["python","pandas","dataframe","in-place"]
Pandas: drop columns with all NaN's

I have this DataFrame: 0 1 2 3 4 5 6 7 0 #0915-8 NaN NaN NaN NaN NaN NaN NaN 1 NaN NaN NaN LIVE WGT NaN AMOUNT NaN TOTAL 2 GBW COD NaN NaN 2,280 NaN $0.60 NaN $1,368.00 3 POLLOCK NaN NaN 1,611 NaN $0.01 NaN $16.11 4 WHAKE NaN NaN 441 NaN $0.70 NaN $308.70 5 GBE HADDOCK NaN NaN 2,788 NaN $0.01 NaN $27.88 6 GBW HADDOCK NaN NaN 16,667 NaN $0.01 NaN $166.67 7 REDFISH NaN NaN 932 NaN $0.01 NaN $9.32 8 GB WINTER FLOUNDER NaN NaN 145 NaN $0.25 NaN $36.25 9 GOM WINTER FLOUNDER NaN NaN 25,070 NaN $0.35 NaN $8,774.50 10 GB YELLOWTAIL NaN NaN 26 NaN $1.75 NaN $45.50 I want to drop all NaNs as well as any columns with more than 3 NaNs (either one, or both, should work I think). I tried this code: fish_frame.dropna() fish_frame.dropna(thresh=len(fish_frame) - 3, axis=1) but it seems not to have any effect on the DataFrame - I see the same results afterward. What is wrong with the code, and how do I fix it?
From the dropna docstring: Drop the columns where all elements are NaN: df.dropna(axis=1, how='all') A B D 0 NaN 2.0 0 1 3.0 4.0 1 2 NaN NaN 5