check if syntax in pandas column meets certain criteria

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I have the dataframe underneath:

df = pd.DataFrame(np.array(['YM.296','MM.305','VO.081.019','VO.081.016','AM.081.002.001','AM081','SR.082','VO.081.012.001','VO.081.012.003']))

I want to know in what row the syntax is similar to 'XX.222.333' (example). So two letters followed by a stop ('.') followed by three numbers followed by a stop ('.') followed by three numbers again.

The desired outcome looks as follows:

tf = pd.DataFrame(np.array([False,False,True,True,False,False,False,False, False]))

Is there a fast and pythonic way to do this?

pandas syntax python
1 Answers

You can do that using str.contains and regex.

As follows:

df[0].str.contains(r'^[A-Z]{2}\.\d{3}\.\d{3}$', regex=True)

Outputs:

0    False
1    False
2     True
3     True
4    False
5    False
6    False
Name: 0, dtype: bool

Here is a visualization of the regex used:

enter image description here

user_45746330
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