How to find all rows in a dataframe that contain a substring?

Question

I have one word and a Pandas dataframe with a column of string values. Now I'm trying to find the rows in that dataframe which contain that word in their string part.

I read about extractall() method but I'm not sure how to use it or if its even the right answer.

Is it similar to the issue referred here stackoverflow.com/a/11531402/5916727, you can use df[df['column_name'].str.contains("your_string")] — niraj
– niraj, Commented Mar 20, 2017 at 17:36

Alex · Accepted Answer · 2017-03-20 20:00:38Z

4

Using this test data (modified and borrowed from Chris Albon):

raw_data = {'regiment': ['Nighthawks Goons', 'Nighthawks Goons', 'Nighthawks', 'Nighthawks', 'Dragoons', 'Dragoons', 'Dragoons', 'Dragoons', 'Scouts', 'Scouts', 'Scouts', 'Scouts'],
        'company': ['1st', '1st', '2nd', '2nd', '1st', '1st', '2nd', '2nd','1st', '1st', '2nd', '2nd'],
        'name': ['Miller', 'Jacobson', 'Ali', 'Milner', 'Cooze', 'Jacon', 'Ryaner', 'Sone', 'Sloan', 'Piger', 'Riani', 'Ali'],
        'preTestScore': [4, 24, 31, 2, 3, 4, 24, 31, 2, 3, 2, 3],
        'postTestScore': [25, 94, 57, 62, 70, 25, 94, 57, 62, 70, 62, 70]}
df = pd.DataFrame(raw_data, columns = ['regiment', 'company', 'name', 'preTestScore', 'postTestScore'])

You can use this to find rows that contain the word goons only (I am ignoring the case):

df[df['regiment'].str.contains(r"\bgoons\b", case = False)]

edited Mar 20, 2017 at 20:00

answered Mar 20, 2017 at 17:43

Alex

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piRSquared · Accepted Answer · 2017-03-20 17:35:56Z

2

use str.contains

df.mycolumn.str.contains(myword)

demonstration

myword = 'foo'
df = pd.DataFrame(dict(mycolumn=['abc', '__foo__']))

df.mycolumn.str.contains(myword)

0    False
1     True
Name: mycolumn, dtype: bool

answered Mar 20, 2017 at 17:35

piRSquared

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Shenglin Chen · Accepted Answer · 2017-03-20 18:51:58Z

0

Use jato's example.

In [148]: df[['Goons' in i for i  in  df.regiment]]
Out[148]:
           regiment company      name  preTestScore  postTestScore
0  Nighthawks Goons     1st    Miller             4             25
1  Nighthawks Goons     1st  Jacobson            24             94

answered Mar 20, 2017 at 18:51

Shenglin Chen

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