title: Why am I getting NANs when concatenating a Data Frame with a Series
body: <p>I have a Pandas Dataframe ('a') and a Series ('b') both with timeseries index (weekends excluded). I am trying to concatenate them.</p>
  <p>Both of them start with the same time point  and the only difference I see is that the series 'b' Index has 3 more days than the dataframe 'a'.</p>
  <p>When I try to concatenate them, I am just getting NANs for all the series values in the resultant Dataframe ('c'). I am unable to fathom the reason. Appreciate inputs.</p>
  <pre><code>print(a.head(10))
  print(a.tail(10))
  print(a.info(10))
  
              DCOILWTICO
  DATE                  
  2022-01-31       89.16
  2022-02-01       88.22
  2022-02-02       88.16
  2022-02-03       90.17
  2022-02-04       92.27
  2022-02-07       91.25
  2022-02-08       89.32
  2022-02-09       89.57
  2022-02-10       89.83
  2022-02-11       93.10
              DCOILWTICO
  DATE                  
  2022-03-29      104.25
  2022-03-30      107.81
  2022-03-31      100.53
  2022-04-01       99.32
  2022-04-04      103.29
  2022-04-05      101.98
  2022-04-06       96.39
  2022-04-07       96.05
  2022-04-08       98.35
  2022-04-11       94.22
  &lt;class 'pandas.core.frame.DataFrame'&gt;
  DatetimeIndex: 51 entries, 2022-01-31 to 2022-04-11
  Data columns (total 1 columns):
   #   Column      Non-Null Count  Dtype  
  ---  ------      --------------  -----  
   0   DCOILWTICO  50 non-null     float64
  dtypes: float64(1)
  memory usage: 2.9 KB
  None
  
  print(type(b))
  print(b.head(10))
  print(b.tail(10))
  
  &lt;class 'pandas.core.series.Series'&gt;
  2022-01-31    51.40
  2022-02-01    52.68
  2022-02-02    53.25
  2022-02-03    53.91
  2022-02-04    55.10
  2022-02-07    55.71
  2022-02-08    54.65
  2022-02-09    54.76
  2022-02-10    54.52
  2022-02-11    55.21
  Name: close, dtype: float64
  2022-04-01    55.72
  2022-04-04    55.82
  2022-04-05    54.91
  2022-04-06    55.30
  2022-04-07    55.42
  2022-04-08    56.09
  2022-04-11    55.32
  2022-04-12    55.99
  2022-04-13    57.04
  2022-04-14    57.11
  Name: close, dtype: float64
  
  c = pd.concat([a,b], axis=1)
  print(c.head(10))
  print(c.tail(10))
  print(c.info)
  
              DCOILWTICO  close
  2022-01-31       89.16    NaN
  2022-02-01       88.22    NaN
  2022-02-02       88.16    NaN
  2022-02-03       90.17    NaN
  2022-02-04       92.27    NaN
  2022-02-07       91.25    NaN
  2022-02-08       89.32    NaN
  2022-02-09       89.57    NaN
  2022-02-10       89.83    NaN
  2022-02-11       93.10    NaN
              DCOILWTICO  close
  2022-04-01       99.32    NaN
  2022-04-04      103.29    NaN
  2022-04-05      101.98    NaN
  2022-04-06       96.39    NaN
  2022-04-07       96.05    NaN
  2022-04-08       98.35    NaN
  2022-04-11       94.22    NaN
  2022-04-12         NaN    NaN
  2022-04-13         NaN    NaN
  2022-04-14         NaN    NaN
  &lt;class 'pandas.core.frame.DataFrame'&gt;
  DatetimeIndex: 54 entries, 2022-01-31 to 2022-04-14
  Freq: B
  Data columns (total 2 columns):
   #   Column      Non-Null Count  Dtype  
  ---  ------      --------------  -----  
   0   DCOILWTICO  50 non-null     float64
   1   close       0 non-null      float64
  dtypes: float64(2)
  memory usage: 1.3 KB
  None
  </code></pre>