英文:
Unable to Filter based on Substring in Pandas
问题
这是一个数据集的形式：
公司网址 名称 收入
mackter.com Mack Sander NaN
nientact.com Neient Dan 321
ventienty.com Richard NaN
所以，我的任务是删除所有在'company_url'或'Name'列中出现字符串'tac'、'bux'或'mvy'的行... 如你所见，'tac'出现在nientact.com中，所以该行应该被删除... 类似地，所有在'company_url'或'Name'中出现这3个字符串中的任何一个的行，都应该被删除... 所以，最初我尝试了针对'company_url'列的代码，并编写了以下代码，但是它显示错误。
lists=['tac', 'bux', 'mvy']
for i in lists:
df = df[~df['company_url'].str.contains(i)]
但是它显示
TypeError: unhashable type: 'list'
英文:
There is a dataset in this form:
company_url Name Revenue
mackter.com Mack Sander NaN
nientact.com Neient Dan 321
ventienty.com Richard NaN
So, my task here is to remove all the rows where string 'tac', 'bux' or 'mvy' is coming in either 'company_url' or 'Name' column.... As you can see, 'tac' is present in nientact.com , so the row should get deleted... Similarly, all the rows where any of these 3 string are present in either company_url or Name, the rows should get deleted.... SO, Initially I tried it for company_url column and written the below code, but it's showing error.
lists=['tac', 'bux', 'mvy']
for i in lists:
df = df[~df['company_url].str.contains(i)]
but its showing
TypeError: unhashable type: 'list'
答案1
得分: 2
你可以使用正则表达式来配合str.contains方法，然后使用any方法进行聚合，再使用~进行取反操作，并进行布尔索引：
import re
lists = ['tac', 'bux', 'mvy']
pattern = '|'.join(map(re.escape, lists))
# 'tac|bux|mvy'
out = df[~df[['company_url', 'Name']]
.apply(lambda s: s.str.contains(pattern, case=False))
.any(axis=1)
]
输出结果为：
company_url Name Revenue
0 mackter.com Mack Sander NaN
2 ventienty.com Richard NaN
只是提供一个修复你循环的方法，但不要使用它，因为这样效率低下：
lists=['tac', 'bux', 'mvy']
for i in lists:
df = df[~df[['company_url', 'Name']]
.apply(lambda s: s.str.contains(i))
.any(axis=1)]
# 或者
lists=['tac', 'bux', 'mvy']
for i in lists:
for col in ['company_url', 'Name']:
df = df[~df[col].str.contains(i)]
英文:
You can craft a regex to use with str.contains, then aggregate with any, invert with ~, and perform boolean indexing:
import re
lists = ['tac', 'bux', 'mvy']
pattern = '|'.join(map(re.escape, lists))
# 'tac|bux|mvy'
out = df[~df[['company_url', 'Name']]
.apply(lambda s: s.str.contains(pattern, case=False))
.any(axis=1)
]
Output:
company_url Name Revenue
0 mackter.com Mack Sander NaN
2 ventienty.com Richard NaN
Just for info a fix of your loop, but don't use it as this is inefficient:
lists=['tac', 'bux', 'mvy']
for i in lists:
df = df[~df[['company_url', 'Name']]
.apply(lambda s: s.str.contains(i))
.any(axis=1)]
# or
lists=['tac', 'bux', 'mvy']
for i in lists:
for col in ['company_url', 'Name']:
df = df[~df[col].str.contains(i)]
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