The simple dataframe replace shown below is not working.
The NewPhone column contains the same value as the original column.
import pandas as pd
SF = pd.read_csv(r"xxx.csv")
SF['NewPhone'] = SF['Phone'].replace("(",'xxx')
print(SF['NewPhone'])
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2 Answers
replace looks for exact matches (by default unless you pass regex=True but you will need to escape the parentheses - see @piRSquared's answer), you want str.replace:
SF['NewPhone'] = SF['Phone'].str.replace("(",'xxx')
which will replace all occurrences of the passed in string with the new string
Example:
In[20]:
df = pd.DataFrame({'phone':['(999)-63266654']})
df
Out[20]:
phone
0 (999)-63266654
In[21]:
df['phone'].str.replace("(",'xxx')
Out[21]:
0 xxx999)-63266654
Name: phone, dtype: object
If we try replace then no match occurs:
In[22]:
df['phone'].replace("(",'xxx')
Out[22]:
0 (999)-63266654
Name: phone, dtype: object
See @piRSquared's answer for how to get replace to work as expected (I don't want to cannibalise his answer)
Comments
The Series.replace method takes a regex argument that is False by default. Set it to True. Also, if the string to be replaced is interpreted as a regex pattern, we'll need to escape the opening parenthesis.
df.phone.replace("\(", 'xxx', regex=True)
0 xxx999)-63266654
Name: phone, dtype: object
Setup from @EdChum
df = pd.DataFrame({'phone':['(999)-63266654']})
1 Comment
piRSquared
I still think I prefer your answer. But it's good to remind people of this option. It's also available for the
DataFrame variant.