Check if all elements in a group are equal using pandas GroupBy

Question

Is there a pythonic way to group by a field and check if all elements of each resulting group have the same value?

Sample data:

              datetime rating  signal
0  2018-12-27 11:33:00     IG       0
1  2018-12-27 11:33:00     HY      -1
2  2018-12-27 11:49:00     IG       0
3  2018-12-27 11:49:00     HY      -1
4  2018-12-27 12:00:00     IG       0
5  2018-12-27 12:00:00     HY      -1
6  2018-12-27 12:49:00     IG       0
7  2018-12-27 12:49:00     HY      -1
8  2018-12-27 14:56:00     IG       0
9  2018-12-27 14:56:00     HY      -1
10 2018-12-27 15:12:00     IG       0
11 2018-12-27 15:12:00     HY      -1
12 2018-12-20 15:14:00     IG       0
13 2018-12-20 15:14:00     HY      -1
14 2018-12-20 15:50:00     IG      -1
15 2018-12-20 15:50:00     HY      -1
16 2018-12-27 13:26:00     IG       0
17 2018-12-27 13:26:00     HY      -1
18 2018-12-27 13:44:00     IG       0
19 2018-12-27 13:44:00     HY      -1
20 2018-12-27 15:06:00     IG       0
21 2018-12-27 15:06:00     HY      -1
22 2018-12-20 15:48:00     IG       0
23 2018-12-20 15:48:00     HY      -1

The grouping part can be done by

df.groupby([datetime.dt.date,'rating'])

However, I'm sure there must be a simple way to leverage the grouper and use a transform statement to return 1 if all the values from signal are the same.

Desired output

2018-12-20  HY            True
            IG            False
2018-12-27  HY            True
            IG            True

Solution 1

Use groupby and nunique, and check whether the result is 1:

df.groupby([df.datetime.dt.date, 'rating']).signal.nunique().eq(1)

datetime    rating
2018-12-20  HY         True
            IG        False
2018-12-27  HY         True
            IG         True
Name: signal, dtype: bool

Or, similarly, using apply with set conversion:

(df.groupby([df.datetime.dt.date, 'rating']).signal
   .apply(lambda x: len(set(x)) == 1))

datetime    rating
2018-12-20  HY         True
            IG        False
2018-12-27  HY         True
            IG         True
Name: signal, dtype: bool

PS., you don't need to assign a temp column, groupby takes arbitrary grouper arguments.

Solution 2

Try to find out alternative without using groupby just for fun

df.datetime=df.datetime.dt.date

s=pd.crosstab(df.datetime,[df.rating,df.signal])


s.eq(s.sum(axis=1,level=0),1).any(level=0,axis=1).stack()
Out[556]: 
datetime    rating
2018-12-20  HY         True
            IG        False
2018-12-27  HY         True
            IG         True
dtype: bool

Comments

[JacobIRR]

Could you check len(set(your_values)) == 1 ?

[RomanPerekhrest]

I don't see any 'temp' key in your input df

[cs95]

Should this be [True, False, True, False]?

[Yuca]

I should have False for 2018-12-20, IG, and True for everything else.

[Yuca]

@RomanPerekhrest it is being generated from the assign statement

[cs95]

Hmm, I see 2018-12-27/IG has [0, -1] as the unique values. Can you take a look?

[Yuca]

@coldspeed fixed the sample data, thanks!

[Yuca]

I have a challenge for you, make it false only if it's not the last entry of the day :)

[Yuca]

follow up: is there an easy way to recover the index of the 'odd-one-out'? we can assume there's only one per day

[cs95]

@Yuca do you mean per day there would only be one odd one out (either true or false)? Is it possible there can be no odd one out?

[Yuca]

yes, it is possible that there is no odd one out, I have a pretty ugly way of doing it that involves using grouby twice :S