Equivalent to the numpy where function in pandas

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Here is my problem, I have two columns as below : 

 A   B
-3   4
-6  -8
12  14

I would like to create a code that replace all the values by 1 if the value is > -2 and by 0 if not.

I know that with numpy it would have been simple by using d["newcolum"]=np.where(d < -2,1,0) but I have more difficulties with pandas as its where function just show the value if the condition is not met.

Do you guys know something that could help me? Thanks

pandas numpy python
1 Answers

If want set 0 and 1 values convert boolean mask to integer:

df = (df < -2).astype(int)
print (df)
   A  B
0  1  0
1  1  1
2  0  0

Using np.where ia absolutely fine, only need DataFrame constructor:

df = pd.DataFrame(np.where(df < -2,1,0), index=df.index, columns=df.columns)
print (df)
   A  B
0  1  0
1  1  1
2  0  0
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