Multiply rows in dataframe, then sum them together Python

Question

I have a function to apply to this table

F(x) = 1.5*x1 + 2*x2 - 1.5*x3

Where xi, i = 1,2,3, is the column value.

And I have the following table below.

X1    | X2   | X3
------|------|------
20    |15    |12    
30    |17    |24    
40    |23    |36

The desired output that I would like is the following below, where I apply the function to each row, taking the value in each column and applying it to the function iteratively then receiving value as a sum and another column appended onto the dataframe.

X1    | X2   | X3   |F(X)
------|------|------|------
20    |15    |12    |42
30    |17    |24    |43
40    |23    |36    |52

Is there a way to do this in Python 2.7?

BENY · Accepted Answer · 2017-10-31 01:33:43Z

6

Something like this ?

df['F(x)']=df.mul([1.5,2,-1.5]).sum(1)
df
Out[1076]: 
   X1  X2  X3  F(x)
0  20  15  12  42.0
1  30  17  24  43.0
2  40  23  36  52.0

answered Oct 31, 2017 at 1:33

BENY

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2 Comments

Leggerless Over a year ago

Welp, that's much shorter than my own answer to this question. Works well too.

s3bw Over a year ago

Don't forget to approve the answer, don't want to leave mighty @Wen high and dry.

Leggerless · Accepted Answer · 2017-10-31 01:37:27Z

-1

Ok. Found a sample code to solve my problem.

var1 = 1.5
var2 = 2
var3 = -1.5
def calculate_fx(row):
return (var1 * row['X1']) + (var2 * row['X2']) + (var3 * row['X3'])

#function_df is the predefined dataframe
function_df['F(X)'] = function_df.apply(calculate_fx, axis=1)
function_df

edited Oct 31, 2017 at 1:37

answered Oct 31, 2017 at 1:29

Leggerless

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