In [2]:
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
In [3]:
df = pd.read_csv('ScoreBoardFinal.csv')
print df.head(5)
MId Home_Team Home_Team_Goals Away_Team_Goals Away_Team Home_Poss \
0 9601 CAR 1 2 CHE 36.8
1 9602 FUL 2 2 CRY 66.1
2 9603 HUL 0 2 EVE 43
3 9604 LIV 2 1 NUFC 66.2
4 9605 MCFC 2 0 WHU 68.1
Away_Poss Home_ShotsT Away_ShotsT Home_Shots ... HTP HTR HTAR \
0 63.2 3 7 10 ... 14 775.406 151.256
1 33.9 5 6 15 ... 14 793.834 182.616
2 57 3 4 12 ... 14 792.189 216.189
3 33.8 5 2 13 ... 14 884.043 269.651
4 31.9 7 0 28 ... 14 912.543 155.313
HTMR HTDR ATP ATR ATAR ATMR ATDR
0 269.512 354.638 14 883.673 166.000 315.499 402.174
1 210.218 401.000 14 790.224 144.288 287.936 358.000
2 219.000 357.000 14 862.968 173.566 293.402 396.000
3 220.392 394.000 14 827.956 84.175 294.260 449.521
4 350.230 407.000 14 808.489 129.325 315.164 364.000
[5 rows x 33 columns]
In [4]:
df_copy = df.loc[df['Home_ShotsT'] != "XX-XX-"]
print df_copy
MId Home_Team Home_Team_Goals Away_Team_Goals Away_Team \
0 9601 CAR 1 2 CHE
1 9602 FUL 2 2 CRY
2 9603 HUL 0 2 EVE
3 9604 LIV 2 1 NUFC
4 9605 MCFC 2 0 WHU
5 9606 NOR 0 2 ARS
6 9607 SOT 1 1 MUFC
7 9608 SUN 1 3 SWAN
8 9609 TOT 3 0 AVL
9 9610 WBA 1 2 STK
10 9599 MCFC 4 0 AVL
11 9600 SUN 2 0 WBA
12 9598 MUFC 3 1 HUL
13 9597 CRY 3 3 LIV
14 9596 CHE 0 0 NOR
15 9595 ARS 1 0 WBA
16 9589 EVE 2 3 MCFC
17 9588 AVL 3 1 HUL
18 9590 MUFC 0 1 SUN
19 9591 NUFC 3 0 CAR
20 9592 STK 4 1 FUL
21 9593 SWAN 0 1 SOT
22 9594 WHU 2 0 TOT
23 9587 ARS 3 0 NUFC
24 9584 CRY 0 2 MCFC
25 9585 LIV 0 2 CHE
26 9586 SUN 4 0 CAR
27 9579 MUFC 4 0 NOR
28 9578 FUL 2 2 HUL
29 9581 STK 0 1 TOT
... ... ... ... ... ...
1110 12144 Arsenal 0 0 Liverpool
1111 12141 Everton 0 2 Man City
1112 12142 Watford 0 0 Southampton
1113 12143 West Brom 2 3 Chelsea
1114 12135 Crystal Palace 2 1 Aston Villa
1115 12136 Leicester 1 1 Spurs
1116 12138 Norwich 1 1 Stoke
1117 12139 Sunderland 1 1 Swansea
1118 12140 West Ham 3 4 Bournemouth
1119 12137 Man Utd 0 0 Newcastle
1120 12134 Liverpool 1 0 Bournemouth
1121 12133 Man City 3 0 Chelsea
1122 12132 Crystal Palace 1 2 Arsenal
1123 12127 Sunderland 1 3 Norwich
1124 12128 Swansea 2 0 Newcastle
1125 12129 Spurs 2 2 Stoke
1126 12130 Watford 0 0 West Brom
1127 12131 West Ham 1 2 Leicester
1128 12126 Southampton 0 3 Everton
1129 12125 Aston Villa 0 1 Man Utd
1130 12124 West Brom 0 3 Man City
1131 12123 Stoke 0 1 Liverpool
1132 12121 Arsenal 0 2 West Ham
1133 12122 Newcastle 2 2 Southampton
1134 12116 Chelsea 2 2 Swansea
1135 12115 Bournemouth 0 1 Aston Villa
1136 12117 Everton 2 2 Watford
1137 12118 Leicester 4 2 Sunderland
1138 12120 Norwich 1 3 Crystal Palace
1139 12119 Man Utd 1 0 Spurs
Home_Poss Away_Poss Home_ShotsT Away_ShotsT Home_Shots ... HTP \
0 36.8 63.2 3 7 10 ... 14
1 66.1 33.9 5 6 15 ... 14
2 43 57 3 4 12 ... 14
3 66.2 33.8 5 2 13 ... 14
4 68.1 31.9 7 0 28 ... 14
5 37.7 62.3 5 8 11 ... 14
6 58.5 41.5 6 2 15 ... 14
7 53.3 46.7 4 4 20 ... 14
8 54.7 45.3 6 1 12 ... 14
9 43 57 4 4 18 ... 14
10 73.3 26.7 9 0 18 ... 14
11 48.3 51.7 4 3 10 ... 14
12 55.3 44.7 7 2 18 ... 14
13 34.9 65.1 6 8 10 ... 14
14 71.3 28.7 4 3 23 ... 14
15 60.3 39.7 4 1 15 ... 14
16 59.3 40.7 4 6 9 ... 13
17 44.2 55.8 9 2 15 ... 14
18 63 37 2 1 17 ... 14
19 53.8 46.2 6 3 22 ... 14
20 63.3 36.7 8 2 23 ... 14
21 38.1 61.9 3 4 12 ... 14
22 47.2 52.8 8 3 20 ... 13
23 61.5 38.5 8 3 20 ... 14
24 46.4 53.6 2 6 3 ... 14
25 73 27 8 4 26 ... 13
26 59.6 40.4 7 1 21 ... 14
27 61.3 38.7 11 2 25 ... 14
28 46 54 3 4 10 ... 14
29 50.5 49.5 3 4 17 ... 13
... ... ... ... ... ... ... ..
1110 65.7 34.3 5 8 19 ... 13
1111 45.7 54.3 1 9 10 ... 14
1112 52 48 0 4 13 ... 13
1113 43.9 56.1 6 5 15 ... 14
1114 49.9 50.1 6 2 16 ... 14
1115 34.8 65.2 2 6 13 ... 14
1116 42.6 57.4 7 1 20 ... 13
1117 39.3 60.7 2 9 10 ... 14
1118 45.8 54.2 4 7 10 ... 14
1119 69.3 30.7 8 0 20 ... 14
1120 54.9 45.1 2 2 18 ... 14
1121 49.2 50.8 8 3 18 ... 14
1122 40.8 59.2 4 7 11 ... 14
1123 43.9 56.1 2 6 6 ... 13
1124 65.8 34.2 6 2 19 ... 14
1125 53.2 46.8 7 7 13 ... 13
1126 63.9 36.1 5 0 16 ... 13
1127 69.8 30.2 3 5 10 ... 14
1128 54.4 45.6 4 4 17 ... 14
1129 45.6 54.4 1 2 5 ... 13
1130 30.9 69.1 2 7 9 ... 14
1131 47 53 1 3 7 ... 14
1132 61.8 38.2 6 4 22 ... 13
1133 56.3 43.7 4 4 9 ... 14
1134 52 48 3 10 11 ... 14
1135 58.4 41.6 2 3 11 ... 14
1136 66.6 33.4 5 5 10 ... 14
1137 44 56 8 5 20 ... 14
1138 62.9 37.1 6 7 17 ... 14
1139 50 50 1 4 9 ... 14
HTR HTAR HTMR HTDR ATP ATR ATAR ATMR \
0 775.406 151.256 269.512 354.638 14 883.673 166.000 315.499
1 793.834 182.616 210.218 401.000 14 790.224 144.288 287.936
2 792.189 216.189 219.000 357.000 14 862.968 173.566 293.402
3 884.043 269.651 220.392 394.000 14 827.956 84.175 294.260
4 912.543 155.313 350.230 407.000 14 808.489 129.325 315.164
5 805.636 93.648 341.988 370.000 14 869.152 230.624 252.528
6 830.718 157.239 296.034 377.445 14 883.726 251.335 229.391
7 798.745 76.958 361.257 360.530 14 795.936 278.427 163.175
8 841.663 148.000 291.936 401.727 13 800.032 181.180 183.000
9 810.201 166.403 349.500 294.298 14 793.845 85.944 349.901
10 906.219 110.639 388.580 407.000 14 801.892 120.336 221.386
11 797.444 72.644 363.800 361.000 14 808.298 216.798 297.500
12 834.715 66.959 437.790 329.966 14 772.178 246.092 209.586
13 791.824 93.616 338.208 360.000 13 882.914 247.614 243.300
14 891.689 95.498 351.191 445.000 12 810.000 55.991 313.009
15 883.867 183.982 299.552 400.333 13 811.443 156.080 359.363
16 846.088 252.292 153.156 440.640 14 903.264 110.368 363.070
17 802.049 130.707 217.468 453.874 14 792.268 221.334 218.500
18 887.480 189.502 291.978 406.000 14 799.090 72.556 365.534
19 832.056 150.000 292.406 389.650 14 781.601 178.689 252.080
20 801.590 58.350 379.240 364.000 14 818.762 145.049 257.853
21 831.746 286.700 169.758 375.288 14 822.914 131.821 313.093
22 808.687 150.015 294.672 364.000 14 860.648 123.042 298.242
23 886.661 164.195 322.466 400.000 14 831.134 96.992 356.410
24 795.602 145.602 290.000 360.000 14 905.322 170.390 327.932
25 882.668 286.148 211.520 385.000 14 873.132 80.134 387.478
26 799.134 72.000 366.134 361.000 14 797.789 151.281 299.316
27 891.129 185.299 299.830 406.000 14 813.722 89.842 352.880
28 824.589 142.414 232.175 450.000 14 787.558 246.584 218.089
29 806.189 97.353 312.828 396.008 14 852.671 148.000 319.768
... ... ... ... ... .. ... ... ...
1110 894.722 239.502 262.220 393.000 14 861.298 219.552 254.052
1111 830.843 238.711 287.132 305.000 14 906.203 87.200 413.003
1112 816.780 153.000 296.780 367.000 14 827.390 144.318 235.072
1113 810.289 103.975 348.314 358.000 14 917.821 84.601 391.224
1114 829.644 146.500 305.144 378.000 12 817.835 147.000 294.835
1115 823.249 155.803 364.446 303.000 14 872.848 103.406 365.442
1116 810.336 138.056 380.280 292.000 14 844.431 181.424 215.157
1117 831.026 74.568 444.794 311.664 12 850.688 155.688 310.000
1118 833.532 99.825 347.000 386.707 14 801.188 75.000 363.418
1119 884.696 85.000 383.080 416.616 14 807.551 139.313 310.238
1120 860.054 253.804 212.550 393.700 14 799.843 75.000 355.843
1121 905.566 87.376 402.674 415.516 14 914.956 153.306 329.874
1122 832.398 155.770 298.628 378.000 14 910.007 248.136 245.709
1123 827.556 78.000 398.874 350.682 14 808.468 124.388 392.080
1124 849.980 152.524 312.456 385.000 14 820.723 52.964 359.759
1125 877.189 141.302 331.887 404.000 14 842.169 255.144 155.825
1126 821.389 153.000 368.389 300.000 14 812.581 158.094 296.487
1127 839.408 87.168 332.138 420.102 14 818.820 131.364 377.692
1128 831.578 216.000 243.578 372.000 14 830.867 237.987 287.880
1129 825.312 175.112 274.200 376.000 14 884.663 85.000 402.663
1130 801.811 142.208 307.603 352.000 14 899.801 82.400 402.743
1131 837.468 228.601 141.025 467.842 13 859.963 230.906 243.057
1132 906.272 255.000 318.272 333.000 14 841.047 98.400 298.928
1133 835.653 84.433 370.220 381.000 13 830.722 176.850 285.872
1134 919.841 139.694 321.023 459.124 14 850.334 155.756 309.578
1135 797.896 104.592 331.304 362.000 14 824.702 148.968 290.025
1136 836.840 185.936 355.480 295.424 14 820.402 89.706 329.616
1137 820.310 147.969 358.397 313.944 13 799.464 78.000 406.841
1138 805.756 146.600 367.156 292.000 14 830.889 147.089 305.800
1139 886.553 85.000 405.153 396.400 14 872.555 176.088 297.467
ATDR
0 402.174
1 358.000
2 396.000
3 449.521
4 364.000
5 386.000
6 403.000
7 354.334
8 435.852
9 358.000
10 460.170
11 294.000
12 316.500
13 392.000
14 441.000
15 296.000
16 429.826
17 352.434
18 361.000
19 350.832
20 415.860
21 378.000
22 439.364
23 377.732
24 407.000
25 405.520
26 347.192
27 371.000
28 322.885
29 384.903
... ...
1110 387.694
1111 406.000
1112 448.000
1113 441.996
1114 376.000
1115 404.000
1116 447.850
1117 385.000
1118 362.770
1119 358.000
1120 369.000
1121 431.776
1122 416.162
1123 292.000
1124 408.000
1125 431.200
1126 358.000
1127 309.764
1128 305.000
1129 397.000
1130 414.658
1131 386.000
1132 443.719
1133 368.000
1134 385.000
1135 385.709
1136 401.080
1137 314.623
1138 378.000
1139 399.000
[1136 rows x 33 columns]
In [5]:
#plt.figure(figsize=(10,10))
df_poss = df_copy[(df_copy['Home_Team']=="MUFC") | (df_copy["Home_Team"] == "Man Utd")]
#print df_poss
df_poss = df_poss[['MId','Home_Team_Goals','Away_Team','Home_Shots','Home_ShotsT','HTR','HTAR','HTMR','HTDR','ATR','ATAR','ATMR','ATDR']]
df_poss['index'] = range(1, len(df_poss) + 1)
df_poss[['MId','Home_Team_Goals','Home_Shots','Home_ShotsT','HTR','HTAR','HTMR','HTDR','ATR','ATAR','ATMR','ATDR']] = df_poss[['MId','Home_Team_Goals','Home_Shots','Home_ShotsT','HTR','HTAR','HTMR','HTDR','ATR','ATAR','ATMR','ATDR']].astype(float)
print df_poss
MId Home_Team_Goals Away_Team Home_Shots Home_ShotsT \
12 9598.0 3.0 HUL 18.0 7.0
18 9590.0 0.0 SUN 17.0 2.0
27 9579.0 4.0 NOR 25.0 11.0
73 9539.0 4.0 AVL 9.0 6.0
77 9533.0 0.0 MCFC 10.0 4.0
130 9479.0 2.0 FUL 31.0 9.0
156 9453.0 2.0 CAR 17.0 5.0
173 9435.0 2.0 SWAN 16.0 5.0
180 9425.0 1.0 TOT 17.0 6.0
215 9394.0 3.0 WHU 21.0 8.0
239 9373.0 0.0 NUFC 8.0 4.0
244 9365.0 0.0 EVE 18.0 6.0
270 9337.0 1.0 ARS 5.0 2.0
297 9314.0 3.0 STK 19.0 5.0
306 9304.0 1.0 SOT 12.0 5.0
327 9284.0 1.0 WBA 14.0 6.0
349 9265.0 2.0 CRY 20.0 8.0
359 9251.0 0.0 CHE 12.0 3.0
392 9977.0 1.0 Arsenal 12.0 4.0
414 9953.0 0.0 West Brom 26.0 9.0
441 9928.0 4.0 Man City 11.0 7.0
457 9914.0 3.0 Aston Villa 20.0 7.0
473 9898.0 3.0 Spurs 11.0 3.0
495 9872.0 2.0 Sunderland 30.0 10.0
513 9857.0 3.0 Burnley 11.0 7.0
535 9835.0 3.0 Leicester 12.0 5.0
550 9820.0 0.0 Southampton 10.0 0.0
585 9787.0 3.0 Newcastle 9.0 4.0
602 9768.0 3.0 Liverpool 11.0 6.0
628 9744.0 2.0 Stoke 10.0 3.0
635 9733.0 3.0 Hull 15.0 7.0
656 9713.0 1.0 Crystal Palace 23.0 5.0
671 9698.0 1.0 Chelsea 19.0 7.0
693 9678.0 2.0 Everton 15.0 4.0
706 9666.0 2.0 West Ham 8.0 3.0
721 9649.0 4.0 QPR 18.0 9.0
759 9613.0 1.0 Swansea 14.0 5.0
760 12494.0 3.0 Bournemouth 12.0 5.0
786 12467.0 1.0 Leicester 21.0 6.0
803 12451.0 2.0 Crystal Palace 16.0 10.0
812 12441.0 1.0 Aston Villa 13.0 4.0
827 12427.0 1.0 Everton 10.0 2.0
863 12390.0 1.0 Watford 14.0 3.0
872 12381.0 3.0 Arsenal 7.0 5.0
903 12348.0 3.0 Stoke 15.0 5.0
915 12337.0 0.0 Southampton 8.0 1.0
945 12307.0 2.0 Swansea 19.0 6.0
953 12298.0 0.0 Chelsea 12.0 2.0
976 12277.0 1.0 Norwich 11.0 2.0
994 12257.0 0.0 West Ham 21.0 1.0
1025 12227.0 2.0 West Brom 13.0 3.0
1042 12213.0 0.0 Man City 6.0 1.0
1075 12177.0 3.0 Sunderland 12.0 7.0
1093 12158.0 3.0 Liverpool 9.0 3.0
1119 12137.0 0.0 Newcastle 20.0 8.0
1139 12119.0 1.0 Spurs 9.0 1.0
HTR HTAR HTMR HTDR ATR ATAR ATMR ATDR \
12 834.715 66.959 437.790 329.966 772.178 246.092 209.586 316.500
18 887.480 189.502 291.978 406.000 799.090 72.556 365.534 361.000
27 891.129 185.299 299.830 406.000 813.722 89.842 352.880 371.000
73 884.713 173.165 353.048 358.500 811.922 211.597 221.483 378.842
77 888.343 252.576 237.767 398.000 912.312 83.000 420.312 409.000
130 899.009 282.106 233.844 383.059 805.947 155.214 209.733 441.000
156 875.515 170.528 179.702 525.285 787.235 143.000 292.468 351.767
173 868.322 78.176 390.000 400.146 826.100 225.100 164.553 436.447
180 878.589 191.404 308.657 378.528 854.106 141.902 323.204 389.000
215 869.221 166.600 229.398 473.223 790.189 90.717 254.816 444.656
239 879.087 171.000 221.954 486.133 846.124 89.310 303.835 452.979
244 872.332 165.400 268.344 438.588 857.148 157.592 317.556 382.000
270 891.868 171.016 272.707 448.145 888.989 175.975 314.014 399.000
297 889.528 196.008 308.376 385.144 813.208 144.325 257.507 411.376
306 884.364 185.297 283.414 415.653 833.542 107.728 273.814 452.000
327 872.152 171.492 258.520 442.140 797.380 204.768 240.368 352.244
349 892.275 175.146 345.344 371.785 780.351 142.071 237.736 400.544
359 892.737 243.626 234.466 414.645 908.503 111.306 328.967 468.230
392 881.498 170.678 393.000 317.820 881.469 234.935 274.134 372.400
414 877.475 287.262 369.300 220.913 813.398 73.222 372.406 367.770
441 885.869 177.708 465.994 242.167 903.084 174.000 323.584 405.500
457 888.850 202.254 443.596 243.000 823.534 216.488 212.684 394.362
473 881.859 173.964 463.387 244.508 853.847 169.516 293.331 391.000
495 886.760 193.472 372.288 321.000 810.596 138.608 285.300 386.688
513 874.440 338.440 154.000 382.000 783.715 147.839 278.876 357.000
535 886.644 331.280 222.480 332.884 780.756 157.675 267.081 356.000
550 889.578 296.630 278.838 314.110 838.100 151.000 308.168 378.932
585 874.441 336.440 226.565 311.436 799.988 207.903 244.700 347.385
602 880.556 308.074 330.669 241.813 844.178 305.000 233.756 305.422
628 875.265 238.660 393.605 243.000 825.482 367.629 6.853 451.000
635 892.090 269.482 379.608 243.000 796.387 116.381 315.006 365.000
656 867.805 277.491 368.314 222.000 806.803 151.500 286.500 368.803
671 875.136 250.136 231.000 394.000 919.180 147.496 357.968 413.716
693 870.744 337.220 158.634 374.890 846.914 156.000 285.170 405.744
706 874.513 318.092 178.750 377.671 807.161 258.387 182.496 366.278
721 883.390 333.691 185.155 364.544 813.732 50.512 395.720 367.500
759 867.255 214.000 325.963 327.292 830.525 158.424 297.512 374.589
760 862.953 145.582 326.371 391.000 794.428 92.956 342.472 359.000
786 871.568 147.859 321.709 402.000 825.574 129.544 254.966 441.064
803 880.904 122.754 353.150 405.000 826.764 77.165 379.599 370.000
812 877.826 121.476 354.350 402.000 817.000 82.497 224.000 510.503
827 870.000 69.000 432.356 368.644 870.901 161.901 314.000 395.000
863 849.792 69.000 467.367 313.425 829.191 153.167 297.024 379.000
872 858.955 61.341 514.999 282.615 898.188 185.438 307.750 405.000
903 877.147 86.518 393.629 397.000 840.000 305.335 155.000 379.665
915 875.856 85.000 400.800 390.056 838.086 152.878 230.208 455.000
945 902.050 85.000 478.373 338.677 847.823 85.853 374.970 387.000
953 899.416 85.000 487.424 326.992 907.504 100.204 396.300 411.000
976 891.999 85.000 477.999 329.000 804.911 129.153 313.758 362.000
994 878.344 0.000 479.844 398.500 845.769 77.000 381.185 387.584
1025 891.239 77.435 463.640 350.164 814.880 170.400 289.288 355.192
1042 894.639 85.000 406.039 403.600 899.057 79.454 397.371 422.232
1075 893.398 85.000 403.220 405.178 814.722 56.794 353.428 404.500
1093 893.010 0.000 485.010 408.000 854.876 215.204 251.131 388.541
1119 884.696 85.000 383.080 416.616 807.551 139.313 310.238 358.000
1139 886.553 85.000 405.153 396.400 872.555 176.088 297.467 399.000
index
12 1
18 2
27 3
73 4
77 5
130 6
156 7
173 8
180 9
215 10
239 11
244 12
270 13
297 14
306 15
327 16
349 17
359 18
392 19
414 20
441 21
457 22
473 23
495 24
513 25
535 26
550 27
585 28
602 29
628 30
635 31
656 32
671 33
693 34
706 35
721 36
759 37
760 38
786 39
803 40
812 41
827 42
863 43
872 44
903 45
915 46
945 47
953 48
976 49
994 50
1025 51
1042 52
1075 53
1093 54
1119 55
1139 56
In [6]:
plt.figure()
df_poss.set_index('Away_Team').plot(kind='bar',y='Home_Team_Goals',figsize=(18,10))
plt.show()
<matplotlib.figure.Figure at 0x9321b38>
In [7]:
df_zero_goals = df_poss[(df_poss["Home_Team_Goals"]== 0.0)]
print df_zero_goals
fig = plt.figure(figsize=(18,10))
ax = fig.add_subplot(111) # Create matplotlib axes
ax2 = ax.twinx()
width = 0.4
#f.amount.plot(kind='bar', color='red', ax=ax, width=width, position=1)
#f.price.plot(kind='bar', color='blue', ax=ax2, width=width, position=0)
df_zero_goals.ATDR.plot(kind='bar', color='red', ax=ax, width=width, position=1)
df_zero_goals.HTAR.plot(kind='bar', color='blue', ax=ax2, width=width, position=0)
plt.show()
#f_zero_goals.set_index('Away_Team').plot(kind='bar',y='ATDR',figsize=(18,10))
#lt.show()
MId Home_Team_Goals Away_Team Home_Shots Home_ShotsT HTR \
18 9590.0 0.0 SUN 17.0 2.0 887.480
77 9533.0 0.0 MCFC 10.0 4.0 888.343
239 9373.0 0.0 NUFC 8.0 4.0 879.087
244 9365.0 0.0 EVE 18.0 6.0 872.332
359 9251.0 0.0 CHE 12.0 3.0 892.737
414 9953.0 0.0 West Brom 26.0 9.0 877.475
550 9820.0 0.0 Southampton 10.0 0.0 889.578
915 12337.0 0.0 Southampton 8.0 1.0 875.856
953 12298.0 0.0 Chelsea 12.0 2.0 899.416
994 12257.0 0.0 West Ham 21.0 1.0 878.344
1042 12213.0 0.0 Man City 6.0 1.0 894.639
1119 12137.0 0.0 Newcastle 20.0 8.0 884.696
HTAR HTMR HTDR ATR ATAR ATMR ATDR index
18 189.502 291.978 406.000 799.090 72.556 365.534 361.000 2
77 252.576 237.767 398.000 912.312 83.000 420.312 409.000 5
239 171.000 221.954 486.133 846.124 89.310 303.835 452.979 11
244 165.400 268.344 438.588 857.148 157.592 317.556 382.000 12
359 243.626 234.466 414.645 908.503 111.306 328.967 468.230 18
414 287.262 369.300 220.913 813.398 73.222 372.406 367.770 20
550 296.630 278.838 314.110 838.100 151.000 308.168 378.932 27
915 85.000 400.800 390.056 838.086 152.878 230.208 455.000 46
953 85.000 487.424 326.992 907.504 100.204 396.300 411.000 48
994 0.000 479.844 398.500 845.769 77.000 381.185 387.584 50
1042 85.000 406.039 403.600 899.057 79.454 397.371 422.232 52
1119 85.000 383.080 416.616 807.551 139.313 310.238 358.000 55
In [40]:
df_goals = pd.DataFrame()
df_goals = df_copy[(df_copy["Home_Team"]=="MCFC")|(df_copy["Home_Team"]=="Man City")]
#df_copy[(df_copy['Home_Team']=="MCFC") | (df_copy["Home_Team"] == "Man City")]
#print df_goals
#print df_goals[['Home_Shots','Home_ShotsT']]
plt.figure()
df_goals.plot(kind='scatter',x='Home_Shots',y='Home_Team_Goals',figsize=(14,10))
#print df_zero_goals[['Home_Shots','Home_ShotsT']]
#df_goals.Home_Shots.plot(kind='bar', color='red', ax=ax, width=width, position=1)
#df_goals.Home_ShotsT.plot(kind='bar', color='blue', ax=ax2, width=width, position=0)
#plt.show()
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-40-5832b032c758> in <module>()
5 #print df_goals[['Home_Shots','Home_ShotsT']]
6 plt.figure()
----> 7 df_goals.plot(kind='scatter',x='Home_Shots',y='Home_Team_Goals',figsize=(14,10))
8 #print df_zero_goals[['Home_Shots','Home_ShotsT']]
9 #df_goals.Home_Shots.plot(kind='bar', color='red', ax=ax, width=width, position=1)
C:\Users\I336006\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\plotting\_core.py in __call__(self, x, y, kind, ax, subplots, sharex, sharey, layout, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, secondary_y, sort_columns, **kwds)
2618 fontsize=fontsize, colormap=colormap, table=table,
2619 yerr=yerr, xerr=xerr, secondary_y=secondary_y,
-> 2620 sort_columns=sort_columns, **kwds)
2621 __call__.__doc__ = plot_frame.__doc__
2622
C:\Users\I336006\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\plotting\_core.py in plot_frame(data, x, y, kind, ax, subplots, sharex, sharey, layout, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, secondary_y, sort_columns, **kwds)
1855 yerr=yerr, xerr=xerr,
1856 secondary_y=secondary_y, sort_columns=sort_columns,
-> 1857 **kwds)
1858
1859
C:\Users\I336006\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\plotting\_core.py in _plot(data, x, y, subplots, ax, kind, **kwds)
1680 plot_obj = klass(data, subplots=subplots, ax=ax, kind=kind, **kwds)
1681
-> 1682 plot_obj.generate()
1683 plot_obj.draw()
1684 return plot_obj.result
C:\Users\I336006\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\plotting\_core.py in generate(self)
236 self._compute_plot_data()
237 self._setup_subplots()
--> 238 self._make_plot()
239 self._add_table()
240 self._make_legend()
C:\Users\I336006\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\plotting\_core.py in _make_plot(self)
829 else:
830 label = None
--> 831 scatter = ax.scatter(data[x].values, data[y].values, c=c_values,
832 label=label, cmap=cmap, **self.kwds)
833 if cb:
C:\Users\I336006\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\core\frame.py in __getitem__(self, key)
2060 return self._getitem_multilevel(key)
2061 else:
-> 2062 return self._getitem_column(key)
2063
2064 def _getitem_column(self, key):
C:\Users\I336006\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\core\frame.py in _getitem_column(self, key)
2067 # get column
2068 if self.columns.is_unique:
-> 2069 return self._get_item_cache(key)
2070
2071 # duplicate columns & possible reduce dimensionality
C:\Users\I336006\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\core\generic.py in _get_item_cache(self, item)
1532 res = cache.get(item)
1533 if res is None:
-> 1534 values = self._data.get(item)
1535 res = self._box_item_values(item, values)
1536 cache[item] = res
C:\Users\I336006\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\core\internals.py in get(self, item, fastpath)
3588
3589 if not isnull(item):
-> 3590 loc = self.items.get_loc(item)
3591 else:
3592 indexer = np.arange(len(self.items))[isnull(self.items)]
C:\Users\I336006\AppData\Local\Continuum\Anaconda2\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance)
2393 return self._engine.get_loc(key)
2394 except KeyError:
-> 2395 return self._engine.get_loc(self._maybe_cast_indexer(key))
2396
2397 indexer = self.get_indexer([key], method=method, tolerance=tolerance)
pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc (pandas\_libs\index.c:5239)()
pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc (pandas\_libs\index.c:5085)()
pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item (pandas\_libs\hashtable.c:20405)()
pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item (pandas\_libs\hashtable.c:20359)()
KeyError: 'Home_Shots'
<matplotlib.figure.Figure at 0xe3d9a58>
In [ ]:
print "test"
Content source: sourabhswain/Prolego
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