pandas applying regex to replace values

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.

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

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

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

regex101 demo

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

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