我怎樣才能實現這一目標DoT Product？我無法將一個附加Pandas DataFrame到另一個。我不需要冗長的解決方案，而是需要一個簡單的Pandas代碼Numpy。產品價格： Apple Price = 3Cherry Price = 4Blueberry Price = 2預期輸出： Apple Cherry Blueberry Mon 13 8 6Tues 9 7 4Wed 7 4 0Thurs 15 6 3Total Rev.($) 83 63 37我的代碼： prod_prices = np.array([3,4,2])prod_pricesprod_days = np.array([[13,9,7,15], [8,7,4,6], [6,4,0,3]]).Tprod_daysdf_week_sales = pd.DataFrame(prod_days, index=["Mon","Tues","Wed","Thurs"], columns=["Apple","Cherry","Blueberry"])df_week_salesweekly_total = df_week_sales.dot(prod_prices)weekly_totaltype(weekly_total)weekly_total_nparray = np.array(weekly_total)type(weekly_total_nparray)weekly_total_nparraydf_weekly_total_nparray = pd.DataFrame(weekly_total_nparray.reshape(1,4))df_weekly_total_nparrayweekly_revenue_data = df_week_sales.append(df_weekly_total_nparray)weekly_revenue_data這給了我以下輸出：
3 回答
料青山看我應如是
TA貢獻1772條經驗 獲得超8個贊
prices = {'Apple': 3, 'Cherry': 4, "Blueberry": 2}
df_week_sales = pd.DataFrame(prod_days,
index=["Mon","Tues","Wed","Thurs"],
columns=["Apple","Cherry","Blueberry"])
df_week_sales
Apple Cherry Blueberry
Mon 13 8 6
Tues 9 7 4
Wed 7 4 0
Thurs 15 6 3
轉置：
df = df_week_sales.transpose()
Mon Tues Wed Thurs
Apple 13 9 7 15
Cherry 8 7 4 6
Blueberry 6 4 0 3
df['Total Revenue'] = [np.sum(df_week_sales[x]*prices[x]) for x in df_week_sales.columns]
df
Mon Tues Wed Thurs Total Revenue
Apple 13 9 7 15 132
Cherry 8 7 4 6 100
Blueberry 6 4 0 3 26
results = df.transpose()
Apple Cherry Blueberry
Mon 13 8 6
Tues 9 7 4
Wed 7 4 0
Thurs 15 6 3
Total Revenue 132 100 26
prod_prices = np.array([3,4,2])
results['Revenue'] = df_week_sales.dot(prod_prices)
Apple Cherry Blueberry Revenue
Mon 13 8 6 83.0
Tues 9 7 4 63.0
Wed 7 4 0 37.0
Thurs 15 6 3 75.0
Total Revenue 132 100 26 NaN
了解您的預期輸出底部是 83、63、37，但這根本沒有意義。83 = 13*3 + 8*4 + 6*2, 63 = 9*3 + 4*2, 同樣對于37. 那么你的75. 數據不應該以該方向顯示。所以我會堅持這種方式，這樣對于表示數據來說更有意義。
素胚勾勒不出你
TA貢獻1827條經驗 獲得超9個贊
你可以嘗試這樣的事情：
prod_prices = np.array([3,4,2])
prod_days = np.array([[13,9,7,15],
[8,7,4,6],
[6,4,0,3]]).T
df_week_sales = pd.DataFrame(prod_days,
index=["Mon","Tues","Wed","Thurs"],
columns=["Apple","Cherry","Blueberry"])
# obtain the dot product
weekly_total = prod_days.dot(prod_prices)
# obtain the last row index of the data frame
jj = df_week_sales.shape[0]
# get the list to append to the data frame (need to be of the same column length)
df_week_sales.loc[jj] = weekly_total[:-1]
# rename the last index as desired
df_week_sales.rename(index={jj: 'Total Rev.($)'}, inplace=True)
df_week_sales
翻閱古今
TA貢獻1780條經驗 獲得超5個贊
您可以通過兩行來實現此目的：
total_revenues = [df_week_sales[x].sum() for x in df_week_sales.columns]* prod_prices
output = pd.concat([df_week_sales, pd.DataFrame(total_revenues, index= df_week_sales.columns, columns = ['Total Revenue']).T])
輸出
| | Apple | Cherry | Blueberry |
|:--------------|--------:|---------:|------------:|
| Mon | 13 | 8 | 6 |
| Tues | 9 | 7 | 4 |
| Wed | 7 | 4 | 0 |
| Thurs | 15 | 6 | 3 |
| Total Revenue | 132 | 100 | 26 |
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