CODEWITHSUNDEEP
pythonpandas
emax
emax

Reputation: 7235

Python: how to assign unique IDs to pandas dataframe entries

I have a dafarame like the following:

df
    Name1   Name2
0   John    Jack
1   John    Albert
2   Jack    Eva
3   Albert  Sara
4   Eva     Sara

I want to assign to each a name a unique ID. So:

df
    Name1   Name2      ID1     ID2
0   John    Jack        0       1
1   John    Albert      0       2
2   Jack    Eva         1       3
3   Albert  Sara        2       5
4   Eva     Sara        3       5

Upvotes: 4

Views: 213

Answers (2)

timgeb
timgeb

Reputation: 78690

If it's not important which name gets which number, you can also consider 

df.join(df.stack().astype('category').cat.codes.unstack() 
          .rename(columns=lambda c: c.replace('Name', 'ID')))

which produces 

    Name1   Name2  ID1  ID2
0    John    Jack    3    2
1    John  Albert    3    0
2    Jack     Eva    2    1
3  Albert    Sara    0    4
4     Eva    Sara    1    4

Upvotes: 1

jezrael
jezrael

Reputation: 862681

First flatten values by numpy.ravel and reshape by original df, use DataFrame constructor and create columns names, last join to original:

df1 = pd.DataFrame(pd.factorize(df.values.ravel())[0].reshape(df.shape))
df1.columns = ['ID{}'.format(x+1) for x in range(len(df1.columns))]
print (df1)
   ID1  ID2
0    0    1
1    0    2
2    1    3
3    2    4
4    3    4

df = df.join(df1)
print (df)
    Name1   Name2  ID1  ID2
0    John    Jack    0    1
1    John  Albert    0    2
2    Jack     Eva    1    3
3  Albert    Sara    2    4
4     Eva    Sara    3    4

Create MultiIndex Series by stack, create ids by factorize and for DataFrame unstack, then rename columns and add to original by join:

s = df.stack()
df = df.join(pd.Series(pd.factorize(s)[0], index=s.index)
               .unstack()
               .rename(columns=lambda x: x.replace('Name','ID')))
print (df)
    Name1   Name2  ID1  ID2
0    John    Jack    0    1
1    John  Albert    0    2
2    Jack     Eva    1    3
3  Albert    Sara    2    4
4     Eva    Sara    3    4

Similar alternative:

s = df.stack()
s[:] = pd.factorize(s)[0]
df = df.join(s.unstack().rename(columns=lambda x: x.replace('Name','ID')))
print (df)
    Name1   Name2  ID1  ID2
0    John    Jack    0    1
1    John  Albert    0    2
2    Jack     Eva    1    3
3  Albert    Sara    2    4
4     Eva    Sara    3    4

Upvotes: 3

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