title: Python sklearn PCA.explained_variance_ratio_ doesn't sum to 1
body: <p>I am using sklearn's PCA module. I am using the following code to set up the analysis.</p>
  
  <pre><code>from sklearn.decomposition import PCA
  pca = PCA(n_components=9)
  p = pca.fit([row[:-1] for row in norm])
  </code></pre>
  
  <p><code>norm</code> here is my normalized dataset, with a unique identifier in the last column, which is why I am removing it in the last line. There are 9 features in this dataset, so I would expect with 9 components that there would be no unexplained variance. When I call <code>p.explained_variance_.cumsum()</code> however, I get:</p>
  
  <pre><code>[ 0.06589563  0.08608778  0.09578116  0.10150195  0.10703567  0.11036608
    0.11241904  0.11422285  0.11591605]
  </code></pre>
  
  <p>Am I misunderstanding something about PCA? I have used this module before with no issue, but it's been a while. Am I setting this up wrong? I stripped my data of any identifying information to post here. Below is a subset of the data that seems to be replicating the issue.</p>
  
  <pre><code>[0.3888888888888889, 0.3888888888888889, 0.3888888888888889, 0.436943311456892, 0.7905900031193156, 0.5020468092219706, 0.8389717734280283, 0.7604923090797432, 0.8206054422776056, '0']
  [0.3888888888888889, 0.3888888888888889, 0.2222222222222222, 0.4457200178477334, 0.8114779465247448, 0.506899600792241, 0.8368566485573798, 0.760617288778523, 0.8195489478905984, '1']
  [0.2777777777777778, 0.2777777777777778, 0.05555555555555555, 0.4426231291814084, 0.7883413226205706, 0.5037172133121759, 0.8370362549229062, 0.7599752704033258, 0.8184218722901648, '2']
  [0.1111111111111111, 0.1111111111111111, 0.16666666666666666, 0.4651807845446571, 0.7983379003654792, 0.5250604537887904, 0.8463875215362144, 0.7533582308429306, 0.8241548325954007, '3']
  [0.5000000000000001, 0.5000000000000001, 0.3333333333333333, 0.4457200178477334, 0.7878040593905666, 0.506899600792241, 0.8368566485573798, 0.7605016058324149, 0.8195489478905984, '4']
  [0.3888888888888889, 0.3888888888888889, 0.2222222222222222, 0.44943322185630036, 0.7843622888520198, 0.5055757644148106, 0.8351253941103399, 0.7604171267769607, 0.8185442945328569, '5']
  [0.3888888888888889, 0.3888888888888889, 0.3333333333333333, 0.4424914587425397, 0.7877430312713435, 0.5029950110274568, 0.836692391332608, 0.760611529525946, 0.8198150075184326, '6']
  [0.3333333333333333, 0.05555555555555555, 0.7777777777777778, 0.4389415113841421, 0.7878040593905666, 0.506899600792241, 0.8368566485573798, 0.7605016058324149, 0.8195489478905984, '7']
  [0.4444444444444444, 0.4444444444444444, 0.4444444444444444, 0.42770705188736874, 0.7976039510596705, 0.5057230657076256, 0.8368566485573798, 0.7605016058324149, 0.8195489478905984, '8']
  [0.2222222222222222, 0.2777777777777778, 0.5000000000000001, 0.43182322765312314, 0.7971732873351607, 0.5072390458086798, 0.84541364942531, 0.7613416598875292, 0.8239037851005895, '9']
  </code></pre>