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：

显示前5行

food_info.head()

运行结果：

food_info.head(3)

运行结果：

显示后5行

food_info.tail()

运行结果：

print(food_info.shape)

运行结果：

读取特定的数据：

print(food_info.loc[0])

运行结果：

切片：

print(food_info.loc[3:6])

运行结果：

列名取数据

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

运行结果：

取某几个列

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

运行结果：

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：

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：

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：

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