01
How do you pivot data in Pandas?
Show answer
pd.pivot_table(df, index='region', columns='month', values='sales', aggfunc='sum') reshapes long data into a summary matrix; melt() does the reverse.
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02
How do you remove duplicates from a DataFrame?
Show answer
df.drop_duplicates(subset=[...], keep='first') removes repeated rows; use duplicated() first to inspect what will be dropped.
03
Pandas: find the top 3 products by revenue in each region.
Show answer
(df.assign(rev=df.price * df.qty)
.groupby(['region', 'product'])['rev'].sum()
.reset_index()
.sort_values('rev', ascending=False)
.groupby('region').head(3))
04
Pandas: merge vs join vs concat?
Show answer
merge joins on columns, SQL-style (how='inner', 'left', ...). join joins on the index. concat stacks DataFrames along rows or columns.
05
Write a function to calculate RMSE without any library.
Show answer
def rmse(y, p):
return (sum((a - b) ** 2 for a, b in zip(y, p)) / len(y)) ** 0.5
06
How do you use groupby with multiple aggregations?
Show answer
df.groupby('region').agg(total=('sales','sum'), avg=('sales','mean')) gives named aggregations per group in one pass.
07
Write Pandas code to read a large CSV in chunks and compute total sales per city.
Show answer
# process 1 million rows at a time
import pandas as pd
totals = {}
for chunk in pd.read_csv('sales.csv', usecols=['city', 'amount'], chunksize=1_000_000):
part = chunk.groupby('city')['amount'].sum()
for city, amt in part.items():
totals[city] = totals.get(city, 0) + amt
result = pd.Series(totals).sort_values(ascending=False)
08
Write Python code to count word frequencies in a text and print the top 5 words.
Show answer
from collections import Counter
import re
def top_words(text, n=5):
words = re.findall(r'[a-z]+', text.lower())
return Counter(words).most_common(n)
print(top_words('Data science is fun and data is everywhere'))
09
Pandas: compute a 7-day rolling average of daily sales for each store.
Show answer
# df has columns: store, date, sales
df['date'] = pd.to_datetime(df['date'])
df = df.sort_values(['store', 'date'])
df['sales_7d_avg'] = (
df.groupby('store')['sales']
.transform(lambda s: s.rolling(7, min_periods=1).mean())
)
10
Write a Python function to flatten a nested list of any depth.
Show answer
def flatten(items):
out = []
for x in items:
if isinstance(x, list):
out.extend(flatten(x))
else:
out.append(x)
return out
print(flatten([1, [2, [3, 4]], 5])) # [1, 2, 3, 4, 5]
11
Pandas: convert wide monthly columns into a long format table.
Show answer
# wide: id, jan, feb, mar -> long: id, month, value
long_df = df.melt(
id_vars=['id'],
value_vars=['jan', 'feb', 'mar'],
var_name='month',
value_name='value'
)
12
Write Python code to compute a 95% bootstrap confidence interval for the mean.
Show answer
import numpy as np
def bootstrap_ci(x, n_boot=10000, seed=42):
rng = np.random.default_rng(seed)
x = np.asarray(x)
means = [rng.choice(x, size=len(x), replace=True).mean() for _ in range(n_boot)]
return np.percentile(means, [2.5, 97.5])
print(bootstrap_ci([12, 15, 9, 20, 14, 11, 18]))
13
How do you check whether a variable is normally distributed?
Show answer
Start visually with a histogram and a Q-Q plot; points close to the straight line suggest normality. Check skewness and kurtosis values. Then use a formal test such as Shapiro-Wilk for small samples or Anderson-Darling. Remember that with very large samples, tests flag tiny harmless deviations, so judge by the plots too. If data is skewed, consider a log transform or a non-parametric method.
14
Write scikit-learn code for a pipeline with scaling and logistic regression, evaluated with 5-fold cross-validation.
Show answer
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
pipe = make_pipeline(StandardScaler(), LogisticRegression(max_iter=1000))
scores = cross_val_score(pipe, X, y, cv=5, scoring='roc_auc')
print(scores.mean(), scores.std())
15
Write scikit-learn code to get precision, recall and a confusion matrix at a custom threshold of 0.3.
Show answer
from sklearn.metrics import confusion_matrix, precision_score, recall_score
proba = model.predict_proba(X_test)[:, 1]
pred = (proba >= 0.3).astype(int)
print(confusion_matrix(y_test, pred))
print('precision', precision_score(y_test, pred))
print('recall', recall_score(y_test, pred))
16
Write a basic PyTorch training loop for one epoch.
Show answer
model.train()
for X, y in train_loader:
X, y = X.to(device), y.to(device)
optimizer.zero_grad()
logits = model(X)
loss = loss_fn(logits, y)
loss.backward()
optimizer.step()
# evaluation
model.eval()
with torch.no_grad():
val_loss = sum(loss_fn(model(Xv.to(device)), yv.to(device)).item() for Xv, yv in val_loader)
17
Write Keras code for a small binary classifier on tabular data.
Show answer
from tensorflow import keras
model = keras.Sequential([
keras.layers.Input(shape=(X_train.shape[1],)),
keras.layers.Dense(64, activation='relu'),
keras.layers.Dropout(0.3),
keras.layers.Dense(1, activation='sigmoid')
])
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['AUC'])
model.fit(X_train, y_train, validation_split=0.2, epochs=20, batch_size=64)
18
How would you quickly build a sentiment analysis model for product reviews?
Show answer
Step 1: collect and label a sample of reviews, checking class balance. Step 2: build a baseline with TF-IDF and logistic regression to get a quick benchmark. Step 3: fine-tune a small pretrained transformer such as DistilBERT for better accuracy, or use a multilingual model if reviews mix languages. Step 4: evaluate with F1 per class and read wrong predictions. Step 5: handle sarcasm and neutral reviews explicitly.
19
Write Pandas code to create useful features from a datetime column.
Show answer
df['ts'] = pd.to_datetime(df['ts'])
df['hour'] = df['ts'].dt.hour
df['dayofweek'] = df['ts'].dt.dayofweek
df['is_weekend'] = df['dayofweek'].isin([5, 6]).astype(int)
df['month'] = df['ts'].dt.month
df['days_since_signup'] = (df['ts'] - df['signup_date']).dt.days
20
Write Pandas code to create lag and rolling features for each customer.
Show answer
df = df.sort_values(['customer_id', 'month'])
g = df.groupby('customer_id')['spend']
df['spend_lag1'] = g.shift(1)
df['spend_lag3'] = g.shift(3)
# shift first so the current month is not included
df['spend_roll3_mean'] = g.transform(lambda s: s.shift(1).rolling(3).mean())
21
Write a scikit-learn ColumnTransformer that preprocesses numeric and categorical columns.
Show answer
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import make_pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
pre = ColumnTransformer([
('num', make_pipeline(SimpleImputer(strategy='median'), StandardScaler()), num_cols),
('cat', make_pipeline(SimpleImputer(strategy='most_frequent'),
OneHotEncoder(handle_unknown='ignore')), cat_cols)
])
model = make_pipeline(pre, LogisticRegression(max_iter=1000))
22
How would you build RFM features for a customer churn model?
Show answer
RFM stands for recency, frequency and monetary value. From the transactions table, for each customer as of a cut-off date: recency is days since the last purchase, frequency is the number of orders in the last 90 days, and monetary is total or average spend in that window. Compute them using only data before the cut-off, add trends like spend this month versus last, and the label from the period after.
23
Write Python code to compute the haversine distance between customer and store locations.
Show answer
import numpy as np
def haversine_km(lat1, lon1, lat2, lon2):
lat1, lon1, lat2, lon2 = map(np.radians, [lat1, lon1, lat2, lon2])
dlat, dlon = lat2 - lat1, lon2 - lon1
a = np.sin(dlat / 2) ** 2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon / 2) ** 2
return 6371 * 2 * np.arcsin(np.sqrt(a))
df['dist_km'] = haversine_km(df.cust_lat, df.cust_lon, df.store_lat, df.store_lon)
24
Write Pandas code to encode hour of day as a cyclical feature.
Show answer
import numpy as np
# hour 23 and hour 0 should be close, so use sin and cos
df['hour_sin'] = np.sin(2 * np.pi * df['hour'] / 24)
df['hour_cos'] = np.cos(2 * np.pi * df['hour'] / 24)
25
Write Pandas code to cap outliers at the 1st and 99th percentile.
Show answer
# fit limits on training data only, then apply to test
low, high = train['income'].quantile([0.01, 0.99])
train['income_capped'] = train['income'].clip(low, high)
test['income_capped'] = test['income'].clip(low, high)
26
Write SQL to find the second highest salary in an employees table.
Show answer
SELECT MAX(salary) AS second_highest
FROM employees
WHERE salary < (SELECT MAX(salary) FROM employees);
-- alternative with window function
SELECT DISTINCT salary
FROM (SELECT salary, DENSE_RANK() OVER (ORDER BY salary DESC) AS rnk
FROM employees) t
WHERE rnk = 2;
27
Write SQL to find the top 3 products by revenue in each category.
Show answer
WITH rev AS (
SELECT category, product_id, SUM(price * qty) AS revenue
FROM order_items
GROUP BY category, product_id
), ranked AS (
SELECT *, ROW_NUMBER() OVER (PARTITION BY category ORDER BY revenue DESC) AS rn
FROM rev
)
SELECT category, product_id, revenue
FROM ranked
WHERE rn <= 3;
28
Write SQL to compute a 7-day rolling average of daily active users.
Show answer
WITH daily AS (
SELECT activity_date, COUNT(DISTINCT user_id) AS dau
FROM events
GROUP BY activity_date
)
SELECT activity_date, dau,
AVG(dau) OVER (ORDER BY activity_date
ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS dau_7d_avg
FROM daily
ORDER BY activity_date;
29
Write SQL to calculate month-over-month revenue growth.
Show answer
WITH monthly AS (
SELECT DATE_TRUNC('month', order_date) AS month, SUM(amount) AS revenue
FROM orders
GROUP BY 1
)
SELECT month, revenue,
LAG(revenue) OVER (ORDER BY month) AS prev_revenue,
ROUND(100.0 * (revenue - LAG(revenue) OVER (ORDER BY month))
/ NULLIF(LAG(revenue) OVER (ORDER BY month), 0), 2) AS mom_pct
FROM monthly;
30
Write SQL to find duplicate emails in a users table.
Show answer
SELECT email, COUNT(*) AS cnt
FROM users
GROUP BY email
HAVING COUNT(*) > 1
ORDER BY cnt DESC;
31
Write SQL to compute week-1 retention by signup week.
Show answer
WITH signups AS (
SELECT user_id, DATE_TRUNC('week', signup_date) AS cohort, signup_date
FROM users
), active AS (
SELECT DISTINCT s.user_id, s.cohort
FROM signups s
JOIN events e ON e.user_id = s.user_id
AND e.event_date >= s.signup_date + INTERVAL '7 day'
AND e.event_date < s.signup_date + INTERVAL '14 day'
)
SELECT s.cohort, COUNT(*) AS users,
ROUND(100.0 * COUNT(a.user_id) / COUNT(*), 1) AS week1_retention_pct
FROM signups s
LEFT JOIN active a ON a.user_id = s.user_id
GROUP BY s.cohort
ORDER BY s.cohort;
32
Write SQL to find customers who ordered in January 2026 but not in February 2026.
Show answer
SELECT DISTINCT o.customer_id
FROM orders o
WHERE o.order_date >= '2026-01-01' AND o.order_date < '2026-02-01'
AND NOT EXISTS (
SELECT 1 FROM orders f
WHERE f.customer_id = o.customer_id
AND f.order_date >= '2026-02-01' AND f.order_date < '2026-03-01'
);
33
Write SQL to find the median order value per city.
Show answer
SELECT city,
PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY amount) AS median_amount
FROM orders
GROUP BY city;
-- PERCENTILE_CONT interpolates; PERCENTILE_DISC returns an actual value
34
Write SQL to pivot order counts by status into columns using CASE WHEN.
Show answer
SELECT DATE_TRUNC('month', order_date) AS month,
SUM(CASE WHEN status = 'delivered' THEN 1 ELSE 0 END) AS delivered,
SUM(CASE WHEN status = 'cancelled' THEN 1 ELSE 0 END) AS cancelled,
SUM(CASE WHEN status = 'returned' THEN 1 ELSE 0 END) AS returned
FROM orders
GROUP BY 1
ORDER BY 1;
35
How do you pull large data from a SQL database into Pandas efficiently?
Show answer
Push as much work as possible into SQL: filter, aggregate and select only needed columns before pulling. Use pd.read_sql with a SQLAlchemy engine and chunksize to stream large results. For very large extracts, export to Parquet from the warehouse and read that, which is much faster than row-by-row transfer. Set dtypes like category early, and never use SELECT star on a big table from a notebook.
36
Write SQL to compute a running total of spend for each user.
Show answer
SELECT user_id, order_date, amount,
SUM(amount) OVER (
PARTITION BY user_id
ORDER BY order_date
ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
) AS running_spend
FROM orders
ORDER BY user_id, order_date;
37
Write SQL to compute funnel conversion from visit to cart to purchase.
Show answer
WITH steps AS (
SELECT user_id,
MAX(CASE WHEN event = 'visit' THEN 1 ELSE 0 END) AS visited,
MAX(CASE WHEN event = 'add_to_cart' THEN 1 ELSE 0 END) AS carted,
MAX(CASE WHEN event = 'purchase' THEN 1 ELSE 0 END) AS purchased
FROM events
WHERE event_date >= '2026-10-01'
GROUP BY user_id
)
SELECT SUM(visited) AS visits, SUM(carted) AS carts, SUM(purchased) AS buyers,
ROUND(100.0 * SUM(carted) / NULLIF(SUM(visited), 0), 1) AS visit_to_cart_pct,
ROUND(100.0 * SUM(purchased) / NULLIF(SUM(carted), 0), 1) AS cart_to_buy_pct
FROM steps;
38
Write Python code to find the top 3 most similar documents to a query using cosine similarity on embeddings.
Show answer
import numpy as np
def top_k(query_vec, doc_vecs, k=3):
q = query_vec / np.linalg.norm(query_vec)
d = doc_vecs / np.linalg.norm(doc_vecs, axis=1, keepdims=True)
scores = d @ q
idx = np.argsort(-scores)[:k]
return list(zip(idx, scores[idx]))
# doc_vecs shape: (n_docs, dim), query_vec shape: (dim,)
39
How do you get reliable JSON output from an LLM?
Show answer
Use the provider's structured output or JSON schema mode when available, which constrains the output to your schema. Otherwise, describe the exact schema in the prompt with an example, and use low temperature. Always parse and validate the response in code, for example with Pydantic, and on failure retry with the error message included. Keep schemas small and flat, and use enums for fields with fixed values.
40
Write a prompt template for classifying customer support tickets.
Show answer
System: You classify support tickets for an e-commerce company. Categories: payment, delivery, refund, account, other. Rules: choose exactly one category; if unsure, choose other; do not invent details. Return JSON with fields category and reason, reason under 20 words. Then two short examples showing a ticket and the expected JSON. User: Ticket: {ticket_text}. Keep the template fixed and test it on 100 labelled tickets before rollout.
41
Write a FastAPI endpoint that serves predictions from a saved scikit-learn model.
Show answer
from fastapi import FastAPI
from pydantic import BaseModel
import joblib
import pandas as pd
app = FastAPI()
model = joblib.load('model.joblib')
class Customer(BaseModel):
tenure: int
monthly_spend: float
plan: str
@app.post('/predict')
def predict(c: Customer):
X = pd.DataFrame([c.model_dump()])
return {'churn_probability': float(model.predict_proba(X)[0, 1])}
42
Write a Dockerfile for a Python model API.
Show answer
FROM python:3.11-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY app/ ./app
COPY model.joblib .
EXPOSE 8000
CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"]
43
Write Python code to compute the Population Stability Index for a feature to detect drift.
Show answer
import numpy as np
def psi(expected, actual, bins=10):
edges = np.quantile(expected, np.linspace(0, 1, bins + 1))
edges[0], edges[-1] = -np.inf, np.inf
e = np.histogram(expected, edges)[0] / len(expected)
a = np.histogram(actual, edges)[0] / len(actual)
e, a = np.clip(e, 1e-6, None), np.clip(a, 1e-6, None)
return float(np.sum((a - e) * np.log(a / e)))
# rule of thumb: <0.1 stable, 0.1-0.25 watch, >0.25 investigate
44
What should you log for a production prediction service?
Show answer
For each request, log a request ID, timestamp, model version, input features or a hash of them, the prediction and score, and latency. Log errors and fallback use separately. Do not log raw personal data unless needed and allowed; mask it. Aggregate these logs into dashboards for traffic, latency percentiles, error rate and score distribution. Joining logged predictions with later outcomes gives the data for monitoring and retraining.
45
Write pytest unit tests for a feature function that computes days since last purchase.
Show answer
import pandas as pd
from features import days_since_last_purchase
def test_basic_case():
df = pd.DataFrame({'customer_id': [1, 1], 'order_date': pd.to_datetime(['2026-01-01', '2026-01-10'])})
out = days_since_last_purchase(df, as_of='2026-01-15')
assert out.loc[1] == 5
def test_no_future_orders_used():
df = pd.DataFrame({'customer_id': [1], 'order_date': pd.to_datetime(['2026-02-01'])})
out = days_since_last_purchase(df, as_of='2026-01-15')
assert out.empty
46
Write Pandas code to resample daily sales into weekly totals and monthly averages.
Show answer
import pandas as pd
df['date'] = pd.to_datetime(df['date'])
s = df.set_index('date')['sales'].sort_index()
# Weekly totals, weeks ending on Sunday
weekly = s.resample('W-SUN').sum()
# Monthly average of daily sales
monthly = s.resample('MS').mean()
print(weekly.head())
print(monthly.head())
47
Write Python code to decompose a monthly series into trend, seasonal and residual parts with STL.
Show answer
import pandas as pd
from statsmodels.tsa.seasonal import STL
s = df.set_index('month')['sales'].asfreq('MS')
res = STL(s, period=12, robust=True).fit()
parts = pd.DataFrame({
'trend': res.trend,
'seasonal': res.seasonal,
'resid': res.resid
})
print(parts.tail())
res.plot()
48
Write Python code to run an ADF stationarity test and difference the series if needed.
Show answer
from statsmodels.tsa.stattools import adfuller
def is_stationary(s, alpha=0.05):
p_value = adfuller(s.dropna())[1]
return p_value < alpha
series = s.copy()
d = 0
while not is_stationary(series) and d < 2:
series = series.diff()
d += 1
print('differencing order d =', d)
49
Write Python code to evaluate a forecasting model with expanding-window time series cross-validation.
Show answer
import numpy as np
from sklearn.model_selection import TimeSeriesSplit
from sklearn.metrics import mean_absolute_error
tscv = TimeSeriesSplit(n_splits=5, test_size=28)
scores = []
for tr, te in tscv.split(X):
model.fit(X.iloc[tr], y.iloc[tr])
pred = model.predict(X.iloc[te])
scores.append(mean_absolute_error(y.iloc[te], pred))
print(np.mean(scores), scores)
50
Write Python code to fit a Holt-Winters model and forecast the next 12 months.
Show answer
from statsmodels.tsa.holtwinters import ExponentialSmoothing
s = df.set_index('month')['sales'].asfreq('MS')
train, test = s[:-12], s[-12:]
model = ExponentialSmoothing(
train, trend='add', seasonal='mul',
seasonal_periods=12
).fit()
forecast = model.forecast(12)
mae = (forecast - test).abs().mean()
print(forecast.round(1), 'MAE:', round(mae, 1))
51
Write a Python function to compute MASE for a seasonal forecast.
Show answer
import numpy as np
def mase(y_train, y_true, y_pred, m=7):
# Scale: in-sample error of seasonal naive
y_train = np.asarray(y_train)
scale = np.mean(np.abs(y_train[m:] - y_train[:-m]))
err = np.mean(np.abs(np.asarray(y_true) - np.asarray(y_pred)))
return err / scale
# MASE below 1 beats seasonal naive
52
Write Python code to group a list of order dictionaries by city and sum the amounts without Pandas.
Show answer
from collections import defaultdict
orders = [
{'city': 'Pune', 'amount': 250},
{'city': 'Delhi', 'amount': 400},
{'city': 'Pune', 'amount': 150},
]
totals = defaultdict(float)
for o in orders:
totals[o['city']] += o['amount']
print(dict(totals)) # {'Pune': 400.0, 'Delhi': 400.0}
53
Pandas: find customers whose monthly spend increased for three consecutive months.
Show answer
m = (df.assign(month=df['date'].dt.to_period('M'))
.groupby(['customer_id', 'month'])['amount'].sum()
.reset_index()
.sort_values(['customer_id', 'month']))
m['up'] = (m.groupby('customer_id')['amount'].diff() > 0).astype(int)
# three rises in a row = three consecutive increases
m['streak'] = (m.groupby('customer_id')['up']
.rolling(3).sum().reset_index(level=0, drop=True))
result = m.loc[m['streak'] == 3, 'customer_id'].unique()
54
Write scikit-learn code to calibrate a gradient boosting classifier's probabilities and check the result.
Show answer
from sklearn.ensemble import HistGradientBoostingClassifier
from sklearn.calibration import CalibratedClassifierCV
from sklearn.metrics import brier_score_loss
base = HistGradientBoostingClassifier()
cal = CalibratedClassifierCV(base, method='isotonic', cv=5)
cal.fit(X_train, y_train)
prob = cal.predict_proba(X_test)[:, 1]
print('Brier:', brier_score_loss(y_test, prob))
55
Write OpenCV code to read an image, convert it to grayscale, resize it and detect edges.
Show answer
import cv2
img = cv2.imread('part.jpg') # loaded as BGR
if img is None:
raise FileNotFoundError('part.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
small = cv2.resize(gray, (224, 224), interpolation=cv2.INTER_AREA)
blur = cv2.GaussianBlur(small, (5, 5), 0)
edges = cv2.Canny(blur, 50, 150)
cv2.imwrite('edges.png', edges)
56
Write torchvision transforms for training and validation of an image classifier using ImageNet normalisation.
Show answer
from torchvision import transforms as T
mean, std = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
train_tf = T.Compose([
T.RandomResizedCrop(224),
T.RandomHorizontalFlip(),
T.ColorJitter(0.2, 0.2, 0.2),
T.ToTensor(),
T.Normalize(mean, std),
])
val_tf = T.Compose([
T.Resize(256), T.CenterCrop(224),
T.ToTensor(), T.Normalize(mean, std),
])
57
Write a Python function to compute IoU between two bounding boxes given as (x1, y1, x2, y2).
Show answer
def iou(a, b):
ix1, iy1 = max(a[0], b[0]), max(a[1], b[1])
ix2, iy2 = min(a[2], b[2]), min(a[3], b[3])
inter = max(0, ix2 - ix1) * max(0, iy2 - iy1)
area_a = (a[2] - a[0]) * (a[3] - a[1])
area_b = (b[2] - b[0]) * (b[3] - b[1])
union = area_a + area_b - inter
return inter / union if union > 0 else 0.0
print(iou((0, 0, 10, 10), (5, 5, 15, 15))) # 0.142...
58
Write PyTorch code to fine-tune a pretrained ResNet-18 for a 5-class problem by replacing its final layer.
Show answer
import torch, torch.nn as nn
from torchvision import models
model = models.resnet18(weights=models.ResNet18_Weights.DEFAULT)
for p in model.parameters():
p.requires_grad = False # freeze backbone
model.fc = nn.Linear(model.fc.in_features, 5)
opt = torch.optim.AdamW(model.fc.parameters(), lr=1e-3)
loss_fn = nn.CrossEntropyLoss()
# train head first, then unfreeze later layers with a lower lr
59
Write NumPy code to compute the Dice coefficient between a predicted and a true binary mask.
Show answer
import numpy as np
def dice(pred, true, eps=1e-7):
pred = pred.astype(bool)
true = true.astype(bool)
inter = np.logical_and(pred, true).sum()
return (2 * inter + eps) / (pred.sum() + true.sum() + eps)
# Dice = 2|A and B| / (|A| + |B|); 1 means perfect overlap
60
Write NumPy code for non-maximum suppression on a set of boxes and scores.
Show answer
import numpy as np
def nms(boxes, scores, thr=0.5):
x1, y1, x2, y2 = boxes.T
areas = (x2 - x1) * (y2 - y1)
order = scores.argsort()[::-1]
keep = []
while order.size:
i = order[0]
keep.append(i)
w = np.maximum(0, np.minimum(x2[i], x2[order[1:]]) - np.maximum(x1[i], x1[order[1:]]))
h = np.maximum(0, np.minimum(y2[i], y2[order[1:]]) - np.maximum(y1[i], y1[order[1:]]))
inter = w * h
iou = inter / (areas[i] + areas[order[1:]] - inter)
order = order[1:][iou < thr]
return keep
61
Write Python code using Hugging Face transformers to run sentiment analysis on a list of reviews.
Show answer
from transformers import pipeline
clf = pipeline(
'sentiment-analysis',
model='distilbert-base-uncased-finetuned-sst-2-english'
)
reviews = ['Delivery was fast and the product is great',
'Packaging was damaged and support did not reply']
for text, out in zip(reviews, clf(reviews, batch_size=16)):
print(out['label'], round(out['score'], 3), '-', text)
62
Write SQL to find users who logged in on at least three consecutive days.
Show answer
WITH d AS (
SELECT DISTINCT user_id, CAST(login_at AS DATE) AS day
FROM logins
),
g AS (
SELECT user_id, day,
day - CAST(ROW_NUMBER() OVER (PARTITION BY user_id ORDER BY day) AS INT) AS grp
FROM d
)
SELECT DISTINCT user_id
FROM g
GROUP BY user_id, grp
HAVING COUNT(*) >= 3;
-- date minus row number is constant within a streak (PostgreSQL syntax)
63
Write SQL to show each customer's first order date, last order date and the gap between them in days.
Show answer
SELECT
customer_id,
MIN(order_date) AS first_order,
MAX(order_date) AS last_order,
MAX(order_date) - MIN(order_date) AS days_between,
COUNT(*) AS orders
FROM orders
GROUP BY customer_id
ORDER BY days_between DESC;
-- PostgreSQL: DATE minus DATE gives days; use DATEDIFF in other engines
64
Write Python code for a two-proportion z-test comparing conversion between control and treatment.
Show answer
from statsmodels.stats.proportion import proportions_ztest, proportion_confint
conversions = [480, 540] # control, treatment
visitors = [10000, 10000]
z, p = proportions_ztest(conversions, visitors)
lift = conversions[1] / visitors[1] - conversions[0] / visitors[0]
ci_t = proportion_confint(conversions[1], visitors[1], method='wilson')
print(f'z={z:.2f} p={p:.4f} abs lift={lift:.4f} treatment CI={ci_t}')
65
Write SQL to compute users, conversions and conversion rate for each experiment variant.
Show answer
SELECT
a.variant,
COUNT(DISTINCT a.user_id) AS users,
COUNT(DISTINCT o.user_id) AS converters,
ROUND(COUNT(DISTINCT o.user_id) * 1.0
/ COUNT(DISTINCT a.user_id), 4) AS conv_rate
FROM assignments a
LEFT JOIN orders o
ON o.user_id = a.user_id
AND o.created_at >= a.assigned_at
WHERE a.experiment_id = 'checkout_v2'
GROUP BY a.variant;
66
Write Python code to calculate the sample size per group needed to detect a lift from 5 percent to 5.5 percent conversion.
Show answer
from statsmodels.stats.power import NormalIndPower
from statsmodels.stats.proportion import proportion_effectsize
baseline, target = 0.05, 0.055
effect = proportion_effectsize(target, baseline) # Cohen's h
n = NormalIndPower().solve_power(
effect_size=effect, alpha=0.05, power=0.8,
ratio=1.0, alternative='two-sided'
)
print(round(n)) # about 31,000 users per group
67
Write a Python function that deterministically assigns users to experiment variants using hashing.
Show answer
import hashlib
def assign(user_id, experiment, variants=('control', 'treatment'), weights=(50, 50)):
key = f'{experiment}:{user_id}'.encode()
bucket = int(hashlib.sha256(key).hexdigest(), 16) % 100
cumulative = 0
for v, w in zip(variants, weights):
cumulative += w
if bucket < cumulative:
return v
# Same user + experiment always gets the same variant;
# salting with the experiment name keeps tests independent
68
Write Python code to compare revenue per user between two groups using Welch's t-test.
Show answer
import numpy as np
from scipy import stats
ctrl = df.loc[df.variant == 'control', 'revenue'].to_numpy()
treat = df.loc[df.variant == 'treatment', 'revenue'].to_numpy()
t, p = stats.ttest_ind(treat, ctrl, equal_var=False) # Welch
diff = treat.mean() - ctrl.mean()
se = np.sqrt(treat.var(ddof=1) / len(treat) + ctrl.var(ddof=1) / len(ctrl))
print(f'diff={diff:.2f} 95% CI=({diff - 1.96*se:.2f}, {diff + 1.96*se:.2f}) p={p:.4f}')
69
Write Python code to apply a CUPED adjustment using each user's pre-experiment metric.
Show answer
import numpy as np
# df has: variant, y (metric during test), x (same metric pre-test)
x, y = df['x'].to_numpy(), df['y'].to_numpy()
theta = np.cov(x, y, ddof=1)[0, 1] / np.var(x, ddof=1)
df['y_cuped'] = y - theta * (x - x.mean())
print(df.groupby('variant')[['y', 'y_cuped']].agg(['mean', 'var']))
# Means stay comparable; variance of y_cuped is lower
70
Write a Python function to split a long text into overlapping chunks by word count for a RAG index.
Show answer
def chunk_words(text, size=200, overlap=40):
if overlap >= size:
raise ValueError('overlap must be smaller than size')
words = text.split()
chunks, step = [], size - overlap
for start in range(0, len(words), step):
chunks.append(' '.join(words[start:start + size]))
if start + size >= len(words):
break
return chunks
print(len(chunk_words(doc_text)))
71
Write Pandas code to compute each customer's average order value excluding the current order.
Show answer
g = df.groupby('customer_id')['amount']
total = g.transform('sum')
count = g.transform('count')
# Leave-one-out mean; NaN when it is the customer's only order
df['avg_other_orders'] = (total - df['amount']) / (count - 1)
df.loc[count == 1, 'avg_other_orders'] = float('nan')
# For time-aware features, use only earlier orders instead
72
Write Python code that validates prediction requests with Pydantic before passing them to a model.
Show answer
from pydantic import BaseModel, Field, ValidationError
class LoanRequest(BaseModel):
age: int = Field(ge=18, le=100)
income: float = Field(gt=0)
city: str = Field(min_length=2)
def predict(payload: dict):
try:
req = LoanRequest(**payload)
except ValidationError as e:
return {'error': e.errors()}
return {'score': float(model.predict_proba([[req.age, req.income]])[0, 1])}
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