I have a pretty simple pandas DataFrame and I want to select the portion of the DataFrame that has data within a column that contains within it another string
So if this is my DataFrame and I want those columns that contain some within the Loc column how can this be done?
Loc
0 'something'
1 'nothing'
I tried two things:
df['some' in df['Loc']]
df[df.Loc.contains('some')]
But neither solution works.
You need to use contains, one of the string accessor methods.
>>> df['Loc'].str.contains('some')
0 True
1 False
Name: Loc, dtype: bool
One would then use boolean indexing on the result to select the relevant rows of your dataframe.
df[df['Loc'].str.contains('some')]
Using replace , a fun way
df[df.Loc.replace({'some':np.nan},regex=True).isnull()]
Out[787]:
Loc
0 'something'
Update since Alex mention it in comment
df[df.Loc.apply(lambda x : 'some' in x)]
Out[801]:
Loc
0 'something'
.isnull() selection.df['Loc'] = ['something', 'nothing', 55, None]. Try lambda x : True if isinstance(x, (str, unicode)) and 'some' in x else False
df[df.Loc.astype(str).apply(lambda x : 'some' in x)], If we are talking about the mixtype