合并具有2列共同索引的DF的熊猫（团队名称（32个团队），2018-2015年）； DF1有9列年度NFL球队年度统计数据AVG，DF2拥有相同的指数球队（32）和年份（2018-2015），但对于17场比赛（或“周”）中的每一项都有11列差异统计：合并每个团队和年份，它会打印该团队和年份的年度平均1st（或Df1的9列），然后是DF2每17（ROWS）个游戏的11列统计数据（“周”） ）的每个团队和年份的索引。
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，我尝试了许多不同的方法，但是它永远不会正确，因此我认为我可以进入列表并从数组中创建数据框。该数组看起来像我想要的，但是当我将其放入数据框时，它不会这样做{在下面检查}
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)....
这是我每年想要的设置，从连续一年的平均平均值开始，然后是当年每个游戏的17行统计数据。---现在，当我尝试放入DataFrame时
new = pd.DataFrame(app, index=["Tm_name", "Year"])
AssertionError: 10 cols passed, passed data had 12 cols
有人可以帮我iv玩了2个星期，尝试做多索引，差异合并，合并，只是看起来好像它看起来不像APP ARRAY并且没有重复... < / p>
再次感谢