title: Python/Pandas - Best practice how to join 2 arrays with dates in Python/Pandas
body: <p>I am a complete newbie in Python - for doing a procedural calculation of interests within a mortgage plan i need to calculate with dates. There is a date array for the payments and another date array for summing up the interests.</p>
  
  <p>I am completely stuck with merging the two date arrays - i would need only one merged array with sorted dates. I read through different approaches (DataFrames, Joins, Merges) but couldn't find any solution.</p>
  
  <pre><code>import pandas as pd
  date_range_1 = pd.date_range(start='1/1/2019', periods=12,freq='M')
  date_range_2 = pd.date_range(start='31/3/2019',periods=4,freq='3M')
  
  Question-1: How to join these 2 arrays
  Question-2: How to sort by date
  
  Output should be:
  '1/1/2019'
  '1/2/2019'
  '1/3/2019'
  '31/3/2019'
  '1/4/2019'
  '1/5/2019'
  '1/6/2019'
  '30/6/2019'
  ...
  </code></pre>
  
  <p>Edit: Full input + solution:</p>
  
  <p><div class="snippet" data-lang="js" data-hide="false" data-console="true" data-babel="false">
  <div class="snippet-code">
  <pre class="snippet-code-html lang-html prettyprint-override"><code>import pandas as pd
  dates_1= pd.date_range('2018-01-01','2020-01-01' , freq='1M')-pd.offsets.MonthBegin(1)
  dates_2= pd.date_range('2018-03-31','2020-03-31' , freq='3M')-pd.offsets.MonthEnd(0)
  Union_dates = pd.Series(sorted(dates_1.union(dates_2)))
  print(Union_dates)</code></pre>
  </div>
  </div>
  </p>