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| """Contains reusable data sets and constants.""" | |
| import logging | |
| from pathlib import Path | |
| import black | |
| import pandas as pd | |
| import vizro.plotly.express as px | |
| from custom_components import CodeClipboard | |
| # To disable logging info messages caused by black.format_str: https://github.com/psf/black/issues/2058 | |
| logging.getLogger("blib2to3").setLevel(logging.ERROR) | |
| def make_code_clipboard_from_py_file(filepath: str): | |
| # Black doesn't yet have a Python API, so format_str might not work at some point in the future. | |
| # https://black.readthedocs.io/en/stable/faq.html#does-black-have-an-api | |
| filepath = Path(__file__).parents[1] / "pages/examples" / filepath | |
| return CodeClipboard( | |
| code=black.format_str(filepath.read_text(encoding="utf-8"), mode=black.Mode(line_length=80)), | |
| language="python", | |
| ) | |
| PAGE_GRID = [[0, 0, 0, 0, 0, 0, 0]] * 2 + [[1, 1, 1, 1, 2, 2, 2]] * 5 | |
| # DATA -------------------------------------------------------------- | |
| gapminder = px.data.gapminder() | |
| iris = px.data.iris() | |
| stocks = px.data.stocks() | |
| tips = px.data.tips() | |
| wind = px.data.wind() | |
| ages = pd.DataFrame( | |
| { | |
| "Age": ["0-19", "20-29", "30-39", "40-49", "50-59", ">=60"], | |
| "Male": [800, 2000, 4200, 5000, 2100, 800], | |
| "Female": [1000, 3000, 3500, 3800, 3600, 700], | |
| } | |
| ) | |
| sankey_data = pd.DataFrame( | |
| { | |
| "Origin": [0, 1, 0, 2, 3, 3], | |
| "Destination": [2, 3, 3, 4, 4, 5], | |
| "Value": [8, 4, 2, 8, 4, 2], | |
| } | |
| ) | |
| funnel_data = pd.DataFrame( | |
| {"Stage": ["Leads", "Sales calls", "Follow-up", "Conversion", "Sales"], "Value": [10, 7, 4, 2, 1]} | |
| ) | |
| stepped_line_data = pd.DataFrame( | |
| { | |
| "year": [1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003], | |
| "rate": [0.10, 0.12, 0.15, 0.13, 0.14, 0.13, 0.14, 0.16, 0.15], | |
| } | |
| ) | |
| carshare = px.data.carshare() | |
| tasks = pd.DataFrame( | |
| [ | |
| {"Task": "Job A", "Start": "2009-01-01", "Finish": "2009-02-28"}, | |
| {"Task": "Job B", "Start": "2009-03-05", "Finish": "2009-04-15"}, | |
| {"Task": "Job C", "Start": "2009-02-20", "Finish": "2009-05-30"}, | |
| ] | |
| ) | |
| waterfall_data = pd.DataFrame( | |
| { | |
| "x": ["Sales", "Consulting", "Net revenue", "Purchases", "Other expenses", "Profit before tax"], | |
| "y": [60, 80, 0, -40, -20, 0], | |
| "measure": ["relative", "relative", "total", "relative", "relative", "total"], | |
| } | |
| ) | |
| pastries = pd.DataFrame( | |
| { | |
| "pastry": [ | |
| "Scones", | |
| "Bagels", | |
| "Muffins", | |
| "Cakes", | |
| "Donuts", | |
| "Cookies", | |
| "Croissants", | |
| "Eclairs", | |
| "Brownies", | |
| "Tarts", | |
| "Macarons", | |
| "Pies", | |
| ], | |
| "Profit Ratio": [-0.10, -0.15, -0.05, 0.10, 0.05, 0.20, 0.15, -0.08, 0.08, -0.12, 0.02, -0.07], | |
| } | |
| ) | |
| salaries = pd.DataFrame( | |
| { | |
| "Job": ["Developer", "Analyst", "Manager", "Specialist"] * 2, | |
| "Salary": [60000, 55000, 70000, 50000, 130000, 110000, 96400, 80000], | |
| "Range": ["Min"] * 4 + ["Max"] * 4, | |
| } | |
| ) | |