Show the screenshot in the README and finish the Run section
Task: MIL-005#2
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# Weather Data (Day 25)
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# Weather Data (Day 25)
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Reading CSV weather data in Python, first with the built-in `csv` library and then
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Reading CSV weather data in Python, first with the built-in `csv` library and then
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with `pandas`. It is the day-25 assignment of Udemy's *100 Days of Code: The
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with `pandas`. It is the day-25 assignment of Udemy's *100 Days of Code: The
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Complete Python Pro Bootcamp*. The lectures read the weather of one week from
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Complete Python Pro Bootcamp*. The lectures read the weather of one week from
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`weather_data.csv`, collect the temperatures as whole numbers, and then show how
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`weather_data.csv`, collect the temperatures as whole numbers, and then show how
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`pandas` does the same in one line: a table is a `DataFrame` and a column of it is
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`pandas` does the same in one line: a table is a `DataFrame` and a column of it is
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a `Series`. They convert both to a dictionary and a list, compute the average and
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a `Series`. They convert both to a dictionary and a list, compute the average and
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the maximum temperature, select rows by a condition (the row of Monday, the row of
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the maximum temperature, select rows by a condition (the row of Monday, the row of
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the hottest day, Monday's temperature in Fahrenheit), create a `DataFrame` from a
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the hottest day, Monday's temperature in Fahrenheit), create a `DataFrame` from a
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dictionary, and export it to a CSV file.
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dictionary, and export it to a CSV file.
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The `csv` part has no runtime dependencies; `pandas` is an optional extra. The code
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The `csv` part has no runtime dependencies; `pandas` is an optional extra. The code
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keeps the names of the lectures (`data`, `temperatures`, `temp_list`, `data_dict`,
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keeps the names of the lectures (`data`, `temperatures`, `temp_list`, `data_dict`,
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`average_temp`, `monday`, `to_dict`, `to_list`, `mean`, `max`, `to_csv`) so that you
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`average_temp`, `monday`, `to_dict`, `to_list`, `mean`, `max`, `to_csv`) so that you
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can compare it with your own solution.
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can compare it with your own solution.
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> **Status:** in progress. The project foundation (configuration, constants,
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> documentation settings and continuous integration) is in place; the readers and
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> the analysis follow in the next milestones, see `docs/project-plan.md`.
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*`python -m weather` in a terminal: the csv part first, then the pandas part.*
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## Requirements
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> **Status:** finished. The `csv` reader, the `pandas` analysis, the creation and
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> export of a `DataFrame` and the program that prints the results are done. A short
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- Python 3.13 or newer.
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> description in English, Danish and Bahasa Melayu is on the wiki of the repository
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- `git`, to clone the repository.
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> (the pages are in `wiki/`).
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- Optional: [Doxygen](https://www.doxygen.nl/) to build the source documentation.
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- No runtime dependencies. `pandas` (the optional extra `pandas`) is needed for the
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## Requirements
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second half of the lectures and is installed by the `dev` extra, together with
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the development tools (`pytest`, `ruff`, `mypy`).
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- Python 3.13 or newer.
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- `git`, to clone the repository.
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## Set up
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- Optional: [Doxygen](https://www.doxygen.nl/) to build the source documentation.
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- No runtime dependencies. `pandas` (the optional extra `pandas`) is needed for the
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Clone the repository, then create a local virtual environment `.venv` in its
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second half of the lectures and is installed by the `dev` extra, together with
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root, upgrade `pip` and install the project with its development tools. The
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the development tools (`pytest`, `ruff`, `mypy`).
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`.venv` folder is ignored by git.
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## Set up
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```bash
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git clone https://git.tirsystem.com/Tirsvad-Udemy-100-days-of-code/025-weather.git
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Clone the repository, then create a local virtual environment `.venv` in its
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cd 025-weather
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root, upgrade `pip` and install the project with its development tools. The
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```
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`.venv` folder is ignored by git.
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### Windows powershell
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```bash
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git clone https://git.tirsystem.com/Tirsvad-Udemy-100-days-of-code/025-weather.git
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```powershell
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cd 025-weather
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python -m venv .venv
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```
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.\.venv\Scripts\Activate.ps1
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python -m pip install --upgrade pip
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### Windows powershell
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python -m pip install -e ".[dev]"
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```
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```powershell
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python -m venv .venv
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If PowerShell refuses to run the activation script, allow scripts for this
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.\.venv\Scripts\Activate.ps1
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window only with `Set-ExecutionPolicy -Scope Process -ExecutionPolicy RemoteSigned`
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python -m pip install --upgrade pip
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and activate again.
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python -m pip install -e ".[dev]"
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```
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### Linux debian
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If PowerShell refuses to run the activation script, allow scripts for this
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Debian 13 or newer ships Python 3.13. On older releases install Python 3.13
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window only with `Set-ExecutionPolicy -Scope Process -ExecutionPolicy RemoteSigned`
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separately.
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and activate again.
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```bash
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### Linux debian
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sudo apt install python3 python3-venv
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python3 -m venv .venv
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Debian 13 or newer ships Python 3.13. On older releases install Python 3.13
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source .venv/bin/activate
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separately.
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python -m pip install --upgrade pip
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python -m pip install -e ".[dev]"
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```bash
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```
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sudo apt install python3 python3-venv
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python3 -m venv .venv
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### MacOS
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source .venv/bin/activate
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python -m pip install --upgrade pip
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```bash
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python -m pip install -e ".[dev]"
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brew install python@3.13
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```
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python3.13 -m venv .venv
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source .venv/bin/activate
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### MacOS
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python -m pip install --upgrade pip
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python -m pip install -e ".[dev]"
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```bash
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```
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brew install python@3.13
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python3.13 -m venv .venv
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To install only what the `csv` part needs, leave out the extras:
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source .venv/bin/activate
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`python -m pip install -e .`. To add `pandas` without the development tools use
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python -m pip install --upgrade pip
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`python -m pip install -e ".[pandas]"`.
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python -m pip install -e ".[dev]"
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```
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## Run
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To install only what the `csv` part needs, leave out the extras:
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With the virtual environment active, the program that prints the results of the
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`python -m pip install -e .`. To add `pandas` without the development tools use
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lectures is added in a later milestone (`python -m weather`); until then the
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`python -m pip install -e ".[pandas]"`.
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package can be imported:
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## Run
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```bash
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python -c "from weather import constants; print(constants.DATA_FILE)"
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With the virtual environment active, print the results of the lectures:
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```
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```bash
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## Run the tests
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python -m weather
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```
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The tests write only into temporary folders.
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The program prints, in the order of the lectures:
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```bash
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pytest
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1. the rows of `weather_data.csv` and the temperatures as whole numbers, read with
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```
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the `csv` library (no `pandas` needed);
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2. the same file read with `pandas` as a `DataFrame`, converted to a dictionary and
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The same checks that continuous integration runs:
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a list;
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3. the average temperature, computed by hand and with `mean`, and the maximum;
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```bash
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4. the row of Monday, the row of the hottest day, and Monday in Fahrenheit;
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python -m pytest
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5. a `DataFrame` created from a dictionary of students and scores.
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python -m ruff check src tests
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python -m ruff format --check src tests
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Nothing is written to disk unless you ask for it. To also export the scores table
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python -m mypy
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to a CSV file, give a path:
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```
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```bash
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## Continuous integration
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python -m weather --export new_data.csv
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```
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`.gitea/workflows/ci.yml` runs on Gitea Actions on every push and on every pull
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request. It sets up Python 3.13, upgrades `pip`, installs `.[dev]`, then runs
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If `pandas` is not installed the `csv` part is still printed, a message names the
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`pytest`, `ruff` (lint and format check) and `mypy`. It does not build the source
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extra to install, and the exit code is 1.
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documentation: run `doxygen Doxyfile` yourself, as the next section shows.
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The functions can also be used from your own code:
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The workflow lives in `.gitea/workflows` and not in `.github/workflows`, so
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GitHub does not run it and pushing to a GitHub mirror needs no workflow
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```python
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permission.
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from weather.csv_reader import read_temperatures
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from weather.analysis import read_data, average_temp, max_temp
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## Build the source documentation
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temperatures = read_temperatures() # [12, 14, 15, 14, 21, 22, 24]
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The source is documented with Doxygen comments and the `Doxyfile` in the root.
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data = read_data() # a pandas DataFrame
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Install Doxygen (`winget install DimitriVanHeesch.Doxygen` on Windows,
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print(average_temp(data), max_temp(data))
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`sudo apt install doxygen` on Debian, `brew install doxygen` on macOS), then:
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```
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```bash
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The default data file is the `weather_data.csv` inside the package, so the
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doxygen Doxyfile
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functions work from any folder; pass a `path` to read another file.
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```
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## Run the tests
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The HTML is written to `build/doxygen/index.html`. A warning fails the build.
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The tests write only into temporary folders.
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## Project layout
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```bash
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```text
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pytest
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.
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```
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├── .gitea/workflows/ci.yml continuous integration (Gitea Actions)
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├── docs/ business case, plan, milestones, reviews
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The same checks that continuous integration runs:
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├── src/weather/ the package
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│ ├── __init__.py
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```bash
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│ └── constants.py every constant of the lectures
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python -m pytest
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├── tests/ pytest tests (conftest.py runs every test in a
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python -m ruff check src tests
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│ temporary folder)
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python -m ruff format --check src tests
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├── Doxyfile source documentation settings
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python -m mypy
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├── LICENSE
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```
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├── pyproject.toml project configuration
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└── README.md
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## Continuous integration
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```
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`.gitea/workflows/ci.yml` runs on Gitea Actions on every push and on every pull
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## License
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request. It sets up Python 3.13, upgrades `pip`, installs `.[dev]`, then runs
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`pytest`, `ruff` (lint and format check) and `mypy`. It does not build the source
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GNU Affero General Public License v3.0 only. See [LICENSE](LICENSE).
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documentation: run `doxygen Doxyfile` yourself, as the next section shows.
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The workflow lives in `.gitea/workflows` and not in `.github/workflows`, so
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GitHub does not run it and pushing to a GitHub mirror needs no workflow
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permission.
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## Build the source documentation
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The source is documented with Doxygen comments and the `Doxyfile` in the root.
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Install Doxygen (`winget install DimitriVanHeesch.Doxygen` on Windows,
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`sudo apt install doxygen` on Debian, `brew install doxygen` on macOS), then:
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```bash
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doxygen Doxyfile
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```
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The HTML is written to `build/doxygen/index.html`. A warning fails the build.
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## Project layout
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```text
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.
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├── .gitea/workflows/ci.yml continuous integration (Gitea Actions)
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├── docs/ business case, plan, milestones, reviews, screenshot
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├── src/weather/ the package
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│ ├── __init__.py
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│ ├── __main__.py prints the results: python -m weather
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│ ├── analysis.py read_data, average_temp, max_temp, row selection (pandas)
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│ ├── constants.py every constant of the lectures
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│ ├── csv_reader.py read_rows and read_temperatures (csv library)
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│ ├── export.py create_scores_frame and export_frame (pandas)
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│ └── weather_data.csv the weather data of the lecture
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├── tests/ pytest tests (conftest.py runs every test in a
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│ temporary folder)
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├── wiki/ wiki pages in English, Danish and Bahasa Melayu
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├── Doxyfile source documentation settings
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├── LICENSE
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├── pyproject.toml project configuration
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└── README.md
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```
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## License
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GNU Affero General Public License v3.0 only. See [LICENSE](LICENSE).
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