I have a dataframe like as shown below
cdf = pd.DataFrame({'Id':[1,2,3,4,5],
'Label':[1,1,1,0,0]})
My objective is to
a) replace 0s as 1s AND 1s as 0s in Label column
I was trying something like the below
cdf.assign(invert_label=cdf.Label.loc[::-1].reset_index(drop=True)) #not work
cdf['invert_label'] = np.where(cdf['Label']==0, '1', '0')
' but this doesn't work. It reverses the order
I expect my output to be like as shown below
Id Label
0 1 0
1 2 0
2 3 0
3 4 1
4 5 1
CodePudding user response：
You can compare 0, so for 0 get Trues and for not 0 get Falses, then converting to integers for mapping True, False to 1, 0:
print (cdf['Label'].eq(0))
0 False
1 False
2 False
3 True
4 True
Name: Label, dtype: bool
cdf['invert_label'] = cdf['Label'].eq(0).astype(int)
print (cdf)
Id Label invert_label
0 1 1 0
1 2 1 0
2 3 1 0
3 4 0 1
4 5 0 1
Another idea is use mapping:
cdf['invert_label'] = cdf['Label'].map({1:0, 0:1})
print (cdf)
Id Label invert_label
0 1 1 0
1 2 1 0
2 3 1 0
3 4 0 1
4 5 0 1
CodePudding user response：
One maybe obvious answer might be to use 1-value:
cdf['Label2'] = 1-cdf['Label']
output:
Id Label Label2
0 1 1 0
1 2 1 0
2 3 1 0
3 4 0 1
4 5 0 1
CodePudding user response：
You could map the not function as well -
import operator
cdf['Label'].map(operator.not_).astype('int')
CodePudding user response：
Another way, and I am adding this as a separate answer as this is probably not "pythonic" enough (in the sense that it is not very explicit) is to use the bitwise xor
cdf['Label'] ^ 1