flyingmeatball - 1 month ago 19

Python 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 expecting this code below to work:

`df = pd.DataFrame([['A','B','C',np.nan,np.nan,np.nan],`

['D','E','F',np.nan,np.nan,np.nan],[np.nan,np.nan,np.nan,'a','b','d'],

[np.nan,np.nan,np.nan,'d','e','f']], columns = ['Col1','Col2','Col3','col1_v2','col2_v2','col3_v2'])

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 NaN NaN NaN a b d

3 NaN NaN NaN d e f

#update

df.loc[df['Col1'].isnull(),['Col1','Col2', 'Col3']] = df[['col1_v2','col2_v2','col3_v2']]

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 NaN NaN NaN a b d

3 NaN NaN NaN d e f

My desired output would be:

`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 c a b d

3 d e f d e f

I'm betting it has to do with updating/setting on a slice, but I always use .loc to update values, just not on multiple columns at once.

I feel like there's an easy way to do this that I'm just missing, any thoughts/suggestions would be welcome!

Thanks for the comment on the indexes. However, I have a question about this as it relates to series. If I wanted to update an individual series in a similar manner, I could do something like this:

`df.loc[df['Col1'].isnull(),['Col1']] = df['col1_v2']`

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 NaN NaN a b d

3 d NaN NaN d e f

Note that I didn't account for the indexes here, I filtered to a 2x1 series and set that equal to a 4x1 series, yet it handled it correctly. Thoughts? I'm trying to understand the functionality a bit better of something I've used for a while, but I guess don't have a full grasp of the underlying mechanism/rule

Answer

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.