Not able to replace the string containing $ in pandas column

Question

I have a dataframe

df = pd.DataFrame({'a':[1,2,3], 'b':[5, '12$sell', '1$sell']})

I want to replace $sell from column b.

So I tried replace() method like below

df['b'] = df['b'].str.replace("$sell","")

but it's doesn't replace the given string and it gives me same dataframe as original.

It's working when I use it with apply

df['b'] = df['b'].apply(lambda x: str(x).replace("$sell",""))

So I want to know why it is not working in previous case?

Note: I tried replacing only $ and shockingly it works.

Answer 1

It is regex metacharacter (end of string), escape it or add parameter regex=False:

df['b'] = df['b'].str.replace("\$sell","")
print (df)
   a    b
0  1  NaN
1  2   12
2  3    1

---

df['b'] = df['b'].str.replace("$sell","", regex=False)

If want also value 5, what is numeric, use Series.replace with regex=True for replace substrings - numeric values are not touched:

df['b'] = df['b'].replace("\$sell","", regex=True)

print (df['b'].apply(type))
0    <class 'int'>
1    <class 'str'>
2    <class 'str'>
Name: b, dtype: object

Or cast to strings all data of column:

df['b'] = df['b'].astype(str).str.replace("$sell","", regex=False)

print (df['b'].apply(type))
0    <class 'str'>
1    <class 'str'>
2    <class 'str'>
Name: b, dtype: object

And for better performance if no missing values is possible use list comprehension:

df['b'] = [str(x).replace("$sell","") for x in  df['b']]

print (df)
   a   b
0  1   5
1  2  12
2  3   1