applying regex to a pandas dataframe

Question

I’m having trouble applying a regex function a column in a python dataframe. Here is the head of my dataframe:

               Name   Season          School   G    MP  FGA  3P  3PA    3P%
 74       Joe Dumars  1982-83   McNeese State  29   NaN  487   5    8  0.625   
 84      Sam Vincent  1982-83  Michigan State  30  1066  401   5   11  0.455   
 176  Gerald Wilkins  1982-83     Chattanooga  30   820  350   0    2  0.000   
 177  Gerald Wilkins  1983-84     Chattanooga  23   737  297   3   10  0.300   
 243    Delaney Rudd  1982-83     Wake Forest  32  1004  324  13   29  0.448

I thought I had a pretty good grasp of applying functions to Dataframes, so maybe my Regex skills are lacking.

Here is what I put together:

import re

def split_it(year):
    return re.findall('(dddd)', year)

 df['Season2'] = df['Season'].apply(split_it(x))

TypeError: expected string or buffer

Output would be a column called Season2 that contains the year before the hyphen. I’m sure theres an easier way to do it without regex, but more importantly, i’m trying to figure out what I did wrong

Thanks for any help in advance.

Asked By: itjcms18

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Source

Answer 1

When I try (a variant of) your code I get NameError: name 'x' is not defined— which it isn’t.

You could use either

df['Season2'] = df['Season'].apply(split_it)

or

df['Season2'] = df['Season'].apply(lambda x: split_it(x))

but the second one is just a longer and slower way to write the first one, so there’s not much point (unless you have other arguments to handle, which we don’t here.) Your function will return a list, though:

>>> df["Season"].apply(split_it)
74     [1982]
84     [1982]
176    [1982]
177    [1983]
243    [1982]
Name: Season, dtype: object

although you could easily change that. FWIW, I’d use vectorized string operations and do something like

>>> df["Season"].str[:4].astype(int)
74     1982
84     1982
176    1982
177    1983
243    1982
Name: Season, dtype: int64

or

>>> df["Season"].str.split("-").str[0].astype(int)
74     1982
84     1982
176    1982
177    1983
243    1982
Name: Season, dtype: int64

Answered By: DSM

Answer 2

The asked problem can be solved by writing the following code :

import re
def split_it(year):
    x = re.findall('([d]{4})', year)
    if x :
      return(x.group())

df['Season2'] = df['Season'].apply(split_it)

You were facing this problem as some rows didn’t had year in the string

Answered By: Pratik409

Answer 3

I had the exact same issue. Thanks for the answers @DSM.
FYI @itjcms, you can improve the function by removing the repetition of the 'dddd'.

def split_it(year):  
    return re.findall('(dddd)', year)

Becomes:

def split_it(year):
    return re.findall('(d{4})', year)

Answered By: Tony Alleven

Answer 4

You can simply use str.extract

df['Season2']=df['Season'].str.extract(r'(d{4})-d{2}')

Here you locate d{4}-d{2} (for example 1982-83) but only extracts the captured group between parenthesis d{4} (for example 1982)

Answered By: Gabriel

Answer 5

you can use pandas native function to do it too.

check this page for the pandas functions that accepts regular expression. for your case, you can do

df["Season"].str.extract(r'([d]{4}))')

Answered By: Julian Eccleshall

Answer 6

I’d extract with:

df['Season2']=df['Season'].str.extract(r'(d{4}))

Answered By: Simon

applying regex to a pandas dataframe

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