Add uuid to a new column in a pandas DataFrame

Question

I'm looking to add a uuid for every row in a single new column in a pandas DataFrame. This obviously fills the column with the same uuid:

import uuid
import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(4,3), columns=list('abc'),
                  index=['apple', 'banana', 'cherry', 'date'])
df['uuid'] = uuid.uuid4()
print(df)

               a         b         c                                  uuid
apple   0.687601 -1.332904 -0.166018  34115445-c4b8-4e64-bc96-e120abda1653
banana -2.252191 -0.844470  0.384140  34115445-c4b8-4e64-bc96-e120abda1653
cherry -0.470388  0.642342  0.692454  34115445-c4b8-4e64-bc96-e120abda1653
date   -0.943255  1.450051 -0.296499  34115445-c4b8-4e64-bc96-e120abda1653

What I am looking for is a new uuid in each row of the 'uuid' column. I have also tried using .apply() and .map() without success.

Answer 1

This is one way:

df['uuid'] = [uuid.uuid4() for _ in range(len(df.index))]

Answer 2

I can't speak to computational efficiency here, but I prefer the syntax here, as it's consistent with the other apply-lambda modifications I usually use to generate new columns:

df['uuid'] = df.apply(lambda _: uuid.uuid4(), axis=1)

You can also pick a random column to remove the axis requirement (why axis=0 is the default, I'll never understand):

df['uuid'] = df['col'].apply(lambda _: uuid.uuid4())

The downside to these is technically you're passing in a variable (_) that you don't actually use. It would be mildly nice to have the capability to do something like lambda: uuid.uuid4(), but apply doesn't support lambas with no args, which is reasonable given its use case would be rather limited.

Answer 3

from uuid import uuid4
df['uuid'] = df.index.to_series().map(lambda x: uuid4())

Answer 4

To create a new column, you must have enough values to fill the column. If we know the number of rows (by calculating the len of the dataframe), we can create a set of values that can then be applied to a column.

import uuid
import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(4,3), columns=list('abc'),
                  index=['apple', 'banana', 'cherry', 'date'])


# you can create a simple list of values using a list comprehension 
#     based on the len (or number of rows) of the dataframe
df['uuid'] = [uuid.uuid4() for x in range(len(df))]
print(df)

apple  -0.775699 -1.104219  1.144653  f98a9c76-99b7-4ba7-9c0a-9121cdf8ad7f
banana -1.540495 -0.945760  0.649370  179819a0-3d0f-43f8-8645-da9229ef3fc3
cherry -0.340872  2.445467 -1.071793  b48a9830-3a10-4ce0-bca0-0cc136f09732
date   -1.286273  0.244233  0.626831  e7b7c65c-0adc-4ba6-88ab-2160e9858fc4