用来自另一个数据框中的字符串匹配的平均值列向pandas数据框附加

Question

Right now I have two dataframes (let's call them A and B) built from Excel imports. Both have different dimensions as well as some empty/NaN cells. Let's say A is data for individual model numbers and B is a set of order information. For every row (unique item) in A, I want to search B for the (possibly) multiple orders for that item number, average the corresponding prices, and append A with a column containing the average price for each item.

The item numbers are alphanumeric so they have to be strings. Not every item will have orders/pricing information for it and I'll be removing those at the next step. This is a large amount of data so efficiency is ideal so iterrows probably isn't the right choice. Thank you in advance!

Here's what I have so far:

avgPrice = []
for index, row in dfA.iterrows():
    def avg_unit_price(item_no, unit_price):
        matchingOrders = []
        for item, price in zip(item_no, unit_price):
            if item == row['itemNumber']:
                matchingOrders.append(price)
        avgPrice.append(np.mean(matchingOrders))  
    avg_unit_price(dfB['item_no'], dfB['unit_price'])
dfA['avgPrice'] = avgPrice

Answer 1

In general, avoid loops as they perform poorly. If you can't vectorise easily, then as a last resort you can try pd.Series.apply. In this case, neither were necessary.

import pandas as pd

# B: pricing data
df_b = pd.DataFrame([['I1', 34.1], ['I2', 541.31], ['I3', 451.3], ['I2', 644.3], ['I3', 453.2]],
                    columns=['item_no', 'unit_price'])

# create avg price dictionary
item_avg_price = df_b.groupby('item_no', as_index=False).mean().set_index('item_no')['unit_price'].to_dict()

# A: product data
df_a = pd.DataFrame([['I1'], ['I2'], ['I3'], ['I4']], columns=['item_no'])

# map price info to product data
df_a['avgPrice'] = df_a['item_no'].map(item_avg_price)

# remove unmapped items
df_a = df_a[pd.notnull(df_a['avgPrice'])]