我有一個這樣的數據框 -我想要表格中這樣的列-所以決賽桌是這樣的——我如何計算這些列。我在 django rest API 中有當前代碼-@api_view(['GET','POST'])def sale_prod(request): if request.method == 'GET': data = sales_products.objects.values() df = pd.DataFrame(data) df = df.groupby(['item_id','item_code'])['amount','quantity'].sum().reset_index() df.dropna(inplace=True) df['amount_per'] = (df.amount / df.amount.sum())*100 # revenue contribution df['quantity_per'] = (df.quantity / df.quantity.sum())*100 # unit sold contribution df = df.round({'quantity': 0, 'amount':2, 'amount_per':2, 'quantity_per':2}) main_list = [] for ind in df.index: dict1 = {} dict1['item_code'] = df['item_code'][ind] dict1['amount'] = df['amount'][ind] dict1['quantity'] = df['quantity'][ind] dict1['amount_per'] = df['amount_per'][ind] dict1['quantity_per'] = df['quantity_per'][ind] main_list.append(dict1) return Response(main_list)這段代碼以數據框的形式給了我輸出 -amount_per = 按金額計算的項目貢獻百分比quantity_per = 按數量計算的項目貢獻百分比請幫我找出正確的答案。
1 回答
神不在的星期二
TA貢獻1963條經驗 獲得超6個贊
您正在尋找df.quantile和一些基本數學。
在表中顯示這些值并沒有多大價值——它的 3 列以上乘以len(df)數據都是一樣的——所以我將它們作為簡單的語句給出：
import pandas as pd
import random
# some data shuffling to see it works on unsorted data
random.seed(42)
data = [[f"product {i+1:3d}",i*10] for i in range(100)]
random.shuffle(data)
df = pd.DataFrame(data, columns=['name', 'price'])
# calculate the quantile series
q25 = df.quantile(.25, numeric_only=True)
q50 = df.quantile(.5, numeric_only=True)
q75 = df.quantile(.75, numeric_only=True)
print (q25, q50, q75, sep="\n\n")
print( f"Bottom 25% of prices are below/equal to {q25.price} thats", end=" ")
print( f"{len(df[df.price <= q25.price]) / (len(df) / 100)}% of all items")
print( f"Bottom 50% of prices are below/equal to {q50.price} thats", end=" ")
print( f"{len(df[df.price <= q50.price]) / (len(df) / 100)}% of all items")
print( f"Bottom 75% of prices are below/equal to {q75.price} thats", end= " ")
print( f"{len(df[df.price <= q75.price]) / (len(df)/ 100)}% of all items")
（未洗牌）數據框看起來像
name price
0 product 1 0
1 product 2 10
2 product 3 20
.. ... ...
97 product 98 970
98 product 99 980
99 product 100 990
[100 rows x 2 columns]
輸出：
price 247.5
Name: 0.25, dtype: float64
price 495.0
Name: 0.5, dtype: float64
price 742.5
Name: 0.75, dtype: float64
Bottom 25% of prices are below/equal to 247.5 thats 25.0% of all items
Bottom 50% of prices are below/equal to 495.0 thats 50.0% of all items
Bottom 75% of prices are below/equal to 742.5 thats 75.0% of all items
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