Looping over a section of data and suming in python
I have a fairly large dataset, I would like to read data from a file and sum values equal to 1 for every row and write it to a file. My code just prints data back to me
Example input file:
1526 0 1 2 1 0
782 0 1 1 1 2
7653 1 1 1 0 0
87bt 1 0 1 2 2
Example desired output file:
1526 2
782 3
7653 3
87bt 2
my code:
df = pd.read_csv("data1", header=None, index_col=0)
df.eq(1).sum(axis=1)
• 319 views
•
link
updated
by
GenoMax
•
written
by
elhamidihay •
•
written
by
elhamidihay •
0 answers
No answers yet.
Log in to answer this question.
More posts like this
-
Classification of samples into metasis and non metastasis using gene expresssion RNASeq
written by maryak •
-
Issue with ANGSD realSFS
written by Begonia_pavonina •
-
Loading the ID mapping file from Uniprot
written by MB •
-
Deleting all the rows from ANY column that contain a key word
written by pramirez •
-
Sum values in a row and compare the results to a value with awk or sed
written by M •
-
Understanding why `Design matrix not of full rank. The following coefficients not estimable` during…
written by O.rka
-
Calculating mutual information from FASTA files in Python
written by nameuser •
-
Sort Sam file by chromosome and position in python
written by hectorbaigessanchez •
-
Mutual Information from Nucleotide Distribution in Python
written by nameuser •
-
right way to working with dataframe and matplotlib
written by heuristic •
Hello elhamidihay!
We believe that this post does not fit the main topic of this site.
This is not a bioinformatics question in its current form. Please tell us how this is related to bioinformatics.
For this reason we have closed your question. This allows us to keep the site focused on the topics that the community can help with.
If you disagree please tell us why in a reply below, we'll be happy to talk about it.
Cheers!
Hello,
Thank you for your feedback. I have a data set of genotypes and I'm trying compute the heterozygosity for each individual. I'm using python to perform the task.
Thank you.