Pandas "Formulas" not working as expected-CodePudding

I am trying to work with data from an accelerometer, trying to get the velocity from acceleration, on a df that looks like this:

{'T': {0: 0.007719999999999999,
  1: 0.016677999999999797,
  2: 0.024630999999996697,
  3: 0.0325849999999983,
  4: 0.040530999999995196},
 'Ax': {0: 0.16, 1: 0.28, 2: 0.28, 3: 0.44, 4: 0.57},
 'Ay': {0: 8.0, 1: 7.9, 2: 7.87, 3: 7.87, 4: 7.9},
 'Az': {0: 3.83, 1: 3.83, 2: 3.79, 3: 3.76, 4: 3.76},
 'delta T': {0: 0.00772,
  1: 0.008957999999999798,
  2: 0.0079529999999969,
  3: 0.007954000000001606,
  4: 0.007945999999996893}}

example of df

First, I set the Velocity of X, Y and Z to 0:

df_yt["Vx"] = 0
df_yt["Vy"] = 0
df_yt["Vz"] = 0

And then I entered the first value of each of these columns manually:

df_yt.loc[0,"Vx"] = 0.16*0.007720
df_yt.loc[0,"Vy"] = 8.00*0.007720
df_yt.loc[0,"Vz"] = 3.83*0.007720

I wanted to create a formula that returned the previous element in Vx (Ax*delta T) of the same column. And to write the "formulas" of these 3 columns, I assumed it would be something like:

df_yt.loc[1:,"Vx"] = df_yt["Vx"].shift(1)   df_yt["Ax"]*df_yt["delta T"]
df_yt.loc[1:,"Vy"] = df_yt["Vy"].shift(1)   df_yt["Ay"]*df_yt["delta T"]
df_yt.loc[1:,"Vz"] = df_yt["Vz"].shift(1)   df_yt["Az"]*df_yt["delta T"]

and this code doesn't return any error but the numbers on the df don't match what they should, for example:

This number

should be 0.005970:

0.003743   0.28*0.007953 = 0.005970

I hope someone can help me with this because I don't know what is causing this mistake and I can't even understand where the wrong numbers are coming from.

CodePudding user response：

Try as follows:

Use df.mul to multiply each column in ['Ax','Ay','Az'] with delta T along axis 0, and apply df.cumsum.

df_yt[['Vx','Vy','Vz']] = df_yt[['Ax','Ay','Az']].mul(df_yt['delta T'], 
                                                      axis=0).cumsum()

print(df_yt)

          T    Ax    Ay    Az   delta T        Vx        Vy        Vz
0  0.007720  0.16  8.00  3.83  0.007720  0.001235  0.061760  0.029568
1  0.016678  0.28  7.90  3.83  0.008958  0.003743  0.132528  0.063877
2  0.024631  0.28  7.87  3.79  0.007953  0.005970  0.195118  0.094019
3  0.032585  0.44  7.87  3.76  0.007954  0.009470  0.257716  0.123926
4  0.040531  0.57  7.90  3.76  0.007946  0.013999  0.320490  0.153803

Incidentally, the problem with your own attempt becomes apparent when you print the values for any of the .shift(1) statements. E.g. you do:

df_yt["Vx"] = 0
df_yt.loc[0,"Vx"] = 0.16*0.007720

print(df_yt["Vx"].shift(1))

0         NaN
1    0.001235
2    0.000000
3    0.000000
4    0.000000
Name: Vx, dtype: float64

So, in a line such as df_yt.loc[1:,"Vx"] = df_yt["Vx"].shift(1) df_yt["Ax"]*df_yt["delta T"], per row you are adding: nothing (NaN), 0.001235, and then just zeros after that. E.g. this adds correct values only for the second row (index 1).