weather
│   ├── analysis.py            read_data, average_temp, max_temp, row selection (pandas)
│   ├── constants.py           every constant of the lectures
│   ├── csv_reader.py          read_rows and read_temperatures (csv library)
│   ├── export.py              create_scores_frame and export_frame (pandas)
│   └── weather_data.csv       the weather data of the lecture
├── tests/                     pytest tests (conftest.py runs every test in a
│                              temporary folder)
├── wiki/                      wiki pages in English, Danish and Bahasa Melayu
├── Doxyfile                   source documentation settings
├── LICENSE
├── pyproject.toml             project configuration
└── README.md

License

GNU Affero General Public License v3.0 only. See LICENSE.