Refactor standard deviation calculation #147
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jeancochrane
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Feb 24, 2025
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| # Vectorized per-row lower and upper thresholds (mean ± std * multiplier) | ||
| for col in [f"sv_price_deviation_{group_str}", f"sv_cgdr_deviation_{group_str}"]: | ||
| df[f"{col}_lower"] = df.groupby(list(groups))[col].transform("mean") - permut[ | ||
| 0 | ||
| ] * df.groupby(list(groups))[col].transform("std") | ||
| df[f"{col}_upper"] = df.groupby(list(groups))[col].transform("mean") + permut[ | ||
| 1 | ||
| ] * df.groupby(list(groups))[col].transform("std") | ||
| if not condos: | ||
| df["sv_which_price"] = df.apply(which_price, args=(holds, groups), axis=1) | ||
|
|
||
| col = f"sv_price_per_sqft_deviation_{group_str}" | ||
| df[f"{col}_lower"] = df.groupby(list(groups))[col].transform("mean") - permut[ | ||
| 0 | ||
| ] * df.groupby(list(groups))[col].transform("std") | ||
| df[f"{col}_upper"] = df.groupby(list(groups))[col].transform("mean") + permut[ | ||
| 1 | ||
| ] * df.groupby(list(groups))[col].transform("std") |
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[Suggestion, non-blocking] Nice generalization! We could go one step further and fold the if not condos branch into the for loop that precedes it:
thresh_cols = [
f"sv_price_deviation_{group_str}",
f"sv_cgdr_deviation_{group_str}"
] + ([] if condos else ["f"sv_price_per_sqft_deviation_{group_str}"])
for col in thresh_cols:
df[f"{col}_lower"] = df.groupby(list(groups))[col].transform("mean") - permut[
0
] * df.groupby(list(groups))[col].transform("std")
...| sqft_val = row[f"sv_price_per_sqft_deviation_{group_str}"] | ||
| sqft_lower = row[f"sv_price_per_sqft_deviation_{group_str}_lower"] | ||
| sqft_upper = row[f"sv_price_per_sqft_deviation_{group_str}_upper"] | ||
| sqft_out = sqft_val > sqft_upper or sqft_val < sqft_lower |
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[Nitpick, non-blocking] Any reason not to use between_two_numbers() here, the way we do for the rest of the threshold checks?
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This PR showcases a potential refactor which reduces lines of code, and may improve readability. A potential cost to this refactor is modularity or computational cost (runtime). I've confirmed that we flag sales in the exact same way comparing outputs from this branch to the master.
Previously there had been 4 functions primarily responsible for the standard deviation calculations.
pricing_infoprice_columnwhich_priceget_threshThe standard deviation information had been held in a nested dictionary structure operated on with the
get_threshhelper function. In this PR we switch to vectorized operations and remove the nested dictionary strategy , which includedget_thresh.Edit:
After testing on the same subset of data it seems like the proposed change decreases runtime for the overarching
pricing_infofunction