PANDAS : consolidate lines with lambda function into one line-CodePudding

I have a data-frame where I need to perform similar steps for a one column like in the example below:

dfd_landing['Supplier name'] = dfd_landing['Supplier name'].apply(lambda x : x.replace(',',''))
dfd_landing['Supplier name'] = dfd_landing['Supplier name'].apply(lambda x : x.replace('.',' '))
dfd_landing['Supplier name'] = dfd_landing['Supplier name'].apply(lambda x : x.replace('-',' '))
dfd_landing['Supplier name'] = dfd_landing['Supplier name'].str.strip()

Is there any way to consolidate all these steps into one line just for sake of not repeating the lines?

CodePudding user response：

You can use method chaining. You can also use .str.replace instead of the lambda function. Finally you can use regex to replace all of those symbols at once:

dfd_landing['Supplier name'] = (
    dfd_landing['Supplier name']
    .str.replace(r'[,.-]','', regex=True)
    .str.strip()
)

I like to format method chains by putting everything inside paratheses and starting each method on a new line. I typically think of the .str method as more of a prefix than a method to I keep it on the same line as the main string method I am trying to use.

Sample input:

dfd_landing = pd.DataFrame({'Supplier name': [', foo.-bar ']})

Output:

  Supplier name
0        foobar

CodePudding user response：

Below mentioned simple code snippet and minor modification in your code should work for you:

dfd_landing['Supplier name'] = dfd_landing['Supplier name'].apply(lambda x : x.replace(',','').replace('.',' ').replace('-',' ').strip())

Sample input:

dfd_landing = pd.DataFrame({'Supplier name': ['A,asda', 'B.asdas', 'C-asdasd', ' D ', 'E '],})

  Supplier name
0        A,asda
1       B.asdas
2      C-asdasd
3            D 
4            E

Code and Sample output:

dfd_landing['Supplier name'] = dfd_landing['Supplier name'].apply(lambda x : x.replace(',','').replace('.',' ').replace('-',' ').strip())

Supplier name
0         Aasda
1       B asdas
2      C asdasd
3             D
4             E