def _compute_boxplot_df()

in chartify/_core/plot.py [0:0]

• 25 lines of code
• 3 McCabe index (conditional complexity)

    def _compute_boxplot_df(data_frame, categorical_columns, numeric_column):
        """Computes the data frames for a boxplot.

        Returns:
            quantlies_and_bounds: data frame for the boxes and whiskers of a
                boxplot
            outliers: data frame with outliers
        """
        # compute quantiles
        q_frame = data_frame.groupby(categorical_columns)[numeric_column].quantile([0.25, 0.5, 0.75])
        q_frame = q_frame.unstack().reset_index()
        q_frame.columns = categorical_columns + ["q1", "q2", "q3"]
        df_with_quantiles = pd.merge(data_frame, q_frame, on=categorical_columns, how="left")

        # compute IQR outlier bounds
        iqr = df_with_quantiles.q3 - df_with_quantiles.q1
        df_with_quantiles["upper"] = df_with_quantiles.q3 + 1.5 * iqr
        df_with_quantiles["lower"] = df_with_quantiles.q1 - 1.5 * iqr

        # adjust outlier bounds to closest observations still within bounds
        # for upper bound
        le_upper = df_with_quantiles[df_with_quantiles[numeric_column].le(df_with_quantiles.upper)]
        group_max_le_upper = le_upper.groupby(categorical_columns, as_index=False)[numeric_column].max()
        group_max_le_upper.columns = categorical_columns + ["upper"]

        df_with_quantiles.drop("upper", axis=1, inplace=True)
        df_with_quantiles = pd.merge(df_with_quantiles, group_max_le_upper, on=categorical_columns, how="left")

        # for lower bound
        ge_lower = df_with_quantiles[df_with_quantiles[numeric_column].ge(df_with_quantiles.lower)]
        group_min_ge_lower = ge_lower.groupby(categorical_columns, as_index=False)[numeric_column].min()
        group_min_ge_lower.columns = categorical_columns + ["lower"]
        df_with_quantiles.drop("lower", axis=1, inplace=True)
        df_with_quantiles = pd.merge(df_with_quantiles, group_min_ge_lower, on=categorical_columns, how="left")

        quantiles_and_bounds = (
            df_with_quantiles.groupby(categorical_columns)[["q1", "q2", "q3", "lower", "upper"]].first().reset_index()
        )

        outliers = df_with_quantiles[
            ~df_with_quantiles[numeric_column].between(df_with_quantiles.lower, df_with_quantiles.upper)
        ]

        return quantiles_and_bounds, outliers