Python pandas: remove everything after a delimiter in a string

Question

I have data frames which contain e.g.:

"vendor a::ProductA"
"vendor b::ProductA"
"vendor a::Productb"

I need to

Answer 1

If it is in a specific column (having name: column) of a data frame (having name: dataframe), you can also use

dataframe.column.str.replace("(::).*","")

It gives you the below result

         column        new_column       
0  vendor a::ProductA  vendor a
1  vendor b::ProductA  vendor b
2  vendor a::Productb  vendor a

By using this you need not specify any position, as it gets rid of anything present after '::'

I guess this might come oh help,Good luck!

Answer 2

there is your function:

def do_it(str):
  integer=0
  while integer<len(str):
      if str[integer]==':' :
        if str[integer+1]==':' :
          str=str.split(':')[0]
          break;
      integer=integer+1    
  return (str)

pass the original string in there. And get the new trimmed string.

Answer 3

You can use str.replace(":", " ") to remove the "::". To split, you need to specify the character you want to split into: str.split(" ")

The trim function is called strip in python: str.strip()

Also, you can do str[:7] to get just "vendor x" in your strings.

Good luck

Answer 4

You can use pandas.Series.str.split just like you would use split normally. Just split on the string '::', and index the list that's created from the split method:

>>> df = pd.DataFrame({'text': ["vendor a::ProductA", "vendor b::ProductA", "vendor a::Productb"]})
>>> df
                 text
0  vendor a::ProductA
1  vendor b::ProductA
2  vendor a::Productb
>>> df['text_new'] = df['text'].str.split('::').str[0]
>>> df
                 text  text_new
0  vendor a::ProductA  vendor a
1  vendor b::ProductA  vendor b
2  vendor a::Productb  vendor a

Here's a non-pandas solution:

>>> df['text_new1'] = [x.split('::')[0] for x in df['text']]
>>> df
                 text  text_new text_new1
0  vendor a::ProductA  vendor a  vendor a
1  vendor b::ProductA  vendor b  vendor b
2  vendor a::Productb  vendor a  vendor a

Edit: Here's the step-by-step explanation of what's happening in pandas above:

# Select the pandas.Series object you want
>>> df['text']
0    vendor a::ProductA
1    vendor b::ProductA
2    vendor a::Productb
Name: text, dtype: object

# using pandas.Series.str allows us to implement "normal" string methods 
# (like split) on a Series
>>> df['text'].str
<pandas.core.strings.StringMethods object at 0x110af4e48>

# Now we can use the split method to split on our '::' string. You'll see that
# a Series of lists is returned (just like what you'd see outside of pandas)
>>> df['text'].str.split('::')
0    [vendor a, ProductA]
1    [vendor b, ProductA]
2    [vendor a, Productb]
Name: text, dtype: object

# using the pandas.Series.str method, again, we will be able to index through
# the lists returned in the previous step
>>> df['text'].str.split('::').str
<pandas.core.strings.StringMethods object at 0x110b254a8>

# now we can grab the first item in each list above for our desired output
>>> df['text'].str.split('::').str[0]
0    vendor a
1    vendor b
2    vendor a
Name: text, dtype: object

I would suggest checking out the pandas.Series.str docs, or, better yet, Working with Text Data in pandas.