Pandas 根据条件查找列平均值

Question

Dataframe has two columns item-1 and item-2, I want to find average of Buy and Sell columns for all the rows where item-1 value is in item-2 and item-2 value is in item-1

Here the value of first row in item-1 is A, item-2 has value A at index 2,4 and 6, so the avg-item-1-buy should be average of buy value at those indexes

similarly the value of first row in item-2 is C, item-1 has value C at index 2 and 5, so the avg-item-2-buy should be average of buy value at those indexes

Expected Result:

Answer 1

I don't think this is the best answer but this surely will do it (i have try it).

First we calculate the average and store to the dictionary. I made 4 dictionary for each desired columns.

def count_avg(col_1, col_2, target_col):
    col_avg= {}
    for i in df[col_1].unique():
        col_avg[i]= df[df[col_2]==i][target_col].mean()

    return col_avg


item1_avg_buy_dict= count_avg('Item-1', 'Item-2', 'buy')
item1_avg_sell_dict= count_avg('Item-1', 'Item-2', 'sell')

item2_avg_buy_dict= count_avg('Item-2', 'Item-1', 'buy')
item2_avg_sell_dict= count_avg('Item-2', 'Item-1', 'sell')

And then we create a function to pair x with the dictionary we just created.

def get_avg(x, dict_):
    try:
        res= dict_[x]
    except:
        res= None

    return res

And then we can apply each new columns for that function by .map()

df['avg-item1-buy']= df['Item-1'].map(lambda x: get_avg(x, item1_avg_buy_dict))
df['avg-item1-sell']= df['Item-1'].map(lambda x: get_avg(x, item1_avg_sell_dict))
df['avg-item2-buy']= df['Item-2'].map(lambda x: get_avg(x, item2_avg_buy_dict))
df['avg-item2-sell']= df['Item-2'].map(lambda x: get_avg(x, item2_avg_sell_dict))