Python中文网 - 问答频道, 解决您学习工作中的Python难题和Bug
Python常见问题
有两列共同索引的DF（团队名称（32个团队），2018-2015年）；DF1有9列球队年度NFL统计数据平均值，DF2有相同的指数球队（32）和年份（2018-2015），但有11列17场比赛（或“周”）的差异统计数据：：所以我试图合并每个球队和年份，它打印该球队和年份的年度平均值第一或（DF1的9列，一行），其次是DF2的每17行11列数据——每队和每一年的比赛（“周”）指数
lvl0 = result.Tm_name.values
lvl1 = result.Year.values
newidx = pd.MultiIndex.from_arrays([lvl0, lvl1], names = ["Tm_name", "Year"])
result.set_index(newidx, inplace = True)
result.drop(["Year", "Tm_name"], axis = 1, inplace = True)
print(result)
W L W_L_Pct PD MoV SoS SRS OSRS DSRS
Tm_name Year
1 2015 13.0 3.0 0.813 176.0 11.0 1.3 12.3 9.0 3.4
2016 7.0 8.0 0.469 56.0 3.5 -1.9 1.6 2.4 -0.8
2017 8.0 8.0 0.500 -66.0 -4.1 0.4 -3.7 -4.0 0.2
2018 3.0 13.0 0.188 -200.0 -12.5 1.0 -11.5 -9.6 -1.9
2 2015 8.0 8.0 0.500 -6.0 -0.4 -3.4 -3.8 -4.0 0.3
2016 11.0 5.0 0.688 134.0 8.4 0.1 8.5 10.5 -2.0
2017 10.0 6.0 0.625 38.0 2.4 1.9 4.3 1.1 3.2
2018 7.0 9.0 0.438 -9.0 -0.6 0.4 -0.1 2.5 -2.6
3 2015 5.0 11.0 0.313 -73.0 -4.6 2.6 -1.9 -0.7 -1.2
2016 8.0 8.0 0.500 22.0 1.4 0.2 1.5 -1.1 2.6
2017 9.0 7.0 0.563 92.0 5.8 -2.4 3.4 2.2 1.2
2018 10.0 6.0 0.625 102.0 6.4 0.6 7.0 0.6 6.4
4 2015 8.0 8.0 0.500 20.0 1.3 -1.2 0.0 0.3 -0.2
2016 7.0 9.0 0.438 21.0 1.3 -1.6 -0.3 1.8 -2.2
2017 9.0 7.0 0.563 -57.0 -3.6 -0.5 -4.0 -3.0 -1.0
2018 6.0 10.0 0.375 -105.0 -6.6 -0.3 -6.9 -6.3 -0.6
5 2015 15.0 1.0 0.938 192.0 12.0 -3.9 8.1 6.0 2.1
2016 6.0 10.0 0.375 -33.0 -2.1 1.1 -1.0 -0.2 -0.8
2017 11.0 5.0 0.688 36.0 2.3 2.1 4.3 1.7 2.7
2018 7.0 9.0 0.438 -6.0 -0.4 1.3 0.9 0.1 0.8
6 2015 6.0 10.0 0.375 -62.0 -3.9 2.6 -1.3 -0.1 -1.2
2016 3.0 13.0 0.188 -120.0 -7.5 0.0 -7.5 -5.2 -2.3
2017 5.0 11.0 0.313 -56.0 -3.5 2.2 -1.3 -4.6 3.3
2018 12.0 4.0 0.750 138.0 8.6 -2.3 6.3 1.5 4.8
7 2015 12.0 4.0 0.750 140.0 8.8 1.9 10.6 4.8 5.8
lvl_0 = result2.Tm_name.values
lvl_1 = result2.Year.values
newidx_2 = newidx = pd.MultiIndex.from_arrays([lvl_0, lvl_1], names=["Tm_name", "Year"])
result2.set_index(newidx, inplace=True)
result2.drop(["Year", "Tm_name"], axis=1, inplace=True)
print(result2)
Week Date win_loss home_away Opp1_team Tm_Pnts \
Tm_name Year
1 2018 1 2018-09-09 0.0 1.0 32.0 6.0
2018 2 2018-09-16 0.0 0.0 18.0 0.0
2018 3 2018-09-23 0.0 1.0 6.0 14.0
2018 4 2018-09-30 0.0 1.0 28.0 17.0
2018 5 2018-10-07 1.0 0.0 29.0 28.0
2018 6 2018-10-14 0.0 0.0 20.0 17.0
2018 7 2018-10-18 0.0 1.0 10.0 10.0
2018 8 2018-10-28 1.0 1.0 29.0 18.0
2018 10 2018-11-11 0.0 0.0 16.0 14.0
2018 11 2018-11-18 0.0 1.0 25.0 21.0
2018 12 2018-11-25 0.0 0.0 17.0 10.0
2018 13 2018-12-02 1.0 0.0 12.0 20.0
2018 14 2018-12-09 0.0 1.0 11.0 3.0
2018 15 2018-12-16 0.0 0.0 2.0 14.0
2018 16 2018-12-23 0.0 1.0 18.0 9.0
2018 17 2018-12-30 0.0 0.0 28.0 24.0
2017 1 2017-09-10 0.0 0.0 11.0 23.0
2017 2 2017-09-17 1.0 0.0 14.0 16.0
2017 3 2017-09-25 0.0 1.0 9.0 17.0
2017 4 2017-10-01 1.0 1.0 29.0 18.0
2017 5 2017-10-08 0.0 0.0 26.0 7.0
2017 6 2017-10-15 1.0 1.0 30.0 38.0
2017 7 2017-10-22 0.0 0.0 18.0 0.0
2017 9 2017-11-05 1.0 0.0 29.0 20.0
2017 10 2017-11-09 0.0 1.0 28.0 16.0
2017 11 2017-11-19 0.0 0.0 13.0 21.0
2017 12 2017-11-26 1.0 1.0 15.0 27.0
2017 13 2017-12-03 0.0 1.0 18.0 16.0
2017 14 2017-12-10 1.0 1.0 31.0 12.0
2017 15 2017-12-17 0.0 0.0 32.0 15.0
... ... ... ... ... ... ...
2016 5 2016-10-06 1.0 0.0 29.0 33.0
2016 6 2016-10-17 1.0 1.0 24.0 28.0
2016 7 2016-10-23 NaN 1.0 28.0 6.0
2016 8 2016-10-30 0.0 0.0 5.0 20.0
2016 10 2016-11-13 1.0 1.0 29.0 23.0
2016 11 2016-11-20 0.0 0.0 20.0 24.0
2016 12 2016-11-27 0.0 0.0 2.0 19.0
2016 13 2016-12-04 1.0 1.0 32.0 31.0
2016 14 2016-12-11 0.0 0.0 19.0 23.0
2016 15 2016-12-18 0.0 1.0 22.0 41.0
2016 16 2016-12-24 1.0 0.0 28.0 34.0
2016 17 2016-01-01 1.0 0.0 18.0 44.0
2015 1 2015-09-13 1.0 1.0 22.0 31.0
2015 2 2015-09-20 1.0 0.0 6.0 48.0
2015 3 2015-09-27 1.0 1.0 29.0 47.0
2015 4 2015-10-04 0.0 1.0 NaN 22.0
2015 5 2015-10-11 1.0 0.0 11.0 42.0
2015 6 2015-10-18 0.0 0.0 27.0 13.0
2015 7 2015-10-26 1.0 1.0 3.0 26.0
2015 8 2015-11-01 1.0 0.0 8.0 34.0
2015 10 2015-11-15 1.0 0.0 28.0 39.0
2015 11 2015-11-22 1.0 1.0 7.0 34.0
2015 12 2015-11-29 1.0 0.0 29.0 19.0
2015 13 2015-12-06 1.0 0.0 NaN 27.0
2015 14 2015-12-10 1.0 1.0 20.0 23.0
2015 15 2015-12-20 1.0 0.0 26.0 40.0
2015 16 2015-12-27 1.0 1.0 12.0 38.0
2015 17 2015-01-03 0.0 1.0 28.0 6.0
2 2018 1 2018-09-06 0.0 0.0 26.0 12.0
2018 2 2018-09-16 1.0 1.0 5.0 31.0
Opp2_pnts Off_1stD Off_TotYd Def_1stD_All Def_TotYd_All
Tm_name Year
1 2018 24.0 14.0 213.0 30.0 429.0
2018 34.0 5.0 137.0 24.0 432.0
2018 16.0 13.0 221.0 21.0 316.0
2018 20.0 18.0 263.0 19.0 331.0
2018 18.0 10.0 220.0 33.0 447.0
2018 27.0 16.0 268.0 20.0 411.0
2018 45.0 14.0 223.0 15.0 309.0
2018 15.0 20.0 321.0 16.0 267.0
2018 26.0 21.0 260.0 20.0 330.0
2018 23.0 13.0 282.0 19.0 325.0
2018 45.0 10.0 149.0 30.0 414.0
2018 17.0 18.0 315.0 22.0 325.0
2018 17.0 22.0 279.0 16.0 218.0
2018 40.0 18.0 253.0 23.0 435.0
2018 31.0 15.0 263.0 33.0 461.0
2018 27.0 12.0 198.0 16.0 291.0
2017 35.0 24.0 308.0 19.0 367.0
2017 13.0 17.0 389.0 18.0 266.0
2017 28.0 22.0 332.0 15.0 273.0
2017 15.0 25.0 368.0 20.0 305.0
2017 34.0 16.0 307.0 19.0 419.0
2017 33.0 23.0 432.0 21.0 412.0
2017 33.0 10.0 196.0 28.0 425.0
2017 10.0 20.0 368.0 17.0 329.0
2017 22.0 24.0 290.0 14.0 287.0
2017 31.0 17.0 292.0 22.0 357.0
2017 24.0 20.0 344.0 19.0 219.0
2017 32.0 19.0 305.0 18.0 303.0
2017 7.0 16.0 261.0 14.0 204.0
2017 20.0 19.0 286.0 14.0 218.0
... ... ... ... ... ...
2016 21.0 17.0 288.0 25.0 286.0
2016 3.0 28.0 396.0 11.0 230.0
2016 6.0 23.0 443.0 11.0 257.0
2016 30.0 22.0 340.0 19.0 349.0
2016 20.0 26.0 443.0 15.0 281.0
2016 30.0 24.0 290.0 16.0 217.0
2016 38.0 23.0 332.0 28.0 360.0
2016 23.0 24.0 369.0 19.0 333.0
2016 26.0 21.0 300.0 15.0 314.0
2016 48.0 26.0 425.0 33.0 488.0
2016 31.0 21.0 370.0 24.0 391.0
2016 6.0 21.0 344.0 9.0 123.0
2015 19.0 25.0 427.0 18.0 408.0
2015 23.0 21.0 300.0 18.0 335.0
2015 7.0 28.0 446.0 10.0 156.0
2015 24.0 26.0 447.0 13.0 328.0
2015 17.0 15.0 345.0 29.0 435.0
2015 25.0 21.0 469.0 14.0 310.0
2015 18.0 21.0 414.0 18.0 276.0
2015 20.0 25.0 491.0 16.0 254.0
2015 32.0 30.0 451.0 18.0 343.0
2015 31.0 21.0 383.0 24.0 377.0
2015 13.0 26.0 337.0 17.0 368.0
2015 3.0 29.0 524.0 9.0 212.0
2015 20.0 22.0 393.0 23.0 389.0
2015 17.0 28.0 493.0 19.0 424.0
2015 8.0 19.0 381.0 16.0 178.0
2015 36.0 16.0 232.0 22.0 354.0
2 2018 18.0 16.0 299.0 18.0 232.0
2018 24.0 23.0 442.0 27.0 439.0
--我不能只是合并它们--我尝试过许多不同的方法，但结果总是不对，所以我想我可以进入一个列表，从数组中生成一个数据帧。这个数组看起来像我想要的，但是当我把它放到一个数据帧中时，它不会这样做{下面检查}
app = []
for row in result.itertuples():
app.append(row)
for row_1 in result2.itertuples():
if row[0] == row_1[0]:
app.append(row_1)
9.0, Off_1stD=25.0, Off_TotYd=427.0, Def_1stD_All=18.0, Def_TotYd_All=408.0)
Pandas(Index=(1, 2015), Week='2', Date=Timestamp('2015-09-20 00:00:00'), win_loss=1.0, home_away=0.0, Opp1_team=6.0, Tm_Pnts=48.0, Opp2_pnts=23.0, Off_1stD=21.0, Off_TotYd=300.0, Def_1stD_All=18.0, Def_TotYd_All=335.0)
Pandas(Index=(1, 2015), Week='3', Date=Timestamp('2015-09-27 00:00:00'), win_loss=1.0, home_away=1.0, Opp1_team=29.0, Tm_Pnts=47.0, Opp2_pnts=7.0, Off_1stD=28.0, Off_TotYd=446.0, Def_1stD_All=10.0, Def_TotYd_All=156.0)
Pandas(Index=(1, 2015), Week='4', Date=Timestamp('2015-10-04 00:00:00'), win_loss=0.0, home_away=1.0, Opp1_team=nan, Tm_Pnts=22.0, Opp2_pnts=24.0, Off_1stD=26.0, Off_TotYd=447.0, Def_1stD_All=13.0, Def_TotYd_All=328.0)
Pandas(Index=(1, 2015), Week='5', Date=Timestamp('2015-10-11 00:00:00'), win_loss=1.0, home_away=0.0, Opp1_team=11.0, Tm_Pnts=42.0, Opp2_pnts=17.0, Off_1stD=15.0, Off_TotYd=345.0, Def_1stD_All=29.0, Def_TotYd_All=435.0)
Pandas(Index=(1, 2015), Week='6', Date=Timestamp('2015-10-18 00:00:00'), win_loss=0.0, home_away=0.0, Opp1_team=27.0, Tm_Pnts=13.0, Opp2_pnts=25.0, Off_1stD=21.0, Off_TotYd=469.0, Def_1stD_All=14.0, Def_TotYd_All=310.0)
Pandas(Index=(1, 2015), Week='7', Date=Timestamp('2015-10-26 00:00:00'), win_loss=1.0, home_away=1.0, Opp1_team=3.0, Tm_Pnts=26.0, Opp2_pnts=18.0, Off_1stD=21.0, Off_TotYd=414.0, Def_1stD_All=18.0, Def_TotYd_All=276.0)
Pandas(Index=(1, 2015), Week='8', Date=Timestamp('2015-11-01 00:00:00'), win_loss=1.0, home_away=0.0, Opp1_team=8.0, Tm_Pnts=34.0, Opp2_pnts=20.0, Off_1stD=25.0, Off_TotYd=491.0, Def_1stD_All=16.0, Def_TotYd_All=254.0)
Pandas(Index=(1, 2015), Week='10', Date=Timestamp('2015-11-15 00:00:00'), win_loss=1.0, home_away=0.0, Opp1_team=28.0, Tm_Pnts=39.0, Opp2_pnts=32.0, Off_1stD=30.0, Off_TotYd=451.0, Def_1stD_All=18.0, Def_TotYd_All=343.0)
Pandas(Index=(1, 2015), Week='11', Date=Timestamp('2015-11-22 00:00:00'), win_loss=1.0, home_away=1.0, Opp1_team=7.0, Tm_Pnts=34.0, Opp2_pnts=31.0, Off_1stD=21.0, Off_TotYd=383.0, Def_1stD_All=24.0, Def_TotYd_All=377.0)
Pandas(Index=(1, 2015), Week='12', Date=Timestamp('2015-11-29 00:00:00'), win_loss=1.0, home_away=0.0, Opp1_team=29.0, Tm_Pnts=19.0, Opp2_pnts=13.0, Off_1stD=26.0, Off_TotYd=337.0, Def_1stD_All=17.0, Def_TotYd_All=368.0)
Pandas(Index=(1, 2015), Week='13', Date=Timestamp('2015-12-06 00:00:00'), win_loss=1.0, home_away=0.0, Opp1_team=nan, Tm_Pnts=27.0, Opp2_pnts=3.0, Off_1stD=29.0, Off_TotYd=524.0, Def_1stD_All=9.0, Def_TotYd_All=212.0)
Pandas(Index=(1, 2015), Week='14', Date=Timestamp('2015-12-10 00:00:00'), win_loss=1.0, home_away=1.0, Opp1_team=20.0, Tm_Pnts=23.0, Opp2_pnts=20.0, Off_1stD=22.0, Off_TotYd=393.0, Def_1stD_All=23.0, Def_TotYd_All=389.0)
Pandas(Index=(1, 2015), Week='15', Date=Timestamp('2015-12-20 00:00:00'), win_loss=1.0, home_away=0.0, Opp1_team=26.0, Tm_Pnts=40.0, Opp2_pnts=17.0, Off_1stD=28.0, Off_TotYd=493.0, Def_1stD_All=19.0, Def_TotYd_All=424.0)
Pandas(Index=(1, 2015), Week='16', Date=Timestamp('2015-12-27 00:00:00'), win_loss=1.0, home_away=1.0, Opp1_team=12.0, Tm_Pnts=38.0, Opp2_pnts=8.0, Off_1stD=19.0, Off_TotYd=381.0, Def_1stD_All=16.0, Def_TotYd_All=178.0)
Pandas(Index=(1, 2015), Week='17', Date=Timestamp('2015-01-03 00:00:00'), win_loss=0.0, home_away=1.0, Opp1_team=28.0, Tm_Pnts=6.0, Opp2_pnts=36.0, Off_1stD=16.0, Off_TotYd=232.0, Def_1stD_All=22.0, Def_TotYd_All=354.0)
Pandas(Index=(1, 2016), W=7.0, L=8.0, W_L_Pct=0.469, PD=56.0, MoV=3.5, SoS=-1.9, SRS=1.6, OSRS=2.4, DSRS=-0.8)
Pandas(Index=(1, 2016), Week=1, Date=Timestamp('2016-09-11 00:00:00'), win_loss=0.0, home_away=1.0, Opp1_team=21.0, Tm_Pnts=21.0, Opp2_pnts=23.0, Off_1stD=21.0, Off_TotYd=344.0, Def_1stD_All=19.0, Def_TotYd_All=363.0)
Pandas(Index=(1, 2016), Week=2, Date=Timestamp('2016-09-18 00:00:00'), win_loss=1.0, home_away=1.0, Opp1_team=30.0, Tm_Pnts=40.0, Opp2_pnts=7.0, Off_1stD=20.0, Off_TotYd=416.0, Def_1stD_All=21.0, Def_TotYd_All=306.0)
Pandas(Index=(1, 2016), Week=3, Date=Timestamp('2016-09-25 00:00:00'), win_loss=0.0, home_away=0.0, Opp1_team=4.0, Tm_Pnts=18.0, Opp2_pnts=33.0, Off_1stD=25.0, Off_TotYd=348.0, Def_1stD_All=16.0, Def_TotYd_All=297.0)
Pandas(Index=(1, 2016), Week=4, Date=Timestamp('2016-10-02 00:00:00'), win_loss=0.0, home_away=1.0, Opp1_team=18.0, Tm_Pnts=13.0, Opp2_pnts=17.0, Off_1stD=26.0, Off_TotYd=420.0, Def_1stD_All=12.0, Def_TotYd_All=288.0)
Pandas(Index=(1, 2016), Week=5, Date=Timestamp('2016-10-06 00:00:00'), win_loss=1.0, home_away=0.0, Opp1_team=29.0, Tm_Pnts=33.0, Opp2_pnts=21.0, Off_1stD=17.0, Off_TotYd=288.0, Def_1stD_All=25.0, Def_TotYd_All=286.0)
Pandas(Index=(1, 2016), Week=6, Date=Timestamp('2016-10-17 00:00:00'), win_loss=1.0, home_away=1.0, Opp1_team=24.0, Tm_Pnts=28.0, Opp2_pnts=3.0, Off_1stD=28.0, Off_TotYd=396.0, Def_1stD_All=11.0, Def_TotYd_All=230.0)
Pandas(Index=(1, 2016), Week=7, Date=Timestamp('2016-10-23 00:00:00'), win_loss=nan, home_away=1.0, Opp1_team=28.0, Tm_Pnts=6.0, Opp2_pnts=6.0, Off_1stD=23.0, Off_TotYd=443.0, Def_1stD_All=11.0, Def_TotYd_All=257.0)
Pandas(Index=(1, 2016), Week=8, Date=Timestamp('2016-10-30 00:00:00'), win_loss=0.0, home_away=0.0, Opp1_team=5.0, Tm_Pnts=20.0, Opp2_pnts=30.0, Off_1stD=22.0, Off_TotYd=340.0, Def_1stD_All=19.0, Def_TotYd_All=349.0)
Pandas(Index=(1, 2016), Week=10, Date=Timestamp('2016-11-13 00:00:00'), win_loss=1.0, home_away=1.0, Opp1_team=29.0, Tm_Pnts=23.0, Opp2_pnts=20.0, Off_1stD=26.0, Off_TotYd=443.0, Def_1stD_All=15.0, Def_TotYd_All=281.0)
Pandas(Index=(1, 2016), Week=11, Date=Timestamp('2016-11-20 00:00:00'), win_loss=0.0, home_away=0.0, Opp1_team=20.0, Tm_Pnts=24.0, Opp2_pnts=30.0, Off_1stD=24.0, Off_TotYd=290.0, Def_1stD_All=16.0, Def_TotYd_All=217.0)
Pandas(Index=(1, 2016), Week=12, Date=Timestamp('2016-11-27 00:00:00'), win_loss=0.0, home_away=0.0, Opp1_team=2.0, Tm_Pnts=19.0, Opp2_pnts=38.0, Off_1stD=23.0, Off_TotYd=332.0, Def_1stD_All=28.0, Def_TotYd_All=360.0)
Pandas(Index=(1, 2016), Week=13, Date=Timestamp('2016-12-04 00:00:00'), win_loss=1.0, home_away=1.0, Opp1_team=32.0, Tm_Pnts=31.0, Opp2_pnts=23.0, Off_1stD=24.0, Off_TotYd=369.0, Def_1stD_All=19.0, Def_TotYd_All=333.0)
Pandas(Index=(1, 2016), Week=14, Date=Timestamp('2016-12-11 00:00:00'), win_loss=0.0, home_away=0.0, Opp1_team=19.0, Tm_Pnts=23.0, Opp2_pnts=26.0, Off_1stD=21.0, Off_TotYd=300.0, Def_1stD_All=15.0, Def_TotYd_All=314.0)
Pandas(Index=(1, 2016), Week=15, Date=Timestamp('2016-12-18 00:00:00'), win_loss=0.0, home_away=1.0, Opp1_team=22.0, Tm_Pnts=41.0, Opp2_pnts=48.0, Off_1stD=26.0, Off_TotYd=425.0, Def_1stD_All=33.0, Def_TotYd_All=488.0)
Pandas(Index=(1, 2016), Week=16, Date=Timestamp('2016-12-24 00:00:00'), win_loss=1.0, home_away=0.0, Opp1_team=28.0, Tm_Pnts=34.0, Opp2_pnts=31.0, Off_1stD=21.0, Off_TotYd=370.0, Def_1stD_All=24.0, Def_TotYd_All=391.0)
Pandas(Index=(1, 2016), Week=17, Date=Timestamp('2016-01-01 00:00:00'), win_loss=1.0, home_away=0.0, Opp1_team=18.0, Tm_Pnts=44.0, Opp2_pnts=6.0, Off_1stD=21.0, Off_TotYd=344.0, Def_1stD_All=9.0, Def_TotYd_All=123.0)....
这就是我希望每年的设置，从一个连续的年平均数开始，然后是那一年每场比赛的17行数据。---现在当我试着放入一个数据框
new = pd.DataFrame(app, index=["Tm_name", "Year"])
AssertionError: 10 cols passed, passed data had 12 cols
有人能帮我玩了两个星期，尝试了多索引，差异合并，合并，只是似乎不能让它看起来像应用程序阵列，没有重复
再次感谢
Tags： homedatedefallwinteamtmweek
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