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python - 在 pandas 数据帧上使用转换函数，向数据帧的每一行返回新值

问题描述

我想对我拥有的数据框的每一行应用一个函数。数据框的片段是这样的：

import pandas as pd
import numpy as np
import math


data = {'EVENT_ID': [112335580,112335580,112335580,112335580,112335580,112335580,112335580,112335580, 112335582,
                     112335582,112335582,112335582,112335582,112335582,112335582,112335582,112335582,112335582,
                     112335582,112335582,112335582],

 'SELECTION_ID': [6356576,2554439,2503211,6297034,4233251,2522967,5284417,7660920,8112876,7546023,8175276,8145908,
                  8175274,7300754,8065540,8175275,8106158,8086265,2291406,8065533,8125015],

 'BSP': [5.080818565,6.651493872,6.374683435,24.69510797,7.776082305,11.73219964,270.0383021,4,8.294425408,335.3223613,
         14.06040142,2.423340019,126.7205863,70.53780982,21.3328554,225.2711962,92.25113066,193.0151362,3.775394142,
         95.3786641,17.86333041],

  'WIN_LOSE':[1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0]}

df = pd.DataFrame(data, columns=['EVENT_ID', 'SELECTION_ID', 'BSP','WIN_LOSE'])

df = df.sort_values(["EVENT_ID","BSP"])
df.set_index(['EVENT_ID', 'SELECTION_ID'], inplace=True)

df['Win_Percentage'] = 1/df['BSP']

df['Lose_Percentage'] = 1 - df['Win_Percentage']

我想将以下函数应用于列Lose_Percentage：

def test(df):

    x_list = df.values

    y_list = []

    for x in x_list:
        y = math.sin(x/1000)*2000

    return y

为此，我使用如下变换函数：

df['Fit'] = df.groupby(level=0)['Lose_Percentage'].transform(test)

问题是它为列的每一行返回相同的值df['Fit']。我希望它返回从该行中获取的值 df['Lose_Percentage']并将其添加到新 df['Fit']列中。

如果这样做正确，该df['Fit']列将包含 index 的值112335580：

 [1.499999859375004, 1.6063624685814168, 1.6862587304992693, 1.6993154622916136, 1.742800855666326, 1.8295287282081318, 1.9190120053704878, 1.992593313611782]

我试图调整这样的功能：

def test(df):

    x_list = df.values

    y_list = []

    for x in x_list:
        y = math.sin(x/1000)*2000

        y_list.append(y)

    for fit in y_list:

        return fit

但这返回与之前的尝试相同。我还尝试更改 return 命令的缩进，但这也不起作用。

标签： pythonpandasdataframetransform

解决方案

信不信由你，你想要的很简单

df['Fit'] = np.sin(df['Lose_Percentage'] / 1000) * 2000

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