How can I replace Python Pandas table text values with unique IDs?

Question

I am using Pandas to read a file in this format:

fp = pandas.read_table("Measurements.txt")
fp.head()

"Aaron", 3, 5, 7  
"Aaron", 3, 6, 9  
"Aaron", 3, 6, 10 
"Brave", 4, 6, 0 
"Brave", 3, 6, 1

I want to replace each name with a unique ID so output looks like:

"1", 3, 5, 7 
"1", 3, 6, 9 
"1", 3, 6, 10 
"2", 4, 6, 0 
"2", 3, 6, 1

How can I do that?

Thanks!

Answer 1

I would make use of categorical dtype:

In [97]: x['ID'] = x.name.astype('category').cat.rename_categories(range(1, x.name.nunique()+1))

In [98]: x
Out[98]:
    name  v1  v2  v3 ID
0  Aaron   3   5   7  1
1  Aaron   3   6   9  1
2  Aaron   3   6  10  1
3  Brave   4   6   0  2
4  Brave   3   6   1  2

if you need string IDs instead of numerical ones, you can use:

x.name.astype('category').cat.rename_categories([str(x) for x in range(1,x.name.nunique()+1)])

or, as @MedAli has mentioned in his answer, using factorize() method - demo:

In [141]: x['cat'] = pd.Categorical((pd.factorize(x.name)[0] + 1).astype(str))

In [142]: x
Out[142]:
    name  v1  v2  v3 ID cat
0  Aaron   3   5   7  1   1
1  Aaron   3   6   9  1   1
2  Aaron   3   6  10  1   1
3  Brave   4   6   0  2   2
4  Brave   3   6   1  2   2

In [143]: x.dtypes
Out[143]:
name      object
v1         int64
v2         int64
v3         int64
ID      category
cat     category
dtype: object

In [144]: x['cat'].cat.categories
Out[144]: Index(['1', '2'], dtype='object')

or having categories as integer numbers:

In [154]: x['cat'] = pd.Categorical((pd.factorize(x.name)[0] + 1))

In [155]: x
Out[155]:
    name  v1  v2  v3 ID cat
0  Aaron   3   5   7  1   1
1  Aaron   3   6   9  1   1
2  Aaron   3   6  10  1   1
3  Brave   4   6   0  2   2
4  Brave   3   6   1  2   2

In [156]: x['cat'].cat.categories
Out[156]: Int64Index([1, 2], dtype='int64')

explanation:

In [99]: x.name.astype('category')
Out[99]:
0    Aaron
1    Aaron
2    Aaron
3    Brave
4    Brave
Name: name, dtype: category
Categories (2, object): [Aaron, Brave]

In [100]: x.name.astype('category').cat.categories
Out[100]: Index(['Aaron', 'Brave'], dtype='object')

In [101]: x.name.astype('category').cat.rename_categories([1,2])
Out[101]:
0    1
1    1
2    1
3    2
4    2
dtype: category
Categories (2, int64): [1, 2]

explanation for the factorize() method:

In [157]: (pd.factorize(x.name)[0] + 1)
Out[157]: array([1, 1, 1, 2, 2])

In [158]: pd.Categorical((pd.factorize(x.name)[0] + 1))
Out[158]:
[1, 1, 1, 2, 2]
Categories (2, int64): [1, 2]