0

Code used:

def fn(x):
    for i in x:
        x=x.replace('Wood','Wooden')
        return x


test['Coming:'] = test['Column:'].apply(fn)

Sample output:

Column:       Coming:      Needed:
 Wood         Wooden       Wooden                                        
Wooden       Woodenen      Wooden

I want Wooden and similare categories to be intact like Woodings, woods etc.. Also Column: could be string e.g "Wood is there on the ground" and needed output is "Wooden is there on the ground"

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2 Answers 2

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1

Here is one way to replace all substrings in a dictionary. Just note that the order may become important if any of the values and keys of the dictionary collide:

import pandas as pd

s = pd.Series(['Wood', 'Wooden', 'Woody Woodpecker', 'wood', 'wood', 'wool suit'])

d = {'Wood': 'Wooden', 'wool': 'soft'}

for k, v in d.items():
    s = s.str.replace(k, v)

# 0                  Wooden
# 1                Woodenen
# 2    Woodeny Woodenpecker
# 3                    wood
# 4                    wood
# 5               soft suit
# dtype: object
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4 Comments

Poignant remark about the dictionary order. But the OP question doesn't even mention a dictionary. I just introduced it in my answer, because I thought one might want to substitute more than one word.
@MrT, I struggled to find a vectorised approach. I hope one exists as this is loopy.
I want Wooden to be intact, but it is getting converted to 'Woodenen'
Can you open a new question? This is going far beyond your original request. That way you can clearly tell us the rules you wish to apply.
1

You can use pandas replace function. Define in a dictionary, what you want to replace and substitute the words in your new column:

import pandas as pd
#test data
df = pd.DataFrame(["Wood", "Wooden", "Woody Woodpecker", "wood", "wool", "wool suit"], columns = ["old"])
#dictionary for substitutions
subst_dict = {"Wood": "Wooden", "wool": "soft"}
df["new"] = df["old"].replace(subst_dict)
#output
                old               new
0              Wood            Wooden
1            Wooden            Wooden
2  Woody Woodpecker  Woody Woodpecker
3              wood              wood
4              wool              soft
5         wool suit         wool suit

Though for more complex substitutions utilising regex, it might be a good idea to write a function and use your apply() approach.

Update after changing the requirements:
If you want to match only whole words in phrases, you can use regex:

import pandas as pd
#test data
df = pd.DataFrame(["Wood", "Wooden", "Woody Woodpecker", "wood", "wool", "wool suit", "Wood is delicious", "A beautiful wool suit"], columns = ["old"])
#dictionary for substitutions
subst_dict = {"Wood": "Wooden", "wool": "soft"}
#create dictionary of regex expressions
temp_dict = {r'(\b){}(\b)'.format(k) : v for k, v in subst_dict.items()}
#and substitute
df["new"] = df["old"].replace(temp_dict, regex = True)
#output
                     old                    new
0                   Wood                 Wooden
1                 Wooden                 Wooden
2       Woody Woodpecker       Woody Woodpecker
3                   wood                   wood
4                   wool                   soft
5              wool suit              soft suit
6      Wood is delicious    Wooden is delicious
7  A beautiful wool suit  A beautiful soft suit
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2 Comments

but it doesn't work on String. e.g. if old= "Wood is there in the garden" What I want is "Wooden is there in the garden" Please help its urgent
Please don't change your question by adding new rules. It is rather suggested to ask a new question.

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