Question

我的数据框users包含不同的列。我的目标是添加列[uses_name]，当密码与每个用户的名字或姓氏相同时，该列应为True。

例如，十二行中的[user_name]包含milford.hubbard。然后在{uses_name]中将True，因为[password]和[last_name]是相同的。

为此，我使用正则表达式创建了两列[first_name]和[last_name]。创建[uses_name]时，我遇到|运算符问题。我在pandas doc中更多地了解布尔索引，但没有找到答案。

我的代码：

import pandas as pd

users = pd.read_csv('datasets/users.csv')

# Extracting first and last names into their own columns

users['first_name'] = users['user_name'].str.extract(r'(^\w+)', expand=False)

users['last_name'] = users['user_name'].str.extract(r'(\w+$)', expand=False)

# Flagging the users with passwords that matches their names

users['uses_name'] = users['password'].isin(users['first_name'] | users['last_name'])

# Counting and printing the number of users using names as passwords

print(users['uses_name'].count())

# Taking a look at the 12 first rows

print(users.head(12))

当我尝试编译时，我给出了一个错误：

TypeError: unsupported operand type(s) for |: 'str' and 'bool'

users数据框中的前12行，其中包含已创建的first_name和last_name列：

id          user_name            password   first_name  last_name
0    1    vance.jennings          joobheco      vance    jennings
1    2    consuelo.eaton        0869347314   consuelo       eaton
2    3   mitchel.perkins        fabypotter    mitchel     perkins
3    4    odessa.vaughan         aharney88     odessa     vaughan
2    3   mitchel.perkins        fabypotter    mitchel     perkins
3    4    odessa.vaughan         aharney88     odessa     vaughan
4    5    araceli.wilder        acecdn3000    araceli      wilder
5    6  shawn.harrington           5278049      shawn  harrington
6    7        evelyn.gay            master     evelyn         gay
7    8       noreen.hale            murphy     noreen        hale
8    9       gladys.ward           lwsves2     gladys        ward
9   10   brant.zimmerman  1190KAREN5572497      brant   zimmerman
10  11     leanna.abbott          aivlys24     leanna      abbott
11  12   milford.hubbard           hubbard    milford     hubbard

Answer 1

你可以连续，因为两者都是系列

users['password'].isin(pd.concat([users['first_name'],users['last_name']]))

由于您更改了问题，请更新一个

df[['first_name','last_name']].eq(df.password,axis=0).any(1)

Answer 2

这有效： users ['uses_name'] =（users ['password'] == users ['first_name']）| （users ['password'] == users ['last_name']）

Answer 3

使用numpy.union1d：

val = np.union1d(users['first_name'], users['last_name'])
users['uses_name'] = users['password'].isin(val)
print (users)
    id         user_name          password first_name   last_name  uses_name
0    1    vance.jennings          joobheco      vance    jennings      False
1    2    consuelo.eaton        0869347314   consuelo       eaton      False
2    3   mitchel.perkins        fabypotter    mitchel     perkins      False
3    4    odessa.vaughan         aharney88     odessa     vaughan      False
2    3   mitchel.perkins        fabypotter    mitchel     perkins      False
3    4    odessa.vaughan         aharney88     odessa     vaughan      False
4    5    araceli.wilder        acecdn3000    araceli      wilder      False
5    6  shawn.harrington           5278049      shawn  harrington      False
6    7        evelyn.gay            master     evelyn         gay      False
7    8       noreen.hale            murphy     noreen        hale      False
8    9       gladys.ward           lwsves2     gladys        ward      False
9   10   brant.zimmerman  1190KAREN5572497      brant   zimmerman      False
10  11     leanna.abbott          aivlys24     leanna      abbott      False
11  12   milford.hubbard           hubbard    milford     hubbard       True

Answer 4

我认为最好的方法是执行set联合并将其传递给isin：

users['uses_name'] = users['password'].isin(
   set(users['first_name']).union(users['last_name'])
)

users 

    id         user_name          password first_name   last_name  uses_name
0    1    vance.jennings          joobheco      vance    jennings      False
1    2    consuelo.eaton        0869347314   consuelo       eaton      False
2    3   mitchel.perkins        fabypotter    mitchel     perkins      False
3    4    odessa.vaughan         aharney88     odessa     vaughan      False
2    3   mitchel.perkins        fabypotter    mitchel     perkins      False
3    4    odessa.vaughan         aharney88     odessa     vaughan      False
4    5    araceli.wilder        acecdn3000    araceli      wilder      False
5    6  shawn.harrington           5278049      shawn  harrington      False
6    7        evelyn.gay            master     evelyn         gay      False
7    8       noreen.hale            murphy     noreen        hale      False
8    9       gladys.ward           lwsves2     gladys        ward      False
9   10   brant.zimmerman  1190KAREN5572497      brant   zimmerman      False
10  11     leanna.abbott          aivlys24     leanna      abbott      False
11  12   milford.hubbard           hubbard    milford     hubbard       True

请注意|是逻辑OR，它对pandas中的字符串列没有意义。

TypeError：|：＆＃39; str＆＃39;不支持的操作数类型并且＆＃39; bool＆＃39;

4 个答案: