# snippets

Misc code snippets I sometimes need and always have to look up how it works...


## linear_regression.py

Calculate the linear regression on two columns of a data frame. The resulting
object has the function `predict()` to calculate x or y values for a given 
counterpart.

```python
from linear_regression import linear_regression

df = pd.DataFrame({"temperature":[...], "signal":[...]})

regression = linear_regression(df, x="temperature", y="signal")

repr(regression) == "Regression(intercept=1, coefficient=3, score=0.9998)"

regression.predict(x=3) == 10
regression.predict(y=7) == 2
```


## split_uniques.py

Splits a data frame on uniques values in a column

Returns a generator of tuples with at least two elements. 
The _last_ element is the resulting partial data frame, 
the element(s) before are the values used to split up the original data.


```python
from split_uniques import split_uniques

df = pd.DataFrame({
        "A": [1, 2, 2], 
        "B": [3, 4, 3], 
        "C": ["x", "y", "z"]
    })

result = list(split_uniques(df, ["B"]))

assert len(result) == 3

value, data = result[0]
assert value == 3
assert data == pd.DataFrame({
        "A": [1, 1], 
        "B": [3, 3], 
        "C": ["x", "z"]
    })

value, data = result[1]
assert value == 4
assert data == pd.DataFrame({
        "A": [2], 
        "B": [4], 
        "C": ["y"]
    })
```

This construct might look a little bit weird, but it makes it easy to use the 
function in a loop definition:

```python
for well, probe, partial_data in split_uniques(full_data, ["Well", "Probe"]):
    ...
```