Phone number cleaning

I have a list of phone numbers in pandas like this:

I want to clean the phone numbers based on two rules

1. If the length of string is 11, number should start with ’03’
2. If the length of string is 12, number should start with ‘923’

I want to discard all other numbers.

So far I have tried creating two seperate columns by following code:

before_cust['digits'] = before_cust['Information'].str.len()
before_cust['starting'] = before_cust['Information'].astype(str).str[:3]
before_cust.loc[((before_cust['digits'] == 11) & before_cust[before_cust['starting'].str.contains("03")==True]) | ((before_cust['digits'] == 12) & (before_cust[before_cust['starting'].str.contains("923")==True]))]

However this code doesn’t work. Is there a more efficient way to do this?

>Solution :

Create 2 boolean masks for each condition then filter out your dataframe:

# If the length of string is 11, number should start with '03'
m1 = df['Information'].str.len().eq(11) & df['Information'].str.startswith('03')

# If the length of string is 12, number should start with '923'
m2 = df['Information'].str.len().eq(12) & df['Information'].str.startswith('923')

out = df.loc[m1|m2]
print(out)

# Output:
    Information
0  923*********

Note: I think it doesn’t work because you use str.contains rather than str.startswith.