Is there a way to do something similar to SQL's LIKE syntax on a pandas text DataFrame column, such that it returns a list of indices, or a list of booleans that can be used for indexing the dataframe? For example, I would like to be able to match all rows where the column starts with 'prefix_', similar to WHERE <col> LIKE prefix_%
in SQL.
You can use the Series method str.startswith
(which takes a regex):
In [11]: s = pd.Series(['aa', 'ab', 'ca', np.nan])
In [12]: s.str.startswith('a', na=False)
Out[12]:
0 True
1 True
2 False
3 False
dtype: bool
You can also do the same with str.contains
(using a regex):
In [13]: s.str.contains('^a', na=False)
Out[13]:
0 True
1 True
2 False
3 False
dtype: bool
So you can do df[col].str.startswith
...
See also the SQL comparison section of the docs.
Note: (as pointed out by OP) by default NaNs will propagate (and hence cause an indexing error if you want to use the result as a boolean mask), we use this flag to say that NaN should map to False.
In [14]: s.str.startswith('a') # can't use as boolean mask
Out[14]:
0 True
1 True
2 False
3 NaN
dtype: object