Pandas数据读取+索引计算

1. 读csv文件

   import pandas
   food_info=pandas.read_csv('food_info.csv')
   print(type(food_info))
   print(food_info.dtypes)
   print(help(pandas.read_csv))
   

   运行结果：
   

2. 显示前5行

   food_info.head()
   

   运行结果：
   

   food_info.head(3)
   

   运行结果：
   

3. 显示后5行

   food_info.tail()
   

   运行结果：
   

   print(food_info.shape)
   

   运行结果：
   

4. 读取特定的数据：

   print(food_info.loc[0])
   

   运行结果：
   

5. 切片：

   print(food_info.loc[3:6])
   

   运行结果：
   

6. 列名取数据

   ndb_col=food_info['NDB_No']
   print(ndb_col)
   

   运行结果：
   

7. 取某几个列

   columns=['Zinc_(mg)','Copper_(mg)']
   zinc_copper=food_info[columns]
   print(zinc_copper)
   

   运行结果：
   

8. endswith()

   col_names=food_info.columns.tolist()
   print(col_names)
   gram_columns=[]
   
   for c in col_names:
       if c.endswith('(g)'):
           gram_columns.append(c)
   gram_df=food_info[gram_columns]
   print(gram_df.head(3))
   

   运行结果：
   

9. 对取出的某些列进行数值运算

   print(food_info['Iron_(mg)'])
   div_1000=food_info['Iron_(mg)']/1000
   print(div_1000)
   

   运行结果：
   

10. 列和列相乘

    water_energy = food_info["Water_(g)"] * food_info["Energ_Kcal"]
    water_energy = food_info["Water_(g)"] * food_info["Energ_Kcal"]
    iron_grams = food_info["Iron_(mg)"] / 1000  
    print(food_info.shape)
    food_info['Iron_(g)']=iron_grams
    print(food_info.shape)
    

    运行结果：
    

11. 求某一列的最大值

    # the "Vit_A_IU" column ranges from 0 to 100000, while the "Fiber_TD_(g)" column ranges from 0 to 79
    #For certain calculations, columns like "Vit_A_IU" can have a greater effect on the result, 
    #due to the scale of the values
    # The largest value in the "Energ_Kcal" column.
    max_calories = food_info["Energ_Kcal"].max()
    # Divide the values in "Energ_Kcal" by the largest value.
    normalized_calories = food_info["Energ_Kcal"] / max_calories
    normalized_protein = food_info["Protein_(g)"] / food_info["Protein_(g)"].max()
    normalized_fat = food_info["Lipid_Tot_(g)"] / food_info["Lipid_Tot_(g)"].max()
    food_info["Normalized_Protein"] = normalized_protein
    food_info["Normalized_Fat"] = normalized_fat