title: reindex to add missing dates to pandas dataframe
body: <p>I try to parse a CSV file which looks like this:</p>
  
  <pre><code>dd.mm.yyyy   value
  
  01.01.2000   1
  02.01.2000   2
  01.02.2000   3
  </code></pre>
  
  <p>I need to add missing dates and fill according values with <strong>NaN</strong>. I used <code>Series.reindex</code> like in <a href="https://stackoverflow.com/questions/19324453/add-missing-dates-to-pandas-dataframe">this question</a>:</p>
  
  <pre><code>import pandas as pd
  
  ts=pd.read_csv(file, sep=';', parse_dates='True', index_col=0)
  
  idx = pd.date_range('01.01.2000', '02.01.2000')
  
  ts.index = pd.DatetimeIndex(ts.index)
  ts = ts.reindex(idx, fill_value='NaN')
  </code></pre>
  
  <p>But in result, values for certain dates are swapped due to date format (i.e. mm/dd instead of dd/mm):</p>
  
  <pre><code>01.01.2000   1
  02.01.2000   3
  03.01.2000   NaN
  ...
  ...
  31.01.2000   NaN
  01.02.2000   2
  </code></pre>
  
  <p>I tried several ways (i.e. add <code>dayfirst=True</code> to <code>read_csv</code>) to do it right but still can't figure it out. Please, help.</p>