python - Sort all columns of a pandas DataFrame independently using sort_values() -

i have dataframe , want sort columns independently in descending or ascending order.

import pandas pd  data = {'a': [5, 2, 3, 6],         'b': [7, 9, 1, 4],         'c': [1, 5, 4, 2]} df = pd.dataframe.from_dict(data)     b  c 0  5  7  1 1  2  9  5 2  3  1  4 3  6  4  2 

when use sort_values() not work expected (to me) , sorts 1 column:

foo = df.sort_values(by=['a', 'b', 'c'], ascending=[false, false, false])     b  c 3  6  4  2 0  5  7  1 2  3  1  4 1  2  9  5 

i can desired result if use solution this answer applies lambda function:

bar = df.apply(lambda x: x.sort_values().values) print(bar)      b  c 0  2  1  1 1  3  4  2 2  5  7  4 3  6  9  5 

but looks bit heavy-handed me.

what's happening in sort_values() example above , how can sort columns in dataframe in pandas-way without lambda function?

you can use numpy.sort dataframe constructor:

df1 = pd.dataframe(np.sort(df.values, axis=0), index=df.index, columns=df.columns) print (df1)     b  c 0  2  1  1 1  3  4  2 2  5  7  4 3  6  9  5