CODEWITHSUNDEEP
pythondataframe
user572575
user572575

Reputation: 1049

Python Pandas DataFrames compare with next rows

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,)

Upvotes: 0

Views: 61

Answers (1)

Henry Ecker
Henry Ecker

Reputation: 35636

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

Upvotes: 1

Related Questions