CODEWITHSUNDEEP
python-3.xpandas
user8970863
user8970863

Reputation:

Assign column contents to categories

I have a data frame with one column of sub-instances of a larger group, and want to categorize this into a smaller number of groups. How do I do this?

Consider the following sample data:

df = pd.DataFrame({     
'a':np.random.randn(60),
'b':np.random.choice( [5,7,np.nan], 60),
'c':np.random.choice( ['panda', 'elephant', 'python', 'anaconda', 'shark', 'clown fish'], 60),

# some ways to create systematic groups for indexing or groupby
'e':np.tile(   range(20), 3 ),

# a date range and set of random dates
})

I now would want, in a new row, e.g. panda and elephant categorized as mammals, etc.

Upvotes: 1

Views: 58

Answers (2)

jezrael
jezrael

Reputation: 862661

I think need map with fillna for replace NaNs if non match values:

#borrowed dict from Ivo's answer
mapping_dict = {'panda': 'mammal', 'elephant': 'mammal', 
               'python': 'snake', 'anaconda': 'snake', 
               'shark': 'fish', 'clown fish': 'fish'}

df['d'] = df['c'].map(mapping_dict).fillna('not_matched')

Also if change format of dictionary is possible generate final dictioanry with swap keys with values:

d = {'mammal':['panda','elephant'], 
     'snake':['python','anaconda'], 
     'fish':['shark','clown fish']}
mapping_dict = {k: oldk for oldk, oldv in d.items() for k in oldv}

df['d'] = df['c'].map(mapping_dict).fillna('not_matched')

Upvotes: 0

Ivo
Ivo

Reputation: 4200

The most intuitive would be to create a new series, create a dict and then remap according to it:

mapping_dict = {'panda': 'mammal', 'elephant': 'mammal', 'python': 'snake', 'anaconda': 'snake', 'shark': 'fish', 'clown fish': 'fish'}

c_Series = pd.Series(df['c'])     # create new series
classified_c = c_Series.map(mapping_dict)     # remap new series
if 'c_classified' not in df.columns: df.insert(3, 'c_classified', classified_c) # insert if not in df already (if you want to run the code multiple times

Upvotes: 1

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