Pandas DataFrame检查列值是否存在列值

Question

我有这样的DataFrame(简化示例)

id  v0  v1  v2  v3  v4
1   10  5   10  22  50
2   22  23  55  60  50
3   8   2   40  80  110
4   15  15  25  100 101

并且如果v0值在v1到v4的值中,则想要创建一个1或0的附加列,如果不是,则创建0.因此,在此示例中,对于id 1,则该值应为1(因为v2 = 10),并且对于id 2值应该为0,因为22不在v1到v4中.

实际上,表格更大(大约100,000行,变量从v1到v99).

Answer 1

您可以使用基础numpy数组来提高性能:

建立

a = df.v0.values
b = df.iloc[:, 2:].values

df.assign(out=(a[:, None]==b).any(1).astype(int))

   id  v0  v1  v2   v3   v4  out
0   1  10   5  10   22   50    1
1   2  22  23  55   60   50    0
2   3   8   2  40   80  110    0
3   4  15  15  25  100  101    1

该解决方案利用广播来进行成对比较:

首先,我们广播a:

>>> a[:, None]
array([[10],
       [22],
       [ 8],
       [15]], dtype=int64)

这允许与以下成对比较b:

>>> a[:, None] == b
array([[False,  True, False, False],
       [False, False, False, False],
       [False, False, False, False],
       [ True, False, False, False]])

然后,我们只需检查True沿第一个轴的任何结果,并转换为整数.

性能

功能

def user_chris(df):
    a = df.v0.values
    b = df.iloc[:, 2:].values
    return (a[:, None]==b).any(1).astype(int)

def rahlf23(df):
    df = df.set_index('id')
    return df.drop('v0', 1).isin(df['v0']).any(1).astype(int)

def chris_a(df):
    return df.loc[:, "v1":].eq(df['v0'], 0).any(1).astype(int)

def chris(df):
    return df.apply(lambda x: int(x['v0'] in x.values[2:]), axis=1)

def anton_vbr(df):
    df.set_index('id', inplace=True)
    return df.isin(df.pop('v0')).any(1).astype(int)

建立

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from timeit import timeit

res = pd.DataFrame(
       index=['user_chris', 'rahlf23', 'chris_a', 'chris', 'anton_vbr'],
       columns=[10, 50, 100, 500, 1000, 5000],
       dtype=float
)

for f in res.index:
    for c in res.columns:
        vals = np.random.randint(1, 100, (c, c))
        vals = np.column_stack((np.arange(vals.shape[0]), vals))
        df = pd.DataFrame(vals, columns=['id'] + [f'v{i}' for i in range(0, vals.shape[0])])
        stmt = '{}(df)'.format(f)
        setp = 'from __main__ import df, {}'.format(f)
        res.at[f, c] = timeit(stmt, setp, number=50)

ax = res.div(res.min()).T.plot(loglog=True)
ax.set_xlabel("N");
ax.set_ylabel("time (relative)");

plt.show()

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