我在df中生成了两列，这些列由“播放器”列中的值填充。因此，当一个值在此列中时，我想在该索引点返回XY。
以下代码适用于样本数据集：
import pandas as pd
import numpy as np
import random
k = 5
N = 10
df = pd.DataFrame({ 'Frame' : range(1, N + 1 ,1),
'John Doe_X' : np.random.uniform(k, k + 100 , size=N),
'John Doe_Y' : np.random.uniform(k, k + 100 , size=N),
'Kevin Lee_X' : np.random.uniform(k, k + 100 , size=N),
'Kevin Lee_Y' : np.random.uniform(k, k + 100 , size=N),
'Liam Smith_X' : np.random.uniform(k, k + -100 , size=N),
'Liam Smith_Y' : np.random.uniform(k, k + 100 , size=N),
'Henry Conr_X' : np.random.uniform(k, k + 100 , size=N),
'Henry Conr_Y' : np.random.uniform(k, k + 100 , size=N),
'Dan On_X' : np.random.uniform(k, k + 100 , size=N),
'Dan On_Y' : np.random.uniform(k, k + 100 , size=N),
'Player' : ['nan','John Doe','nan','Liam Smith','Henry john-Conr','John Doe','nan','nan','Kevin Lee','Kevin Lee']})
df['X'] = df.apply(lambda row: row.get(row['Player']+'_X'), axis=1)
df['Y'] = df.apply(lambda row: row.get(row['Player']+'_Y'), axis=1)
输出：
Dan On_X Dan On_Y Henry john-Conr_X Henry john-Conr_Y John Doe_X \
0 44.768998 84.473350 73.295603 9.432975 45.098229
1 22.078255 83.308328 68.302211 96.222105 45.511418
2 8.958403 32.589984 36.570954 11.388089 76.967684
3 88.710772 39.136471 46.045362 11.080126 73.837371
4 51.959727 95.460223 8.558665 57.413331 43.943096
5 32.703784 50.699055 54.201836 81.972841 35.237445
6 10.533631 53.555805 81.192208 63.985641 88.664571
7 31.100324 76.484070 62.607465 59.920721 36.455336
8 58.747071 16.464910 104.331561 102.078546 28.662882
9 28.593463 15.175946 87.434676 101.343336 63.139973
John Doe_Y Kevin Lee_X Kevin Lee_Y Liam Smith_X Liam Smith_Y \
0 32.444921 75.818689 56.684665 1.367917 67.193555
1 48.496147 17.040589 13.027425 -16.125411 75.731070
2 103.802791 24.569848 89.321086 -32.929826 28.512003
3 103.249107 64.077139 81.212017 -11.560951 18.367170
4 21.053600 65.848794 42.016315 -17.002813 67.181597
5 38.703567 43.069560 74.596956 -34.210479 91.396546
6 35.942260 22.064457 24.871318 -4.745078 81.645923
7 78.317289 5.980761 74.924581 -72.922074 97.187079
8 53.819660 102.053136 77.254865 -57.583882 24.007903
9 20.381099 31.395646 27.660293 -20.299473 19.465666
Player X Y
0 nan NaN NaN
1 John Doe 45.511418 48.496147
2 nan NaN NaN
3 Liam Smith -11.560951 18.367170
4 Henry john-Conr 8.558665 57.413331
5 John Doe 35.237445 38.703567
6 nan NaN NaN
7 nan NaN NaN
8 Kevin Lee 102.053136 77.254865
9 Kevin Lee 31.395646 27.660293
但是当我导入我的数据集时，它返回一个TypeError：
TypeError: ("unsupported operand type(s) for +: 'float' and 'str'", 'occurred at index 0')
我检查过每个数据类型是否相同。就像布局一样。所有值都在浮点数中。
答案 0 :(得分：0)
问题有些值为NaN s，因此建议您只需将string添加到列中：
df = pd.DataFrame({ 'Frame' : range(1, N + 1 ,1),
'John Doe_X' : np.random.uniform(k, k + 100 , size=N),
'John Doe_Y' : np.random.uniform(k, k + 100 , size=N),
'Kevin Lee_X' : np.random.uniform(k, k + 100 , size=N),
'Kevin Lee_Y' : np.random.uniform(k, k + 100 , size=N),
'Liam Smith_X' : np.random.uniform(k, k + -100 , size=N),
'Liam Smith_Y' : np.random.uniform(k, k + 100 , size=N),
'Henry Conr_X' : np.random.uniform(k, k + 100 , size=N),
'Henry Conr_Y' : np.random.uniform(k, k + 100 , size=N),
'Dan On_X' : np.random.uniform(k, k + 100 , size=N),
'Dan On_Y' : np.random.uniform(k, k + 100 , size=N),
'Player' : [np.nan,'John Doe','nan','Liam Smith','Henry Conr','John Doe','nan','nan','Kevin Lee','Kevin Lee']})
df['X'] = df['Player']+'_X'
df['Y'] = df['Player']+'_Y'
print (df)
Frame John Doe_X ... X Y
0 1 10.240240 ... NaN NaN
1 2 91.760964 ... John Doe_X John Doe_Y
2 3 76.286604 ... nan_X nan_Y
3 4 24.907703 ... Liam Smith_X Liam Smith_Y
4 5 57.319837 ... Henry Conr_X Henry Conr_Y
5 6 18.380069 ... John Doe_X John Doe_Y
6 7 6.361868 ... nan_X nan_Y
7 8 24.305204 ... nan_X nan_Y
8 9 22.669279 ... Kevin Lee_X Kevin Lee_Y
9 10 97.934949 ... Kevin Lee_X Kevin Lee_Y
如果需要apply添加if-else，但它比第一个解决方案更慢：
df['X'] = df.apply(lambda row: row.get(row['Player']+'_X') if pd.notnull(row['Player']) else np.nan, axis=1)
df['Y'] = df.apply(lambda row: row.get(row['Player']+'_Y') if pd.notnull(row['Player']) else np.nan, axis=1)