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 have 'None' in any of it's columns? I though I could use df.dropna and set the value of na, but I can't seem to be able to.

Thanks

I think this is a good representation of the dataframe:

temp = pd.DataFrame(data=[['str1','str2',2,3,5,6,76,8],['str3','str4',2,3,'None',6,76,8]])

Answer 1

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 2

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.