Rename the less frequent categories by "OTHER" python

Convert your series to categorical, extract categories whose counts are not in the top 3, add a new category e.g. 'Other', then replace the previously calculated categories:

df['Jobrol'] = df['Jobrol'].astype('category')

others = df['Jobrol'].value_counts().index[3:]
label = 'Other'

df['Jobrol'] = df['Jobrol'].cat.add_categories([label])
df['Jobrol'] = df['Jobrol'].replace(others, label)

Note: It's tempting to combine categories by renaming them via df['Jobrol'].cat.rename_categories(dict.fromkeys(others, label)), but this won't work as this will imply multiple identically labeled categories, which isn't possible.

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The above solution can be adapted to filter by count. For example, to include only categories with a count of 1 you can define others as so:

counts = df['Jobrol'].value_counts()
others = counts[counts == 1].index

Use value_counts with numpy.where:

need = df['Jobrol'].value_counts().index[:3]
df['Jobrol'] = np.where(df['Jobrol'].isin(need), df['Jobrol'], 'OTHER')

valCount = df['Jobrol'].value_counts()
print (valCount)
Research Scientist       7
Sales Executive          7
Laboratory Technician    5
OTHER                    2
Name: Jobrol, dtype: int64

Another solution:

N = 3
s = df['Jobrol'].value_counts()
valCount = s.iloc[:N].append(pd.Series(s.iloc[N:].sum(), index=['OTHER']))
print (valCount)
Research Scientist       7
Sales Executive          7
Laboratory Technician    5
OTHER                    2
dtype: int64