TypeError: unsupported operand type(s) for *: 'PCA' and 'float'
<p>EDIT:</p>

<p>Here is the head of the data csv:</p>

<pre><code>    Fresh   Milk    Grocery Frozen  Detergents_Paper    Delicatessen
0   12669   9656    7561    214 2674    1338
1   7057    9810    9568    1762    3293    1776
2   6353    8808    7684    2405    3516    7844
3   13265   1196    4221    6404    507 1788
4   22615   5410    7198    3915    1777    5185
</code></pre>

<p>Error I'm seeing:</p>

<pre><code>TypeError: unsupported operand type(s) for *: 'PCA' and 'float'
</code></pre>

<p>Code:</p>

<pre><code>from sklearn.decomposition import PCA

log_data = np.log(data)

# TODO: Apply PCA to the good data with the same number of dimensions as features
pca = PCA(n_components=4)

# TODO: Apply a PCA transformation to the sample log-data
pca_samples = pca.fit(log_data)

# Generate PCA results plot
pca_results = rs.pca_results(good_data, pca)

display(pd.DataFrame(np.round(pca_samples, 4), columns = pca_results.index.values))
</code></pre>

<p>It's complaining about the last line</p>

<p>data is from a csv that has been shown to work fine.  </p>

<p><code>PCA.fit()</code> tansforms the model in-place and returns <code>self</code> so that you can chain other model operations. So, after</p>

<pre><code>pca_samples = pca.fit(log_data)
</code></pre>

<p><code>pca_samples</code> is just another reference to <code>pca</code>.</p>

python scikit-learn sklearn-pandas