The nature of wanting to include the row where A == 5 and all rows upto but not including the row where A == 8 means we will end up using iloc (loc includes both ends of slice).
In order to get the index labels we use idxmax. This will return the first position of the maximum value. I run this on a boolean series where A == 5 (then when A == 8) which returns the index value of when A == 5 first happens (same thing for A == 8).
Then I use searchsorted to find the ordinal position of where the index label (that I found above) occurs. This is what I use in iloc.
i5, i8 = df.index.searchsorted([df.A.eq(5).idxmax(), df.A.eq(8).idxmax()])
df.iloc[i5:i8]
numpy
you can further enhance this by using the underlying numpy objects the analogous numpy functions. I wrapped it up into a handy function.
def find_between(df, col, v1, v2):
vals = df[col].values
mx1, mx2 = (vals == v1).argmax(), (vals == v2).argmax()
idx = df.index.values
i1, i2 = idx.searchsorted([mx1, mx2])
return df.iloc[i1:i2]
find_between(df, 'A', 5, 8)
timing
