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42 lines
998 B
Python
42 lines
998 B
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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df[category_col] = df[category_col].astype("string")
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df.loc[df[category_col].isna(), category_col] = na_as_category
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df[category_col] = df[category_col].astype("category")
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# ... then carry on.
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group = df[[category_col, value_col]].groupby(category_col)
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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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