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https://github.com/koodiklinikka/palkkakysely.git
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663cd3d349
The 2025 survey uses a single English-only xlsx (instead of separate fi/en files) with a restructured schema: compensation is split into base salary, commission, lomaraha, bonus, and equity components; working time is h/week instead of percentage; and competitive salary is categorical instead of boolean. Vuositulot is now synthesized from the component fields. Drop COLUMN_MAP_2024, COLUMN_MAP_2024_EN_TO_FI, VALUE_MAP_2024_EN_TO_FI, read_initial_dfs_2024, read_data_2024, map_sukupuoli, map_vuositulot, split_boolean_column_to_other, apply_fixups, and the associated gender value lists and boolean text maps. All of this exists in version history. - KKPALKKA now includes base salary + commission (median 5500 → 5800) - Apply map_numberlike to tuntilaskutus and vuosilaskutus columns to handle string values like "60 000" and "100 000" - Filter out zeros when computing tunnusluvut on the index page so stats reflect actual reported values Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
71 lines
1.6 KiB
Python
71 lines
1.6 KiB
Python
from typing import Optional
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import pandas as pd
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def q25(x):
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return x.quantile(0.25)
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def q50(x):
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return x.quantile(0.5)
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def q75(x):
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return x.quantile(0.75)
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def q90(x):
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return x.quantile(0.9)
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def get_categorical_stats(
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df: pd.DataFrame,
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category_col: str,
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value_col: str,
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*,
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na_as_category: Optional[str] = None,
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) -> pd.DataFrame:
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# Drop records where value is not numeric before grouping...
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df = df.copy()
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df[value_col] = pd.to_numeric(df[value_col], errors="coerce")
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df = df[df[value_col].notna() & df[value_col] > 0]
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if na_as_category:
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rename_na(df, category_col, na_as_category)
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# ... then carry on.
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group = df[[category_col, value_col]].groupby(category_col, observed=False)
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return group[value_col].agg(
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["mean", "min", "max", "median", "count", q25, q50, q75, q90],
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)
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def rename_na(df: pd.DataFrame, col: str, na_name: str) -> None:
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df[col] = df[col].astype("string")
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df.loc[df[col].isna(), col] = na_name
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df[col] = df[col].astype("category")
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def explode_multiselect(
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series: pd.Series,
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*,
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sep: str = ", ",
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top_n: int | None = None,
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) -> pd.Series:
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"""
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Explode a comma-separated multiselect column into value counts.
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Returns a Series of counts indexed by individual values,
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sorted descending. Optionally limited to top_n entries.
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"""
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counts = (
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series.dropna()
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.str.split(sep)
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.explode()
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.str.strip()
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.loc[lambda s: s != ""]
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.value_counts()
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)
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if top_n is not None:
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counts = counts.head(top_n)
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return counts
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