Как я могу вызвать значения из pandas dataframe в функцию?

Question

У меня есть большой кадр данных с двумя столбцами и функцией, которая принимает значения из каждой строки и выполняет итерацию по кадру данных.Ниже заголовок кадра данных.

xG_Team1  xG_Team2
0  1.440539  1.380095
1  2.123673  0.946116
2  1.819697  0.921660
3  1.132676  1.375717
4  1.244837  1.269933

x1, x2, x3 are constants.
    x1 = [1,0,0] 
    x2 = [0,1,0] 
    x3 = [0,0,1] 

For index 0, 
y  = np.array([1-(xG_Team1[0] + xG_Team2[0])/k, xG_Team1[0]/k, xG_Team2[0]/k])
i.e.   y  = np.array([1-(1.440539 + 1.380095)/k, 1.440539/k, 1.380095/k])


For index 1, 
        y  = np.array([1-(xG_Team1[1] + xG_Team2[1])/k, xG_Team1[1]/k, xG_Team2[1]/k])

Где k - это total_timeslot и константа.

total_timeslot = 180 
Home_Goal = [] # No Goal
Away_Goal = [] # No Goal
    def sum_squared_diff(x1, x2, x3, y):
        ssd=[]
        for k in range(total_timeslot):
            if k in Home_Goal:
                ssd.append( sum((x2 - y)**2))
            elif k in Away_Goal:
                ssd.append(sum((x3 - y)**2))
            else:
                ssd.append(sum((x1 - y)**2))
        return ssd

y_0 =  sum_squared_diff(x1, x2, x3, y)

План состоит в суммировании выходных данных с sum_squared_diff для всехЧто-то вроде for all i sum(y_i).

So for i = 0,
    y_0 =  sum_squared_diff(x1, x2, x3, y_0)
    len(y_0) = 180
    sum(y_0) = 0.0663099498972334
Then I will have n numbers of sum(y_i) for n xGs.
using @Dillon code, for the above datframe, n=5
sum(results.sum()) = 0.31885730707076826

Answer 1

data = {'xG_Team1': {0: 1.440539, 1: 2.123673, 2: 1.819697, 3: 1.132676, 4: 1.244837},
 'xG_Team2': {0: 1.380095, 1: 0.946116, 2: 0.92166, 3: 1.375717, 4: 1.269933}}

df = pd.DataFrame(data)

x1 = [1,0,0] 
x2 = [0,1,0] 
x3 = [0,0,1]

# Constants
total_timeslot = 180
k = 180

# Measures
Home_Goal = [] # No Goal
Away_Goal = [] # No Goal

def sum_squared_diff(x1, x2, x3, y):
    ssd = []
    for k in range(total_timeslot):  # k will take multiple values
        if k in Home_Goal:
            ssd.append(sum((x2 - y) ** 2))
        elif k in Away_Goal:
            ssd.append(sum((x3 - y) ** 2))
        else:
            ssd.append(sum((x1 - y) ** 2))
    return ssd

def my_function(row):
    xG_Team1 = row.xG_Team1
    xG_Team2 = row.xG_Team2
    return np.array([1-(xG_Team1 + xG_Team2)/k, xG_Team1/k, xG_Team2/k])

# You can use the apply function
results = df.apply(lambda row: sum_squared_diff(x1, x2, x3, my_function(row)), axis=1)

# Each item in results is a 180 item list
results
Out[]: 
0    [0.0003683886105401867, 0.0003683886105401867,...
1    [0.0004576767592872215, 0.0004576767592872215,...
2    [0.00036036396694006056, 0.0003603639669400605...
3    [0.00029220949467635905, 0.0002922094946763590...
4    [0.00029279065228265494, 0.0002927906522826549...

# For each list, calculate the sum
results.map(lambda x: sum(x))
Out[]: 
0    0.066310
1    0.082382
2    0.064866
3    0.052598
4    0.052702

# Get the sum of all these values
results.map(lambda x: sum(x)).sum()
Out[]: 
0.3188573070707662