Checking a DataFrame's size

print(len(df))     # number of rows
print(df.shape)    # (rows, columns)

Filtering rows with a condition

Putting a condition inside the square brackets keeps only the rows where it's true:

high_scorers = df[df["score"] > 70]
print(high_scorers)

df["score"] > 70 on its own produces a True/False Series, one value per row -- df[...] then keeps only the rows marked True.

Combining conditions

Use & for "and" and | for "or" -- each condition needs its own parentheses:

result = df[(df["score"] > 60) & (df["score"] < 90)]

Plain Python's and/or don't work here -- they're not defined for comparing a whole column at once, only &/| are.

Counting matches

A filtered DataFrame is still a DataFrame, so len() works the same way on it:

print(len(df[df["score"] > 70]))

Selecting specific columns

print(df[["name", "score"]])   # a list of column names keeps a DataFrame, not a Series

Why This Matters for Your Exams

df[condition] is the single most-used pattern in pandas -- almost every real question ("how many students scored above X," "which rows match Y") starts here. Mixing up &/and is the most common beginner error -- remember: & and | for pandas conditions, always with parentheses around each side.

Try it yourself