What is the best way to calculate the RMS of a column in sections in python/pandas. Here is a example for a better understanding what I mean:
| index | x | x_rms |
|---|---|---|
| 0 | 2 | |
| 1 | 3 | 2.55 |
| 2 | 10 | |
| 3 | 22 | 17.09 |
| ... | ... | ... |
So 2.55 is the RMS of 2 and 3, 17.09 is the RMS of 10 and 22 and so on.
the following will work
import pandas as pd
df = pd.DataFrame([2,3,10,22], columns=["x"])
def rms(a, b):
# return round(np.sqrt((a**2+b**2)/2), 2) # for only two decimals
return np.sqrt((a**2+b**2)/2)
df["rms"] = [rms(df.loc[idx-1,"x"], val["x"]) if idx%2 != 0 else np.nan
for idx, val in df.iterrows()]
output
x rms
0 2 NaN
1 3 2.549510
2 10 NaN
3 22 17.088007
EDIT regarding comment
if your index is a date you should do this to have the same output
values = [2,3,10,22]
tidx = pd.date_range('2019-01-01', periods=len(values), freq='D')
df = pd.DataFrame([2,3,10,22], columns=["x"], index=tidx)
def rms(a, b):
# return round(np.sqrt((a**2+b**2)/2), 2) # for only two decimals
return np.sqrt((a**2+b**2)/2)
df = df.reset_index()
df["rms"] = [rms(df.loc[idx-1,"x"], val["x"]) if idx%2 != 0 else np.nan
for idx, val in df.iterrows()]
df.set_index("index")