如何从两个不同的pandas dataframe列中添加值

Question

How to add first rows from two different columns of a dataframe together, such that if column A's first row is NaN , replace with value from balance 's first row, otherwise, add column A's first row and balance 's first row. And so likewise column B. The idea is using balnce first row to transform first row of other columns' A and B. I attempted using df.iloc[0] to get the first row but I'm unable to set the value or add it:

data = {'id': [1, 2, 3, 4, 5, 6],
        'A': [None, None, 20, 10, 39, 30],
        'B': [13, 98, 23, 45, 64, 10],
        'balance': [23, 41, 12, 22, 32, 0]}

df = pd.DataFrame(data)
df = df.set_index('id')
print df

    A   B    balance
id                 
1  NaN  13   23
2  NaN  98   41
3   20  23   12
4   10  45   22
5   39  64   32
6   30  10   0

for i in df.columns:
    if i not in ['balance']:
        if df[i].iloc[0] == None:
            df[i].iloc[0] = df['balance'].iloc[0]
        else:
            df[i].iloc[0] = df[i].iloc[0] + df['balance'].iloc[0]

        print df[i]


id
1   NaN
2   NaN
3    20
4    10
5    39
6    30
Name: A, dtype: float64


id
1    36
2    98
3    23
4    45
5    64
6    10
Name: B, dtype: int64
#A[0] should be 23, and B[0] should be 13 + 23 = 36

desired output:

id  A   B     balance               
1   23  36       23
2  NaN  98       41
3   20  23       12
4   10  45       22
5   39  64       32
6   30  10        0

Answer 1

IIUC you need combine_first or fillna if need replace NaN by values of other column:

print (df.A.combine_first(df.B))
id
1    13.0
2    98.0
3    20.0
4    10.0
5    39.0
6    30.0
Name: A, dtype: float64

Or:

print (df.A.fillna(df.B))
id
1    13.0
2    98.0
3    20.0
4    10.0
5    39.0
6    30.0
Name: A, dtype: float64


print (df.A.combine_first(df.B) + df.B)
id
1     26.0
2    196.0
3     43.0
4     55.0
5    103.0
6     40.0
dtype: float64

If need sum two columns with replacing NaN to 0 use add with parameter fill_value :

print (df.A.add(df.B, fill_value=0))
id
1     13.0
2     98.0
3     43.0
4     55.0
5    103.0
6     40.0
dtype: float64

EDIT:

You need:

df.ix[1,'A'] = df.ix[1,'balance']
print (df)
       A   B  balance
id                   
1   23.0  13       23
2    NaN  98       41
3   20.0  23       12
4   10.0  45       22
5   39.0  64       32
6   30.0  10        0

EDIT1:

df.ix[1,'A'] = df.ix[1,'balance']
df.ix[1,'B'] = df.ix[1,'B'] + df.ix[1,'balance']

print (df)
       A   B  balance
id                   
1   23.0  36       23
2    NaN  98       41
3   20.0  23       12
4   10.0  45       22
5   39.0  64       32
6   30.0  10        0