title: How do you loop and test multiple "numpy.isclose" values on a column of a dataframe?
body: <p>I'm using this line of code to determine values in a column of a large dataframe, df, that are close to the value of A (within a tolerance):</p>
  
  <pre><code>df[df[['column']].apply(numpy.isclose, b=A, atol=0.004).any(1)]
  </code></pre>
  
  <p>However, in some instances, A can have multiple values (i.e. 2-4 different values). Is there a method that would allow me to loop through each of the values of A to test each one? I know that the line of code shown above will only let me use A if it has one value assigned to it.</p>
  
  <p>An example of this would be (using a much shorter dataframe): </p>
  
  <pre><code>   column1   column2
  0  0.902062    5.8
  1  0.557808    3.3
  2  0.655985    3.9
  3  0.832471    4.1  
  4  0.199884    1.2
  5  0.127254    1.8
  6  0.771439    4.9
  7  0.432289    2.8
  8  0.385282    2.2
  9  0.783643    3.7
  </code></pre>
  
  <p>Where A has values:</p>
  
  <pre><code>A=[0.432, 0.783, 0.902]
  </code></pre>
  
  <p>But in another example, it may have the values:</p>
  
  <pre><code>A=[0.558, 0.002]
  </code></pre>
  
  <p>(where it is obvious here that nothing from the dataframe will actually match 0.002).</p>
  
  <p>I'd like some code which would be able to return the rows from the dataframe where the column1 values match the values of A for ALL examples, regardless of the number of different A values (and if there is no match, return "NaN" instead).</p>