如何使用 python 中的 pandas 将多个关键字与数据框列值映射
How to map sevaral keywords with a dataframe column values using pandas in python
你好,我有一个关键字列表。
keyword_list=['one','two']
DF,
Name Description
Sri Sri is one of the good singer in this two
Ram Ram is one of the good cricket player
我想找到包含我 keyword_list 中所有值的行。
我想要的输出是,
output_Df,
Name Description
Sri Sri is one of the good singer in this two
I tried, mask=DF['Description'].str.contains() method but I can do this only for a single word pls help.
使用 list comprehension
创建的所有掩码中的 np.logical_and + reduce:
keyword_list=['one','two']
m = np.logical_and.reduce([df['Description'].str.contains(x) for x in keyword_list])
df1 = df[m]
print (df1)
Name Description
0 Sri Sri is one of the good singer in this two
面膜的替代品:
m = np.all([df['Description'].str.contains(x) for x in keyword_list], axis=0)
#if no NaNs
m = [set(x.split()) >= set(keyword_list) for x in df['Description']]
你好,我有一个关键字列表。
keyword_list=['one','two']
DF,
Name Description
Sri Sri is one of the good singer in this two
Ram Ram is one of the good cricket player
我想找到包含我 keyword_list 中所有值的行。
我想要的输出是,
output_Df,
Name Description
Sri Sri is one of the good singer in this two
I tried, mask=DF['Description'].str.contains() method but I can do this only for a single word pls help.
使用 list comprehension
创建的所有掩码中的 np.logical_and + reduce:
keyword_list=['one','two']
m = np.logical_and.reduce([df['Description'].str.contains(x) for x in keyword_list])
df1 = df[m]
print (df1)
Name Description
0 Sri Sri is one of the good singer in this two
面膜的替代品:
m = np.all([df['Description'].str.contains(x) for x in keyword_list], axis=0)
#if no NaNs
m = [set(x.split()) >= set(keyword_list) for x in df['Description']]