Conditionals & Loops

score = 75
if score >= 90:
    grade = "A"
elif score >= 75:
    grade = "B"
elif score >= 60:
    grade = "C"
else:
    grade = "F"
print(grade)  # "B"

In Pandas, this logic is done with np.where() for two conditions or np.select() for multiple. But knowing the plain Python form is the foundation interviewers expect.

# for — iterates over a sequence
for i in range(5):
    print(i)  # 0 1 2 3 4

# while — runs until condition is False
n = 0
while n < 5:
    print(n)
    n += 1

Use for when you know the number of iterations. Use while for retry logic or reading streaming data until a condition is met. Forgetting to increment in while loops causes infinite loops.

# enumerate — get index + value
cols = ["name", "age", "city"]
for i, col in enumerate(cols):
    print(i, col)   # 0 name, 1 age, 2 city

# zip — loop over two lists together
keys = ["a", "b", "c"]
vals = [1, 2, 3]
for k, v in zip(keys, vals):
    print(k, v)

enumerate() replaces the pattern for i in range(len(lst)): — cleaner and Pythonic. zip() is used to pair column names with values, or two DataFrames row by row.

# break — exit the loop entirely
for i in range(10):
    if i == 5:
        break  # stops at 5

# continue — skip to next iteration
for i in range(5):
    if i == 2:
        continue  # skips 2
    print(i)  # 0 1 3 4

# pass — do nothing (placeholder)
if True:
    pass  # no error, loop/block continues

break is used to stop searching once you find what you need. continue skips bad rows in a loop. pass is a placeholder when building stub functions or empty branches.

x = 10
result = "positive" if x > 0 else "non-positive"
print(result)  # "positive"

# In list comprehension:
flags = ["high" if v > 100 else "low" for v in [50, 150, 80, 200]]

The ternary form is used heavily in list comprehensions and Pandas .apply() lambdas. It is cleaner than a full if-else for simple single-value decisions.