Drop row in dataframe if equal to previous row

I have a dataframe like the one below, where I have a daily count of points for each team. However, it's a tough task to earn points and on many days the points stay the same. Since I'm turning the dataframe into a chart, I want to remove the rows where the point values are the same as that of the previous day. So in this case we keep row 0, row 1 is the same so we omit it, then keep row 2 because it's different from row 1.

And of course, we don't want to compare the date. The idea for what I want to accomplish is something like this, but keeping it as a dataframe:

df = [
    df.iloc[[i]] for i in df.index[1:] 
    if
    any(df.iloc[:, 1:].iloc[[i]] != df.iloc[:, 1:].shift(1).iloc[[i]])
]

Example

For clarity, i've prepared a slightly different example.

import pandas as pd
data = {'row' : [0, 1, 2, 3, 4], 
        'date': ['07-01-24', '07-02-24', '07-03-24', '07-04-24', '07-05-24'], 
        'Team A': ['2pts', '2pts', '2pts', '4pts', '2pts'], 
        'Team B': '2pts'}
df = pd.DataFrame(data)

df

   row      date Team A Team B
0    0  07-01-24   2pts   2pts
1    1  07-02-24   2pts   2pts <-- delete
2    2  07-03-24   2pts   2pts <-- delete
3    3  07-04-24   4pts   2pts
4    4  07-05-24   2pts   2pts

check your logic to make sure that the row labelled delete is deleted. If yes, try the code below

Code

tmp = df.filter(like='Team')
out = tmp[tmp.ne(tmp.shift()).any(axis=1)]

out

   row      date Team A Team B
0    0  07-01-24   2pts   2pts
1    3  07-04-24   4pts   2pts
2    4  07-05-24   2pts   2pts