Missing data in Python nested loop-CodePudding

I'm working with a multidimensional data array where I have various data points for individuals. I created a nested loop that allows me to make metric calculations throughout the entire dataset, however, once rearranging it I loose data points. From my initial 253 individuals, I end up with the calculated metrics for 182. The code works, but I don't know at which moment I'm letting data out.

data_array -- containing 253 individuals, each with several subcategories 

mos0_ids=[]
mos0_dt = []
mos0_x_dpos = []
mos0_y_dpos = []
mos0_z_dpos = []

for i in range (0,252): 
    mos0=data_array[i]
    mos0_id= mos0[0][0]                                                                             
    mos0_time=mos0[:,1]                                                                                      
    mos0_x_pos=mos0[:,2]
    mos0_y_pos=mos0[:,3]
    mos0_z_pos=mos0[:,4]
    mos0_speed=mos0[:,6]

    for j in range(0,len(mos0_id)):  
        mos0_ids.append(mos0_id)
        
    for k in range(0,len(mos0_time)):
        first_mov_time=mos0_time[k]
        last_mov_time=mos0_time[k-1]
        first_movement = dt.datetime.strptime(first_mov_time, '%Y-%m-%d %H:%M:%S.%f')
        last_movement = dt.datetime.strptime(last_mov_time, '%Y-%m-%d %H:%M:%S.%f')
        x = first_movement - last_movement
        total_seconds = x.total_seconds()  
        mos0_dt.append(total_seconds)
    
    for l in range(0,len(mos0_x_pos)):
        first_mov_pos=mos0_x_pos[l]
        last_mov_pos=mos0_x_pos[l-1]
        x = first_mov_pos - last_mov_pos
        mos0_x_dpos.append(x)
    
    for m in range(0,len(mos0_y_pos)):
        first_mov_pos=mos0_y_pos[m]
        last_mov_pos=mos0_y_pos[m-1]
        x = first_mov_pos - last_mov_pos
        mos0_y_dpos.append(x)
    
    for n in range(0,len(mos0_z_pos)):
        first_mov_pos=mos0_z_pos[n]
        last_mov_pos=mos0_z_pos[n-1]
        x = first_mov_pos - last_mov_pos
        mos0_z_dpos.append(x)
        
mos0_ids
mos0_dt
mos0_x_dpos 
mos0_y_dpos 
mos0_z_dpos       

time_pos=list(zip(mos0_ids, mos0_dt, mos0_x_dpos, mos0_y_dpos, mos0_z_dpos))                                                 
time_pos=pd.DataFrame(time_pos,columns=['mos_id','dtime', 'x_position', 'y_position','z_position'])               #  transform into a dataframe         
time_pos['x_velocity'] = time_pos['x_position']/time_pos['dtime']
time_pos['y_velocity'] = time_pos['y_position']/time_pos['dtime']
time_pos['z_velocity'] = time_pos['z_position']/time_pos['dtime']

time_pos['x_acceleration'] = time_pos['x_velocity']/time_pos['dtime']
time_pos['y_acceleration'] = time_pos['y_velocity']/time_pos['dtime']
time_pos['z_acceleration'] = time_pos['z_velocity']/time_pos['dtime']

time_pos=time_pos.groupby('mos_id')
time_pos = np.array(time_pos, dtype=object)    
time_pos

CodePudding user response：

You probably miss one "specific" behavior of range(). Your first very simplified loop will have only 252 values, instead of having 253

Try this out in console:
len(range(0,252)) -> 252

So I presume as it's nested arr (matrices), it looses lots of data according to several calculations it should make for every col/row. Solution:
for i in range(0, 253) or for i in range(len(data_array) 1)

I assume same happened to all of your provided for loops