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POReturn sorted indexes skipping nan values in pandas?
 

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    <p>I have the following data set:</p> <pre><code>PID,RUN_START_DATE,PUSHUP_START_DATE,SITUP_START_DATE,PULLUP_START_DATE 1,2013-01-24,2013-01-02,,2013-02-03 2,2013-01-30,2013-01-21,2013-01-13,2013-01-06 3,2013-01-29,2013-01-28,2013-01-01,2013-01-29 4,2013-02-16,2013-02-12,2013-01-04,2013-02-11 5,2013-01-06,2013-02-07,2013-02-25,2013-02-12 6,2013-01-26,2013-01-28,2013-02-12,2013-01-10 7,2013-01-26,,2013-01-12,2013-01-30 8,2013-01-03,2013-01-24,2013-01-19,2013-01-02 9,2013-01-22,2013-01-13,2013-02-03, 10,2013-02-06,2013-01-16,2013-02-07,2013-01-11 </code></pre> <p>I know I can use <code>numpy.argsort</code> to return the sorted indexes of the values: </p> <pre><code>SQ_AL_INDX = numpy.argsort(df_sequence[['RUN_START_DATE', 'PUSHUP_START_DATE', 'SITUP_START_DATE', 'PULLUP_START_DATE']], axis=1) </code></pre> <p>...returns...</p> <pre><code> RUN_START_DATE PUSHUP_START_DATE SITUP_START_DATE PULLUP_START_DATE 0 2 1 0 3 1 3 2 1 0 2 2 1 0 3 3 2 3 1 0 4 0 1 3 2 5 3 0 1 2 6 1 2 0 3 7 3 0 2 1 8 3 1 0 2 9 3 1 0 2 </code></pre> <p>But, it seems to put <code>pandas.NaT</code> values into the first position. So in this example <code>where PID == 1</code> the sort order returns <code>2 1 0 3</code>. But, the second index position is a <code>pandas.Nat</code> value.</p> <p>How can I get the sorted indexes while skipping the <code>pandas.NaT</code> values (e.g., the return index values would be <code>2 1 np.NaN 3</code> or <code>2 1 pandas.NaT 3</code> or better yet <code>1 0 2</code> for <code>PID 1</code> instead of <code>2 1 0 3</code>)?</p>
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