I have dataframe like this.
col1
0 1
1 3
2 3
3 1
4 2
5 3
6 2
7 2
I want to create column out by compare each row. If row 0 less than row 1 then out is 1. If row 1 more than row 2 then out is 0. like this sample.
col1 out
0 1 1 # 1<3 = 1
1 3 0 # 3<3 = 0
2 3 0 # 3<1 = 0
3 1 1 # 1<2 = 1
4 2 1 # 2<3 = 1
5 3 0 # 3<2 = 0
6 2 0 # 2<2 = 0
7 2 -
I try with this code.
def comp_out(a):
return np.concatenate(([1],a[1:] > a[2:]))
df['out'] = comp_out(df.col1.values)
It show error like this.
ValueError: operands could not be broadcast together with shapes (11,) (10,)
CodePudding user response：
Let's use shift instead to "shift" the column up so that rows are aligned with the previous, then use lt to compare less than and astype convert the booleans to 1/0:
df['out'] = df['col1'].lt(df['col1'].shift(-1)).astype(int)
col1 out
0 1 1
1 3 0
2 3 0
3 1 1
4 2 1
5 3 0
6 2 0
7 2 0
We can strip the last value with iloc if needed:
df['out'] = df['col1'].lt(df['col1'].shift(-1)).iloc[:-1].astype(int)
df:
col1 out
0 1 1.0
1 3 0.0
2 3 0.0
3 1 1.0
4 2 1.0
5 3 0.0
6 2 0.0
7 2 NaN
If we want to use the function we should make sure both are the same length, by slicing off the last value:
def comp_out(a):
return np.concatenate([a[0:-1] < a[1:], [np.NAN]])
df['out'] = comp_out(df['col1'].to_numpy())
df:
col1 out
0 1 1.0
1 3 0.0
2 3 0.0
3 1 1.0
4 2 1.0
5 3 0.0
6 2 0.0
7 2 NaN