Updating NaN's in a dataframe from a prior specific column

#!/usr/bin/env python3

from pprint import pprint
import numpy as np
import pandas as pd

A = [3,42,4,90,22,5,np.nan,np.nan,2,5,2,np.nan,23,61,32,24]
B = [6,44,2,83,23,2,np.nan,np.nan,12,7,13,np.nan,np.nan,np.nan,43,2]
C = [7,1,3,53,24,5,np.nan,np.nan,65,32,6,np.nan,23,63,12,34]
D = [4,3,62,23,np.nan,34,np.nan,np.nan,1,7,12,np.nan,34,3,76,2]

df = pd.DataFrame({ 'A':A, 'B':B, 'C':C, 'D':D })

pprint(df)

# Output 
#        A     B     C     D
# 0    3.0   6.0   7.0   4.0
# 1   42.0  44.0   1.0   3.0
# 2    4.0   2.0   3.0  62.0
# 3   90.0  83.0  53.0  23.0
# 4   22.0  23.0  24.0   NaN
# 5    5.0   2.0   5.0  34.0
# 6    NaN   NaN   NaN   NaN
# 7    NaN   NaN   NaN   NaN
# 8    2.0  12.0  65.0   1.0
# 9    5.0   7.0  32.0   7.0
# 10   2.0  13.0   6.0  12.0
# 11   NaN   NaN   NaN   NaN
# 12  23.0   NaN  23.0  34.0
# 13  61.0   NaN  63.0   3.0
# 14  32.0  43.0  12.0  76.0
# 15  24.0   2.0  34.0   2.0

def fix_abc(row, column, df):

# If the row/column value is null/nan 
	if pd.isnull( row[column] ):

# Get the value of row[column] from the row before
		prior = row.name
		value = df[prior-1:prior]['B'].values[0]

# If that values empty, go to the row before that
		while pd.isnull( value ) and prior >= 1 :
			prior = prior - 1
			value = df[prior-1:prior]['B'].values[0]

else:
		value = row[column]

return value

df['A'] = df.apply( lambda x: fix_abc(x,'A',df), axis=1 )
df['B'] = df.apply( lambda x: fix_abc(x,'B',df), axis=1 )
df['C'] = df.apply( lambda x: fix_abc(x,'C',df), axis=1 )

def fix_d(x):
	if pd.isnull(x['D']):
		return 0
	return x

df['D'] = df.apply( lambda x: fix_d(x), axis=1 )

pprint(df)

#        A     B     C     D
# 0    3.0   6.0   7.0   3.0
# 1   42.0  44.0   1.0  42.0
# 2    4.0   2.0   3.0   4.0
# 3   90.0  83.0  53.0  90.0
# 4   22.0  23.0  24.0   0.0
# 5    5.0   2.0   5.0   5.0
# 6    2.0   2.0   2.0   0.0
# 7    2.0   2.0   2.0   0.0
# 8    2.0  12.0  65.0   2.0
# 9    5.0   7.0  32.0   5.0
# 10   2.0  13.0   6.0   2.0
# 11  13.0  13.0  13.0   0.0
# 12  23.0  13.0  23.0  23.0
# 13  61.0  13.0  63.0  61.0
# 14  32.0  43.0  12.0  32.0
# 15  24.0   2.0  34.0  24.0

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