pandas基础用法

 

import pandas
food_info=pandas.read_csv('food_info.csv')#将csv文件读进来
print(type(food_info))
print(food_info.dtypes)
print(help(pandas.read_csv))

food_info.head(3)#将读入数据显示出来，3-显示前3行，括号内不写数字默认显示5行
food_info.tail()#将读入数据显示出来，3-显示前3行，括号内不写数字默认显示5行
print(food_info.columns)#打印csv文件中的列名
print(food_info.shape)#此样本一共8618个数据，36个样本
print(food_info.loc[6])#打印第七行的数据，（列名 数据）
print(food_info.loc[3:6])#打印第四到七行
print(food_info["NDB_No"])#打印文件名是NDB_No的那一列
print(food_info[["Zinc_(mg)", "Copper_(mg)"]])#取两列列名Zinc_(mg), Copper_(mg)，一定注意是两个[]
columns = ["Zinc_(mg)", "Copper_(mg)"]#取两列，方法2
zinc_copper = food_info[columns]
print (zinc_copper)

object - For string values
int - For integer values
float - For float values
datetime - For time values
bool - For Boolean values
print(food_info.dtypes)

a=food_info.columns.tolist()#取出列名，并组成一个list
print(a)
b=[]
for c in a:
    if c.endswith("(g)"):#判断列名是否以"g"结尾
        b.append(c)
print(b)        
d=food_info[b]#food_info中以"g"结尾的数据
print(d.head(3))

print(food_info["Iron_(mg)"])
print(food_info.shape)
d=food_info["Iron_(mg)"]/1000  #将Iron_(mg)列元素都除以1000
#print(d)
food_info["Iron_(g)"]=d  #将新的列"Iron_(g)"加入food_info
print(food_info.shape)

>>>
(8618, 36)
(8618, 37)

water_energy = food_info["Water_(g)"] * food_info["Energ_Kcal"]#维度一样，两列的对应位置可以相乘
weighted_protein = food_info["Protein_(g)"] * 2 #整个一列乘以2
weighted_fat = -0.75 * food_info["Lipid_Tot_(g)"]
initial_rating = weighted_protein + weighted_fat#维度一样，两列的对应位置可以相加

max_calories = food_info["Energ_Kcal"].max()

food_info.sort_values("Sodium_(mg)", inplace=False)#sort_value升序排列，inplace=true,更新原有列的排序，=false生成新的数列
food_info.sort_values("Sodium_(mg)", inplace=True, ascending=True)#ascending=false降序排列,=true升序排列