pandas数据处理中常用的map，apply，applymap三种方法

#制作数据源
boolean=[True,False]
gender=["男","女"]
color=["white","black","yellow"]
data=pd.DataFrame({
    "height":np.random.randint(150,190,100),
    "weight":np.random.randint(40,90,100),
    "smoker":[boolean[x] for x in np.random.randint(0,2,100)],
    "gender":[gender[x] for x in np.random.randint(0,2,100)],
    "age":np.random.randint(15,90,100),
    "color":[color[x] for x in np.random.randint(0,len(color),100) ]
}
)

#三种方法之一：map用法
#使用字典进行映射
data["gender"] = data["gender"].map({"男":1, "女":0})
​
#三种方法之二：使用函数
def gender_map(x):
    gender = 1 if x == "男" else 0
    return gender

data["gender"] = data["gender"].map(gender_map)

def apply_age(x,bias):
    return x+bias
​
#三种方法之二：apply用法
data["age"] = data["age"].apply(apply_age,args=(-3,))

########################
#处理行
def BMI(series):
    weight = series["weight"]
    height = series["height"]/100
    BMI = weight/height**2
    return BMI

def BMI(df):
    weight = df["weight"]
    height = df["height"]/100
    BMI = weight/height**2
    return BMI
​
data["BMI"]=data.apply(BMI,axis=1)


# 处理列
a=data[["height","weight","age"]].apply(np.sum, axis=0).reset_index()
​
# 处理列
data[["height","weight","age"]].apply(np.log, axis=0)


#三种方法之三：applymap用法
#applymap的用法比较简单，会对DataFrame中的每个单元格执行指定函数的操作，
#虽然用途不如apply广泛，但在某些场合下还是比较有用的，如下面这个例子。

df = pd.DataFrame(
    {
        "A":np.random.randn(5),
        "B":np.random.randn(5),
        "C":np.random.randn(5),
        "D":np.random.randn(5),
        "E":np.random.randn(5),
    }
)
df
df.applymap(lambda x:"%.2f" % x)
df.apply(lambda x:"%.2f" % x)