As suggested by François Chollet (creator of Keras) in his book, one way to handle missing values is to replace them with zero: In general, with neural networks, it’s safe to input missing values as 0, with the condition that 0 isn’t already a meaningful value. The network will learn from exposure to the data … Read more
Make sure to do the following (from Support Library Setup): Right-click the library project folder and select Build Path > Configure Build Path. In the Order and Export tab, check the .jar files you just added to the build path, so they are available to projects that depend on this library project. For example, the … Read more
The recommendation in PEP 3101 is to subclass Formatter: import string class PartialFormatter(string.Formatter): def __init__(self, missing=’~~’, bad_fmt=”!!”): self.missing, self.bad_fmt=missing, bad_fmt def get_field(self, field_name, args, kwargs): # Handle a key not found try: val=super(PartialFormatter, self).get_field(field_name, args, kwargs) # Python 3, ‘super().get_field(field_name, args, kwargs)’ works except (KeyError, AttributeError): val=None,field_name return val def format_field(self, value, spec): # handle … Read more
tidyr::complete() fills missing values add id and date as the columns (…) to expand for library(tidyverse) complete(dat, id, date) # A tibble: 16 x 3 id date value <dbl> <date> <dbl> 1 1.00 2017-01-01 30.0 2 1.00 2017-02-01 30.0 3 1.00 2017-03-01 NA 4 1.00 2017-04-01 25.0 5 2.00 2017-01-01 NA 6 2.00 2017-02-01 25.0 … Read more
As commented the axis argument to fillna is NotImplemented. df.fillna(df.mean(axis=1), axis=1) Note: this would be critical here as you don’t want to fill in your nth columns with the nth row average. For now you’ll need to iterate through: m = df.mean(axis=1) for i, col in enumerate(df): # using i allows for duplicate columns # … Read more
If you are using Python 3.2+, use can use str.format_map(). For bond, bond: from collections import defaultdict ‘{bond}, {james} {bond}’.format_map(defaultdict(str, bond=’bond’)) Result: ‘bond, bond’ For bond, {james} bond: class SafeDict(dict): def __missing__(self, key): return ‘{‘ + key + ‘}’ ‘{bond}, {james} {bond}’.format_map(SafeDict(bond=’bond’)) Result: ‘bond, {james} bond’ In Python 2.6/2.7 For bond, bond: from collections import … Read more
So, you can simulate your situation like this; # Simulate some data: # Create 332 data frames set.seed(1) df.list<-replicate(332,data.frame(sulfate=rnorm(100),nitrate=rnorm(100)),simplify=FALSE) # Generate names like 001.csv and 010.csv file.names<-paste0(‘specdata/’,sprintf(‘%03d’,1:332),’.csv’) # Write them to disk invisible(mapply(write.csv,df.list,file.names)) And here is a function that would read those files: pollutantmean <- function(directory, pollutant, id = 1:332) { file.names <- list.files(directory) file.numbers … Read more
You can also use fill from tidyr: library(dplyr) library(tidyr) df1 %>% group_by(ID) %>% fill(v1) %>% fill(v1, .direction = “up”) Result: # A tibble: 9 x 2 # Groups: ID [3] ID v1 <chr> <int> 1 A 1 2 A 1 3 A 1 4 B 2 5 B 2 6 B 2 7 C NA … Read more
You may try pmax df$c <- pmax(df$a, df$b) df # a b c # 1 dog <NA> dog # 2 mouse <NA> mouse # 3 <NA> cat cat # 4 bird <NA> bird …or ifelse: df$c <- ifelse(is.na(df$a), df$b, df$a) For more general solutions in cases with more than two columns, you find several ways … Read more
A relatively simple modification of your code should solve the issue: for(i in 1:ncol(data)){ data[is.na(data[,i]), i] <- mean(data[,i], na.rm = TRUE) }