New to pandas development. How do I forward fill a DataFrame with the value contained in one previously seen column?

Self-contained example:

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']]

This yields the following DataFrame:

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

I want to go from that last DataFrame to something like this:

                     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

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:

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

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?

It seems you need assign with ffill and then bfill per row by axis=1, but necessary full NaNs rows:

df = ohlc.assign(Close=ohlc['Close'].ffill()).bfill(axis=1)
print (df)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

What is same as:

ohlc['Close'] = ohlc['Close'].ffill()
df = ohlc.bfill(axis=1)
print (df)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