我刚刚开始使用Python进行编码,并希望构建一个解决方案,您可以在其中搜索字符串以查看它是否包含一组给定的值。
我在R中找到了一个使用字符串库的类似解决方案:Search for a value in a string and if the value exists, print it all by itself in a new column
以下代码似乎有效,但我也希望输出我正在寻找的三个值,此解决方案只输出一个值:
#Inserting new column
df.insert(5, "New_Column", np.nan)
#Searching old column
df['New_Column'] = np.where(df['Column_with_text'].str.contains('value1|value2|value3', case=False, na=False), 'value', 'NaN')
------编辑------
所以我意识到我没有给出那么好的解释,抱歉。
下面是一个示例,其中我匹配字符串中的水果名称,并且根据它是否在字符串中找到任何匹配项,它将在新列中打印出true或false。这是我的问题:我想打印出它在字符串中找到的名称,而不是打印出真或假。苹果,橘子等。
import pandas as pd
import numpy as np
text = [('I want to buy some apples.', 0),
('Oranges are good for the health.', 0),
('John is eating some grapes.', 0),
('This line does not contain any fruit names.', 0),
('I bought 2 blueberries yesterday.', 0)]
labels = ['Text','Random Column']
df = pd.DataFrame.from_records(text, columns=labels)
df.insert(2, "MatchedValues", np.nan)
foods =['apples', 'oranges', 'grapes', 'blueberries']
pattern = '|'.join(foods)
df['MatchedValues'] = df['Text'].str.contains(pattern, case=False)
print(df)
结果
Text Random Column MatchedValues
0 I want to buy some apples. 0 True
1 Oranges are good for the health. 0 True
2 John is eating some grapes. 0 True
3 This line does not contain any fruit names. 0 False
4 I bought 2 blueberries yesterday. 0 True
通缉结果
Text Random Column MatchedValues
0 I want to buy some apples. 0 apples
1 Oranges are good for the health. 0 oranges
2 John is eating some grapes. 0 grapes
3 This line does not contain any fruit names. 0 NaN
4 I bought 2 blueberries yesterday. 0 blueberries
答案 0 :(得分:4)
您需要设置正则表达式标志(将您的搜索解释为正则表达式):
whatIwant = df['Column_with_text'].str.contains('value1|value2|value3',
case=False, regex=True)
df['New_Column'] = np.where(whatIwant, df['Column_with_text'])
------编辑------
根据更新的问题陈述,这是一个更新的答案:
您需要使用括号在正则表达式中定义捕获组,并使用extract()
函数返回捕获组中找到的值。 lower()
函数处理任何大写字母
df['MatchedValues'] = df['Text'].str.lower().str.extract( '('+pattern+')', expand=False)
答案 1 :(得分:1)
这是一种方式:
foods =['apples', 'oranges', 'grapes', 'blueberries']
def matcher(x):
for i in foods:
if i.lower() in x.lower():
return i
else:
return np.nan
df['Match'] = df['Text'].apply(matcher)
# Text Match
# 0 I want to buy some apples. apples
# 1 Oranges are good for the health. oranges
# 2 John is eating some grapes. grapes
# 3 This line does not contain any fruit names. NaN
# 4 I bought 2 blueberries yesterday. blueberries