Add two elements in a dataframe (based on the index) - pandas

Question

I have a dataframe in which some rows are useless except for one variable.
I want to add that the variable in those rows to the previous row and then delete the useless rows.
In the data frame there are some rows in which the only useful information is on a variable, so I want to preserve this information.
More precisely, my dataframe looks something like
|cat1| cat2|var1|var2|
|A |x |1 |2 |
|A |x |1 |0 |
|A |x |. |5 |
|A |y |1 |2 |
|A |y |1 |2 |
|A |y |1 |3 |
|A |y |. |6 |
|B |x |1 |2 |
|B |x |1 |4 |
|B |x |1 |2 |
|B |x |1 |1 |
|B |x |. |3 |
and i want to get
|cat1| cat2|var1|var2|
|A |x |1 |2 |
|A |x |1 |5(5+0)|
|A |y |1 |2 |
|A |y |1 |2 |
|A |y |1 |9(6+3)|
|B |x |1 |2 |
|B |x |1 |4 |
|B |x |1 |2 |
|B |x |1 |4(3+1)|
iI've tried code like
test = df[df['var1'] == '.'].index
for num in test:
df['var2][num - 1] = df['var2][num - 1] + df['var2][num]
but it doesn't work.
Any help would be appreciated.

Answer 1

For a very readable solution combine np.where to select the rows where the shifted rows of var1 contain .. Use the -1 to select the next row. If that's the case add the next row, otherwise just fill the original row. Afterwards, just drop all the rows with a .
df['var2_new'] = np.where(df['var1'].shift(-1) == '.',
df['var2'] + df['var2'].shift(-1), df['var2'])
df[df['var1'] != '.']
# cat1 cat2 var1 var2 var2_new
#0 A x 1 2 2.0
#1 A x 1 0 5.0
#3 A y 1 2 2.0
#4 A y 1 2 2.0
#5 A y 1 3 9.0
#7 B x 1 2 2.0
#8 B x 1 4 4.0
#9 B x 1 2 2.0
#10 B x 1 1 4.0