Unique values python

Question

I am trying to basically look through a column and if that column has a unique value then enter 1 but if it doesn't it just becomes a NaN, my dataframe looks like this:

    Street             Number
0   1312 Oak Avenue     1
1   14212 central Ave   2
2   981 franklin way    1

the code I am using to put the number 1 next to unique values is as follows:

df.loc[(df['Street'].unique()), 'Unique'] = '1'

however when I run this I get this error KeyError: "not in index" I don't know why. I tried running this on the Number column and I get my desired result which is:

    Street            Number    Unique
0   1312 Oak Avenue     1         NaN
1   14212 central Ave   2          1
2   981 franklin way    1          1

so my column that specifies which ones are unique is called Unique and it puts a one by the rows that are unique and NaNs by ones that are duplicates. So in this case I have 2 ones and it notices that and makes the first NaN and the second it provides a 1 and since their is only 1 two then it provides us for a 1 their as well since it is unique. I just don't know why I am getting that error for the street column.

EFT · Accepted Answer · 2017-06-23 19:15:18Z

1

That's not really producing your desired result. The output of df['Number'].unique(), array([1, 2], dtype=int64), just happened to be in the index. You'd encounter the same issue on that column if Number instead was [3, 4, 3], say.

For what you're looking for, selecting where not duplicated, or where you have left after dropping duplicates, might be better than unique:

df.loc[~(df['Number'].duplicated()), 'Unique'] = 1
df
Out[51]: 
              Street  Number Unique
0    1312 Oak Avenue       1    1.0
1  14212 central Ave       2    1.0
2   981 franklin way       1    NaN


df.loc[df['Number'].drop_duplicates(), 'Unique'] = 1
df
Out[63]: 
              Street  Number  Unique
0    1312 Oak Avenue       1     NaN
1  14212 central Ave       2     1.0
2   981 franklin way       1     1.0

answered Jun 23, 2017 at 19:15

EFT

2,3691 gold badge12 silver badges11 bronze badges

Sign up to request clarification or add additional context in comments.

Collectives™ on Stack Overflow

Unique values python

1 Answer 1

Comments

Your Answer

Hot Network Questions

Collectives™ on Stack Overflow

1 Answer 1

Comments

Your Answer

Sign up or log in

Post as a guest

Related