# ak.corr¶

Defined in awkward.operations.reducers on line 1193.

ak.corr(x, y, weight=None, axis=None, keepdims=False, mask_identity=True)
Parameters
• x – one coordinate to use in the correlation.

• y – the other coordinate to use in the correlation.

• weight – data that can be broadcasted to `x` and `y` to give each point a weight. Weighting points equally is the same as no weights; weighting some points higher increases the significance of those points. Weights can be zero or negative.

• axis (None or int) – If None, combine all values from the array into a single scalar result; if an int, group by that axis: `0` is the outermost, `1` is the first level of nested lists, etc., and negative `axis` counts from the innermost: `-1` is the innermost, `-2` is the next level up, etc.

• keepdims (bool) – If False, this function decreases the number of dimensions by 1; if True, the output values are wrapped in a new length-1 dimension so that the result of this operation may be broadcasted with the original array.

• mask_identity (bool) – If True, the application of this function on empty lists results in None (an option type); otherwise, the calculation is followed through with the reducers’ identities, usually resulting in floating-point `nan`.

Computes the correlation of `x` and `y` (many types supported, including all Awkward Arrays and Records, must be broadcastable to each other). The grouping is performed the same way as for reducers, though this operation is not a reducer and has no identity.

This function has no NumPy equivalent.

Passing all arguments to the reducers, the correlation is calculated as

```ak.sum((x - ak.mean(x))*(y - ak.mean(y))*weight)
/ np.sqrt(ak.sum((x - ak.mean(x))**2))
/ np.sqrt(ak.sum((y - ak.mean(y))**2))
```

See `ak.sum` for a complete description of handling nested lists and missing values (None) in reducers, and `ak.mean` for an example with another non-reducer.