Or clamp.(df, 0.0, Inf) which is nice and explicit (at least if you know what “clamp” means I guess…)
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In short - data frame supports broadcasting in full 
.
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It would sure be nice to have a function replacenan(df,fillwith) that could iterate through a df finding all Floats with NaNs and replacing them with fillwith. I have a slightly complex dataframe:
And I’ve been trying to figure out how to replace all on the nan’s so I can export as a Parquet file.
I’m sure people have written this… seems like a common task
Try
replace.(df, NaN => 0)
which basically applies replace(::Array, NaN => 0) to each cell. Use replace! for in-place version. Nice, huh?
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@Jollywatt that is a dream! Love it.
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Not perfect but this is how I did it…
transform(addCols, nanCols .=> ByRow(x → replace.(x, r"NaN"=> 0)) .=> nanCols)
apparently this work in all cases except NaN … oops
@bkamins This will fail in case if df has missings right? how to handle such scenarios?
You mean the above? But what do you want to do with missings? Keep them being missing? In this case you need:
ifelse.(isless.(df, 0), 0, df)
(this works in this specific case, in case of other conditions you would need double condition or passmissing)
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I apologize for not mentioning this earlier but below operation will fail in case there are any missing’s in df columns.
df .= ifelse.(isnan.(df), 0, df)
I assume you want to keep missing to be still missing
df .= ifelse.(isnan.(coalesce.(df, false)), 0, df)
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Thank You!!