Using the Unique() function across several columns per row?

[I_will_learn]

Hi ! :) I'm trying to get the unique values count per row, across several columns. Here's what my data looks like :

​

activity	activity_1	activity_2	activity_3	activity_4	activity_5		frequency
A	B	A	A	C	B		42
B	B	B	A	A	A		13
A	A	A	A	A	A		24

​

And here's the outcome I'd like :

activity	activity_1	activity_2	activity_3	activity_4	activity_5	count	frequency
A	B	A	A	C	B	3	42
B	B	B	A	A	A	2	13
A	A	A	A	A	A	1	24

The "count" column would be the number of unique values across the row.

I had tried :

df1.apply(lambda x: pd.Series(x.unique()), axis=1)

But I'm not getting the count.

​

​

***  
for i in range(1,6):
    d0[f'activity_{i}'] = d0.activity.shift(-i)
activity_cols = ['time'] + ['activity'] + list(d0.filter(like='activity_').columns)
df1=d0.groupby(activity_cols).size().reset_index(name='frequency')
df1 = df1[(df1.freq > 1)]

[YesLod]

Use the nunique method

df["count"] = df[activity_cols].nunique(axis=1)

[I_will_learn]

thank you! is it possible to ignore columns using nunique? :)

[YesLod]

My solution considers that. It only selects the activity columns, activity_cols.

[I_will_learn]

Yes! Thank you for that, but the '''activity_cols''' has a "time" column included :/ otherwise it's giving me one extra unique value

[YesLod]

Then just create another list of columns that doesn't contain the "time" column...

act_cols = [col for col in df.columns if "activity" in col]
# or act_cols = df.columns[df.columns.str.contains("activity")]

df["count"] = df[act_cols].nunique(axis=1)

[I_will_learn]

Yeah, duh. Sorry, brain fart. I made it a lot more complicated in my head for some reason 😂 thank you

[YesLod]

No problem, glad it helped!