np.where() do nothing if condition fails

Question

I have a sample from my dataframe:

       Created      Insert Time   MatchKey              In Previous    New Type
18593  2016-08-12   2018-02-19    LXGS090393APIN040640       No        New Existing
5517   2016-08-12   2018-02-19    LIN380076CI166203726       No        New Existing
2470   2018-02-12   2018-02-19    CI164414649APIN160672      No        New Existing
13667  2016-08-12   2018-02-19    LIN257400APIN015446       Yes        New Existing
10998  2016-08-12   2018-02-19    LXSV225786APIN158860      Yes        New Existing
20149  2016-08-12   2018-02-19    LIN350167APIN158284       Yes        New Existing
20143  2016-08-12   2018-02-19    LIN350167APIN161348       Yes        New Existing
30252  2016-08-12   2018-02-19    LXGS120737APIN153339      Yes        New Existing
12583  2016-08-09   2018-02-19    WIN556410APIN157186       Yes        New Existing
28591  2018-05-03   2018-02-19    CI195705185APIN009076      No        New Created

I wanted to replace values in New Type column in a way that if the condition is failed the function does nothing:

current['New Type'] = np.where(current['In Previous']=='Yes','In Previous',pass)

but apparently it causes a syntax error as np.where() doesn't handle pass:

File "<ipython-input-9-7f68cda12cbe>", line 1
current['New Type'] = np.where(current['In Previous']=='Yes','In Previous',pass)

                                                                          ^
SyntaxError: invalid syntax

What would be an alternative to achieve the same?

`where` isn't an `if/else` clone. – hpaulj Aug 10 '18 at 14:30 — hpaulj, Aug 10 '18 at 14:30

score 19 · Accepted Answer · answered Aug 10 '18 at 08:43

Just return the column instead of pass this is the same as doing nothing when the condition is False:

current['New Type'] = np.where(current['In Previous']=='Yes','In Previous',current['New Type'] )

Or you can just mask those rows:

current['New Type'] = current.loc[current['In Previous']=='Yes', 'In Previous']

score 6 · Answer 2 · answered Aug 10 '18 at 08:52

6

You can use pd.Series.mask for exactly this purpose:

df['New Type'].mask(df['In Previous']=='Yes', 'In Previous', inplace=True)

Somewhat confusingly, you have to remember that pd.Series.mask updates a value when a condition is met, while pd.Series.where updates a value when a condition is not met.

answered Aug 10 '18 at 08:52

jpp

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I have TypeError: mask() got multiple values for argument 'inplace'. Why? – Bartek Malysz Aug 11 '18 at 17:31

np.where() do nothing if condition fails

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