title: Different result for std between pandas and numpy
body: <p>I am trying to subtract every element in the column from its mean and divide by the standard deviation. I did it in two different ways (<code>numeric_data1</code> and <code>numeric_data2</code>):</p>
  
  <pre><code>import pandas as pd
  data = pd.read_csv("https://s3.amazonaws.com/demo-datasets/wine.csv")
  numeric_data = data.drop("color", 1)
  numeric_data1 = ((numeric_data - numeric_data.mean()) /
                   numeric_data.std())
  numeric_data2 = ((numeric_data - np.mean(numeric_data, axis=0)) /
                   np.std(numeric_data, axis=0))
  
  type(numeric_data1)  # -&gt; pandas.core.frame.DataFrame
  type(numeric_data2)  # -&gt; pandas.core.frame.DataFrame
  </code></pre>
  
  <p>Both are pandas dataframes and they should have the same result. However, I get different results:</p>
  
  <pre><code>numeric_data2 == numeric_data1  # -&gt; False
  </code></pre>
  
  <p>I think the problem stems from how numpy and pandas handle numeric precision:</p>
  
  <pre><code>numeric_data.mean() == np.mean(numeric_data, axis=0)      # -&gt; True
  numeric_data.std(axis=0) == np.std(numeric_data, axis=0)  # -&gt; False
  </code></pre>
  
  <p>For mean numpy and pandas gave me the same thing, but for standard deviation, I got little different results.</p>
  
  <p>Is my assessment correct or am I making some blunder?</p>