Sort all columns of a dataframe
I think you can use numpy.sort with DataFrame constructor or apply with sort_values with convert to numpy array by values:
df = pd.DataFrame(np.sort(df.values, axis=0), index=df.index, columns=df.columns)
Another solution, slowier:
df = df.apply(lambda x: x.sort_values().values)
print (df)
0 1 2 3 4 5 6 7 8 9 ... 490 491 492 \
0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0
1 0 0 0 0 0 0 0 0 0 0 ... 0 0 0
2 0 0 0 0 0 0 0 0 0 0 ... 0 0 0
3 0 0 0 0 0 0 0 0 0 0 ... 0 0 0
4 0 0 0 0 0 0 0 0 0 0 ... 0 0 0
5 0 0 0 0 0 0 0 0 0 0 ... 0 0 0
6 0 0 0 0 0 0 0 0 0 0 ... 0 0 0
7 0 0 0 0 0 0 0 0 0 0 ... 0 0 0
8 0 0 0 0 0 0 0 0 0 0 ... 0 0 0
9 0 0 0 0 0 0 0 0 0 0 ... 0 0 0
10 0 0 0 0 0 0 0 0 0 0 ... 0 0 0
11 0 0 0 0 0 0 0 0 0 0 ... 0 0 0
12 0 0 0 0 0 0 0 0 0 0 ... 0 0 0
13 0 0 0 0 0 0 0 0 0 0 ... 0 0 0
14 0 0 0 0 0 0 0 0 0 0 ... 0 0 0
15 0 0 0 0 0 1 0 0 0 0 ... 0 0 0
16 0 0 0 0 0 1 1 0 0 0 ... 0 0 0
17 0 0 0 0 0 1 1 0 0 0 ... 0 0 0
18 0 0 0 0 0 1 1 0 0 0 ... 0 0 0
19 0 0 0 0 0 1 1 1 1 0 ... 0 0 0
20 0 0 1 0 0 1 1 1 1 0 ... 0 0 0
21 0 0 1 0 0 1 1 1 1 1 ... 0 1 0
22 0 1 1 0 0 1 1 1 1 1 ... 0 1 0
23 1 1 1 0 0 1 1 1 1 1 ... 0 1 0
24 1 1 1 0 0 1 1 1 1 1 ... 0 1 0
25 1 1 1 1 0 1 1 1 1 1 ... 0 1 0
26 1 1 1 1 0 1 1 1 1 1 ... 1 1 1
27 1 1 1 1 0 1 1 1 1 1 ... 1 1 1
28 1 1 1 1 0 1 1 1 1 1 ... 1 1 1
29 1 1 1 1 0 1 1 1 1 1 ... 1 1 1
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1970 97 98 98 98 98 98 99 98 98 98 ... 98 98 98
1971 97 98 98 98 98 98 99 98 98 98 ... 98 98 98
1972 98 98 98 98 98 98 99 98 98 98 ... 98 98 98
1973 98 98 98 99 98 98 99 98 98 98 ... 98 98 98
1974 98 98 98 99 98 98 99 98 98 98 ... 98 98 98
1975 98 98 98 99 98 98 99 98 98 98 ... 98 98 98
1976 98 98 98 99 98 98 99 98 99 99 ... 98 98 98
1977 98 98 98 99 98 98 99 98 99 99 ... 98 98 99
1978 98 98 98 99 98 98 99 98 99 99 ... 98 98 99
1979 98 98 98 99 99 99 99 98 99 99 ... 98 98 99
1980 98 98 98 99 99 99 99 98 99 99 ... 98 98 99
1981 99 99 98 99 99 99 99 98 99 99 ... 99 98 99
1982 99 99 98 99 99 99 99 98 99 99 ... 99 98 99
1983 99 99 98 99 99 99 99 98 99 99 ... 99 98 99
1984 99 99 98 99 99 99 99 99 99 99 ... 99 99 99
1985 99 99 98 99 99 99 99 99 99 99 ... 99 99 99
1986 99 99 98 99 99 99 99 99 99 99 ... 99 99 99
1987 99 99 99 99 99 99 99 99 99 99 ... 99 99 99
1988 99 99 99 99 99 99 99 99 99 99 ... 99 99 99
1989 99 99 99 99 99 99 99 99 99 99 ... 99 99 99
1990 99 99 99 99 99 99 99 99 99 99 ... 99 99 99
1991 99 99 99 99 99 99 99 99 99 99 ... 99 99 99
1992 99 99 99 99 99 99 99 99 99 99 ... 99 99 99
1993 99 99 99 99 99 99 99 99 99 99 ... 99 99 99
1994 99 99 99 99 99 99 99 99 99 99 ... 99 99 99
1995 99 99 99 99 99 99 99 99 99 99 ... 99 99 99
1996 99 99 99 99 99 99 99 99 99 99 ... 99 99 99
1997 99 99 99 99 99 99 99 99 99 99 ... 99 99 99
1998 99 99 99 99 99 99 99 99 99 99 ... 99 99 99
1999 99 99 99 99 99 99 99 99 99 99 ... 99 99 99
493 494 495 496 497 498 499
0 0 0 0 0 0 0 0
1 0 0 0 0 0 0 0
2 0 0 0 0 0 0 0
3 0 0 0 0 0 0 0
4 0 0 0 0 0 0 0
5 0 0 0 0 0 0 0
6 0 0 0 0 0 0 0
7 0 0 0 0 0 0 0
8 0 0 0 0 0 0 0
9 0 0 0 0 0 0 0
10 0 0 0 0 0 0 0
11 0 0 0 0 0 0 0
12 0 0 0 0 0 0 0
13 0 0 0 0 0 0 0
14 0 0 0 0 0 0 0
15 0 0 0 0 1 0 0
16 0 1 0 0 1 0 0
17 0 1 0 0 1 0 0
18 1 1 0 0 1 0 0
19 1 1 1 0 1 0 0
20 1 1 1 0 1 0 1
21 1 1 1 0 1 0 1
22 1 1 1 0 1 0 1
23 1 1 1 0 1 0 1
24 1 1 1 0 1 0 1
25 1 1 1 0 1 0 1
26 1 1 1 0 1 0 1
27 1 1 1 1 1 0 1
28 1 1 1 1 1 0 1
29 1 1 1 1 1 0 1
... ... ... ... ... ... ... ...
1970 98 98 98 98 98 98 98
1971 98 98 98 98 98 98 98
1972 98 98 98 98 98 98 98
1973 98 98 98 98 98 98 98
1974 98 98 98 99 98 98 98
1975 98 98 98 99 98 98 98
1976 99 98 98 99 98 98 98
1977 99 98 98 99 98 98 98
1978 99 98 98 99 99 98 98
1979 99 99 98 99 99 98 98
1980 99 99 98 99 99 99 99
1981 99 99 98 99 99 99 99
1982 99 99 98 99 99 99 99
1983 99 99 99 99 99 99 99
1984 99 99 99 99 99 99 99
1985 99 99 99 99 99 99 99
1986 99 99 99 99 99 99 99
1987 99 99 99 99 99 99 99
1988 99 99 99 99 99 99 99
1989 99 99 99 99 99 99 99
1990 99 99 99 99 99 99 99
1991 99 99 99 99 99 99 99
1992 99 99 99 99 99 99 99
1993 99 99 99 99 99 99 99
1994 99 99 99 99 99 99 99
1995 99 99 99 99 99 99 99
1996 99 99 99 99 99 99 99
1997 99 99 99 99 99 99 99
1998 99 99 99 99 99 99 99
1999 99 99 99 99 99 99 99
I think the most elegant solution nowadays is df.transform(np.sort).