I have a big sparse dataframe and I'd like to remove automatically the columns (the column keys/names are dynamic, so in principle I don't know the column number and the naming) with a number of non zero element below a certain percentage of the total row number.
Thank you!
Pandas has a dropna
function, which has a thresh
parameter. Simply set that to the number of non-zero values you need to keep. So
df.dropna(thres=int(len(df)*0.8), axis=1)
Will drop columns where less than 80% of the rows are complete.
Firebase Cloud Functions: PubSub, "res.on is not a function"
TypeError: Cannot read properties of undefined (reading 'createMessageComponentCollector')
I have used the code below to send some DataFrames as tables in an emailThe tables contain 4 columns each, the first is a label and the next three are numbers
I am currently testing a web app using pytest and SeleniumAll pages have "Home" and "Log Out" links, so I have written a test like this: