Q.Write a statement to count the number of values in a row.
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Start your 14-day free trial to unlock the full solution →DataFrame.count() counts non-null values; the direction it counts in flips with axis — axis=0 (the default) counts DOWN each column, axis=1 counts ACROSS each row. At this point in the chapter df has no missing values yet, so both directions currently return a "clean" flat number.
The default direction — down each column
print(df.count())
Output:
Name 12
UT 12
Maths 12
Science 12
S.St 12
Hindi 12
Eng 12
dtype: int64
Every column reports 12 — unsurprising, since the case-study df has 12 rows and (at this point in the chapter) no missing data at all. This is count()'s default behaviour (axis=0): for each column, count how many of its 12 cells actually hold a value.
What Program 3-5 asks for — across each row instead
print(df.count(axis=1))
Output:
0 7
1 7
2 7
3 7
4 7
5 7
6 7
7 7
8 7
9 7
10 7
11 7
dtype: int64
Passing axis=1 flips the direction: for each row, count how many of its columns have a value. Every row has exactly 7 fields (Name, UT, Maths, Science, S.St, Hindi, Eng), and every one of them is filled in, so every row reports 7.
Why this matters beyond a "no missing data" flex …
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