title: Fill other columns with missing dates Nan Pandas DataFrane
body: <p>I am actually extracting data from several excel files monitoring my daily calorie intake. I managed to use list comprehension to generate the dates. I tried to use merge or join and it does not work.
  ValueError: You are trying to merge on object and float64 columns.</p>
  <pre><code>date_list = ['2021-05-22','2021-05-24','2021-05-26','2021-05-27']
  idx = pd.date_range(date_list[0], date_list[-1]) # To find missing dates
  df_dates = pd.DataFrame(idx) # To convert list to DataFrame
  df1_dates = pd.DataFrame(np.repeat(df_dates.values,2,axis=0)) # However, there is no column title and it is default at 0.
  </code></pre>
  <p>I have another set of data with my calorie intake and exercise minutes.</p>
  <pre><code># These are Lists.
  Time = [Morning, Afternoon, Morning, Afternoon, Morning, Afternoon, Morning, Afternoon]
  Calories = [420,380,390,400,350,280,300,430]
  Duration = [50,40,45,50,45,50,44,58]
  </code></pre>
  <p>The problems I am facing is that I don't know how to create a column title ('Date') for df1_dates dataframe after using np.repeat. I want to fill the other columns that corresponds to the missing dates with 'NaN'.</p>
  <p>The output should look something like this:</p>
  <pre><code>         Date       Time calories duration
  0   22/5/2021    Morning      420       50
  1   22/5/2021  Afternoon      380       40
  2   23/5/2021    Morning      Nan      Nan
  3   23/5/2021  Afternoon      Nan      Nan
  4   24/5/2021    Morning      390       45
  5   24/5/2021  Afternoon      400       50
  6   25/5/2021    Morning      Nan      Nan
  7   25/5/2021  Afternoon      Nan      Nan
  8   26/5/2021    Morning      350       45
  9   26/5/2021  Afternoon      280       50
  10  27/5/2021    Morning      300       44
  11  27/5/2021  Afternoon      430       58
  </code></pre>