Python-apply(lambda x: )的使用及说明

def instant_order_deal(plat, special_product, clearance_goods, new_product_instant,orders):
    """
    :param plat: 要计算的平台
    :param special_product: 特定库龄产品，其他平台的，amazon的在下面单独读取
    :param clearance_goods: 清仓产品
    :param new_product: 新品
    :param orders: 订单
    :return:
    """
    # 退款订单处理
    orders['订单总金额(包含客户运费、平台补贴)'] = orders.apply(lambda x: 0 if (x['订单类型'] == 'refund') else x['订单总金额(包含客户运费、平台补贴)'], axis=1)
    "中间特定sku处理毛利"
    # orders['毛利'] = orders.apply(lambda x: (x['平均采购价']* 0.4 + x['毛利']) if (x['产品代码'] == 'S4338867210')| (x['产品代码']=='S2130010010') else x['毛利'],axis=1)
    orders['毛利'] = orders.apply(lambda x: (x['毛利'] + 5) if (x['产品代码'] == 'S1416028410') | (x['产品代码'] == 'S1416028440') | (x['产品代码'] == 'S1416028470')  else x['毛利'], axis=1)
    """折价商品毛利计算 + 额温枪"""
    depreciate = read_data().read_depreciate()
    orders['毛利'] = orders.apply(lambda x: (x['平均采购价'] * 0.4 * x['数量']  + x['毛利']) if (x['产品代码'] in depreciate) and x['订单类型'] == 'sale' else x['毛利'],axis=1)


    orders['平均采购价'] = orders.apply(lambda x: 0 if (x['订单类型'] == 'resend') else x['平均采购价'], axis=1)
    # 中英仓处理
    orders['仓库分类'] = orders.apply(lambda x: '中仓' if (x['发运仓库'] =='SH [上海奉贤仓]') | (x['发运仓库'] =='WZC [温州仓]') | (x['发运仓库'] =='SZC [深圳仓]') else '海外仓', axis=1)
    # 处理新品
    # if plat == 'ebay' or plat == 'shopee' or plat == 'amazon' :
    newproduct = read_data().read_newproduct()
    orders['仓库分类'] = orders.apply(lambda x: '新品' if (x['产品代码'] in newproduct) else x['仓库分类'], axis=1)

    #处理海运产品
    shipping = read_data().read_shipping()
    orders['仓库分类'] =orders.apply(lambda x: '海运产品' if(x['产品代码'] in shipping  and  x['仓库分类'] != '海外仓') else x['仓库分类'],axis=1)

    # 当月转清仓处理
    orders['仓库分类'] = orders.apply(lambda x: '特定库龄'if isClearance(x['付款时间'], x['产品代码'], clearance_goods) != None  else x['仓库分类'], axis=1)

    # 特定库龄处理
    orders['仓库分类'] = orders.apply(lambda x: '特定库龄' if (x['发运仓库'] == 'GSE [古斯美东仓]' and x['平台']!='ebay') else x['仓库分类'], axis=1)
    if plat == 'amazon':
        # amazon的特定库龄需要单独读取
        special_product_a = read_data().read_special_product(plat)
        special_product_as = read_data().read_special_product('amazon特殊')

        orders['仓库分类'] = orders.apply(lambda x: '特定库龄' if (x['产品代码'] in special_product_as) else x['仓库分类'], axis=1)
        orders['仓库分类'] = orders.apply(lambda x: '特定库龄' if ((x['发运仓库'] + x['产品代码']) in special_product_a) else x['仓库分类'], axis=1)

    else:
        special_product = read_data().read_special_product('其他平台')
        orders['仓库分类'] = orders.apply(lambda x: '特定库龄' if (x['产品代码'] in special_product) else x['仓库分类'], axis=1)
        orders['仓库分类']=orders.apply(lambda x:'稳定期' if (x['仓库分类']=='中仓')| (x['仓库分类']=='海外仓' )else x['仓库分类'],axis=1 )
    # 处理好仓库分类，接下来判断是否是开发新品
    orders = pd.merge(orders, new_product_instant, on='产品代码', how='left')
    orders['开发新品'] = orders['开发新品'].fillna('非开发新品')
    # 然后处理货值
    orders['货值'] = orders['数量'] * orders['平均采购价']

    # orders = pd.merge(orders,mask_instant, on='产品代码', how='left')
    # orders['口罩'] = orders['口罩'].fillna('非口罩')

    return orders

lambda argument_list: expression

def sqr(x):
 
    return x ** 2
 
print(sqr)
 
# <function sqr at 0x000000BABD3A4400>
 
y = [sqr(x) for x in range(10)]
 
print(y)
 
# [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
 
lbd_sqr = lambda x: x ** 2
 
print(lbd_sqr)
 
# <function <lambda> at 0x000000BABB6AC1E0>
 
y = [lbd_sqr(x) for x in range(10)]
 
print(y)
 
# [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
 
 
sumary = lambda arg1, arg2: arg1 + arg2
 
print(sumary(10, 20))  # 30
 
func = lambda *args: sum(args)
 
print(func(1, 2, 3, 4, 5))  # 15

def f(x):
 
    for i in range(0, len(x)):
 
        x[i] += 10
 
    return x
 
x = [1, 2, 3]
 
f(x)
 
print(x)
 
# [11, 12, 13]
 
def f(x):
 
    y = []
 
    for item in x:
 
        y.append(item + 10)
 
    return y
 
x = [1, 2, 3]
 
f(x)
 
print(x)
 
# [1, 2, 3]

odd = lambda x: x % 2 == 1 
templist = filter(odd, [1, 2, 3, 4, 5, 6, 7, 8, 9]) 
print(list(templist))  # [1, 3, 5, 7, 9]

m1 = map(lambda x: x ** 2, [1, 2, 3, 4, 5])
 
print(list(m1))  
 
# [1, 4, 9, 16, 25]
 
m2 = map(lambda x, y: x + y, [1, 3, 5, 7, 9], [2, 4, 6, 8, 10])
 
print(list(m2))  
 
# [3, 7, 11, 15, 19]

def apply_to_list(fun, some_list):
 
    return fun(some_list)
 
lst = [1, 2, 3, 4, 5]
 
print(apply_to_list(sum, lst))
 
# 15
 
print(apply_to_list(len, lst))
 
# 5
 
print(apply_to_list(lambda x: sum(x) / len(x), lst))
 
# 3.0