Replace all values with NaN in the dataframe in pandas

Question

I have a small dataframe (df):

unique  a     b     c     d 
  0    None  None  None  None
  1    None  None  None  None
  2    None  0132  None  None
  3    None  None  None  0231
  4    None  None  None  None
  5    None  None  0143  None
  6    0121  None  None  None
  7    None  None  None  0432

I need to replace all values with NaN. I tried to apply df.fillna(np.NAN), but it does not change the value in cells, where there is a number. How do I make all the values have been replaced? Dataframe should look like this:

unique  a     b     c     d 
  0    NaN   NaN   NaN   NaN
  1    NaN   NaN   NaN   NaN
  2    NaN   NaN   NaN   NaN
  3    NaN   NaN   NaN   NaN
  4    NaN   NaN   NaN   NaN
  5    NaN   NaN   NaN   NaN
  6    NaN   NaN   NaN   NaN
  7    NaN   NaN   NaN   NaN

df.fillna() kind of does the opposite of what you want. It replaces all NaN-values with another value. So df.fillna(np.NAN) replaces all NaN with NaN which makes no sense. — Cleared
– Cleared, Commented Jul 3, 2017 at 9:32

piRSquared · Accepted Answer · 2017-07-03 09:30:36Z

11

Use loc to assign np.nan

df.loc[:] = np.nan

iloc also works

df.iloc[:] = np.nan

answered Jul 3, 2017 at 9:30

piRSquared

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zipa · Accepted Answer · 2017-07-03 09:36:37Z

6

Just to add to the pool, this also works:

df[:] = np.nan

answered Jul 3, 2017 at 9:36

zipa

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jezrael · Accepted Answer · 2017-07-03 09:33:41Z

4

Use DataFrame constructor and pass index and columns names by df:

df = pd.DataFrame(columns=df.columns, index=df.index)

edited Jul 3, 2017 at 9:33

answered Jul 3, 2017 at 9:28

jezrael

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