[英]python pandas column conditional on two other column values
Is there a way in python pandas to apply a conditional if one or another column have a value? 如果一个或另一个列有值,python pandas中是否有一种方法可以应用条件?
For one column, I know I can use the following code, to apply a test flag if the column Title includes the word "test". 对于一列,我知道我可以使用以下代码,如果列标题包含单词“test”,则应用测试标志。
df['Test_Flag'] = np.where(df['Title'].str.contains("test|Test"), 'Y', '')
But if I would like to say if column title or column subtitle include the word "test", add the test flag, how could I do that? 但是,如果我想说列标题或列字幕是否包含单词“test”,请添加测试标志,我该怎么做?
This obviously didn't work 这显然不起作用
df['Test_Flag'] = np.where(df['Title'|'Subtitle'].str.contains("test|Test"), 'Y', '')
If many columns then simplier is create subset df[['Title', 'Subtitle']]
and apply
contains
, because works only with Series
and check at least one True
per row by any
: 如果多列然后simplier是创建子
df[['Title', 'Subtitle']]
和apply
contains
,因为只能与Series
和检查至少一个True
每行的any
:
mask = df[['Title', 'Subtitle']].apply(lambda x: x.str.contains("test|Test")).any(axis=1)
df['Test_Flag'] = np.where(mask,'Y', '')
Sample: 样品:
df = pd.DataFrame({'Title':['test','Test','e', 'a'], 'Subtitle':['b','a','Test', 'a']})
mask = df[['Title', 'Subtitle']].apply(lambda x: x.str.contains("test|Test")).any(axis=1)
df['Test_Flag'] = np.where(mask,'Y', '')
print (df)
Subtitle Title Test_Flag
0 b test Y
1 a Test Y
2 Test e Y
3 a a
pattern = "test|Test"
match = df['Title'].str.contains(pattern) | df['Subtitle'].str.contains(pattern)
df['Test_Flag'] = np.where(match, 'Y', '')
Using @jezrael's setup 使用@ jezrael的设置
df = pd.DataFrame(
{'Title':['test','Test','e', 'a'],
'Subtitle':['b','a','Test', 'a']})
pandas
you can stack
+ str.contains
+ unstack
你可以
stack
+ str.contains
+ unstack
import re
df.stack().str.contains('test', flags=re.IGNORECASE).unstack()
Subtitle Title
0 False True
1 False True
2 True False
3 False False
Bring it all together with 把它全部带到一起
truth_map = {True: 'Y', False: ''}
truth_flag = df.stack().str.contains(
'test', flags=re.IGNORECASE).unstack().any(1).map(truth_map)
df.assign(Test_flag=truth_flag)
Subtitle Title Test_flag
0 b test Y
1 a Test Y
2 Test e Y
3 a a
numpy
if performance is a concern 如果表现是一个问题
v = df.values.astype(str)
low = np.core.defchararray.lower(v)
flg = np.core.defchararray.find(low, 'test') >= 0
ys = np.where(flg.any(1), 'Y', '')
df.assign(Test_flag=ys)
Subtitle Title Test_flag
0 b test Y
1 a Test Y
2 Test e Y
3 a a
naive time test 天真的时间测试
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