Extract two specified words from the dataframe and place them in a new column, then delete the rows

This is the dataframe:

data = {"Company" : [["ConsenSys"] , ["Cognizant"], ["IBM"], ["IBM"], ["Reddit, Inc"], ["Reddit, Inc"], ["IBM"]],
"skills" : [['services', 'scientist technical expertise', 'databases'], ['datacomputing tools experience', 'deep learning models', 'cloud services'], ['quantitative analytical projects', 'financial services', 'field experience'],
['filesystems server architectures', 'systems', 'statistical analysis', 'data analytics', 'workflows', 'aws cloud services'], ['aws services'], ['data mining statistics', 'statistical analysis', 'aws cloud', 'services', 'data discovery', 'visualization'], ['communication skills experience', 'services', 'manufacturing environment', 'sox compliance']]}

dff = pd.DataFrame(data)
dff

I need to create a new column, and I want to start by taking specific words out of the skills column.
The row that does not include those specific words should then be deleted.
Specific words: 'services', 'statistical analysis'

Expected Output:

	Company	skills	new_col
0	[ConsenSys]	[services, scientist technical expertise, databases]	[services]
1	[IBM]	[filesystems server architectures, systems, statistical analysis, data analytics, workflows, aws cloud services]	[services, statistical analysis]
2	[Reddit, Inc]	[data mining statistics, statistical analysis, aws cloud, services, data discovery, visualization]	[statistical analysis]
3	[IBM]	['communication skills experience', 'services', 'manufacturing environment', 'sox compliance']	[services]

I tried quite a lot of code in an effort to extract a specific word from the one that was available on Stack Overflow, but I was unsuccessful.

Solution

word = ['services', 'statistical analysis']
s1 = df['skills'].apply(lambda x: [i for i in word if i in x])

output(s1):

0                          [services]
1                                  []
2                                  []
3              [statistical analysis]
4                                  []
5    [services, statistical analysis]
6                          [services]
Name: skills, dtype: object

make s1 to new_col and boolean indexing

df.assign(new_col=s1)[lambda x: x['new_col'].astype('bool')]

result:

    Company skills  new_col
0   [ConsenSys] [services, scientist technical expertise, data...   [services]
3   [IBM]   [filesystems server architectures, systems, st...   [statistical analysis]
5   [Reddit, Inc]   [data mining statistics, statistical analysis,...   [services, statistical analysis]
6   [IBM]   [communication skills experience, services, ma...   [services]

i think you should make more simple example