Python

cleanedList = [x for x in countries if str(x) != 'nan']

a = a[~(np.isnan(a).any(axis=1))] # removes rows containing at least one nan
a = a[~(np.isnan(a).all(axis=1))] # removes rows containing all nan

df = df[df.index.notnull()]

function removeNaN(arr) {
  return arr.filter(item => !isNaN(item));
}

df = df[df['my_var'].notna()]

df.dropna(subset = ["column2"], inplace=True)

a[ ~np.isnan(a).any(axis=1),:]

cleanedList = [x for x in countries if str(x) != 'nan']

cleanedList = [x for x in countries if str(x) != 'nan']

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