适用于熊猫的替代方法-基于多个列创建新列

Question

I have a Pandas dataframe and I would like to add a new column based on the values of the other columns. A minimal example illustrating my usecase is below.

df = pd.DataFrame([[4,5,19],[1,2,0],[2,5,9],[8,2,5]], columns=['a','b','c'])
df

    a   b   c
---------------
0   4   5   19
1   1   2   0
2   2   5   9
3   8   2   5

x = df.sample(n=2)
x

    a   b   c
---------------
3   8   2   5
1   1   2   0

def get_new(row):
    a, b, c = row
    return random.choice(df[(df['a'] != a) & (df['b'] == b) & (df['c'] != c)]['c'].values)

y = x.apply(lambda row: get_new(row), axis=1)
x['new'] = y
x

    a   b   c   new
--------------------
3   8   2   5   0
1   1   2   0   5

Note: The original dataframe has ~4 million rows and ~6 columns. The number of rows in the sample might vary between 50 and 500. I am running on a 64-bit machine with 8 GB RAM.

The above works, except that it is quite slow (takes about 15 seconds for me). I also tried using x.itertuples() instead of apply and there is not much of an improvement in this case.

1. It seems that apply(with axis=1) is slow since it does not make use of the vectorized operations. Is there some way I could achieve this in a faster way?

2. Can the filtering(in the get_new function) be modified or made more efficient compared to using conditional boolean variables, as I currently have?

3. Can I in some way use numpy here for some speedup?

Edit: df.sample() is also quite slow and I cannot use .iloc or .loc since I am further modifying the sample and do not wish for this to affect the original dataframe.

Answer 1

I see a reasonable performance improvement by using .loc rather than chained indexing:

import random, pandas as pd, numpy as np

df = pd.DataFrame([[4,5,19],[1,2,0],[2,5,9],[8,2,5]], columns=['a','b','c'])

df = pd.concat([df]*1000000)

x = df.sample(n=2)

def get_new(row):
    a, b, c = row
    return random.choice(df[(df['a'] != a) & (df['b'] == b) & (df['c'] != c)]['c'].values)

def get_new2(row):
    a, b, c = row
    return random.choice(df.loc[(df['a'] != a) & (df['b'] == b) & (df['c'] != c), 'c'].values)


%timeit x.apply(lambda row: get_new(row), axis=1)   # 159ms
%timeit x.apply(lambda row: get_new2(row), axis=1)  # 119ms