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5yler
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Jul 4, 2023
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| # Step 2: Add random time offset to "Date" column | ||
| if "Date" in df.columns: | ||
| df["Date"] = pd.to_datetime(df["Date"]) | ||
| random_offset = pd.tseries.offsets.DateOffset( | ||
| years=np.random.randint(-1000, 1000) | ||
| ) | ||
| df["Date"] += random_offset |
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This should be applied to all the dataframes equally so there is still time correspondence
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all reflections get shifted by a random year offset?
oinoom
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Jul 5, 2023
| # and apply a random linear transformation to numerical columns | ||
| # and replace non-empty strings with a random word | ||
| for col in df.columns: | ||
| if df[col].dtype == "object": |
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object means string? is there any way to narrow this down further?
oinoom
reviewed
Jul 5, 2023
| df[col] = df[col].apply(lambda x: random_word() if x else x) | ||
| elif df[col].dtype in ["int64", "float64"]: | ||
| k = random.uniform(0.5, 10) * random.choice([-1, 1]) | ||
| df[col] = df[col] * k |
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if it's a rating type, won't this be obvious given our current fixed range? what is this protecting from?
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