Compare Dataframe column with numpy ndarray and update value in dataframe

Question

Dateframe df:A  B  C  D        E
             1  2  4  6        #Value to be updated for this column  
             12 34 5  54
             4  8  12 4
             3  5  6  2
             5  7  11 27
numpy ndarray(shape(4*1)):
         npar=  ([12]
           [6]
           [2]
           [27]
          )

I have above dataframe df and array npar, I want to compare value of column D in array npar. if value of column D is found in array npar anywhere . I want to update column E with 1 else 0 for that row of dataframe df. Kindly suggest how I can do this with sample code.

df['E'] = df.D.isin(npar).astype(int)

Ami Tavory
– Ami Tavory

2018-04-27 07:17:44 +00:00
Commented Apr 27, 2018 at 7:17 — Ami Tavory
– Ami Tavory, Commented Apr 27, 2018 at 7:17

jezrael · Accepted Answer · 2018-04-27 07:26:01Z

1

You need isin, but first is necessery flatten array by numpy.ravel and last convert boolean mask to integers - Trues are 1s and Falses are 0s:

df['E'] = df.D.isin(npar.ravel()).astype(int)
print (df)
    A   B   C   D  E
0   1   2   4   6  1
1  12  34   5  54  0
2   4   8  12   4  0
3   3   5   6   2  1
4   5   7  11  27  1

Detail:

npar = np.array([[12],[6],[2],[27]])
print (npar)
[[12]
 [ 6]
 [ 2]
 [27]]

print (npar.ravel())
[12  6  2 27]

print (df.D.isin(npar.ravel()))
0     True
1    False
2    False
3     True
4     True
Name: D, dtype: bool

edited Apr 27, 2018 at 7:26

answered Apr 27, 2018 at 7:18

jezrael

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1 Comment

ASING Over a year ago

Thanks a lot it worked. npar.ravel() is the key here.

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