Candidate telemetry diagnostic, error autopsy, and step-by-step query construction walkthrough.
Live aggregated metrics across candidate sandbox attempts
11 solved
First attempt fail
Evaluated submissions
Median time to solve
Unlocked answer
Parse Apache-style access log lines and return a list of dicts, one per line, with these keys: ip, method, path, status (int), bytes (int).
Each line follows the pattern:
<IP> - - [date] "<METHOD> <PATH> HTTP/1.1" <STATUS> <BYTES>Use a single regex with named groups for clarity.
Operating on a DataFrame slice without `.copy()`, producing `SettingWithCopyWarning`, or using `.apply(axis=1)` with custom Python functions when vectorized NumPy / pandas column operations could run 50x faster with 0 memory overhead.
Interviewers assess whether you write idiomatic, vectorized pandas code instead of slow row-by-row procedural Python loops.
Construct the solution logically from first principles to avoid typical edge case pitfalls.
Use boolean masks with `.loc[row_mask, col_list]` to avoid chained assignment warnings.
subset = df.loc[df['status'] == 'completed'].copy()
Use numpy/pandas native operations like `.groupby()`, `.transform()`, or column arithmetic.
df['rev_share'] = df['revenue'] / df.groupby('country')['revenue'].transform('sum')Sort values and reset index to match the required evaluation output contract.
result = df.sort_values('rev_share', ascending=False).reset_index(drop=True)import re
log = '''192.168.1.1 - - [25/May/2026:10:01:23] "GET /index.html HTTP/1.1" 200 4523
10.0.0.15 - - [25/May/2026:10:02:11] "POST /api/login HTTP/1.1" 401 89
192.168.1.1 - - [25/May/2026:10:02:48] "GET /dashboard HTTP/1.1" 200 18472
172.16.0.5 - - [25/May/2026:10:03:02] "GET /static/app.css HTTP/1.1" 304 0
10.0.0.15 - - [25/May/2026:10:03:30] "POST /api/login HTTP/1.1" 200 412
192.168.1.1 - - [25/May/2026:10:04:55] "DELETE /api/comments/42 HTTP/1.1" 500 215'''
pat = re.compile(
r'(?P<ip>\d+\.\d+\.\d+\.\d+) - - \[[^\]]+\] '
r'"(?P<method>\w+) (?P<path>\S+) HTTP/\d\.\d" '
r'(?P<status>\d+) (?P<bytes>\d+)'
)
result = []
for line in log.strip().split('\n'):
m = pat.match(line)
if m:
d = m.groupdict()
d['status'] = int(d['status'])
d['bytes'] = int(d['bytes'])
result.append(d)
Real code patterns candidates submit that fail the grading suite.
results = []
for i, row in df.iterrows():
results.append(row['val'] * 2)Three recurring syntax and semantic traps relevant to this problem domain.
Modifying a filtered slice like df[df.age > 30]['salary'] = 5000 modifies an ephemeral copy instead of the underlying dataframe.
df[df['active'] == True]['status'] = 'verified' # ❌ SettingWithCopyWarning
df.loc[df['active'] == True, 'status'] = 'verified' # ✅ Idiomatic inplace assignment
Calling df.groupby('category').agg(...) places 'category' into the MultiIndex/Index, making subsequent column references fail.
res = df.groupby('dept')['salary'].mean(); print(res['dept']) # ❌ KeyErrorres = df.groupby('dept', as_index=False)['salary'].mean() # ✅ Keeps column intactIterating rows with for index, row in df.iterrows() is an anti-pattern in data science interviews.
for i, r in df.iterrows(): df.at[i, 'total'] = r['price'] * r['qty'] # ❌ Slow
df['total'] = df['price'] * df['qty'] # ✅ C-speed vectorization
Launch our in-browser coding environment. Run queries, view execution plans, and get instant comparative diff grading with no setup.