Question

背景

我有一个类似于以下内容的数据集：

product_name    price
Women's pant    20.00
Men's Shirt     30.00
Women's Dress   40.00
Blue Shirt      30.00
...

我希望创建一个新列

性别

将基于product_name中的字符串包含值Women，Men或Unisex

所需的结果如下：

product_name    price   gender
Women's pant    20.00   women
Men's Shirt     30.00   men
Women's Dress   40.00   women
Blue Shirt      30.00   unisex

我的方法

我认为首先应该创建一个新列，每一行的空白值都应为空白。然后，我应该遍历数据帧中的每一行，并检查字符串df [product_name]，看看它是男装，女装还是男女通用，并填写相应的性别行值。

这是我的代码：

df['gender'] = ""
for product_name in df['product_name']:
    if 'women' in product_name.lower():
        df['gender'] = 'women'
    elif 'men' in product_name.lower():
        df['gender'] = 'men'
    else:
        df['gender'] = 'unisex'

但是，我得到以下结果：

product_name    price   gender
Women's pant    20.00   men
Men's Shirt     30.00   men
Women's Dress   40.00   men
Blue Shirt      30.00   men

我真的很感谢这里的帮助，因为我是python和pandas库的新手。

Answer 1

您可以对if/else使用列表推导来获取输出：

df['gender'] = ['women' if 'women' in word 
                else "men" if "men" in word
                else "unisex" 
                for word in df.product_name.str.lower()]

df

   product_name    price    gender
0   Women's pant    20.0    women
1   Men's Shirt     30.0    men
2   Women's Dress   40.0    women
3   Blue Shirt      30.0    unisex

或者，您可以使用numpy select获得相同的结果：

cond1 = df.product_name.str.lower().str.contains("women")
cond2 = df.product_name.str.lower().str.contains("men")
condlist = [cond1, cond2]
choicelist = ["women", "men"]
df["gender"] = np.select(condlist, choicelist, default="unisex")

通常，对于字符串，python的迭代要快得多；但是您必须进行测试。

Answer 2

尝试将for语句转换为函数并使用apply。所以像-

def label_gender(product_name):
    '''product_name is a str'''
    if 'women' in product_name.lower():
        return 'women'
    elif 'men' in product_name.lower():
        return 'men'
    else:
        return 'unisex'

df['gender'] = df.apply(lambda x: label_gender(x['product_name']),axis=1)

可以在以下位置找到使用apply / lambda的详细信息：https://towardsdatascience.com/apply-and-lambda-usage-in-pandas-b13a1ea037f7

Answer 3

您还可以使用np.where + Series.str.contains，

import numpy as np

df['gender'] = (
    np.where(df.product_name.str.contains("women", case=False), 'women',
             np.where(df.product_name.str.contains("men", case=False), "men", 'unisex'))
)

    product_name  price  gender
0   Women's pant   20.0   women
1    Men's Shirt   30.0     men
2  Women's Dress   40.0   women
3     Blue Shirt   30.0  unisex

Answer 4

在词组中使用np.where .str.contains和regex first单词`。这样；

#np.where(if product_name has WomenORMen, 1st Word in Phrase, otherwise;unisex)



 df['Gender']=np.where(df.product_name.str.contains('Women|Men')\
                      ,df.product_name.str.split('(^[\w]+)').str[1],'Unisex')


 
    product_name  price  gender
0   Women's pant   20.0   Women
1    Men's Shirt   30.0     Men
2  Women's Dress  640.0   Women
3    Blue Shirt    30.0  Unisex

根据条件填充数据框行的值

4 个答案: