So far we've seen how to write Python code to **that loads and process Astro Pi CSV data files** and how to **filter the data for a specific date**.

In this post we'll using some simple statistics functions to analyse each day in our data set to spot where something out of the ordinary happened.

To begin load up the **date filter code** and add a new import at the top (note you'll need to use Python 3.4 or later for this library module):.

**import** statistics

Now add these two new function definitions:

The `get_first_date()`

function finds the first date in the date set. While the second `collect_stats()`

calls this function before looping through all the rows to build date-specific data list for the specified column.

Then we call the statistics `mean`

and `variance`

statistical functions of our date-specific data collection. These values are then printed out.

Finally, in the main code area, we simply call the `collect_daily_stats()`

function with our chosen column number.

As you can see from the output there was a big variance in the humidity data on 21st February 2016.

```
2016-02-17 humidity: count=4740 mean=45.41 variance=0.12
```

2016-02-18 humidity: count=8565 mean=45.54 variance=0.68

2016-02-19 humidity: count=8555 mean=45.22 variance=1.97

2016-02-20 humidity: count=8579 mean=44.90 variance=0.12

2016-02-21 humidity: count=8557 mean=49.07 variance=20.28

2016-02-22 humidity: count=8559 mean=47.65 variance=0.47

2016-02-23 humidity: count=8559 mean=47.12 variance=0.73

2016-02-24 humidity: count=8564 mean=47.34 variance=0.31

2016-02-25 humidity: count=8559 mean=46.86 variance=0.12

2016-02-26 humidity: count=8570 mean=46.49 variance=0.23

2016-02-27 humidity: count=8560 mean=45.73 variance=0.14

2016-02-28 humidity: count=8561 mean=45.71 variance=0.08

2016-02-29 humidity: count=8575 mean=45.02 variance=0.08

The **Python statistics library model** has many other functions, so have some fun experimenting with some other statistical techniques.

Start coding today with my **Learn Python on the Raspberry Pi** tutorial.