2

I been trying to get my code to work but I am having some trouble here. It would be great if someone could assist me

DF

  Col1              Col2          
  2017-01-01        Coffee
  2017-01-01        Muffin
  2017-01-01        Donut
  2017-01-01        Toast
  2017-01-01        
  2017-01-01

How can I change Col2 so that every value that isn't Coffee or Muffin or null becomes 'Other'?

  Col1              Col2          
  2017-01-01        Coffee
  2017-01-01        Muffin
  2017-01-01        Other
  2017-01-01        Other
  2017-01-01        
  2017-01-01

Edit:

df.loc[~df.Col2.isin(['Coffee','Muffin']), 'Col2'] = 'Other'

^this is where I am right now, but how can I add a null statement in the isin

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3
  • "get my code to work" - please include your code. Commented Jan 30, 2018 at 3:33
  • Is that NaN or an empty string? They aren't the same thing. Commented Jan 30, 2018 at 3:42
  • it is blank, missing value according to df.describe() Commented Jan 30, 2018 at 3:43

3 Answers 3

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3

You were almost there. If you're working with NaNs, you'll need an additional check with isnull. Create a mask and set values with loc - 

m = ~(df.Col2.isin(['Coffee', 'Muffin']) | df.Col2.isnull())
df.loc[m, 'Col2'] = 'Other'

df

         Col1    Col2
0  2017-01-01  Coffee
1  2017-01-01  Muffin
2  2017-01-01   Other
3  2017-01-01   Other
4  2017-01-01     NaN
5  2017-01-01     NaN

Or, if they're blanks (empty string, not NaN - they're different!), perform an equality comparison for the second condition -

m = ~(df.Col2.isin(['Coffee', 'Muffin']) | df.Col2.eq(''))

Here are some more possibilities with np.where/pd.Series.where/pd.Series.mask -

df.Col2 = np.where(m, 'Other', df.Col2)

Or,

df.Col2 = df.Col2.where(~m, 'Other')

Or,

df.Col2 = df.Col2.mask(m, 'Other')


df

         Col1    Col2
0  2017-01-01  Coffee
1  2017-01-01  Muffin
2  2017-01-01   Other
3  2017-01-01   Other
4  2017-01-01     NaN
5  2017-01-01     NaN
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Comments

2 
df = pd.DataFrame({'Col1':['2017-01-01','2017-01-01','2017-01-01','2017-01-01','2017-01-01','2017-01-01'],
 'Col2':['Coffee','Muffin','Donut','Toast',pd.np.nan,pd.np.nan]})

conditions = (df['Col2'] != 'Coffee') & (df['Col2'] != 'Muffin') & (df['Col2'].isnull() == False)

df['Col2'][conditions] = 'Other'
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Comments

2

isin can include the np.nan

df.loc[df.Col2.isin(['Donut', 'Toast',np.nan]),'Col2']='Other'
df
Out[112]: 
         Col1    Col2
0  2017-01-01  Coffee
1  2017-01-01  Muffin
2  2017-01-01   Other
3  2017-01-01   Other
4  2017-01-01   Other
5  2017-01-01   Other
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1 Comment

df.loc[df.Col2.isin(['Coffee', 'Muffin',pd.np.nan]) == False,'Col2']='Other'

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