title: Handling a column with dates and missing dates
body: <p>I have the following code to estimate profit from buy and sell price of crypto token.</p>
  <pre><code>import pandas as pd
  
  
  # Read text file into pandas DataFrame
  # --------------------------------------
  df = pd.read_csv(&quot;crypto.txt&quot;, comment=&quot;#&quot;, skip_blank_lines=True, delim_whitespace=True).dropna()
  
  # Display DataFrame
  # -----------------
  print(df)
  print()
  
  # Replace commas in number
  # --------------------------------------
  df['BuyPrice'] = df['BuyPrice'].str.replace(',', '').astype(float)
  df['SellPrice'] = df['SellPrice'].str.replace(',', '').astype(float)
  df['Size'] = df['Size'].str.replace(',', '').astype(float)
  
  df['Profit'] = df.SellPrice - df.BuyPrice
  
  # Sort BuyPrice column in ascending way
  # --------------------------------------
  df = df.sort_values('BuyPrice', ignore_index=True)
  #df = df.sort_values('BuyPrice').reset_index(drop=True)
  print()
  
  
  # Sum all the numerical values and create a 'Total' row
  # -----------------------------------------------------
  df.loc['Total'] = df.sum(numeric_only=True)
  
  # Replace NaN by empty space
  # ---------------------------
  df = df.fillna('')
  
  
  df = df.rename({'BuyPrice': 'Buy Price', 'SellPrice': 'Sell Price'}, axis=1)
  
  # Display Final DataFrame
  # -----------------
  print(df)
  </code></pre>
  <p>Now the output does only show the rows with sensible entries in the 'Date' column. I get</p>
  <pre><code>       Coin BuyPrice SellPrice     Size           Date
  1  1INCH    2,520      3180       10     23-10-2021
  3   SHIB      500       450  200,000     27-10-2021
  4    DOT     1650      2500        1  June 01, 2021
  
  
          Coin  Buy Price  Sell Price      Size           Date  Profit
  0       SHIB      500.0       450.0  200000.0     27-10-2021   -50.0
  1        DOT     1650.0      2500.0       1.0  June 01, 2021   850.0
  2      1INCH     2520.0      3180.0      10.0     23-10-2021   660.0
  Total            4670.0      6130.0  200011.0                 1460.0
  </code></pre>
  <p>Clearly, we can see the rows without dates have been ignored. How could one tackle this issue? How can <code>Pandas</code> understand they are dates?</p>
  <p><code>crypto.txt</code> file contains:</p>
  <pre><code>Coin        BuyPrice       SellPrice      Size        Date
  #---        ---------       ----------    ----     -----------
  
  ADA         1,580            1,600             1            NA
  
  
  1INCH       2,520            3180         10         23-10-2021
  
  
  SHIB        261.6           450         200,000          NA 
  SHIB        500             450         200,000       27-10-2021 
  
  
  DOT         1650            2500           1          &quot;June 01, 2021&quot;
  </code></pre>
  <p>It seems I couldn't write the last row and column entry within single inverted commas. Is it possible to convert all the dates in one single kind of format (say  , )?</p>