I want to create categories based on particular string of keyword found true than assigned category else "other".

For example - if "health" is found in a column, name that keyword row as "HEALTH", if "therapist" then 'THERAPIST'

1. create a "category" column through code
2. assign categories based on condition

I was able to do on Excel by creating a table and using index match and want to switch to Python to apply this on large dataset,

Below is the sample data,

CodePudding user response：

1. Pandas has apply methods for this:

def f(s):
    if s.found('health')>=0:
        return 'health'
    if s.found('thera...')>=0:
        return 'thera'
    ...

df['category'] = df['text'].apply(f)

Read: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.apply.html

2. pandas has map methods for this. First create a dictionary and then use map methods.

d ={keyword1:category1, ...}
df['category'] = df['keyword'].map(d)

This is for when you have a specific dictionary. Read: https://pandas.pydata.org/docs/reference/api/pandas.Series.map.html

CodePudding user response：

You can create first a list of keywords to test or research on it . Then the empty ones fill it with "other".

import pandas as pd

#example df
df=pd.DataFrame(data=['health xxxxx','yyyy therapist','zzzzz'],columns=["keyword"])

keywords=['health','therapist']


df['category']=df['keyword'].str.findall('|'.join(keywords)).apply(set).str.join(',')
df = df.apply(lambda x: x.str.strip()).replace('', "other")

CodePudding user response：

The easiest solution would be

import pandas as pd
df = your dataframe

assigning the default value first. You could set to None, or np.nan too

df['category'] = 'others'
df.loc[df.keyword.str.contains('therapist'),'category'] = 'therapist'