Math over list values of a dictionary with unlimited items

How do I loop through all keys list values one by one until I reach the end of the last list's array value? All lists are the same length for each key.
I mean:

 my_dict1 = {'c1': [10, 11, 12], 'c2': [100, 110, 120], 'c3': [200, 210, 220]}
 my_dict2 = {'c1': 3, 'c2': 1, 'c3': 2}

The result I need to get is:

result = 0.5 * ([10 * 3 + 100 * 1 + 200 * 2] + [11 * 3 + 110 * 1 + 210 * 2] + [12 * 3 + 120 * 1 + 220 * 2])

I checked https://stackoverflow.com/questions/46550213/multiplying-the-values-of-dictionaries-with-different-keys and https://stackoverflow.com/questions/53078068/how-to-multiply-list-elements-in-dictionary but they did not come in handy here.

The following just worked when two dictionary have the similar keys.

dict1 = {2: [10, 11, 12], 2: [100, 110, 120]}
dict2 = {2: [100, 110, 120], 2: [100, 110, 120]}
result = {i :[x*y for x, y in zip(dict1[i], dict2[i])] for i in dict1.keys()}
print(result)

Result:

{2: [10000, 12100, 14400]}

Should I work by the NumPy or Pandas to handle it? In my real job there is a dictionary or a data frame with unknown number of keys.

One way just using zip:

arr = []
for k, v in my_dict1.items():
    c = my_dict2[k]
    arr.append([i * c for i in v])
    
out = [sum(a)/2 for a in zip(*arr)]

Or using pandas:

out = pd.DataFrame(my_dict1).mul(my_dict2).sum(axis=1).mul(0.5)

Output:

[265.0, 281.5, 298.0]

If you just want to do the math...

my_dict1 = {'c1': [10, 11, 12], 'c2': [100, 110, 120], 'c3': [200, 210, 220]}
my_dict2 = {'c1': 3, 'c2': 1, 'c3': 2}

result=0
for i in my_dict1:
    for j in range(len(my_dict1[i])):
        result+=my_dict1[i][j]*my_dict2[i]
result/=2
print(result)

You must use the key from my_dict1 to find the multiplier in my dict2. From there on, you can just zip the lists and sum their items. It can be a plain Python inliner:

[sum(i)/2 for i in zip(*([i * my_dict2[k] for i in v] 
                         for k, v in my_dict1.items()))]

Above gives as expected:

[265.0, 281.5, 298.0]

You could also build 2 numpy arrays for the same operation:

arr1 = np.array(list(my_dict1.values()))
arr2 = np.array([my_dict2[k] for k in my_dict1])

Then:

np.sum(np.transpose(arr1) * arr2, axis=1)/2

gives as expected

array([265. , 281.5, 298. ])

It seems here using the Dataframe, Numpy and just python this question can be solved.
As Ballesta have replied based on Python:

[sum(i)/2 for i in zip(*([i * my_dict2[k] for i in v] 
                         for k, v in my_dict1.items()))]

Using Ballesta answer and Numpy:

arr1 = np.array(list(my_dict1.values()))
arr2 = np.array([my_dict2[k] for k in my_dict1])     
np.sum(np.transpose(arr1) * arr2, axis=1)/2

Or using pandas as Chris has answerd:

 out = pd.DataFrame(my_dict1).mul(my_dict2).sum(axis=1).mul(0.5)

A simpler way using the DataFrame could be:

import pandas as pd

df = pd.DataFrame({'c1': [10, 11, 12], 'c2': [100, 110, 120]})
df = df.reset_index()  # make sure indexes pair with number of rows

for index, row in df.iterrows():
    print((row['c1'] + row['c2'] )/ 2)