11.Pandas+索引及运算

import pandas as pd
food_info=pd.read_csv("food_info.csv")
col_names=food_info.columns.tolist()
print(col_names)
print(food_info.head(3))

['NDB_No', 'Shrt_Desc', 'Water_(g)', 'Energ_Kcal', 'Protein_(g)', 'Lipid_Tot_(g)', 'Ash_(g)', 'Carbohydrt_(g)', 'Fiber_TD_(g)', 'Sugar_Tot_(g)', 'Calcium_(mg)', 'Iron_(mg)', 'Magnesium_(mg)', 'Phosphorus_(mg)', 'Potassium_(mg)', 'Sodium_(mg)', 'Zinc_(mg)', 'Copper_(mg)', 'Manganese_(mg)', 'Selenium_(mcg)', 'Vit_C_(mg)', 'Thiamin_(mg)', 'Riboflavin_(mg)', 'Niacin_(mg)', 'Vit_B6_(mg)', 'Vit_B12_(mcg)', 'Vit_A_IU', 'Vit_A_RAE', 'Vit_E_(mg)', 'Vit_D_mcg', 'Vit_D_IU', 'Vit_K_(mcg)', 'FA_Sat_(g)', 'FA_Mono_(g)', 'FA_Poly_(g)', 'Cholestrl_(mg)']
   NDB_No                 Shrt_Desc  Water_(g)  Energ_Kcal  Protein_(g)  
0    1001          BUTTER WITH SALT      15.87         717         0.85   
1    1002  BUTTER WHIPPED WITH SALT      15.87         717         0.85   
2    1003      BUTTER OIL ANHYDROUS       0.24         876         0.28   

   Lipid_Tot_(g)  Ash_(g)  Carbohydrt_(g)  Fiber_TD_(g)  Sugar_Tot_(g)  
0          81.11     2.11            0.06           0.0           0.06   
1          81.11     2.11            0.06           0.0           0.06   
2          99.48     0.00            0.00           0.0           0.00   

        ...        Vit_A_IU  Vit_A_RAE  Vit_E_(mg)  Vit_D_mcg  Vit_D_IU  
0       ...          2499.0      684.0        2.32        1.5      60.0   
1       ...          2499.0      684.0        2.32        1.5      60.0   
2       ...          3069.0      840.0        2.80        1.8      73.0   

   Vit_K_(mcg)  FA_Sat_(g)  FA_Mono_(g)  FA_Poly_(g)  Cholestrl_(mg)  
0          7.0      51.368       21.021        3.043           215.0  
1          7.0      50.489       23.426        3.012           219.0  
2          8.6      61.924       28.732        3.694           256.0  

[3 rows x 36 columns]

对列进行统一处理

#print(food_info["Iron_(mg)"])
div_100=food_info["Iron_(mg)"]*100
print(div_100[:3])

0     2.0
1    16.0
2     0.0
Name: Iron_(mg), dtype: float64

列与列运算做为新的列（特征）

water_energy=food_info["Water_(g)"]* food_info["Energ_Kcal"]
print(food_info.shape)
food_info["Iron_(g)"]=water_energy
print(food_info.shape)

(8618, 36)
(8618, 37)

print(water_energy[:3])

0    11378.79
1    11378.79
2      210.24
dtype: float64