pandas applying regex to replace values

Question

I have read some pricing data into a pandas dataframe the values appear as:

$40,000*
$40000 conditions attached

I want to strip it down to just the numeric values.
I know I can loop through and apply regex

[0-9]+

to each field then join the resulting list back together but is there a not loopy way?

Thanks

Asked By: KillerSnail

||

Source

Answer 1

You could remove all the non-digits using re.sub():

value = re.sub(r"[^0-9]+", "", value)

regex101 demo

Answered By: Jerry

Answer 2

You could use Series.str.replace:

import pandas as pd

df = pd.DataFrame(['$40,000*','$40000 conditions attached'], columns=['P'])
print(df)
#                             P
# 0                    $40,000*
# 1  $40000 conditions attached

df['P'] = df['P'].str.replace(r'D+', '', regex=True).astype('int')
print(df)

yields

       P
0  40000
1  40000

since D matches any character that is not a decimal digit.

Answered By: unutbu

Answer 3

You don’t need regex for this. This should work:

df['col'] = df['col'].astype(str).convert_objects(convert_numeric=True)

Answered By: samthebrand

Answer 4

You could use pandas’ replace method; also you may want to keep the thousands separator ‘,’ and the decimal place separator ‘.’

import pandas as pd

df = pd.DataFrame(['$40,000.32*','$40000 conditions attached'], columns=['pricing'])
df['pricing'].replace(to_replace="$([0-9,.]+).*", value=r"1", regex=True, inplace=True)
print(df)
pricing
0  40,000.32
1      40000

Answered By: Pluto

Answer 5

In case anyone is still reading this. I’m working on a similar problem and need to replace an entire column of pandas data using a regex equation I’ve figured out with re.sub

To apply this on my entire column, here’s the code.

#add_map is rules of replacement for the strings in pd df.
add_map = dict([
    ("AV", "Avenue"),
    ("BV", "Boulevard"),
    ("BP", "Bypass"), 
    ("BY", "Bypass"),
    ("CL", "Circle"),
    ("DR", "Drive"),
    ("LA", "Lane"),
    ("PY", "Parkway"),
    ("RD", "Road"),
    ("ST", "Street"),
    ("WY", "Way"),
    ("TR", "Trail"),
    
      
])

obj = data_909['Address'].copy() #data_909['Address'] contains the original address'
for k,v in add_map.items(): #based on the rules in the dict
    rule1 = (r"(b)(%s)(b)" % k) #replace the k only if they're alone (lookup 
b)
    rule2 = (lambda m: add_map.get(m.group(), m.group())) #found this online, no idea wtf this does but it works
    obj = obj.str.replace(rule1, rule2, regex=True, flags=re.IGNORECASE) #use flags here to avoid the dictionary iteration problem
data_909['Address_n'] = obj #store it!

Hope this helps anyone searching for the problem I had. Cheers

Answered By: E. Goldsmi

pandas applying regex to replace values

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