I have a pandas dataframe with a salary column which contains values like:

£36,000 - £40,000 per year plus excellent bene..., £26,658 to £32,547 etc

I isolated this column and split it with the view to recombining into the data frame later via a column bind in pandas.

I now have an object with columns like the below. The columns I split the original data frame column I think are blank because I didn't specify them (I called df['salary']=df['salary'].astype(str).str.split() )

So my new object contains this type of information:

[£26,658, to, £32,547], [Competitive, with, Excellent, Benefits]

What I want to do is:

  1. Create three columns called minvalue and maxvalue and realvalue
  2. List items starting with £ (something to do with "^£"?
  3. Take till the end of the items found ignoring the £ (get the number out) (something to do with (substr(x,2,nchar(x)))?
  4. If there are two such items found, call the first number "minvalue" and call the second number "maxvalue" and put it below the right column. If there is only one value in the row, put it below the realvalue column.

I am very new to pandas and programming in general, but keen on learning, your help would be appreciated.


2 Answers 2


This is more of a general regex question, rather than pandas specific.

I would first create a function that extracts the numbers you need from strings, and then use the pandas.DataFrame.apply function to apply it on the pandas column containing the strings. Here is what I would do:

import re
def parseNumbers(salary_txt):
    return [int(item.replace(',','')) for item in re.findall('£([\d,]+)',salary_txt)]

#testing if this works
testcases = ['£23,000 to £100,000','£34,000','£10000']
for testcase in testcases:
    print testcase,parseNumbers(testcase)

Here, I just used re.findall, which finds all patterns that look like £([\d,]+). This is anything that starts with £ and is followed by an arbitrary sequence of digits and commas. The parenthesis tells python to extract only the bit after the £ sign. The last thing I do is I remove commas, and parse the remaining string into an integer. You could be more elegant about this I guess, but it works.

using this function in pandas

df['salary_list'] = df['salary'].apply(parseNumbers)
df['minsalary'] = df['salary'].apply(parseNumbers).apply(min)
df['maxsalary'] = df['salary'].apply(parseNumbers).apply(max)

Checking if this all works:

import pandas
df = pandas.DataFrame(testcases,columns = ['salary'])
df['minsalary'] = df['salary'].apply(parseNumbers).apply(min)
df['maxsalary'] = df['salary'].apply(parseNumbers).apply(max)

    salary  minsalary   maxsalary
0   £23,000 to £100,000 23000   100000
1   £34,000 34000   34000
2   £10000  10000   10000

The advantages of moving the parsing logic to a separate function is that:

  1. it may be reusable in other code
  2. it is easier to read for others, even if they aren't pandas experts
  3. it's easier to develop and test the parsing functionality in isolation
  • $\begingroup$ This is extremely helpful, and works. One complication I have which maybe wasn't clear is that each cell in each row df['salary'] is a list, not a string (i.e. [£26,658, to, £32,547], [Competitive, with, Excellent, Benefits], not [£26,658 to £32,547],[Competitive with Excellent Benefits] . So when I run your function I will get TypeError: expected string or buffer. I tried solving this via df['salary']=df['salary'].apply(''.join(df['salary'])) just after you define df in your second line of code of the second section, but get TypeError: sequence item 0: expected string, list found. $\endgroup$
    – Dhruv
    Feb 22, 2015 at 14:04
  • $\begingroup$ first, apply takes a function as argument, so you should write df['salary'].apply(' '.join). Note also that I would do ' '.join as opposed to ''.join to avoid accidentally concatenating strings that should not be. Secondly, the only reason you have the lists is because you did df['salary']=df['salary'].astype(str).str.split(). My solution doesn't require this step, so you can just work with the un-splitted strings as they are. re.findall will scan through an entire string and extract everything that looks like it's a reference to money. $\endgroup$ Feb 22, 2015 at 14:29
  • $\begingroup$ I am still getting TypeError: expected string or buffer even when I removed df['salary']=df['salary'].astype(str).str.split(). When I print df['salary'] it certainly looks now like a column of strings. Even when I did df['salary']=df['salary'].astype(int32) based on stackoverflow.com/questions/21841402/… I get ValueError: invalid literal for long() with base 10: '\xc2\xa336,000 - \xc2\xa340,000 per year plus excellent benefits'. I read my CSV in as UTF-8 by the way. $\endgroup$
    – Dhruv
    Feb 23, 2015 at 13:27

You can check the data types of your columns by doing df.dtypes, and if 'salary' isn't a string, you can convert it using df['salary'] = df['salary'].astype(str). This is what you were already doing before splitting. From there, Ferenc's method should work!

  • $\begingroup$ I tried this and now I have gone down a rabbithole of encoding problems after this error when trying to convert to string (I read my CSV as encoding=UTF-8') - UnicodeEncodeError: 'ascii' codec can't encode character u'\xa3' in position 0: ordinal not in range(128) (stackoverflow.com/questions/3588083/…). Then, when I try to encode or decode df['salary'] it says AttributeError: 'Series' object has no attribute 'encode'. Basically what I think is that the pound signs which are now in UTF-8 cannot be converted to strings? $\endgroup$
    – Dhruv
    Feb 28, 2015 at 13:12

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