Delete all rows which have null values/missing values in 'all' the columns

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1

I am trying to find a similar functionality like in Python where in I can delete the rows which have ‘all’ null values in a row -

code in python - Using Pandas dataframe ‘closed_prices’
closed_prices.dropna(axis=0, how=‘all’, inplace=True)

Basically I want to drop rows which have missing values for all columns - I am using a stock data and want to remove the weekends and holidays, each column represent closing price of a particular stock. So if all the column values are null/missing for a particular row I want to delete that row.

2

Here is one example

julia> df = DataFrame(a = [1, missing], b = [missing, missing])
2×2 DataFrame
 Row │ a        b       
     │ Int64?   Missing 
─────┼──────────────────
   1 │       1  missing 
   2 │ missing  missing 

julia> filter(AsTable(:) => t -> !all(ismissing, t), df)
1×2 DataFrame
 Row │ a       b       
     │ Int64?  Missing 
─────┼─────────────────
   1 │      1  missing
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