Pandas update multiple columns at once

Question

I\'m trying to update a couple fields at once - I have two data sources and I\'m trying to reconcile them. I know I could do some ugly merging and then delete columns, but was

Answer 1

In the "take the hill" spirit, I offer the below solution which yields the requested result.

I realize this is not exactly what you are after as I am not slicing the df (in the reasonable - but non functional - way in which you propose).

#Does not work when indexing on np.nan, so I fill with some arbitrary value. 
df = df.fillna('AAA')

#mask to determine which rows to update
mask = df['Col1'] == 'AAA'

#dict with key value pairs for columns to be updated
mp = {'Col1':'col1_v2','Col2':'col2_v2','Col3':'col3_v2'}

#update
for k in mp: 
     df.loc[mask,k] = df[mp.get(k)]

#swap back np.nans for the arbitrary values
df = df.replace('AAA',np.nan)

Output:

Col1    Col2    Col3    col1_v2     col2_v2     col3_v2
A       B       C       NaN         NaN         NaN
D       E       F       NaN         NaN         NaN
a       b       d       a           b           d
d       e       f       d           e           f

The error I get if I do not replace nans is below. I'm going to research exactly where that error stems from.

ValueError: array is not broadcastable to correct shape

Answer 2

you want to replace

print df.loc[df['Col1'].isnull(),['Col1','Col2', 'Col3']]

  Col1 Col2 Col3
2  NaN  NaN  NaN
3  NaN  NaN  NaN

With:

replace_with_this = df.loc[df['Col1'].isnull(),['col1_v2','col2_v2', 'col3_v2']]
print replace_with_this

  col1_v2 col2_v2 col3_v2
2       a       b       d
3       d       e       f

Seems reasonable. However, when you do the assignment, you need to account for index alignment, which includes columns.

So, this should work:

df.loc[df['Col1'].isnull(),['Col1','Col2', 'Col3']] = replace_with_this.values

print df

  Col1 Col2 Col3 col1_v2 col2_v2 col3_v2
0    A    B    C     NaN     NaN     NaN
1    D    E    F     NaN     NaN     NaN
2    a    b    d       a       b       d
3    d    e    f       d       e       f

I accounted for columns by using .values at the end. This stripped the column information from the replace_with_this dataframe and just used the values in the appropriate positions.