python - Divide certain columns by another column in pandas - Stack Overflow

[kemistep]

For performance, I would suggest using the underlying array data and array-slicing as the two columns to be modified come in sequence to use view into it -

a = df.values
df.iloc[:,1:3] = a[:,1:3]/a[:,0,None] 

To eloborate a bit more on the array-slicing part, with a[:,[1,2]] would have forced a copy there and would have slowed it down. a[:,[1,2]] on the dataframe side is equivalent to df[['open','close']] and that I am guessing is slowing things down too. df.iloc[:,1:3] is thus improving upon it.

Sample run -

In [64]: df
Out[64]: 
    prev   open  close  volume
0  20.77  20.87  19.87  962816
1  19.87  19.89  19.56  668076
2  19.56  19.96  20.10  578987
3  20.10  20.40  20.53  418597

In [65]: a = df.values
    ...: df.iloc[:,1:3] = a[:,1:3]/a[:,0,None]
    ...: 

In [66]: df
Out[66]: 
    prev      open     close  volume
0  20.77  1.004815  0.956668  962816
1  19.87  1.001007  0.984399  668076
2  19.56  1.020450  1.027607  578987
3  20.10  1.014925  1.021393  418597 

Runtime test

Approaches -

def numpy_app(df): 
    a = df.values
    df.iloc[:,1:3] = a[:,1:3]/a[:,0,None]
    return df

def pandas_app1(df): 
    df[['open','close']] = df[['open','close']].div(df['prev'].values,axis=0)
    return df 

Timings -

In [44]: data = np.random.randint(15, 25, (100000,4)).astype(float)
    ...: df1 = pd.DataFrame(data, columns=(('prev','open','close','volume')))
    ...: df2 = df1.copy()
    ...: 

In [45]: %timeit pandas_app1(df1)
    ...: %timeit numpy_app(df2)
    ...: 
100 loops, best of 3: 2.68 ms per loop
1000 loops, best of 3: 885 µs per loop 

https://stackoverflow.com/questions/45899613/divide-certain-columns-by-another-column-in-pandas