The essential idea here is to select the data you want to sum, and then sum them. This selection of data can be done in several different ways, a few of which are shown below.
Arguably the most common way to select the values is to use Boolean indexing.
With this method, you find out where column 'a' is equal to 1 and then sum the corresponding rows of column 'b'. You can use loc to handle the indexing of rows and columns:
>>> df.loc[df['a'] == 1, 'b'].sum()
15
The Boolean indexing can be extended to other columns. For example if df also contained a column 'c' and we wanted to sum the rows in 'b' where 'a' was 1 and 'c' was 2, we'd write:
df.loc[(df['a'] == 1) & (df['c'] == 2), 'b'].sum()
Another way to select the data is to use query to filter the rows you're interested in, select column 'b' and then sum:
>>> df.query("a == 1")['b'].sum()
15
Again, the method can be extended to make more complicated selections of the data:
df.query("a == 1 and c == 2")['b'].sum()
Note this is a little more concise than the Boolean indexing approach.
The alternative approach is to use groupby to split the DataFrame into parts according to the value in column 'a'. You can then sum each part and pull out the value that the 1s added up to:
>>> df.groupby('a')['b'].sum()[1]
15
This approach is likely to be slower than using Boolean indexing, but it is useful if you want check the sums for other values in column a:
>>> df.groupby('a')['b'].sum()
a
1 15
2 8