How to remove unchanged values from a timestamped pandas dataframe?

Question

So I have the following pandas dataframe, sorted by Timestamp ascending:

Timestamp,Point,Value
2019-09-01,A,1
2019-09-01,B,2
2019-09-02,A,1
2019-09-02,B,2
2019-09-03,A,3
2019-09-03,B,4
2019-09-04,A,3
2019-09-04,B,4
2019-09-05,A,1
2019-09-05,B,2

This dataframe contains a reading of the value of different "points" at different moments in time. In this example, A and B have readings every 1 day, but some of those values are the same as the previous reading.

I need to apply a transformation that will only leave rows whose Value column has changed from the previous reading for the same point.

|Timestamp |Point|Value|
|----------|-----|-----|
|2019-09-01|A    |1    | // A = 1
|2019-09-01|B    |2    | // B = 2 
|2019-09-02|A    |1    | // A unchanged, should be removed
|2019-09-02|B    |2    | // B unchanged, should be removed
|2019-09-03|A    |3    | // A = 3
|2019-09-03|B    |4    | // B = 4
|2019-09-04|A    |3    | // A unchanged, should be removed
|2019-09-04|B    |4    | // B unchanged, should be removed
|2019-09-05|A    |1    | // A = 1
|2019-09-05|B    |2    | // B = 2

In this simplified example, I'd want to get a dataframe like the following. Only including values that are different from the previous reading for the same point.

|Timestamp |Point|Value|
|----------|-----|-----|
|2019-09-01|A    |1    |
|2019-09-01|B    |2    |
|2019-09-03|A    |3    |
|2019-09-03|B    |4    |
|2019-09-05|A    |1    |
|2019-09-05|B    |2    |

Answer 1

You can reshape the dataframe to have unique Timestamps as row and Points as columns , then check if there is a change else assign nan and stack() :

m = df.set_index(['Timestamp','Point']).unstack().where(lambda x:
                           x.ne(x.shift())).stack().reset_index()

Or breaking into 2 lines:

m = df.set_index(['Timestamp','Point']).unstack()
m = m.where(m.ne(m.shift())).stack().reset_index()

---

print(m) 

    Timestamp Point  Value
0  2019-09-01     A    1.0
1  2019-09-01     B    2.0
2  2019-09-03     A    3.0
3  2019-09-03     B    4.0
4  2019-09-05     A    1.0
5  2019-09-05     B    2.0

Answer 2

You can try boolean indexing, first sort by Timestamp , groupby Point and check that diff (difference between 2 consecutive rows) of Value is not equal to 0 :

df[df.sort_values('Timestamp').groupby('Point')['Value'].diff().ne(0)]

[out]

   Timestamp Point  Value
0 2019-09-01     A      1
1 2019-09-01     B      2
4 2019-09-03     A      3
5 2019-09-03     B      4
8 2019-09-05     A      1
9 2019-09-05     B      2