I have two dataframes with only somewhat overlapping indices and columns.
old = pd.DataFrame(index = ['A', 'B', 'C'],
columns = ['k', 'l', 'm'],
data = abs(np.floor(np.random.rand(3, 3)*10)))
new = pd.DataFrame(index = ['A', 'B', 'C', 'D'],
columns = ['k', 'l', 'm', 'n'],
data = abs(np.floor(np.random.rand(4, 4)*10)))
I want to calculate the difference between them and tried
delta = new - old
This gives lots of NaNs where indices and columns do not match. I would like to treat the abscence of the indices and columns as zeroes, (old['n', 'D'] = 0). old will always be a subspace of new.
Any ideas?
EDIT: I guess I didn't explain it thoroughly enough. I don't want to fill the delta dataframe with zeroes. I want to treat missing indices and columns in old as if they were zeroes. I would then get the value in new['n', 'D'] in delta instead of a NaN.
Use sub
with fill_value=0
:
In [15]:
old = pd.DataFrame(index = ['A', 'B', 'C'],
columns = ['k', 'l', 'm'],
data = abs(np.floor(np.random.rand(3, 3)*10)))
new = pd.DataFrame(index = ['A', 'B', 'C', 'D'],
columns = ['k', 'l', 'm', 'n'],
data = abs(np.floor(np.random.rand(4, 4)*10)))
delta = new.sub(old, fill_value=0)
delta
Out[15]:
k l m n
A 0 3 -9 7
B 0 -2 1 8
C -4 1 1 7
D 8 6 0 6