Recently, a pandas user, Wouter Overmeire, contributed a to_html function to DataFrame in a pull request. It's very handy:
In [31]: df
Out[31]:
ls lsc pop ccode
year cname agefrom
1950 Australia 15 64.3 15.4 558 AUS
40 38.9 20.1 555 AUS
65 24.7 10.7 273 AUS
1955 Australia 25 48.4 26.2 705 AUS
50 34 16.6 478 AUS
75 26.3 11.7 240 AUS
1960 Australia 35 47.9 24.7 755 AUS
60 29.6 13.5 389 AUS
1965 Australia 20 57.7 31.1 832 AUS
45 41.3 20.3 665 AUS
70 30.2 12.9 273 AUS
1970 Australia 30 57.7 31.1 789 AUS
55 42.1 19.4 619 AUS
1975 Australia 15 91.1 23.1 1225 AUS
40 56.3 30.9 744 AUS
65 48.8 22.5 440 AUS
1980 Australia 25 74 44.5 1205 AUS
50 60.2 32.8 774 AUS
75 62.5 30.8 500 AUS
1985 Australia 35 72.7 42.8 1180 AUS
In [32]: df.to_html()
which outputs:
| ls | lsc | pop | ccode | |||
|---|---|---|---|---|---|---|
| year | cname | agefrom | ||||
| 1950 | Australia | 15 | 64.3 | 15.4 | 558 | AUS |
| 1955 | Australia | 50 | 34 | 16.6 | 478 | AUS |
| 1965 | Australia | 20 | 57.7 | 31.1 | 832 | AUS |
| 1970 | Australia | 55 | 42.1 | 19.4 | 619 | AUS |
| 1980 | Australia | 25 | 74 | 44.5 | 1205 | AUS |
| 1985 | Australia | 60 | 59.1 | 30.8 | 685 | AUS |
| 1995 | Australia | 30 | 72 | 45.7 | 1430 | AUS |
| 2000 | Australia | 65 | 60 | 31.5 | 663 | AUS |
| 2010 | Australia | 35 | 59.9 | 39.1 | 1506 | AUS |
| 1950 | Austria | 70 | 2.8 | 1.9 | 218 | AUT |
| 1960 | Austria | 40 | 4.5 | 3 | 324 | AUT |
| 1965 | Austria | 75 | 13 | 8.6 | 322 | AUT |
| 1975 | Austria | 45 | 42.1 | 28 | 455 | AUT |
| 1985 | Austria | 15 | 22.4 | 4.2 | 626 | AUT |
| 1990 | Austria | 50 | 47.3 | 23.4 | 448 | AUT |
| 2000 | Austria | 20 | 73.2 | 63.2 | 465 | AUT |
| 2005 | Austria | 55 | 55.1 | 36.2 | 486 | AUT |