import pandas as pd varnames = [‘Student_id’,’First_Name’,’Last_Name’,’Grade’] values = [[‘156841′,’Mark’,’Smith’,’85’], [‘785496′,’Jason’,’Gross’,’90’], [‘785612′,’Laura’,’Clarkson’,’76’], [‘125465′,’Tria’,’Carr’,’100′]] data1 = pd.DataFrame(values, columns=varnames) data1 solved python – how do i assign columns to my dataframe?
You could run one apply() which will use for-loop to convert list to Series with headers – it can use enumerate to put correct number in headers. Because some rows have less participants so it puts NaN which you can later fill with empty strings. And next you can use join() to add all as … Read more
We can use pmax df$a <- do.call(pmax, c(df, na.rm = TRUE)) Or with coalesce library(dplyr) df %>% mutate(a = coalesce(a,b)) # a b #1 1 <NA> #2 2 <NA> #3 3 3 #4 4 4 1 solved r simple way to merge 2 columns with not NA values [duplicate]
Introduction Merging two columns with non-NA values can be a tricky task, especially when the data is large and complex. Fortunately, there are a few simple methods that can be used to quickly and easily merge two columns with non-NA values. In this article, we will discuss some of the most common methods for merging … Read more
We could split the dataset (‘df’) with ‘1416’ columns to equal size ‘118’ columns by creating a grouping index with gl lst <- setNames(lapply(split(1:ncol(df), as.numeric(gl(ncol(df), 118, ncol(df)))), function(i) df[,i]), paste0(‘site’, 1:12)) Or you can create the ‘lst’ without using the split lst <- setNames(lapply(seq(1, ncol(df), by = 118), function(i) df[i:(i+117)]), paste0(‘site’, 1:12)) If we need … Read more
You are actually close to the solution. I also want to stress to first check out how to make a good reproducible example (at least for your next question) –> How to make a great R reproducible example? Here comes an example how it might look for your data: # Make up some demo data … Read more
csv files does not have a datatype definition header or something similar. So when your read a csv pandas tries to guess the types and this can change the datatypes. You have two possibile solutions: Provide the datatype list when you do read_csv with dtype and parse_dates keywords (https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html) Use a different file format that … Read more
You basically have two steps here: First is exploding the arrays (using the explode functions) to get a row for each value in the array, then fixing each element. You do not have the schema here so the internal structure of each element in the array is not clear, however, I would assume it is … Read more
This is a stack trace. It shows not just where the actual error occurred directly, but also what the program was doing while it occurred. The code df.loc[‘origin’ == ‘US’] (on line 6) is at the top of the trace, meaning it’s the root cause, but this by itself is not an error. This will … Read more
Here’s a base R one-liner use ave: df$A <- ave(df$X, df$Subject, FUN = function(x) if (max(x) == 3) 1 else 0) > df Subject Year X A 1 A 1990 1 0 2 A 1991 1 0 3 A 1992 2 0 4 A 1993 3 0 5 A 1994 4 0 6 A 1995 … Read more
This answer assumes reading of the earlier question and comments so is not “code only”. qt = expand.grid(Month=c(“G”,”J”,”M”,”Q”,”V”), Year=1975:2016) query_names_vec <- apply(qt, 1, function(x) paste0(“CME/GC”, paste0( x, collapse=””) ) ) > head( query_names_vec ) [1] “CME/GCG1975” “CME/GCJ1975” “CME/GCM1975” “CME/GCQ1975” [5] “CME/GCV1975” “CME/GCG1976” 10 solved Loop R with Quandl as optional
One solution is as follows: df2[, (df2[‘Row’, ] %in% df1$Row) & (df2[‘Column’, ] %in% df1$Column)] Head of the output is as follows: V1 V2 V3 Row 1 2 3 Column 5 4 3 3 49 29 34 4 45 42 18 5 9 15 45 6 34 35 19 2 solved Matching information from different … Read more
try this, filtered_column=df.filter(regex=r’^p’,axis=1).columns.values df=df.drop(filtered_column, axis=1) As you didn’t provide a sample data I created this my self. Input: p101 p102 p103 q21 qw32 kwp 0 68 17 54 67 93 36 1 32 22 56 69 38 6 2 58 48 89 68 60 79 3 64 14 63 53 7 86 4 67 94 … Read more
This is how I would do it: data = data.frame( Well = c(“A01”, “A02”, “A03”, “A04”, “A05”, “A06”, “A07”, “A08”), Call = c(“No Call”, “No Call”, “Allele 1”, “Heterozygote”, “Allele 1”, “Allele 2”, “Heterozygote”, “Heterozygote”), stringsAsFactors=FALSE ) g121map = c(“No Call”=””, “Allele 1″=”+”, “Allele 2″=”G1^{1384M}”, “Heterozygote”=”G1^{1384M}”) g122map = c(“No Call”=””, “Allele 1″=”+”, “Allele 2″=”G1^{1384M}”, “Heterozygote”=”+”) … Read more
import pandas as pd, numpy as np # filter results by showing records that meet condition # sample df d = {‘SBP’: [111, 100], ‘DBP’: [81, 40], ‘HEARTRATE’:[1,50]} df = pd.DataFrame(data=d) df # if an alert is found, prints alert, else normal if len(df[(df[ ‘SBP’ ] > 110) & (df[ ‘DBP’ ] > 80) & … Read more