Pandas Cumcount() over multiple columns
Question:
I have a dataframe that looks like this:
data = {'exercise': ['squat', 'squat', 'squat', 'squat', 'bench', 'bench', 'bench', 'bench', 'squat', 'squat', 'squat', 'squat', 'bench', 'bench', 'bench', 'bench'],
'session': [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],
'weight': [100, 100, 120, 120, 80, 80, 100, 110, 120, 130, 140, 150, 80, 90, 100, 110],
'velocity': [0.30, 0.25, 0.20, 0.15, 0.30, 0.25, 0.20, 0.15, 0.30, 0.25, 0.20, 0.15, 0.30, 0.25, 0.20, 0.15]}
df = pd.DataFrame(data, columns = data.keys())
print(df)
exercise session weight velocity
0 squat 0 100 0.30
1 squat 0 100 0.25
2 squat 0 120 0.20
3 squat 0 120 0.15
4 bench 0 80 0.30
5 bench 0 80 0.25
6 bench 0 100 0.20
7 bench 0 110 0.15
8 squat 1 120 0.30
9 squat 1 130 0.25
10 squat 1 140 0.20
11 squat 1 150 0.15
12 bench 1 80 0.30
13 bench 1 90 0.25
14 bench 1 100 0.20
15 bench 1 110 0.15
What I want to do is to add two columns, one for set number and one for rep number.
Set number should increase by 1 for each weight change exercise & session is the same, else reset to 0.
Rep number should increase by 1 for each velocity change if exercise, session & weight is the same, else reset to 0.
The logic I wrote above was flawed. What I meant was that the set number should increase for each change in weight (per line), but reset to 0 if either exercise or session changes.
Rep count should then be the number of lines in each set.
Like this:
data = {'exercise': ['squat', 'squat', 'squat', 'squat', 'bench', 'bench', 'bench', 'bench', 'squat', 'squat', 'squat', 'squat', 'bench', 'bench', 'bench', 'bench'],
'session': [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],
'weight': [100, 100, 120, 120, 80, 80, 100, 110, 120, 130, 140, 150, 80, 90, 100, 110],
'velocity': [0.30, 0.25, 0.20, 0.15, 0.30, 0.25, 0.20, 0.15, 0.30, 0.25, 0.20, 0.15, 0.30, 0.25, 0.20, 0.15],
'set': [0, 0, 1, 1, 0, 0, 1, 2, 0, 1, 2, 3, 0, 1, 2, 3],
'rep': [0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}
df = pd.DataFrame(data, columns = data.keys())
print(df)
exercise session weight velocity set rep
0 squat 0 100 0.30 0 0
1 squat 0 100 0.25 0 1
2 squat 0 120 0.20 1 0
3 squat 0 120 0.15 1 1
4 bench 0 80 0.30 0 0
5 bench 0 80 0.25 0 1
6 bench 0 100 0.20 1 0
7 bench 0 110 0.15 2 0
8 squat 1 120 0.30 0 0
9 squat 1 130 0.25 1 0
10 squat 1 140 0.20 2 0
11 squat 1 150 0.15 3 0
12 bench 1 80 0.30 0 0
13 bench 1 90 0.25 1 0
14 bench 1 100 0.20 2 0
15 bench 1 110 0.15 3 0
I think this should be possible using groupby and cumcount, but I struggle to make it work.
Answers:
Use GroupBy.transform with factorize and GroupBy.cumcount:
df['set1'] = (df.groupby(['exercise','session'])['weight']
.transform(lambda x: pd.factorize(x)[0]))
df['rep1'] = df.groupby(['exercise','session','weight']).cumcount()
print (df)
exercise session weight velocity set rep set1 rep1
0 squat 0 100 0.30 0 0 0 0
1 squat 0 100 0.25 0 1 0 1
2 squat 0 120 0.20 1 0 1 0
3 squat 0 120 0.15 1 1 1 1
4 bench 0 80 0.30 0 0 0 0
5 bench 0 80 0.25 0 1 0 1
6 bench 0 100 0.20 1 0 1 0
7 bench 0 110 0.15 1 1 2 0
8 squat 1 120 0.30 0 0 0 0
9 squat 1 130 0.25 1 0 1 0
10 squat 1 140 0.20 2 0 2 0
11 squat 1 150 0.15 3 0 3 0
12 bench 1 80 0.30 0 0 0 0
13 bench 1 90 0.25 1 0 1 0
14 bench 1 100 0.20 2 0 2 0
15 bench 1 110 0.15 3 0 3 0
As the question describe, we should count when there is a change, I suggest the following solution:
data = {'exercise': ['squat', 'squat', 'squat', 'squat', 'bench', 'bench', 'bench', 'bench', 'squat', 'squat', 'squat', 'squat', 'bench', 'bench', 'bench', 'bench'],
'session': [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],
'weight': [100, 100, 120, 120, 80, 80, 100, 110, 120, 130, 140, 150, 80, 90, 100, 110],
'velocity': [0.30, 0.25, 0.20, 0.15, 0.30, 0.25, 0.20, 0.15, 0.30, 0.25, 0.20, 0.15, 0.30, 0.25, 0.20, 0.15],
'set': [0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 2, 3, 0, 1, 2, 3],
'rep': [0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0]}
df = pd.DataFrame(data, columns = data.keys())
# df['velocity'] = 0.30
# df['weight'] = 100
# print(df)
grp_rep = ['exercise', 'session', 'weight']
grp_set = ['exercise','session']
def is_diff(arr):
return [0] + [int(d) for d in arr[:-1]!=arr[1:]]
df['rep_sol'] = df.groupby(grp_rep)['velocity'].transform(lambda x: is_diff(x.values))
df['set_sol'] = df.groupby(grp_set)['weight'].transform(lambda x: is_diff(x.values))
df['rep_sol'] = df.groupby(grp_rep)['rep_sol'].cumsum().astype(int)
df['set_sol'] = df.groupby(grp_set)['set_sol'].cumsum().astype(int)
cols = ['exercise', 'session', 'weight', 'velocity', 'set', 'set_sol', 'rep', 'rep_sol']
print(df[cols])
which return:
exercise session weight velocity set set_sol rep rep_sol
0 squat 0 100 0.30 0 0 0 0
1 squat 0 100 0.25 0 0 1 1
2 squat 0 120 0.20 1 1 0 0
3 squat 0 120 0.15 1 1 1 1
4 bench 0 80 0.30 0 0 0 0
5 bench 0 80 0.25 0 0 1 1
6 bench 0 100 0.20 1 1 0 0
7 bench 0 110 0.15 1 2 1 0
8 squat 1 120 0.30 0 0 0 0
9 squat 1 130 0.25 1 1 0 0
10 squat 1 140 0.20 2 2 0 0
11 squat 1 150 0.15 3 3 0 0
12 bench 1 80 0.30 0 0 0 0
13 bench 1 90 0.25 1 1 0 0
14 bench 1 100 0.20 2 2 0 0
15 bench 1 110 0.15 3 3 0 0
This solution still hold if we test for weightand velocityconstant.
data = {'exercise': ['squat', 'squat', 'squat', 'squat', 'bench', 'bench', 'bench', 'bench', 'squat', 'squat', 'squat', 'squat', 'bench', 'bench', 'bench', 'bench'],
'session': [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],
'weight': [100, 100, 120, 120, 80, 80, 100, 110, 120, 130, 140, 150, 80, 90, 100, 110],
'velocity': [0.30, 0.25, 0.20, 0.15, 0.30, 0.25, 0.20, 0.15, 0.30, 0.25, 0.20, 0.15, 0.30, 0.25, 0.20, 0.15],
'set': [0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 2, 3, 0, 1, 2, 3],
'rep': [0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0]}
df = pd.DataFrame(data, columns = data.keys())
df['velocity'] = 0.30
df['weight'] = 100
# print(df)
grp_rep = ['exercise', 'session', 'weight']
grp_set = ['exercise','session']
def is_diff(arr):
return [0] + [int(d) for d in arr[:-1]!=arr[1:]]
df['rep_sol'] = df.groupby(grp_rep)['velocity'].transform(lambda x: is_diff(x.values))
df['set_sol'] = df.groupby(grp_set)['weight'].transform(lambda x: is_diff(x.values))
df['rep_sol'] = df.groupby(grp_rep)['rep_sol'].cumsum().astype(int)
df['set_sol'] = df.groupby(grp_set)['set_sol'].cumsum().astype(int)
#cols = ['exercise', 'session', 'weight', 'velocity', 'set', 'set_sol', 'rep', 'rep_sol']
cols = ['exercise', 'session', 'weight', 'velocity', 'set_sol', 'rep_sol']
print(df[cols])
which will return no changes as what should be expected:
exercise session weight velocity set_sol rep_sol
0 squat 0 100 0.3 0 0
1 squat 0 100 0.3 0 0
2 squat 0 100 0.3 0 0
3 squat 0 100 0.3 0 0
4 bench 0 100 0.3 0 0
5 bench 0 100 0.3 0 0
6 bench 0 100 0.3 0 0
7 bench 0 100 0.3 0 0
8 squat 1 100 0.3 0 0
9 squat 1 100 0.3 0 0
10 squat 1 100 0.3 0 0
11 squat 1 100 0.3 0 0
12 bench 1 100 0.3 0 0
13 bench 1 100 0.3 0 0
14 bench 1 100 0.3 0 0
15 bench 1 100 0.3 0 0
After experimenting a bit, I ended up using this solution:
df['set_'] = df.groupby(['exercise', 'session'])['weight'].apply(lambda x: (x != x.shift()).cumsum() - 1)
df['rep_'] = df.groupby(['exercise', 'session', 'set_']).cumcount()
It works as expected for the posted example and in my real dataset with much more data and more scenarios.
data = {'exercise': ['squat', 'squat', 'squat', 'squat', 'bench', 'bench', 'bench', 'bench', 'squat', 'squat', 'squat', 'squat', 'bench', 'bench', 'bench', 'bench'],
'session': [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],
'weight': [100, 100, 120, 120, 80, 80, 100, 110, 120, 130, 140, 150, 80, 90, 100, 110],
'velocity': [0.30, 0.25, 0.20, 0.15, 0.30, 0.25, 0.20, 0.15, 0.30, 0.25, 0.20, 0.15, 0.30, 0.25, 0.20, 0.15],
'set': [0, 0, 1, 1, 0, 0, 1, 2, 0, 1, 2, 3, 0, 1, 2, 3],
'rep': [0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}
df = pd.DataFrame(data, columns = data.keys())
df['set_'] = df.groupby(['exercise', 'session'])['weight'].apply(lambda x: (x != x.shift()).cumsum() - 1)
df['rep_'] = df.groupby(['exercise', 'session', 'set_']).cumcount()
df['as_expected'] = (df['set_'] == df['set']) & (df['rep_'] == df['rep'])
print(df)
exercise session weight velocity set rep set_ rep_ as_expected
0 squat 0 100 0.30 0 0 0 0 True
1 squat 0 100 0.25 0 1 0 1 True
2 squat 0 120 0.20 1 0 1 0 True
3 squat 0 120 0.15 1 1 1 1 True
4 bench 0 80 0.30 0 0 0 0 True
5 bench 0 80 0.25 0 1 0 1 True
6 bench 0 100 0.20 1 0 1 0 True
7 bench 0 110 0.15 2 0 2 0 True
8 squat 1 120 0.30 0 0 0 0 True
9 squat 1 130 0.25 1 0 1 0 True
10 squat 1 140 0.20 2 0 2 0 True
11 squat 1 150 0.15 3 0 3 0 True
12 bench 1 80 0.30 0 0 0 0 True
13 bench 1 90 0.25 1 0 1 0 True
14 bench 1 100 0.20 2 0 2 0 True
15 bench 1 110 0.15 3 0 3 0 True
I have a dataframe that looks like this:
data = {'exercise': ['squat', 'squat', 'squat', 'squat', 'bench', 'bench', 'bench', 'bench', 'squat', 'squat', 'squat', 'squat', 'bench', 'bench', 'bench', 'bench'],
'session': [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],
'weight': [100, 100, 120, 120, 80, 80, 100, 110, 120, 130, 140, 150, 80, 90, 100, 110],
'velocity': [0.30, 0.25, 0.20, 0.15, 0.30, 0.25, 0.20, 0.15, 0.30, 0.25, 0.20, 0.15, 0.30, 0.25, 0.20, 0.15]}
df = pd.DataFrame(data, columns = data.keys())
print(df)
exercise session weight velocity
0 squat 0 100 0.30
1 squat 0 100 0.25
2 squat 0 120 0.20
3 squat 0 120 0.15
4 bench 0 80 0.30
5 bench 0 80 0.25
6 bench 0 100 0.20
7 bench 0 110 0.15
8 squat 1 120 0.30
9 squat 1 130 0.25
10 squat 1 140 0.20
11 squat 1 150 0.15
12 bench 1 80 0.30
13 bench 1 90 0.25
14 bench 1 100 0.20
15 bench 1 110 0.15
What I want to do is to add two columns, one for set number and one for rep number.
Set number should increase by 1 for each weight change exercise & session is the same, else reset to 0.
Rep number should increase by 1 for each velocity change if exercise, session & weight is the same, else reset to 0.
The logic I wrote above was flawed. What I meant was that the set number should increase for each change in weight (per line), but reset to 0 if either exercise or session changes.
Rep count should then be the number of lines in each set.
Like this:
data = {'exercise': ['squat', 'squat', 'squat', 'squat', 'bench', 'bench', 'bench', 'bench', 'squat', 'squat', 'squat', 'squat', 'bench', 'bench', 'bench', 'bench'],
'session': [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],
'weight': [100, 100, 120, 120, 80, 80, 100, 110, 120, 130, 140, 150, 80, 90, 100, 110],
'velocity': [0.30, 0.25, 0.20, 0.15, 0.30, 0.25, 0.20, 0.15, 0.30, 0.25, 0.20, 0.15, 0.30, 0.25, 0.20, 0.15],
'set': [0, 0, 1, 1, 0, 0, 1, 2, 0, 1, 2, 3, 0, 1, 2, 3],
'rep': [0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}
df = pd.DataFrame(data, columns = data.keys())
print(df)
exercise session weight velocity set rep
0 squat 0 100 0.30 0 0
1 squat 0 100 0.25 0 1
2 squat 0 120 0.20 1 0
3 squat 0 120 0.15 1 1
4 bench 0 80 0.30 0 0
5 bench 0 80 0.25 0 1
6 bench 0 100 0.20 1 0
7 bench 0 110 0.15 2 0
8 squat 1 120 0.30 0 0
9 squat 1 130 0.25 1 0
10 squat 1 140 0.20 2 0
11 squat 1 150 0.15 3 0
12 bench 1 80 0.30 0 0
13 bench 1 90 0.25 1 0
14 bench 1 100 0.20 2 0
15 bench 1 110 0.15 3 0
I think this should be possible using groupby and cumcount, but I struggle to make it work.
Use GroupBy.transform with factorize and GroupBy.cumcount:
df['set1'] = (df.groupby(['exercise','session'])['weight']
.transform(lambda x: pd.factorize(x)[0]))
df['rep1'] = df.groupby(['exercise','session','weight']).cumcount()
print (df)
exercise session weight velocity set rep set1 rep1
0 squat 0 100 0.30 0 0 0 0
1 squat 0 100 0.25 0 1 0 1
2 squat 0 120 0.20 1 0 1 0
3 squat 0 120 0.15 1 1 1 1
4 bench 0 80 0.30 0 0 0 0
5 bench 0 80 0.25 0 1 0 1
6 bench 0 100 0.20 1 0 1 0
7 bench 0 110 0.15 1 1 2 0
8 squat 1 120 0.30 0 0 0 0
9 squat 1 130 0.25 1 0 1 0
10 squat 1 140 0.20 2 0 2 0
11 squat 1 150 0.15 3 0 3 0
12 bench 1 80 0.30 0 0 0 0
13 bench 1 90 0.25 1 0 1 0
14 bench 1 100 0.20 2 0 2 0
15 bench 1 110 0.15 3 0 3 0
As the question describe, we should count when there is a change, I suggest the following solution:
data = {'exercise': ['squat', 'squat', 'squat', 'squat', 'bench', 'bench', 'bench', 'bench', 'squat', 'squat', 'squat', 'squat', 'bench', 'bench', 'bench', 'bench'],
'session': [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],
'weight': [100, 100, 120, 120, 80, 80, 100, 110, 120, 130, 140, 150, 80, 90, 100, 110],
'velocity': [0.30, 0.25, 0.20, 0.15, 0.30, 0.25, 0.20, 0.15, 0.30, 0.25, 0.20, 0.15, 0.30, 0.25, 0.20, 0.15],
'set': [0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 2, 3, 0, 1, 2, 3],
'rep': [0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0]}
df = pd.DataFrame(data, columns = data.keys())
# df['velocity'] = 0.30
# df['weight'] = 100
# print(df)
grp_rep = ['exercise', 'session', 'weight']
grp_set = ['exercise','session']
def is_diff(arr):
return [0] + [int(d) for d in arr[:-1]!=arr[1:]]
df['rep_sol'] = df.groupby(grp_rep)['velocity'].transform(lambda x: is_diff(x.values))
df['set_sol'] = df.groupby(grp_set)['weight'].transform(lambda x: is_diff(x.values))
df['rep_sol'] = df.groupby(grp_rep)['rep_sol'].cumsum().astype(int)
df['set_sol'] = df.groupby(grp_set)['set_sol'].cumsum().astype(int)
cols = ['exercise', 'session', 'weight', 'velocity', 'set', 'set_sol', 'rep', 'rep_sol']
print(df[cols])
which return:
exercise session weight velocity set set_sol rep rep_sol
0 squat 0 100 0.30 0 0 0 0
1 squat 0 100 0.25 0 0 1 1
2 squat 0 120 0.20 1 1 0 0
3 squat 0 120 0.15 1 1 1 1
4 bench 0 80 0.30 0 0 0 0
5 bench 0 80 0.25 0 0 1 1
6 bench 0 100 0.20 1 1 0 0
7 bench 0 110 0.15 1 2 1 0
8 squat 1 120 0.30 0 0 0 0
9 squat 1 130 0.25 1 1 0 0
10 squat 1 140 0.20 2 2 0 0
11 squat 1 150 0.15 3 3 0 0
12 bench 1 80 0.30 0 0 0 0
13 bench 1 90 0.25 1 1 0 0
14 bench 1 100 0.20 2 2 0 0
15 bench 1 110 0.15 3 3 0 0
This solution still hold if we test for weightand velocityconstant.
data = {'exercise': ['squat', 'squat', 'squat', 'squat', 'bench', 'bench', 'bench', 'bench', 'squat', 'squat', 'squat', 'squat', 'bench', 'bench', 'bench', 'bench'],
'session': [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],
'weight': [100, 100, 120, 120, 80, 80, 100, 110, 120, 130, 140, 150, 80, 90, 100, 110],
'velocity': [0.30, 0.25, 0.20, 0.15, 0.30, 0.25, 0.20, 0.15, 0.30, 0.25, 0.20, 0.15, 0.30, 0.25, 0.20, 0.15],
'set': [0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 2, 3, 0, 1, 2, 3],
'rep': [0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0]}
df = pd.DataFrame(data, columns = data.keys())
df['velocity'] = 0.30
df['weight'] = 100
# print(df)
grp_rep = ['exercise', 'session', 'weight']
grp_set = ['exercise','session']
def is_diff(arr):
return [0] + [int(d) for d in arr[:-1]!=arr[1:]]
df['rep_sol'] = df.groupby(grp_rep)['velocity'].transform(lambda x: is_diff(x.values))
df['set_sol'] = df.groupby(grp_set)['weight'].transform(lambda x: is_diff(x.values))
df['rep_sol'] = df.groupby(grp_rep)['rep_sol'].cumsum().astype(int)
df['set_sol'] = df.groupby(grp_set)['set_sol'].cumsum().astype(int)
#cols = ['exercise', 'session', 'weight', 'velocity', 'set', 'set_sol', 'rep', 'rep_sol']
cols = ['exercise', 'session', 'weight', 'velocity', 'set_sol', 'rep_sol']
print(df[cols])
which will return no changes as what should be expected:
exercise session weight velocity set_sol rep_sol
0 squat 0 100 0.3 0 0
1 squat 0 100 0.3 0 0
2 squat 0 100 0.3 0 0
3 squat 0 100 0.3 0 0
4 bench 0 100 0.3 0 0
5 bench 0 100 0.3 0 0
6 bench 0 100 0.3 0 0
7 bench 0 100 0.3 0 0
8 squat 1 100 0.3 0 0
9 squat 1 100 0.3 0 0
10 squat 1 100 0.3 0 0
11 squat 1 100 0.3 0 0
12 bench 1 100 0.3 0 0
13 bench 1 100 0.3 0 0
14 bench 1 100 0.3 0 0
15 bench 1 100 0.3 0 0
After experimenting a bit, I ended up using this solution:
df['set_'] = df.groupby(['exercise', 'session'])['weight'].apply(lambda x: (x != x.shift()).cumsum() - 1)
df['rep_'] = df.groupby(['exercise', 'session', 'set_']).cumcount()
It works as expected for the posted example and in my real dataset with much more data and more scenarios.
data = {'exercise': ['squat', 'squat', 'squat', 'squat', 'bench', 'bench', 'bench', 'bench', 'squat', 'squat', 'squat', 'squat', 'bench', 'bench', 'bench', 'bench'],
'session': [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1],
'weight': [100, 100, 120, 120, 80, 80, 100, 110, 120, 130, 140, 150, 80, 90, 100, 110],
'velocity': [0.30, 0.25, 0.20, 0.15, 0.30, 0.25, 0.20, 0.15, 0.30, 0.25, 0.20, 0.15, 0.30, 0.25, 0.20, 0.15],
'set': [0, 0, 1, 1, 0, 0, 1, 2, 0, 1, 2, 3, 0, 1, 2, 3],
'rep': [0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}
df = pd.DataFrame(data, columns = data.keys())
df['set_'] = df.groupby(['exercise', 'session'])['weight'].apply(lambda x: (x != x.shift()).cumsum() - 1)
df['rep_'] = df.groupby(['exercise', 'session', 'set_']).cumcount()
df['as_expected'] = (df['set_'] == df['set']) & (df['rep_'] == df['rep'])
print(df)
exercise session weight velocity set rep set_ rep_ as_expected
0 squat 0 100 0.30 0 0 0 0 True
1 squat 0 100 0.25 0 1 0 1 True
2 squat 0 120 0.20 1 0 1 0 True
3 squat 0 120 0.15 1 1 1 1 True
4 bench 0 80 0.30 0 0 0 0 True
5 bench 0 80 0.25 0 1 0 1 True
6 bench 0 100 0.20 1 0 1 0 True
7 bench 0 110 0.15 2 0 2 0 True
8 squat 1 120 0.30 0 0 0 0 True
9 squat 1 130 0.25 1 0 1 0 True
10 squat 1 140 0.20 2 0 2 0 True
11 squat 1 150 0.15 3 0 3 0 True
12 bench 1 80 0.30 0 0 0 0 True
13 bench 1 90 0.25 1 0 1 0 True
14 bench 1 100 0.20 2 0 2 0 True
15 bench 1 110 0.15 3 0 3 0 True