Python Pandas Dataframe, remove all rows where 'None' is the value in any column

Question

I have a large dataframe. When it was created \'None\' was used as the value where a number could not be calculated (instead of \'nan\')

How can I delete all rows that h

Answer 1

UPDATE:

In [70]: temp[temp.astype(str).ne('None').all(1)]
Out[70]:
      0     1  2  3  4  5   6  7
0  str1  str2  2  3  5  6  76  8

Old answer:

In [35]: x
Out[35]:
      a     b   c
0     1     2   3
1     4  None   6
2  None     7   8
3     9    10  11

In [36]: x = x[~x.astype(str).eq('None').any(1)]

In [37]: x
Out[37]:
   a   b   c
0  1   2   3
3  9  10  11

or bit nicer variant from @roganjosh:

In [47]: x = x[x.astype(str).ne('None').all(1)]

In [48]: x
Out[48]:
   a   b   c
0  1   2   3
3  9  10  11

Answer 2

Setup
Borrowed @MaxU's df

df = pd.DataFrame([
    [1, 2, 3],
    [4, None, 6],
    [None, 7, 8],
    [9, 10, 11]
], dtype=object)

Solution
You can just use pd.DataFrame.dropna as is

df.dropna()

   0   1   2
0  1   2   3
3  9  10  11

---

Supposing you have None strings like in this df

df = pd.DataFrame([
    [1, 2, 3],
    [4, 'None', 6],
    ['None', 7, 8],
    [9, 10, 11]
], dtype=object)

Then combine dropna with mask

df.mask(df.eq('None')).dropna()

   0   1   2
0  1   2   3
3  9  10  11

You can ensure that the entire dataframe is object when you compare with.

df.mask(df.astype(object).eq('None')).dropna()

   0   1   2
0  1   2   3
3  9  10  11

Answer 3

Thanks for all your help. In the end I was able to get

df = df.replace(to_replace='None', value=np.nan).dropna()

to work. I'm not sure why your suggestions didn't work for me.