title: Python Pandas -- Forward filling entire rows with value of one previous column
body: <p>New to pandas development. How do I forward fill a DataFrame with the value contained in one previously seen column?</p>
  
  <p>Self-contained example:</p>
  
  <pre><code>import pandas as pd
  import numpy as np
  O = [1, np.nan, 5, np.nan]
  H = [5, np.nan, 5, np.nan]
  L = [1, np.nan, 2, np.nan]
  C = [5, np.nan, 2, np.nan]
  timestamps = ["2017-07-23 03:13:00", "2017-07-23 03:14:00", "2017-07-23 03:15:00", "2017-07-23 03:16:00"]
  dict = {'Open': O, 'High': H, 'Low': L, 'Close': C}
  df = pd.DataFrame(index=timestamps, data=dict)
  ohlc = df[['Open', 'High', 'Low', 'Close']]
  </code></pre>
  
  <p>This yields the following DataFrame:</p>
  
  <pre><code>print(ohlc)
                       Open  High  Low  Close
  2017-07-23 03:13:00   1.0   5.0  1.0    5.0
  2017-07-23 03:14:00   NaN   NaN  NaN    NaN
  2017-07-23 03:15:00   5.0   5.0  2.0    2.0
  2017-07-23 03:16:00   NaN   NaN  NaN    NaN
  </code></pre>
  
  <p>I want to go from that last DataFrame to something like this:</p>
  
  <pre><code>                     Open  High  Low  Close
  2017-07-23 03:13:00   1.0   5.0  1.0    5.0
  2017-07-23 03:14:00   5.0   5.0  5.0    5.0
  2017-07-23 03:15:00   5.0   5.0  2.0    2.0
  2017-07-23 03:16:00   2.0   2.0  2.0    2.0
  </code></pre>
  
  <p>So that the previously-seen value in 'Close' forward fills entire rows until there's a new populated row seen. It's simple enough to fill column 'Close' like so: </p>
  
  <pre><code>column2fill = 'Close'
  ohlc[column2fill] = ohlc[column2fill].ffill()
  print(ohlc)
                       Open  High  Low  Close
  2017-07-23 03:13:00   1.0   5.0  1.0    5.0
  2017-07-23 03:14:00   NaN   NaN  NaN    5.0
  2017-07-23 03:15:00   5.0   5.0  2.0    2.0
  2017-07-23 03:16:00   NaN   NaN  NaN    2.0
  </code></pre>
  
  <p>But is there a way to fill across the  03:14:00 and 03:16:00 rows with the 'Close' value of those rows? And is there a way to do it in one step using one forward fill instead of filling the 'Close' column first?</p>