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)

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))

CodePudding user response：

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)

CodePudding user response：

The code you've written should work fine (just write df instead of df1, which is undefined):

df["Euclidean Distance from ideal best"] = np.sqrt(
    (df["Battery(mAh)"] - df["Battery(mAh)"].max()).pow(2)
      (df1["RAM(GB)"] - df["RAM(GB)"].max()).pow(2)
      (df1["Storage(GB)"] - df["Storage(GB)"].max()).pow(2)
)

you'll get

>>> df
      Mobile  ...  Euclidean Distance from ideal best
0   Sansung   ...                         7000.000357
1    iPhone   ...                            0.000000
2  Motorola   ...                         5000.001300

which matches your expected output

For the other column:

df["Euclidean Distance from ideal worst"] = np.sqrt(
    (df["Battery(mAh)"] - df["Battery(mAh)"].min()).pow(2)
      (df1["RAM(GB)"] - df["RAM(GB)"].min()).pow(2)
      (df1["Storage(GB)"] - df["Storage(GB)"].min()).pow(2)
)