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d2d30e9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 | """
Synthetic dataset generation with a fixed seed for full reproducibility.
All datasets are generated purely from numpy/random — no external downloads.
"""
import random
import numpy as np
import pandas as pd
SEED = 42
# ---------------------------------------------------------------------------
# Task 1 — Employee records with missing values
# ---------------------------------------------------------------------------
def generate_task1_datasets():
"""Returns (dirty_df, clean_df) for Task 1."""
rng = np.random.default_rng(SEED)
random.seed(SEED)
n = 100
departments = ["Engineering", "Marketing", "Sales", "HR", "Finance"]
first_names = ["Alice", "Bob", "Carol", "David", "Eve", "Frank", "Grace",
"Heidi", "Ivan", "Judy", "Karl", "Laura", "Mallory", "Niaj",
"Oscar", "Peggy", "Quinn", "Romeo", "Sybil", "Trent"]
last_names = ["Smith", "Jones", "Brown", "Taylor", "Wilson", "Davis",
"Miller", "Anderson", "Thomas", "Jackson"]
names = [f"{random.choice(first_names)} {random.choice(last_names)}" for _ in range(n)]
ages = rng.integers(22, 60, size=n).astype(float)
salaries = rng.integers(40_000, 120_000, size=n).astype(float)
depts = rng.choice(departments, size=n)
experience = rng.integers(0, 30, size=n).astype(float)
clean_df = pd.DataFrame({
"name": names,
"age": ages,
"salary": salaries,
"department": depts,
"experience": experience,
})
dirty_df = clean_df.copy()
# Inject ~20 % NaN into age, salary, department
for col, frac in [("age", 0.20), ("salary", 0.20), ("department", 0.10)]:
idx = rng.choice(n, size=int(n * frac), replace=False)
dirty_df.loc[idx, col] = np.nan
return dirty_df.reset_index(drop=True), clean_df.reset_index(drop=True)
# ---------------------------------------------------------------------------
# Task 2 — Product catalog with format & duplicate issues
# ---------------------------------------------------------------------------
def _scramble_phone(phone: str, rng) -> str:
digits = phone.replace("-", "")
fmt = rng.integers(0, 3)
if fmt == 0:
return digits # 5551234567
elif fmt == 1:
return f"({digits[:3]}){digits[3:]}" # (555)1234567
else:
return phone # 555-123-4567 (canonical)
def _scramble_date(date_str: str, rng) -> str:
dt = pd.to_datetime(date_str)
fmt = rng.integers(0, 3)
if fmt == 0:
return dt.strftime("%Y-%m-%d")
elif fmt == 1:
return dt.strftime("%b %d %Y")
else:
return dt.strftime("%d/%m/%Y")
def generate_task2_datasets():
"""Returns (dirty_df, clean_df) for Task 2."""
rng = np.random.default_rng(SEED)
random.seed(SEED)
n = 200
categories = ["Electronics", "Clothing", "Food", "Books", "Toys"]
product_ids = [f"P{str(i).zfill(4)}" for i in range(1, n + 1)]
product_names = [f"Product_{i}" for i in range(1, n + 1)]
prices = np.round(rng.uniform(5.0, 500.0, size=n), 2)
categories_col = rng.choice(categories, size=n)
phones = [
f"{rng.integers(100,999)}-{rng.integers(100,999)}-{rng.integers(1000,9999)}"
for _ in range(n)
]
days_offset = rng.integers(0, 1000, size=n)
dates = [
(pd.Timestamp("2020-01-01") + pd.Timedelta(days=int(d))).strftime("%Y-%m-%d")
for d in days_offset
]
clean_df = pd.DataFrame({
"product_id": product_ids,
"product_name": product_names,
"price": prices,
"category": categories_col,
"phone": phones,
"listed_date": dates,
})
dirty_df = clean_df.copy()
# Scramble ~60 % of phone formats
phone_idx = rng.choice(n, size=int(n * 0.6), replace=False)
dirty_df.loc[phone_idx, "phone"] = [
_scramble_phone(dirty_df.loc[i, "phone"], rng) for i in phone_idx
]
# Scramble ~60 % of date formats
date_idx = rng.choice(n, size=int(n * 0.6), replace=False)
dirty_df.loc[date_idx, "listed_date"] = [
_scramble_date(dirty_df.loc[i, "listed_date"], rng) for i in date_idx
]
# Add 15 duplicate rows
dup_idx = rng.choice(n, size=15, replace=False)
dup_rows = dirty_df.iloc[dup_idx].copy()
dirty_df = pd.concat([dirty_df, dup_rows], ignore_index=True)
return dirty_df.reset_index(drop=True), clean_df.reset_index(drop=True)
# ---------------------------------------------------------------------------
# Task 3 — Customer database: full pipeline
# ---------------------------------------------------------------------------
def generate_task3_datasets():
"""Returns (dirty_df, clean_df) for Task 3."""
rng = np.random.default_rng(SEED)
random.seed(SEED)
n = 300
countries = ["USA", "UK", "Canada", "Australia", "Germany"]
first_names = ["Alice", "Bob", "Carol", "David", "Eve", "Frank", "Grace",
"Heidi", "Ivan", "Judy"]
last_names = ["Smith", "Jones", "Brown", "Taylor", "Wilson"]
names = [f"{random.choice(first_names)} {random.choice(last_names)}" for _ in range(n)]
ages = rng.integers(18, 75, size=n).astype(float)
purchase_amounts = np.round(rng.uniform(10.0, 500.0, size=n), 2)
countries_col = rng.choice(countries, size=n)
emails = [f"user{i}@example.com" for i in range(1, n + 1)]
days_offset = rng.integers(0, 730, size=n)
signup_dates = [
(pd.Timestamp("2022-01-01") + pd.Timedelta(days=int(d))).strftime("%Y-%m-%d")
for d in days_offset
]
clean_df = pd.DataFrame({
"name": names,
"age": ages,
"purchase_amount": purchase_amounts,
"country": countries_col,
"email": emails,
"signup_date": signup_dates,
})
dirty_df = clean_df.copy()
# Missing values (~15 % in age, purchase_amount, country, signup_date)
for col, frac in [("age", 0.15), ("purchase_amount", 0.15),
("country", 0.10), ("signup_date", 0.10)]:
idx = rng.choice(n, size=int(n * frac), replace=False)
dirty_df.loc[idx, col] = np.nan
# Outliers in purchase_amount (~3 %)
out_idx = rng.choice(n, size=int(n * 0.03), replace=False)
dirty_df.loc[out_idx, "purchase_amount"] = (
dirty_df.loc[out_idx, "purchase_amount"] * 10
)
# Mixed case in country (~40 %)
case_idx = rng.choice(n, size=int(n * 0.40), replace=False)
dirty_df.loc[case_idx, "country"] = dirty_df.loc[case_idx, "country"].str.lower()
# Mixed date formats (~50 %) — only scramble non-null entries
date_idx = rng.choice(n, size=int(n * 0.50), replace=False)
valid_date_idx = [i for i in date_idx if pd.notna(dirty_df.loc[i, "signup_date"])]
for i in valid_date_idx:
dirty_df.loc[i, "signup_date"] = _scramble_date(dirty_df.loc[i, "signup_date"], rng)
# 20 duplicate rows
dup_idx = rng.choice(n, size=20, replace=False)
dup_rows = dirty_df.iloc[dup_idx].copy()
dirty_df = pd.concat([dirty_df, dup_rows], ignore_index=True)
return dirty_df.reset_index(drop=True), clean_df.reset_index(drop=True)
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