# DataMatrix: Exercises and Solutions

```
from datamatrix import io
dm = io.readxlsx('data/movies.xlsx')
dm = (dm.year >= 1990) & (dm.year < 2000)
for year in dm.year.unique:
year_dm = dm.year == year
print('For movies from year {}, the mean rating is {:.2f}'.format(
year,
year_dm.rating.mean
))
```

**Output:**

```
/home/sebastiaan/anaconda3/envs/pydata/lib/python3.10/site-packages/datamatrix/py3compat.py:105: UserWarning: Some rows miss column title
warnings.warn(safe_str(msg), *args)
[32mâ §[0m Generating...For movies from year 1990, the mean rating is 4.86
For movies from year 1991, the mean rating is 4.99
For movies from year 1992, the mean rating is 5.15
For movies from year 1993, the mean rating is 5.30
For movies from year 1994, the mean rating is 4.91
For movies from year 1995, the mean rating is 4.80
For movies from year 1996, the mean rating is 4.94
For movies from year 1997, the mean rating is 5.12
For movies from year 1998, the mean rating is 5.20
For movies from year 1999, the mean rating is 5.27
```

An alternative solution makes use of `datamatrix.operations.split()`

:

```
from datamatrix import io
from datamatrix import operations as ops
dm = io.readxlsx('data/movies.xlsx')
dm = (dm.year >= 1990) & (dm.year < 2000)
for year, year_dm in ops.split(dm.year):
print('For movies from year {}, the mean rating is {:.2f}'.format(
year,
year_dm.rating.mean
))
```

**Output:**

```
For movies from year 1990, the mean rating is 4.86
For movies from year 1991, the mean rating is 4.99
For movies from year 1992, the mean rating is 5.15
For movies from year 1993, the mean rating is 5.30
For movies from year 1994, the mean rating is 4.91
For movies from year 1995, the mean rating is 4.80
For movies from year 1996, the mean rating is 4.94
For movies from year 1997, the mean rating is 5.12
For movies from year 1998, the mean rating is 5.20
For movies from year 1999, the mean rating is 5.27
```

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