如何使用AND运算符过滤包含特定字符串值的行

Question

My question is kind of an extension of the question answered quite well in this link:

• How to filter rows containing a string pattern from a Pandas dataframe

I've posted the answer here below where the strings are filtered out when they contain the word "ball":

In [3]: df[df['ids'].str.contains("ball")]
Out[3]:
     ids     vals
0  aball     1
1  bball     2
3  fball     4

Now my question is: what if I have long sentences in my data, and I want to identify strings with the words "ball" AND "field"? So that it throws away data that contains the word "ball" or "field" when only one of them occur, but keeps the ones where the string has both words in it.

Answer 1

df[df['ids'].str.contains("ball")]

Would become:

df[df['ids'].str.contains("ball") & df['ids'].str.contains("field")]

If you are into neater code:

contains_balls = df['ids'].str.contains("ball")
contains_fields = df['ids'].str.contains("field")

filtered_df = df[contains_balls & contains_fields]

Answer 2

If you have more than 2 , You can using this ..(Notice the speed is not as good as foxyblue's method )

l = ['ball', 'field']
df.ids.apply(lambda x: all(y in x for y in l))

Answer 3

You could use np.logical_and.reduce and str.contains takes care of multiple words.

df[np.logical_and.reduce([df['ids'].str.contains(w) for w in ['ball', 'field']])]

In [96]: df
Out[96]:
             ids
0  ball is field
1     ball is wa
2  doll is field

In [97]: df[np.logical_and.reduce([df['ids'].str.contains(w) for w in ['ball', 'field']])]
Out[97]:
             ids
0  ball is field

Answer 4

Yet another RegEx approach:

In [409]: df
Out[409]:
               ids
0   ball and field
1  ball, just ball
2      field alone
3  field and ball

In [410]: pat = r'(?:ball.*field|field.*ball)'

In [411]: df[df['ids'].str.contains(pat)]
Out[411]:
               ids
0   ball and field
3  field and ball