summarize all columns by mean and std

R::

mtcars %>% summarize_all(funs(mean, sd)) # Non-numerical columns turn into NA + a warning mtcars %>% select_if(is.numeric) %>% summarize_all(funs(mean, sd)

plydata:

mtcars >> dp.summarise_all((np.mean, np.std))  # exception due to the string column

mtcars >> dp.call('select_dtypes',int) >>dp.summarise_all((np.mean, np.std))

mtcars >> dp.summarize_if('is_numeric', (np.mean, np.std))

dpylthon:

# won't return - no comprehensions with X.anything
dp.DplyFrame(mtcars) >> dp.summarize(**{f"{x}_mean": X[x].mean() for x in X.columns})

#have to use the datafram, and the merge-dicts syntax (python 3.5+)
dp.DplyFrame(mtcars) >> dp.summarize(**{
  **{f"{x}_mean": X[x].mean() for x in mtcars.select_dtypes(int).columns},
  **{f"{x}_std": X[x].std() for x in mtcars.select_dtypes(int).columns}
  })

dfplyr:

mtcars >> dp.summarize(**{
  **{f"{x}_mean": X[x].mean() for x in mtcars.select_dtypes(int).columns},
  **{f"{x}_std": X[x].std() for x in mtcars.select_dtypes(int).columns}
  })

can’t select first, because I can’t iterate over X.columns

dpdp:

dp(mtcars).select_dtypes(np.number).summarize(*
  [(c, np.mean) for c in X.columns]
  + [(c, np.std) for c in X.columns]
).pd