Write the python statements to print the mean, variance, standard deviation and quartile of the marks scored in Mathematics by each student across the UTs.

Question

Accepted Answer

> [!TLDR]
> `groupby()` splits `df` into one group per student, following a **split → apply → combine** pattern; `['Maths']` then picks that column within each group, and `.agg([...])` applies several statistics to it at once, producing one summary row per student.

## The split-apply-combine idea

`groupby()` works in three conceptual steps: **split** the DataFrame into groups sharing some key (here, `Name`); **apply** a function to each group separately; **combine** the per-group results back into one summary table. A small illustration: if a two-column table held keys A/B/C paired with several numbers each, `groupby('key').sum()` would split it into three little tables (one per key), sum each one independently, then stitch the three sums back into a single 3-row result — that's exactly the same mechanism used here, just with `Name` as the key and four richer statistics instead of one sum.

## What a GroupBy object actually holds, before aggregating

It helps to see what `df.groupby('Name')` produces *before* jumping straight to `.agg()`:

```python
g1 = df.groupby('Name')
print(g1.first())      # the first row of each student's group
print(g1.size())        # how many rows are in each group
print(g1.get_group('Raman'))   # pull out one student's full group
```
```
         UT  Maths  Science  S.St  Hindi  Eng
Name
Ashravy   1     23       19    20     15   22
Mishti    1     15       22    25     22   22
Raman     1     22       21    18     20   21
Zuhaire   1     20       17    22     24   19

Name
Ashravy    3
Mishti     3
Raman      3
Zuhaire    3
dtype: int64

UT  Maths  Science  S.St  Hindi  Eng
0   1     22       21    18     20   21
1   2     21       20    17     22   24
2   3     14       19    15     24   23
```

Each student's group is genuinely just their 3 original rows, held together under one label — `.agg()` is what turns each of those 3-row groups into a single summary row.

## Program 3-10 — four statistics in one call

```python
print(df.gr

Q.Write the python statements to print the mean, variance, standard deviation and quartile of the marks scored in Mathematics by each student across the UTs.

Concept understanding — Five Number Summary

The split-apply-combine idea

What a GroupBy object actually holds, before aggregating

Program 3-10 — four statistics in one call

Interpreting what the four numbers mean …

Q.Write the python statements to print the mean, variance, standard deviation and quartile of the marks scored in Mathematics by each student across the UTs.

The split-apply-combine idea

What a GroupBy object actually holds, before aggregating

Program 3-10 — four statistics in one call

Interpreting what the four numbers mean …

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