Guided Project: Exploring Ebay Car Sales Data step 7, combining two displays

volkswagen       21.091133
bmw              11.118456
opel             10.880487
mercedes_benz     9.371144
audi              8.798255
ford              6.998061
renault           4.814472
peugeot           3.067160
fiat              2.564781
seat              1.877314
Name: brand, dtype: float64
brands_10 = ['volkswagen', 'bmw', 'opel', 'mercedes_benz', 'audi', 'ford', 'renault', 'peugeot', 'fiat', 'seat'] 

avg_cost_10brands = {}

for each_brand in brands_10:
    brand_price = autos.loc[autos["brand"] == each_brand, ["brand", "dollar_price"]]
    avg_cost_10brands[each_brand] = round(brand_price["dollar_price"].mean())

{'volkswagen': 5399,
 'bmw': 8361,
 'opel': 2954,
 'mercedes_benz': 8582,
 'audi': 9420,
 'ford': 3448,
 'renault': 2420,
 'peugeot': 3088,
 'fiat': 2705,
 'seat': 4402}

so i have a “percent of sales per brand” and an “average sale value per brand” i want to make one dictionary to display both. not sure how to go about it.

altered first cell

brand_sales = autos['brand'].value_counts(normalize=True).mul(100).head(10)

and shortened the code to one line and started trying pd.concat and pd.append. they didn’t work how i’ve used them.

brands_10 = ['volkswagen', 'bmw', 'opel', 'mercedes_benz', 'audi', 'ford', 'renault', 'peugeot', 'fiat', 'seat'] 

avg_cost_10brands = {}

for each_brand in brands_10:
    avg_cost_10brands[each_brand] = round(autos.loc[autos["brand"] == each_brand, ["brand", "dollar_price"]]["dollar_price"].mean())

# avg_cost_10brands
pd.append([brand_sales, avg_cost_10brands])