Euclidean Distance from Ideal best and from Ideal Worst

Let’s consider this table below:

Formula for ED from Ideal best = √(( Battery(mAh)- Max of Battery(mAh))^2 + (RAM(GB) – Max of RAM(GB) )^2 + ( Storage(GB) – Max of Storage(GB))^2)

Formula for ED from Ideal Worst = √(( Battery(mAh)- Min of Battery(mAh))^2 + (RAM(GB) – Minof RAM(GB) )^2 + ( Storage(GB) – Min of Storage(GB))^2)

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For instance, value for Samsung mobile should be √((1000-8000)^2+(4-6)^2+(2-3)^2) = 7000.00035714

Can Anyone help me to write a function in Python where these two formulas can apply for each rows for ED from Ideal best and ED from Ideal worst?

So far I’ve tried this:

df["Euclidean Distance from ideal best"]= np.sqrt((df["Battery(mAh)"]-df["Battery(mAh)"].max()).pow(2) + (df["RAM(GB)"]-df["RAM(GB)"].max()).pow(2) + (df["Storage(GB)"]-df["Storage(GB)"].max()).pow(2))

>Solution :

It works fine by me. Just change the typo df1->df:

df["Euclidean Distance from ideal best"] = np.sqrt(
    (df["Battery(mAh)"] - df["Battery(mAh)"].max())**2 + 
    (df["RAM(GB)"]-df["RAM(GB)"].max())**2 + 
    (df["Storage(GB)"]-df["Storage(GB)"].max())**2)