Processing my dataframes with conditions - python jupyter notebook

This is my first post and im not so much into programming and python. Hope i can discribe this well pls be patient with me

For my studies i use jupyter notebook (numpy pandas etc.) to process and plot my data.
Here it has something to do with xray beams and a Geiger-Müller-counter.
It looks like this :

# loading my txt
data1_1_KBr = pd.read_csv("1_1_SpektrumCuKBr_Daten.txt",skiprows=2,usecols=[0,1], 
                           names=["Winkel1_1_KBr","Rate1_1_KBr"],delimiter="\t",decimal=",")
#putting into dataframe
df1_1_KBr = pd.DataFrame(data1_1_KBr,columns=["Winkel1_1_KBr","Rate1_1_KBr"])

#this is the correction of my values
N_1_1_KBr = (df1_1_KBr.Rate1_1_KBr/(1-T*df1_1_KBr.Rate1_1_KBr))

The correction should only apply for higher values of "Rate1_1_KBr".

The processed data should be a panda dataframe or array like data that just looks the same like my orignal data with the entries that are corrected.

I tried to write loop and i failed.

It should be something like this (no code just thoughts) :

If the values of "Rate1_1_KBr" are smaller then 200, then put these into my new dataframe, if the values of "Rate1_1_KBr" are bigger then 200 then process them with my correction and afterwards put them into my new dataframe.

Would be awesome if somebody got an nice explanation for a beginner like me

I'm not sure if you're familiar with numpy, but you can use indexing with dataframes the same way. Also, the second line of your code is redundant as the pd.read_csv returns a dataframe itself. So the code you want looks like this:

df1_1_KBr = pd.read_csv("1_1_SpektrumCuKBr_Daten.txt",skiprows=2,usecols=[0,1], 
                           names=["Winkel1_1_KBr","Rate1_1_KBr"],delimiter="\t",decimal=",")
correction_df = df1_1_KBr[df1_1_KBr.Rate1_1_KBr>200]
N_1_1_KBr = df1_1_KBr.copy()
N_1_1_KBr[correction_df.index, 'Rate1_1_KBr'] = (correction_df.Rate1_1_KBr/(1-T*correction_df.Rate1_1_KBr))

Note that there is no need to use apply as it is much slower than indexing this way.

Also not sure if T is a constant value or part of a dataframe. If the former, this code should work.

Welcome Dennis,

def proc_rate(rate, threshold, T):
    if rate<threshold:
        return rate
    else:
        return (rate/(1-T*rate))

ans = df1_1_KBr.Rate1_1_KBr.apply(proc_rate, threshold=200, T=T)

if You want to write the series back to the original data frame:

df1_1_KBr.loc[:, 'Rate1_1_KBr'] = df1_1_KBr.Rate1_1_KBr.apply(proc_rate, threshold=200, T=T)

You can also filter the rates and then apply the correction method:

sub_frame = df1_1_KBr.Rate1_1_KBr[df1_1_KBr.Rate1_1_KBr<200].apply(proc_rate, threshold=200, T=T)