如何找到嵌入在 Pandas 数据框列中的元素列表的平均值

Question

I'm the process of cleaning a data frame, and one particular column contains values that are comprised of lists. I'm trying to find the average of those lists and update the existing column with an int while preserving the indices. I can successfully and efficiently convert those values to a list, but I lose the index values in the process. The code I've written below is too memory-tasking to execute. Is there a simpler code that would work?

data: https://docs.google.com/spreadsheets/d/1Od7AhXn9OwLO-SryT--erqOQl_NNAGNuY4QPSJBbI18/edit?usp=sharing

def Average(lst):
    sum1 = 0
    average = 0
    if len(x) == 1:
        for obj in x:
            sum1 = int(obj)

    if len(x)>1:
        for year in x:
            sum1 += int(year)
        average = sum1/len(x)

    return mean(average) 

hello = hello[hello.apply([lambda x: mean(x) for x in hello])]

Here's the loop I used to convert the values into a list:

df_list1 = []

for x in hello:
        sum1 = 0
        average = 0
        if len(x) == 1:
            for obj in x:
                df_list1.append(int(obj))

        if len(x)>1:
            for year in x:
                sum1 += int(year)
                average = sum1/len(x)
            df_list1.append(int(average))

Answer 1

Use apply and np.mean .

import numpy as np

df = pd.DataFrame(data={'listcol': [np.random.randint(1, 10, 5) for _ in range(3)]}, index=['a', 'b', 'c'])

# np.mean will return NaN on empty list
df['listcol'] = df['listcol'].fillna([])

# can use this if all elements in lists are numeric
df['listcol'] = df['listcol'].apply(lambda x: np.mean(x))

# use this instead if list has numbers stored as strings
df['listcol'] = df['listcol'].apply(lambda x: np.mean([int(i) for i in x])) 

Output

>>>df
   listcol
a      5.0
b      5.2
c      4.4