I am trying to calculate valocity in Python with for loop. But when I run my code, I get "-inf" as a result for every row.
This is my code:
Vx = 0
for i in range(1, len(x_axis.timestamp)):
Vx += Vx + (x_axis.diff_ts[i]) * float(x_axis.value_x[i-1])
x_axis['Vx'] = Vx
print(x_axis)
Vx is my new column. I am not sure what I am doing wrong.
When I check dtypes, I get this:
timestamp float64
value_x object
y_axis float64
z_axis float64
diff_ts float64
My data looks like:
timestamp value_x ... lag(timestamp,1) diff_ts
0 1.661847e+09 -8.579621315 ... 0.000000e+00 0.000
1 1.661847e+09 -8.586804389953 ... 1.661847e+09 0.020
2 1.661847e+09 -8.56884765625 ... 1.661847e+09 0.020
3 1.661847e+09 -8.579621315 ... 1.661847e+09 0.020
4 1.661847e+09 -8.58800125122 ... 1.661847e+09 0.021
Reproducible example:
import pandas as pd
data = {'timestamp': [1.661848e+09, 1.661848e+09, 1.661848e+09, 1.661848e+09, 1.661848e+09],
'value_x': [-8.56884765625, -8.573636055, -8.58201599121, -8.565256118774, -8.58201599121],
'diff_ts': [0.000, 0.020, 0.021, 0.020, 0.020]
}
df = pd.DataFrame(data)
df['value_x'] = df['value_x'].astype(object, errors='raise')
print(df.info())
print(df)
Vx = 0
for i in range(1, len(df.timestamp)):
Vx = Vx + (df.diff_ts[i]) * float(df.value_x[i-1])
df['Vx'] = Vx
print(df)
df['Vx'] = Vx will set Vx as value for the whole column.
So if at the last iteration Vx = -inf, the column will have -inf at every row.
You can use df.at[<row>, <col>] = <val> to set a single value at (<row>, <col>) position.
As @Paul Hankin mentioned in the comments, the += in Vx += Vx + (x_axis.diff_ts[i]) * float(x_axis.value_x[i-1]) might result in -inf at some point if the number of iteration is high enough.
By the way, there is a mismatch between the first code snippet and the reproducible example, since in the latter you don't use the +=.
Try using at as mentioned above and Vx = Vx + ... instead of Vx += Vx ... and see if you still encounter the same issue.