**120**wrote:

this is a continuation from my previous post, where I wanted a faster and more efficient alternative to a standard Python loop, which performs some summing and multiplication on elements of each row.

Basically, what I have are two file inputs. One is a list of all combinations for a group of SNPs, for example below for 3 SNPs:

```
AA CC TT
AT CC TT
TT CC TT
AA CG TT
AT CG TT
TT CG TT
AA GG TT
AT GG TT
TT GG TT
AA CC TA
AT CC TA
TT CC TA
AA CG TA
AT CG TA
TT CG TA
AA GG TA
AT GG TA
TT GG TA
AA CC AA
AT CC AA
TT CC AA
AA CG AA
AT CG AA
TT CG AA
AA GG AA
AT GG AA
TT GG AA
```

And the second is a table, containing some information for each SNP, notably their log(OR) for a disease and the frequency of the risk allele:

```
SNP1 A T 1.25 0.223143551314 0.97273
SNP2 C G 1.07 0.0676586484738 0.3
SNP3 T A 1.08 0.0769610411361 0.1136
```

Below is my main code, in which I am looking to calculate a 'score' and a 'frequency' for each 'profile. The score is the sum of log(ORs) for each risk allele present in the profile, while the frequency is the frequencies multiplied together, assuming Hardy Weinberg equilibrium:

```
import pandas as pd
numbers = pd.read_csv(table2, sep="\t", header=None)
combinations = pd.read_csv(table1, sep=" ", header=None)
def score_freq(line):
score=0
freq=1
for j in range(len(line)):
if line[j][1] != numbers.values[j][1]: # homozygous for ref
score+=0
freq*=(float(1-float(numbers.values[j][6]))*float(1-float(numbers.values[j][6])))
elif line[j][0] != numbers.values[j][1] and line[j][1] == numbers.values[j][1]: # heterozygous
score+=(float(numbers.values[j][5]))
freq*=(2*(float(1-float(numbers.values[j][6]))*float(numbers.values[j][6])))
elif line[j][0] == numbers.values[j][1]: # homozygous for risk
score+=2*(float(numbers.values[j][5]))
freq*=(float(numbers.values[j][6])*float(numbers.values[j][6]))
if freq < 1e-05: # threshold to stop loop in interest of efficiency
break
return pd.Series([score, freq])
combinations[['score', 'freq']] = combinations.apply(lambda row: score_freq(row), axis=1)
#combinations[['score', 'freq']] = score_freq(combinations.values) # vectorization?
print(combinations)
```

I was referring to this site, where they go over the fastest way to loop over a Pandas dataframe. I have been able to use the Pandas apply method, but I am not sure how to perform the vectorization method over the Pandas series. Other than that, do suggest any way in which I can improve my script to make it more efficient, thanks!

Hello Volka!

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46kIn fact it is most likely an ill-posed or XY-problem. Until OP accepts that, repeated posts without further details will not help.

48k