Converting Negative Number in String Format to Numeric when Sign as at the end - pandas

Question

I have certain numbers within a column of my dataframe that have negative numbers in a string format like this: "500.00-" I need to convert every negative number within the column to numeric format. I'm sure there's an easy way to do this, but I have struggled finding one specific to pandas dataframe. Any help would be greatly appreciated.
I have tried the basic to_numeric function as shown below, but it doesn't read it in correctly. Also, only some of the numbers within the column are negative, therefore I can't simply remove all the negative signs and multiply the column by 1.
Q1['Credit'] = pd.to_numeric(Q1['Credit'])

Answer 1

Sample data:
df:
num
0 50.00
1 60.00-
2 70.00+
3 -80.00
Using series str accessor to check last digit. If it is '-' or '+', swap it to front. Use df.mask to apply it only to rows having -/+ as suffix. Finally, astype column to float
df.num.mask(df.num.str[-1].isin(['-','+']), df.num.str[-1].str.cat(df.num.str[:-1])).astype('float')
Out[1941]:
0 50.0
1 -60.0
2 70.0
3 -80.0
Name: num, dtype: float64

Answer 2

Possibly a bit explicit but would work
# build a mask of negative numbers
m_neg = Q1["Credit"].str.endswith("-")
# remove - signs
Q1["Credit"] = Q1["Credit"].str.rstrip("-")
# convert to number
Q1["Credit"] = pd.to_numeric(Q1["Credit"])
# Apply the mask to create the negatives
Q1.loc[m_neg, "Credit"] *= -1

Answer 3

Let us consider the following example dataframe:
Q1 = pd.DataFrame({'Credit':['500.00-', '100.00', '300.00-']})
Credit
0 500.00-
1 100.00
2 300.00-
We can use str.endswith to create a mask which indicates the negative numbers. Then we use np.where to conditionally convert the numbers to negative:
m1 = Q1['Credit'].str.endswith('-')
m2 = Q1['Credit'].str[:-1].astype(float)
Q1['Credit'] = np.where(m1, -m2, m2)
Output
Credit
0 -500.0
1 100.0
2 -300.0