what is the use of .str[0] in pandas dataframe

I am having a dataframe df which has sales_area column i have to remove the MV- from the beginning of the columns_value

             Sales_area
          MV-Schmidt Carsten
          MV-Schmidt Carsten
          MV-Schmidt Carsten
          MV-Schmidt Carsten
          MV-Schmidt Carsten

so i used split function

d['SALES_AREA'].str.split('-')[1]

getting result

['MV', 'Schmidt Carsten']

when i try this

d['SALES_AREA'].str.split('-').str[1]

i got correct result

         Schmidt Carsten
         Schmidt Carsten
         Schmidt Carsten
         Schmidt Carsten

i got results. but i am not able to understand why i am getting result when i use str[0] instead of [0]

The str attribute produces a proxy of type StringMethods that wraps d['Sales_AREA']. The split method then is not invoked on the column itself, but on each str value in that column. The results are then returned in a new Series object with the same shape.

df[...].str[0] works because ...[0] invokes the __getitem__ method of the object, so df[...].str[0] is the same as df[...].str.__getitem__(0).

When you use d['SALES_AREA'].str.split('-')[1] you are effectively doing this:

d = pd.DataFrame(['MV-Schmidt Carsten', 'MV-Schmidt Carsten', 'MV-Schmidt Carsten'], columns=['Sales_area'])
d['Split'] = d['Sales_area'].str.split('-')
print(d)

           Sales_area                  Split
0  MV-Schmidt Carsten  [MV, Schmidt Carsten]
1  MV-Schmidt Carsten  [MV, Schmidt Carsten]
2  MV-Schmidt Carsten  [MV, Schmidt Carsten]

Your first line of code is the equivalent of d['Split'][1]; It returns the second element of the dataframe after applying the split function, i.e. the list of the two split values.

['MV', 'Schmidt Carsten']

By doing d['SALES_AREA'].str.split('-').str[1] you are instead returning the second element of the list created by the split() function. You can also index this like d['Sales_area'].str.split('-')[1][1] to return the second value of a specific row

In pandas, when you use the "str.split()" function on a column, it splits the values into a list of substrings based on the delimiter specified making a new column where each value is a list.

To remove the 'MV-' from the beginning of the values in the SALES_AREA column

d['SALES_AREA'] = d['SALES_AREA'].str.replace('MV-', '')

This replaces 'MV-' with an empty string.

Or you can use

.strip('MV-')

This will remove MV- from the start and end of string.