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# Weather Data (Day 25) # Weather Data (Day 25)
Reading CSV weather data in Python, first with the built-in `csv` library and then 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 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 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 `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 `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 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 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 the hottest day, Monday's temperature in Fahrenheit), create a `DataFrame` from a
dictionary, and export it to a CSV file. dictionary, and export it to a CSV file.
The `csv` part has no runtime dependencies; `pandas` is an optional extra. The code 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`, 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 `average_temp`, `monday`, `to_dict`, `to_list`, `mean`, `max`, `to_csv`) so that you
can compare it with your own solution. can compare it with your own solution.
> **Status:** in progress. The project foundation (configuration, constants, ![The program running in a terminal: the rows and temperatures read with the csv library, then the data frame, the dictionary, the list, the average 17.428571428571427, the maximum 24, the row of Monday, the hottest row Sunday, Monday in Fahrenheit 53.6 and the scores frame](docs/images/weather-run.png)
> documentation settings and continuous integration) is in place; the readers and
> the analysis follow in the next milestones, see `docs/project-plan.md`. *`python -m weather` in a terminal: the csv part first, then the pandas part.*
## Requirements > **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
- Python 3.13 or newer. > description in English, Danish and Bahasa Melayu is on the wiki of the repository
- `git`, to clone the repository. > (the pages are in `wiki/`).
- Optional: [Doxygen](https://www.doxygen.nl/) to build the source documentation.
- No runtime dependencies. `pandas` (the optional extra `pandas`) is needed for the ## Requirements
second half of the lectures and is installed by the `dev` extra, together with
the development tools (`pytest`, `ruff`, `mypy`). - Python 3.13 or newer.
- `git`, to clone the repository.
## Set up - Optional: [Doxygen](https://www.doxygen.nl/) to build the source documentation.
- No runtime dependencies. `pandas` (the optional extra `pandas`) is needed for the
Clone the repository, then create a local virtual environment `.venv` in its second half of the lectures and is installed by the `dev` extra, together with
root, upgrade `pip` and install the project with its development tools. The the development tools (`pytest`, `ruff`, `mypy`).
`.venv` folder is ignored by git.
## Set up
```bash
git clone https://git.tirsystem.com/Tirsvad-Udemy-100-days-of-code/025-weather.git Clone the repository, then create a local virtual environment `.venv` in its
cd 025-weather root, upgrade `pip` and install the project with its development tools. The
``` `.venv` folder is ignored by git.
### Windows powershell ```bash
git clone https://git.tirsystem.com/Tirsvad-Udemy-100-days-of-code/025-weather.git
```powershell cd 025-weather
python -m venv .venv ```
.\.venv\Scripts\Activate.ps1
python -m pip install --upgrade pip ### Windows powershell
python -m pip install -e ".[dev]"
``` ```powershell
python -m venv .venv
If PowerShell refuses to run the activation script, allow scripts for this .\.venv\Scripts\Activate.ps1
window only with `Set-ExecutionPolicy -Scope Process -ExecutionPolicy RemoteSigned` python -m pip install --upgrade pip
and activate again. python -m pip install -e ".[dev]"
```
### Linux debian
If PowerShell refuses to run the activation script, allow scripts for this
Debian 13 or newer ships Python 3.13. On older releases install Python 3.13 window only with `Set-ExecutionPolicy -Scope Process -ExecutionPolicy RemoteSigned`
separately. and activate again.
```bash ### Linux debian
sudo apt install python3 python3-venv
python3 -m venv .venv Debian 13 or newer ships Python 3.13. On older releases install Python 3.13
source .venv/bin/activate separately.
python -m pip install --upgrade pip
python -m pip install -e ".[dev]" ```bash
``` sudo apt install python3 python3-venv
python3 -m venv .venv
### MacOS source .venv/bin/activate
python -m pip install --upgrade pip
```bash python -m pip install -e ".[dev]"
brew install python@3.13 ```
python3.13 -m venv .venv
source .venv/bin/activate ### MacOS
python -m pip install --upgrade pip
python -m pip install -e ".[dev]" ```bash
``` brew install python@3.13
python3.13 -m venv .venv
To install only what the `csv` part needs, leave out the extras: source .venv/bin/activate
`python -m pip install -e .`. To add `pandas` without the development tools use python -m pip install --upgrade pip
`python -m pip install -e ".[pandas]"`. python -m pip install -e ".[dev]"
```
## Run
To install only what the `csv` part needs, leave out the extras:
With the virtual environment active, the program that prints the results of the `python -m pip install -e .`. To add `pandas` without the development tools use
lectures is added in a later milestone (`python -m weather`); until then the `python -m pip install -e ".[pandas]"`.
package can be imported:
## Run
```bash
python -c "from weather import constants; print(constants.DATA_FILE)" With the virtual environment active, print the results of the lectures:
```
```bash
## Run the tests python -m weather
```
The tests write only into temporary folders.
The program prints, in the order of the lectures:
```bash
pytest 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
The same checks that continuous integration runs: a list;
3. the average temperature, computed by hand and with `mean`, and the maximum;
```bash 4. the row of Monday, the row of the hottest day, and Monday in Fahrenheit;
python -m pytest 5. a `DataFrame` created from a dictionary of students and scores.
python -m ruff check src tests
python -m ruff format --check src tests Nothing is written to disk unless you ask for it. To also export the scores table
python -m mypy to a CSV file, give a path:
```
```bash
## Continuous integration python -m weather --export new_data.csv
```
`.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 If `pandas` is not installed the `csv` part is still printed, a message names the
`pytest`, `ruff` (lint and format check) and `mypy`. It does not build the source extra to install, and the exit code is 1.
documentation: run `doxygen Doxyfile` yourself, as the next section shows.
The functions can also be used from your own code:
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 ```python
permission. from weather.csv_reader import read_temperatures
from weather.analysis import read_data, average_temp, max_temp
## Build the source documentation
temperatures = read_temperatures() # [12, 14, 15, 14, 21, 22, 24]
The source is documented with Doxygen comments and the `Doxyfile` in the root. data = read_data() # a pandas DataFrame
Install Doxygen (`winget install DimitriVanHeesch.Doxygen` on Windows, print(average_temp(data), max_temp(data))
`sudo apt install doxygen` on Debian, `brew install doxygen` on macOS), then: ```
```bash The default data file is the `weather_data.csv` inside the package, so the
doxygen Doxyfile functions work from any folder; pass a `path` to read another file.
```
## Run the tests
The HTML is written to `build/doxygen/index.html`. A warning fails the build.
The tests write only into temporary folders.
## Project layout
```bash
```text pytest
. ```
├── .gitea/workflows/ci.yml continuous integration (Gitea Actions)
├── docs/ business case, plan, milestones, reviews The same checks that continuous integration runs:
├── src/weather/ the package
│ ├── __init__.py ```bash
│ └── constants.py every constant of the lectures python -m pytest
├── tests/ pytest tests (conftest.py runs every test in a python -m ruff check src tests
│ temporary folder) python -m ruff format --check src tests
├── Doxyfile source documentation settings python -m mypy
├── LICENSE ```
├── pyproject.toml project configuration
└── README.md ## Continuous integration
```
`.gitea/workflows/ci.yml` runs on Gitea Actions on every push and on every pull
## License 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
GNU Affero General Public License v3.0 only. See [LICENSE](LICENSE). 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:
```bash
doxygen Doxyfile
```
The HTML is written to `build/doxygen/index.html`. A warning fails the build.
## Project layout
```text
.
├── .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](LICENSE).
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| PP | Project Plan | docs/project-plan.md | 002 | | PP | Project Plan | docs/project-plan.md | 002 |
| MIL | Milestone / Gateway | docs/milestones/*.md | 006 | | MIL | Milestone / Gateway | docs/milestones/*.md | 006 |
| DICT | Domain Dictionary | docs/dictionary.md | 002 | | DICT | Domain Dictionary | docs/dictionary.md | 002 |
| RC | SQA Review Record | docs/sqa/reviews/rc-*.md | 013 | | RC | SQA Review Record | docs/sqa/reviews/rc-*.md | 014 |
## Languages ## Languages
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# RC-013: Code review of MIL-005 (screenshot, README, wiki; no Python file changed)
## Metadata
| Key | Value |
| --- | --- |
| ID | RC-013 |
| CrossReference | [MIL-005], [QC-PY-001] |
## Version History
| Date | Status | Author | Reviewer | Change | Commit |
| --- | --- | --- | --- | --- | --- |
| 2026-10-13 | Proposed | Jens Tirsvad Nielsen | AI as stand-in for S01 | Initial version | [deaed38] |
---
## Artifact Under Review
- Instance reviewed: the source and tests that [MIL-005] delivers (screenshot, README, wiki; no Python file changed)
- Checklist used: [QC-PY-001]
- Scope: full review of the files changed on the branch of the milestone
- Language and domain: n/a
- Language reviewer: none
- Mode: autonomous, not independent (the reviewer is `AI as stand-in for S01`; the assistant also wrote the code; the review of the files was done by the `python-developer` agent of the session)
## Checklist Results
| # | Criterion | Status | Evidence/Notes |
| --- | --- | --- | --- |
| 1 | Packages, modules, functions, variables, classes and constants follow PEP 8 casing (`snake_case`, `PascalCase`, `UPPER_SNAKE`) | N-A | MIL-005 changes no Python file (`git diff main --stat -- src tests` is empty); the files are a PNG, `README.md` and `wiki/`. |
| 2 | Names state purpose in the domain's language; no unexplained abbreviations, no single-letter names outside tiny scopes | N-A | No Python file changed. |
| 3 | Code is produced by the project's formatter and passes its linter with no unexplained suppressions | Pass | `ruff check` and `ruff format --check` still exit 0 on the unchanged `src` and `tests`, checked in a fresh clone of the branch. |
| 4 | Every function and method signature is type-annotated, including `-> None` | N-A | No Python file changed. |
| 5 | No bare `except:`, no swallowed exceptions; specific exceptions are raised and the cause is kept (`raise ... from`) | N-A | No Python file changed. |
| 6 | No mutable default arguments and no shadowed builtins | N-A | No Python file changed. |
| 7 | Files, locks and connections are managed with context managers | N-A | No Python file changed. |
| 8 | Public modules, classes and functions have docstrings that say what, not how | Pass | `doxygen Doxyfile` exits 0 with no warnings in the fresh clone. |
| 9 | Logging uses `logging`, not `print`; no secrets or personal data in log output | N-A | No Python file changed. |
| 10 | Classes and operations trace to the Design Class Diagram they implement; deviations are recorded | N-A | No Python file changed. |
| 11 | Tests exist for new behaviour, are named for the behaviour, and do not depend on order or the network | Pass | In a fresh clone of the branch, following the README on Windows PowerShell (`python -m venv .venv`, `python -m pip install --upgrade pip`, `python -m pip install -e ".[dev]"`), `pytest` reports 39 passed and `python -m weather` exits 0 and prints the results of the lectures. |
| 12 | Type checker runs in strict mode without errors; `Any` is justified in a comment | Pass | `python -m mypy` runs with `strict = true`: no issues in 12 source files in the fresh clone. |
| 13 | Dependencies are declared and pinned in the project's dependency file, none unused | N-A | No dependency change. |
| 14 | Each piece of knowledge (a business rule, constant, format, validation or query) is defined in one place; code is merged only where it expresses the same knowledge, not where it merely looks alike | N-A | No Python file changed. |
| 15 | Imports point inward: business-rule modules import no framework, database, UI or delivery-mechanism code, and there are no import cycles | N-A | No Python file changed. |
| 16 | SOLID holds: a class or module has one reason to change; new behaviour is added by extension, not by editing a type test in several places; subclasses honour the contract of their base; protocols are narrow; high-level code depends on protocols or abstract types and receives its implementations | N-A | No Python file changed. |
## Overall Verdict
Go — all Mandatory criteria pass or are not applicable. Assessed on 2026-10-13 by the assistant that wrote the code, as stand-in for S01 in autonomous mode. Mode: autonomous, not independent. The pull request is the second look. The deliverables of [MIL-005] that are not code were checked against its Go/No-Go list: the screenshot is a capture of a real console window that ran `python -m weather` (taken once with a one-off script outside the repository, not part of the change; the capture was cropped to the window so that no neighbouring window shows); the README and the three wiki pages show it; the wiki has `Home`, `Home.da`, `Home.ms` and `_Sidebar`; the marker `wiki/.publish` is added (a small addition to the task list: it is what makes the instance's workflow publish the pages). The address of the image on `main` and the publication of the wiki are checked after the merge and reported in the run summary.
## Action Items
| Action | Owner | Due |
| --- | --- | --- |
| Challenge this `Go` in the run summary or on the pull request if the stand-in decision is not wanted | S01 | 2026-10-23 |
---
[MIL-005]: ../../milestones/mil-005-screenshot-wiki-and-finish.md
[QC-PY-001]: ../../../framework/qc/code/qc-programming-python.md
[PP-001]: ../../project-plan.md
[deaed38]: https://git.tirsystem.com/Tirsvad-Udemy-100-days-of-code/025-weather/commit/deaed3899c379d04d6df00d616086f46aefea029
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> Denne side er genereret fra README.md og wiki/. Ændringer her bliver overskrevet, næste gang wikien udgives.
> Den engelske side er den gældende; denne oversættelse følger den.
[English](Home) | Dansk | [Bahasa Melayu](Home.ms)
# Vejrdata (dag 25)
At læse CSV-vejrdata i Python. Det er opgaven fra dag 25 i Udemys *100 Days of Code: The Complete Python Pro Bootcamp*. Lektionerne læser en uges vejr fra `weather_data.csv` med Pythons indbyggede `csv`-bibliotek og samler temperaturerne som hele tal, og viser derefter, hvordan biblioteket `pandas` gør det samme på én linje. En tabel er en `DataFrame`, og en kolonne i den er en `Series`; begge kan omdannes til en ordbog og en liste, gennemsnits- og maksimumtemperaturen kan beregnes, rækker kan udvælges ud fra en betingelse, og en `DataFrame` kan oprettes ud fra en ordbog og eksporteres til en CSV-fil.
![Programmet kører i en terminal: rækkerne og temperaturerne læst med csv-biblioteket, derefter datarammen, ordbogen, listen, gennemsnittet 17,43, maksimum 24, mandagens række, den varmeste række søndag, mandag i Fahrenheit 53,6 og tabellen med point](https://git.tirsystem.com/Tirsvad-Udemy-100-days-of-code/025-weather/raw/branch/main/docs/images/weather-run.png)
*`python -m weather` i en terminal: først csv-delen, derefter pandas-delen.*
## Hvad programmet skriver
| Del | Resultat |
| --- | --- |
| `csv`-biblioteket | Filens rækker og temperaturerne `[12, 14, 15, 14, 21, 22, 24]` |
| `pandas` | `DataFrame`, ordbogen og listen med temperaturer |
| Statistik | Gennemsnit 17,43 (i hånden og med `mean`) og maksimum 24 |
| Rækkeudvælgelse | Mandagens række, den varmeste dags række (søndag), mandag i Fahrenheit (53,6) |
| Opret og eksportér | En `DataFrame` med elever og point lavet ud fra en ordbog; skrives kun til en fil med `--export` |
## Funktioner
| Funktion | Modul | Lektion |
| --- | --- | --- |
| `read_rows`, `read_temperatures` | `weather.csv_reader` | Læsning med `csv`-biblioteket |
| `read_data` | `weather.analysis` | `pandas.read_csv` |
| `to_data_dict`, `to_temp_list` | `weather.analysis` | `to_dict`, `to_list` |
| `average_temp`, `average_temp_mean`, `max_temp` | `weather.analysis` | Sum og længde, `mean`, `max` |
| `row_for_day`, `hottest_row`, `day_in_fahrenheit` | `weather.analysis` | Udvælgelse af rækker |
| `create_scores_frame`, `export_frame` | `weather.export` | `DataFrame` ud fra en ordbog, `to_csv` |
## Kør programmet
Der skal bruges Python 3.13 eller nyere. `csv`-delen har ingen afhængigheder ved kørsel; `pandas` er et valgfrit tilvalg, som `dev`-tilvalget installerer.
```bash
git clone https://git.tirsystem.com/Tirsvad-Udemy-100-days-of-code/025-weather.git
cd 025-weather
python -m venv .venv
python -m pip install --upgrade pip
python -m pip install -e ".[dev]"
python -m weather
```
Tilføj `--export new_data.csv` for også at skrive tabellen med point til en fil. Repositoriets README har den fulde opsætning til Windows, Linux og macOS.
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> This page is generated from README.md and wiki/. Changes made here are overwritten the next time the wiki is published.
English | [Dansk](Home.da) | [Bahasa Melayu](Home.ms)
# Weather Data (Day 25)
Reading CSV weather data in Python. 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` with Python's built-in `csv` library and collect the temperatures as whole numbers, and then show how the `pandas` library does the same in one line. A table is a `DataFrame` and a column of it is a `Series`; both can be converted to a dictionary and a list, the average and the maximum temperature can be computed, rows can be selected by a condition, and a `DataFrame` can be created from a dictionary and exported to a CSV file.
![The program running in a terminal: the rows and temperatures read with the csv library, then the data frame, the dictionary, the list, the average 17.43, the maximum 24, the row of Monday, the hottest row Sunday, Monday in Fahrenheit 53.6 and the scores frame](https://git.tirsystem.com/Tirsvad-Udemy-100-days-of-code/025-weather/raw/branch/main/docs/images/weather-run.png)
*`python -m weather` in a terminal: the csv part first, then the pandas part.*
## What the program prints
| Part | Result |
| --- | --- |
| `csv` library | The rows of the file and the temperatures `[12, 14, 15, 14, 21, 22, 24]` |
| `pandas` | The `DataFrame`, the dictionary and the list of temperatures |
| Statistics | Average 17.43 (by hand and with `mean`) and maximum 24 |
| Row selection | The row of Monday, the row of the hottest day (Sunday), Monday in Fahrenheit (53.6) |
| Create and export | A `DataFrame` of students and scores made from a dictionary; written to a file only with `--export` |
## Functions
| Function | Module | Lecture |
| --- | --- | --- |
| `read_rows`, `read_temperatures` | `weather.csv_reader` | Reading with the `csv` library |
| `read_data` | `weather.analysis` | `pandas.read_csv` |
| `to_data_dict`, `to_temp_list` | `weather.analysis` | `to_dict`, `to_list` |
| `average_temp`, `average_temp_mean`, `max_temp` | `weather.analysis` | Sum and length, `mean`, `max` |
| `row_for_day`, `hottest_row`, `day_in_fahrenheit` | `weather.analysis` | Selecting rows |
| `create_scores_frame`, `export_frame` | `weather.export` | `DataFrame` from a dictionary, `to_csv` |
## Run
Python 3.13 or newer is needed. The `csv` part has no runtime dependencies; `pandas` is an optional extra that the `dev` extra installs.
```bash
git clone https://git.tirsystem.com/Tirsvad-Udemy-100-days-of-code/025-weather.git
cd 025-weather
python -m venv .venv
python -m pip install --upgrade pip
python -m pip install -e ".[dev]"
python -m weather
```
Add `--export new_data.csv` to also write the scores table to a file. The README of the repository has the full set-up for Windows, Linux and macOS.
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> Halaman ini dijana daripada README.md dan wiki/. Perubahan di sini akan ditimpa apabila wiki diterbitkan lagi.
> Halaman Bahasa Inggeris adalah yang rasmi; terjemahan ini mengikutinya.
[English](Home) | [Dansk](Home.da) | Bahasa Melayu
# Data Cuaca (hari 25)
Membaca data cuaca CSV dalam Python. Ia ialah tugasan hari ke-25 dalam *100 Days of Code: The Complete Python Pro Bootcamp* di Udemy. Pelajaran membaca cuaca satu minggu daripada `weather_data.csv` dengan pustaka `csv` terbina dalam Python dan mengumpul suhu sebagai nombor bulat, kemudian menunjukkan bagaimana pustaka `pandas` melakukan perkara yang sama dalam satu baris. Satu jadual ialah `DataFrame` dan satu lajur daripadanya ialah `Series`; kedua-duanya boleh ditukar kepada kamus dan senarai, suhu purata dan suhu maksimum boleh dikira, baris boleh dipilih mengikut syarat, dan `DataFrame` boleh dicipta daripada kamus serta dieksport ke fail CSV.
![Program berjalan dalam terminal: baris dan suhu yang dibaca dengan pustaka csv, kemudian data frame, kamus, senarai, purata 17.43, maksimum 24, baris hari Isnin, baris terpanas hari Ahad, Isnin dalam Fahrenheit 53.6 dan jadual skor](https://git.tirsystem.com/Tirsvad-Udemy-100-days-of-code/025-weather/raw/branch/main/docs/images/weather-run.png)
*`python -m weather` dalam terminal: bahagian csv dahulu, kemudian bahagian pandas.*
## Apa yang dipaparkan oleh program
| Bahagian | Hasil |
| --- | --- |
| Pustaka `csv` | Baris fail dan suhu `[12, 14, 15, 14, 21, 22, 24]` |
| `pandas` | `DataFrame`, kamus dan senarai suhu |
| Statistik | Purata 17.43 (secara manual dan dengan `mean`) dan maksimum 24 |
| Pemilihan baris | Baris hari Isnin, baris hari terpanas (Ahad), Isnin dalam Fahrenheit (53.6) |
| Cipta dan eksport | `DataFrame` pelajar dan skor yang dibuat daripada kamus; ditulis ke fail hanya dengan `--export` |
## Fungsi
| Fungsi | Modul | Pelajaran |
| --- | --- | --- |
| `read_rows`, `read_temperatures` | `weather.csv_reader` | Membaca dengan pustaka `csv` |
| `read_data` | `weather.analysis` | `pandas.read_csv` |
| `to_data_dict`, `to_temp_list` | `weather.analysis` | `to_dict`, `to_list` |
| `average_temp`, `average_temp_mean`, `max_temp` | `weather.analysis` | Jumlah dan panjang, `mean`, `max` |
| `row_for_day`, `hottest_row`, `day_in_fahrenheit` | `weather.analysis` | Memilih baris |
| `create_scores_frame`, `export_frame` | `weather.export` | `DataFrame` daripada kamus, `to_csv` |
## Jalankan
Python 3.13 atau lebih baharu diperlukan. Bahagian `csv` tiada kebergantungan semasa dijalankan; `pandas` ialah tambahan pilihan yang dipasang oleh tambahan `dev`.
```bash
git clone https://git.tirsystem.com/Tirsvad-Udemy-100-days-of-code/025-weather.git
cd 025-weather
python -m venv .venv
python -m pip install --upgrade pip
python -m pip install -e ".[dev]"
python -m weather
```
Tambah `--export new_data.csv` untuk turut menulis jadual skor ke fail. README repositori mempunyai persediaan penuh untuk Windows, Linux dan macOS.
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**Weather Data (Day 25)**
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