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Data Wrangling in Python – How to Break Up A String Into Columns Using Regex In pandas

By on Monday, May 31, 2021

Breaking Up A String Into Columns Using Regex In pandas

Import modules


import re
import pandas as pd

Create a dataframe of raw strings

/* Create a dataframe with a single column of strings */
data = {'raw': ['Arizona 1 2014-12-23       3242.0',
                'Iowa 1 2010-02-23       3453.7',
                'Oregon 0 2014-06-20       2123.0',
                'Maryland 0 2014-03-14       1123.6',
                'Florida 1 2013-01-15       2134.0',
                'Georgia 0 2012-07-14       2345.6']}
df = pd.DataFrame(data, columns = ['raw'])
df
raw
0 Arizona 1 2014-12-23 3242.0
1 Iowa 1 2010-02-23 3453.7
2 Oregon 0 2014-06-20 2123.0
3 Maryland 0 2014-03-14 1123.6
4 Florida 1 2013-01-15 2134.0
5 Georgia 0 2012-07-14 2345.6

Search a column of strings for a pattern

/* Which rows of df['raw'] contain 'xxxx-xx-xx'? */
df['raw'].str.contains('....-..-..', regex=True)
0    True
1    True
2    True
3    True
4    True
5    True
Name: raw, dtype: bool

Extract the column of single digits

/* In the column 'raw', extract single digit in the strings */
df['female'] = df['raw'].str.extract('(d)', expand=True)
df['female']
0    1
1    1
2    0
3    0
4    1
5    0
Name: female, dtype: object

Extract the column of dates

/* In the column 'raw', extract xxxx-xx-xx in the strings */
df['date'] = df['raw'].str.extract('(....-..-..)', expand=True)
df['date']
0    2014-12-23
1    2010-02-23
2    2014-06-20
3    2014-03-14
4    2013-01-15
5    2012-07-14
Name: date, dtype: object

Extract the column of thousands

/* In the column 'raw', extract ####.## in the strings */
df['score'] = df['raw'].str.extract('(dddd.d)', expand=True)
df['score']
0    3242.0
1    3453.7
2    2123.0
3    1123.6
4    2134.0
5    2345.6
Name: score, dtype: object

Extract the column of words

/* In the column 'raw', extract the word in the strings */
df['state'] = df['raw'].str.extract('([A-Z]w{0,})', expand=True)
df['state']
0     Arizona
1        Iowa
2      Oregon
3    Maryland
4     Florida
5     Georgia
Name: state, dtype: object

View the final dataframe

df
raw female date score state
0 Arizona 1 2014-12-23 3242.0 1 2014-12-23 3242.0 Arizona
1 Iowa 1 2010-02-23 3453.7 1 2010-02-23 3453.7 Iowa
2 Oregon 0 2014-06-20 2123.0 0 2014-06-20 2123.0 Oregon
3 Maryland 0 2014-03-14 1123.6 0 2014-03-14 1123.6 Maryland
4 Florida 1 2013-01-15 2134.0 1 2013-01-15 2134.0 Florida
5 Georgia 0 2012-07-14 2345.6 0 2012-07-14 2345.6 Georgia

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