# Preparing a heatmap

You have a table of results from optimizing two hyperparameters of a machine learning algorithm. The table looks like

alpha beta accuracy
1 0.1 0.1 0.9
2 0.2 0.1 0.92
3 0.3 0.1 0.95

Which was generated by looping over values of both alpha and beta and calculating the accuracy given those settings.

```import numpy as np

results = []
for alpha in np.linspace(0, 1, 10):
for beta in np.linspace(0, 1, 10):
results.append(dict(alpha=alpha, beta=beta,
accuracy=accuracy_with_settings(alpha, beta)))
df = pd.DataFrame(results)
```

Write a function, `prepare_for_heatmap(df)` which has one column for each value of alpha, one row for each value of beta and the accuracy associated with the pair as the value.

0.1 0.2 0.3
0.1 0.9 0.8 0.7
0.2 0.8 0.7 0.6
0.3 0.7 0.6 0.5

You should be able to use `prepare_for_heatmap` along with `seaborn.heatmap(prepare_for_heatmap(df))` to generate a heatmap like: # Example Input

## Code to generate input

```results = []
for alpha in np.round(np.linspace(0, 1, 4), 1):
for beta in np.round(np.linspace(0, 1, 4), 1):
alpha_contrib = 0.5 - (alpha - 0.5)**2
beta_contrib = 0.5 - (beta - 0.8)**2
acc = np.minimum(1, alpha_contrib+beta_contrib+np.random.randn()*0.01)
results.append(dict(alpha=alpha, beta=beta, accuracy=acc))
df = pd.DataFrame(results)
```

## Table generated

alpha beta accuracy
0 0 0 0.112186
1 0 0.3 0.513274
2 0 0.7 0.722594
3 0 1 0.720604
4 0.3 0 0.319968
5 0.3 0.3 0.711226
6 0.3 0.7 0.946665
7 0.3 1 0.93472
8 0.7 0 0.330959
9 0.7 0.3 0.706973
10 0.7 0.7 0.934994
11 0.7 1 0.910779
12 1 0 0.108678
13 1 0.3 0.510815
14 1 0.7 0.742989
15 1 1 0.703605

# Example Output

beta 0.0 0.3 0.7 1.0
0 0.112186 0.319968 0.330959 0.108678
0.3 0.513274 0.711226 0.706973 0.510815
0.7 0.722594 0.946665 0.934994 0.742989
1 0.720604 0.93472 0.910779 0.703605