import pandas
food_info = pandas.read_csv("food_info.csv")
# print(type(food_info))
print(food_info.shape)
print(food_info.loc[0])
# print(food_info.dtypes)
# print(help(pandas.read_csv))
(8618, 36)
NDB_No 1001
Shrt_Desc BUTTER WITH SALT
Water_(g) 15.87
Energ_Kcal 717
Protein_(g) 0.85
Lipid_Tot_(g) 81.11
Ash_(g) 2.11
Carbohydrt_(g) 0.06
Fiber_TD_(g) 0
Sugar_Tot_(g) 0.06
Calcium_(mg) 24
Iron_(mg) 0.02
Magnesium_(mg) 2
Phosphorus_(mg) 24
Potassium_(mg) 24
sodium_(mg) 643
Zinc_(mg) 0.09
copper_(mg) 0
Manganese_(mg) 0
Selenium_(mcg) 1
Vit_C_(mg) 0
Thiamin_(mg) 0.005
Riboflavin_(mg) 0.034
Niacin_(mg) 0.042
Vit_B6_(mg) 0.003
Vit_B12_(mcg) 0.17
Vit_A_IU 2499
Vit_A_RAE 684
Vit_E_(mg) 2.32
Vit_D_mcg 1.5
Vit_D_IU 60
Vit_K_(mcg) 7
FA_Sat_(g) 51.368
FA_Mono_(g) 21.021
FA_poly_(g) 3.043
Cholestrl_(mg) 215
Name: 0, dtype: object
print(food_info.head()) #默认显示前5条数据
print(food_info.tail(4))
print(food_info.columns)
print(food_info.shape)
NDB_No Shrt_Desc Water_(g) Energ_Kcal Protein_(g) \
0 1001 BUTTER WITH SALT 15.87 717 0.85
1 1002 BUTTER WHIPPED WITH SALT 15.87 717 0.85
2 1003 BUTTER OIL ANHYDROUS 0.24 876 0.28
3 1004 CHEESE BLUE 42.41 353 21.40
4 1005 CHEESE BRICK 41.11 371 23.24
Lipid_Tot_(g) Ash_(g) Carbohydrt_(g) Fiber_TD_(g) Sugar_Tot_(g) \
0 81.11 2.11 0.06 0.0 0.06
1 81.11 2.11 0.06 0.0 0.06
2 99.48 0.00 0.00 0.0 0.00
3 28.74 5.11 2.34 0.0 0.50
4 29.68 3.18 2.79 0.0 0.51
... Vit_A_IU Vit_A_RAE Vit_E_(mg) Vit_D_mcg Vit_D_IU \
0 ... 2499.0 684.0 2.32 1.5 60.0
1 ... 2499.0 684.0 2.32 1.5 60.0
2 ... 3069.0 840.0 2.80 1.8 73.0
3 ... 721.0 198.0 0.25 0.5 21.0
4 ... 1080.0 292.0 0.26 0.5 22.0
Vit_K_(mcg) FA_Sat_(g) FA_Mono_(g) FA_poly_(g) Cholestrl_(mg)
0 7.0 51.368 21.021 3.043 215.0
1 7.0 50.489 23.426 3.012 219.0
2 8.6 61.924 28.732 3.694 256.0
3 2.4 18.669 7.778 0.800 75.0
4 2.5 18.764 8.598 0.784 94.0
[5 rows x 36 columns]
NDB_No Shrt_Desc Water_(g) Energ_Kcal Protein_(g) \
8614 90240 SCALLOP (BAY&SEA) CKD STMD 70.25 111 20.54
8615 90480 SYRUP CANE 26.00 269 0.00
8616 90560 SNAIL RAW 79.20 90 16.10
8617 93600 TURTLE GREEN RAW 78.50 89 19.80
Lipid_Tot_(g) Ash_(g) Carbohydrt_(g) Fiber_TD_(g) Sugar_Tot_(g) \
8614 0.84 2.97 5.41 0.0 0.0
8615 0.00 0.86 73.14 0.0 73.2
8616 1.40 1.30 2.00 0.0 0.0
8617 0.50 1.20 0.00 0.0 0.0
... Vit_A_IU Vit_A_RAE Vit_E_(mg) Vit_D_mcg Vit_D_IU \
8614 ... 5.0 2.0 0.0 0.0 2.0
8615 ... 0.0 0.0 0.0 0.0 0.0
8616 ... 100.0 30.0 5.0 0.0 0.0
8617 ... 100.0 30.0 0.5 0.0 0.0
Vit_K_(mcg) FA_Sat_(g) FA_Mono_(g) FA_poly_(g) Cholestrl_(mg)
8614 0.0 0.218 0.082 0.222 41.0
8615 0.0 0.000 0.000 0.000 0.0
8616 0.1 0.361 0.259 0.252 50.0
8617 0.1 0.127 0.088 0.170 50.0
[4 rows x 36 columns]
Index(['NDB_No', 'Shrt_Desc', 'Water_(g)', 'Energ_Kcal', 'Protein_(g)',
'Lipid_Tot_(g)', 'Ash_(g)', 'Carbohydrt_(g)', 'Fiber_TD_(g)',
'Sugar_Tot_(g)', 'Calcium_(mg)', 'Iron_(mg)', 'Magnesium_(mg)',
'Phosphorus_(mg)', 'Potassium_(mg)', 'sodium_(mg)', 'Zinc_(mg)',
'copper_(mg)', 'Manganese_(mg)', 'Selenium_(mcg)', 'Vit_C_(mg)',
'Thiamin_(mg)', 'Riboflavin_(mg)', 'Niacin_(mg)', 'Vit_B6_(mg)',
'Vit_B12_(mcg)', 'Vit_A_IU', 'Vit_A_RAE', 'Vit_E_(mg)', 'Vit_D_mcg',
'Vit_D_IU', 'Vit_K_(mcg)', 'FA_Sat_(g)', 'FA_Mono_(g)', 'FA_poly_(g)',
'Cholestrl_(mg)'],
dtype='object')
(8618, 36)
print(food_info.loc[0]) #输出第一行数据
NDB_No 1001
Shrt_Desc BUTTER WITH SALT
Water_(g) 15.87
Energ_Kcal 717
Protein_(g) 0.85
Lipid_Tot_(g) 81.11
Ash_(g) 2.11
Carbohydrt_(g) 0.06
Fiber_TD_(g) 0
Sugar_Tot_(g) 0.06
Calcium_(mg) 24
Iron_(mg) 0.02
Magnesium_(mg) 2
Phosphorus_(mg) 24
Potassium_(mg) 24
sodium_(mg) 643
Zinc_(mg) 0.09
copper_(mg) 0
Manganese_(mg) 0
Selenium_(mcg) 1
Vit_C_(mg) 0
Thiamin_(mg) 0.005
Riboflavin_(mg) 0.034
Niacin_(mg) 0.042
Vit_B6_(mg) 0.003
Vit_B12_(mcg) 0.17
Vit_A_IU 2499
Vit_A_RAE 684
Vit_E_(mg) 2.32
Vit_D_mcg 1.5
Vit_D_IU 60
Vit_K_(mcg) 7
FA_Sat_(g) 51.368
FA_Mono_(g) 21.021
FA_poly_(g) 3.043
Cholestrl_(mg) 215
Name: 0, dtype: object
#切片
food_info.loc[3:6]
#切片第2，5，10行
food_info.loc[[2,5,10]]
| NDB_No | Shrt_Desc | Water_(g) | Energ_Kcal | Protein_(g) | Lipid_Tot_(g) | Ash_(g) | Carbohydrt_(g) | Fiber_TD_(g) | Sugar_Tot_(g) | ... | Vit_A_IU | Vit_A_RAE | Vit_E_(mg) | Vit_D_mcg | Vit_D_IU | Vit_K_(mcg) | FA_Sat_(g) | FA_Mono_(g) | FA_poly_(g) | Cholestrl_(mg) | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2 | 1003 | BUTTER OIL ANHYDROUS | 0.24 | 876 | 0.28 | 99.48 | 0.00 | 0.00 | 0.0 | 0.00 | ... | 3069.0 | 840.0 | 2.80 | 1.8 | 73.0 | 8.6 | 61.924 | 28.732 | 3.694 | 256.0 |
| 5 | 1006 | CHEESE BRIE | 48.42 | 334 | 20.75 | 27.68 | 2.70 | 0.45 | 0.0 | 0.45 | ... | 592.0 | 174.0 | 0.24 | 0.5 | 20.0 | 2.3 | 17.410 | 8.013 | 0.826 | 100.0 |
| 10 | 1011 | CHEESE COLBY | 38.20 | 394 | 23.76 | 32.11 | 3.36 | 2.57 | 0.0 | 0.52 | ... | 994.0 | 264.0 | 0.28 | 0.6 | 24.0 | 2.7 | 20.218 | 9.280 | 0.953 | 95.0 |
3 rows × 36 columns
#定位到列名
ndb_col = food_info["NDB_No"]
print(ndb_col)
0 1001
1 1002
2 1003
3 1004
4 1005
5 1006
6 1007
7 1008
8 1009
9 1010
10 1011
11 1012
12 1013
13 1014
14 1015
15 1016
16 1017
17 1018
18 1019
19 1020
20 1021
21 1022
22 1023
23 1024
24 1025
25 1026
26 1027
27 1028
28 1029
29 1030
...
8588 43544
8589 43546
8590 43550
8591 43566
8592 43570
8593 43572
8594 43585
8595 43589
8596 43595
8597 43597
8598 43598
8599 44005
8600 44018
8601 44048
8602 44055
8603 44061
8604 44074
8605 44110
8606 44158
8607 44203
8608 44258
8609 44259
8610 44260
8611 48052
8612 80200
8613 83110
8614 90240
8615 90480
8616 90560
8617 93600
Name: NDB_No, dtype: int64
#指定列名
columns = ["Zinc_(mg)", "copper_(mg)"]
zinc_copper = food_info[columns]
print(zinc_copper)
Zinc_(mg) copper_(mg)
0 0.09 0.000
1 0.05 0.016
2 0.01 0.001
3 2.66 0.040
4 2.60 0.024
5 2.38 0.019
6 2.38 0.021
7 2.94 0.024
8 3.43 0.056
9 2.79 0.042
10 3.07 0.042
11 0.40 0.029
12 0.33 0.040
13 0.47 0.030
14 0.51 0.033
15 0.38 0.028
16 0.51 0.019
17 3.75 0.036
18 2.88 0.032
19 3.50 0.025
20 1.14 0.080
21 3.90 0.036
22 3.90 0.032
23 2.10 0.021
24 3.00 0.032
25 2.92 0.011
26 2.46 0.022
27 2.76 0.025
28 3.61 0.034
29 2.81 0.031
... ... ...
8588 3.30 0.377
8589 0.05 0.040
8590 0.05 0.030
8591 1.15 0.116
8592 5.03 0.200
8593 3.83 0.545
8594 0.08 0.035
8595 3.90 0.027
8596 4.10 0.100
8597 3.13 0.027
8598 0.13 0.000
8599 0.02 0.000
8600 0.09 0.037
8601 0.21 0.026
8602 2.77 0.571
8603 0.41 0.838
8604 0.05 0.028
8605 0.03 0.023
8606 0.10 0.112
8607 0.02 0.020
8608 1.49 0.854
8609 0.19 0.040
8610 0.10 0.038
8611 0.85 0.182
8612 1.00 0.250
8613 1.10 0.100
8614 1.55 0.033
8615 0.19 0.020
8616 1.00 0.400
8617 1.00 0.250
[8618 rows x 2 columns]
#把当前的列名做成一个list
col_names = food_info.columns.tolist()
print(col_names)
gram_columns = []
for c in col_names:
if c.endswith("(g)"):
gram_columns.append(c)
gram_df = food_info[gram_columns]
print(gram_df.head(3))
['NDB_No', 'Shrt_Desc', 'Water_(g)', 'Energ_Kcal', 'Protein_(g)', 'Lipid_Tot_(g)', 'Ash_(g)', 'Carbohydrt_(g)', 'Fiber_TD_(g)', 'Sugar_Tot_(g)', 'Calcium_(mg)', 'Iron_(mg)', 'Magnesium_(mg)', 'Phosphorus_(mg)', 'Potassium_(mg)', 'sodium_(mg)', 'Zinc_(mg)', 'copper_(mg)', 'Manganese_(mg)', 'Selenium_(mcg)', 'Vit_C_(mg)', 'Thiamin_(mg)', 'Riboflavin_(mg)', 'Niacin_(mg)', 'Vit_B6_(mg)', 'Vit_B12_(mcg)', 'Vit_A_IU', 'Vit_A_RAE', 'Vit_E_(mg)', 'Vit_D_mcg', 'Vit_D_IU', 'Vit_K_(mcg)', 'FA_Sat_(g)', 'FA_Mono_(g)', 'FA_poly_(g)', 'Cholestrl_(mg)']
Water_(g) Protein_(g) Lipid_Tot_(g) Ash_(g) Carbohydrt_(g) \
0 15.87 0.85 81.11 2.11 0.06
1 15.87 0.85 81.11 2.11 0.06
2 0.24 0.28 99.48 0.00 0.00
Fiber_TD_(g) Sugar_Tot_(g) FA_Sat_(g) FA_Mono_(g) FA_poly_(g)
0 0.0 0.06 51.368 21.021 3.043
1 0.0 0.06 50.489 23.426 3.012
2 0.0 0.00 61.924 28.732 3.694
import pandas
food_info = pandas.read_csv("food_info.csv")
print(food_info)
NDB_No Shrt_Desc Water_(g) \
0 1001 BUTTER WITH SALT 15.87
1 1002 BUTTER WHIPPED WITH SALT 15.87
2 1003 BUTTER OIL ANHYDROUS 0.24
3 1004 CHEESE BLUE 42.41
4 1005 CHEESE BRICK 41.11
5 1006 CHEESE BRIE 48.42
6 1007 CHEESE CAMEMBERT 51.80
7 1008 CHEESE CaraWAY 39.28
8 1009 CHEESE CHEDDAR 37.10
9 1010 CHEESE CHESHIRE 37.65
10 1011 CHEESE COLBY 38.20
11 1012 CHEESE CottAGE CRMD LRG OR SML CURD 79.79
12 1013 CHEESE CottAGE CRMD W/FRUIT 79.64
13 1014 CHEESE CottAGE NONFAT UNCRMD DRY LRG OR SML CURD 81.01
14 1015 CHEESE CottAGE LOWFAT 2% MILKFAT 81.24
15 1016 CHEESE CottAGE LOWFAT 1% MILKFAT 82.48
16 1017 CHEESE CREAM 54.44
17 1018 CHEESE Edam 41.56
18 1019 CHEESE FETA 55.22
19 1020 CHEESE FONTINA 37.92
20 1021 CHEESE GJetoST 13.44
21 1022 CHEESE GOUDA 41.46
22 1023 CHEESE GRUYERE 33.19
23 1024 CHEESE LIMBURGER 48.42
24 1025 CHEESE MONTEREY 41.01
25 1026 CHEESE MOZZARELLA WHL MILK 50.01
26 1027 CHEESE MOZZARELLA WHL MILK LO MOIST 48.38
27 1028 CHEESE MOZZARELLA PART SKIM MILK 53.78
28 1029 CHEESE MOZZARELLA LO MOIST PART-SKIM 45.54
29 1030 CHEESE MUENSTER 41.77
... ... ... ...
8588 43544 BABYFOOD CRL RICE W/ PEARS & APPL DRY INST 2.00
8589 43546 BABYFOOD BANANA NO TAPIOCA STR 76.70
8590 43550 BABYFOOD BANANA APPL DSSRT STR 83.10
8591 43566 SNACKS TORTILLA CHIPS LT (BAKED W/ LESS OIL) 1.30
8592 43570 CEREALS RTE POST HONEY BUNCHES OF OATS HONEY RSTD 5.00
8593 43572 POPCORN MICROWAVE LOFAT&NA 2.80
8594 43585 BABYFOOD FRUIT SUPREME DSSRT 81.60
8595 43589 CHEESE SWISS LOW FAT 59.60
8596 43595 BREAKFAST BAR CORN FLAKE CRUST W/FRUIT 14.50
8597 43597 CHEESE MOZZARELLA LO NA 49.90
8598 43598 MAYONNAISE DRSNG NO CHOL 21.70
8599 44005 OIL CORN PEANUT AND OLIVE 0.00
8600 44018 SWEETENERS TABLetoP FRUCTOSE LIQ 23.90
8601 44048 CHEESE FOOD IMITATION 55.50
8602 44055 CELERY FLAKES DRIED 9.00
8603 44061 PuddiNGS CHOC FLAVOR LO CAL INST DRY MIX 4.20
8604 44074 BABYFOOD GRAPE JUC NO SUGAR CND 84.40
8605 44110 JELLIES RED SUGAR HOME PRESERVED 53.00
8606 44158 PIE FILLINGS BLUEBerry CND 54.66
8607 44203 COCKTAIL MIX NON-ALCOHOLIC CONCD FRZ 28.24
8608 44258 PuddiNGS CHOC FLAVOR LO CAL REG DRY MIX 6.80
8609 44259 PuddiNGS ALL FLAVORS XCPT CHOC LO CAL REG DRY MIX 10.40
8610 44260 PuddiNGS ALL FLAVORS XCPT CHOC LO CAL INST DRY... 6.84
8611 48052 VITAL WHEAT gluTEN 8.20
8612 80200 FROG LEGS RAW 81.90
8613 83110 MACKEREL SALTED 43.00
8614 90240 SCALLOP (BAY&SEA) CKD STMD 70.25
8615 90480 SYRUP CANE 26.00
8616 90560 SNAIL RAW 79.20
8617 93600 TURTLE GREEN RAW 78.50
Energ_Kcal Protein_(g) Lipid_Tot_(g) Ash_(g) Carbohydrt_(g) \
0 717 0.85 81.11 2.11 0.06
1 717 0.85 81.11 2.11 0.06
2 876 0.28 99.48 0.00 0.00
3 353 21.40 28.74 5.11 2.34
4 371 23.24 29.68 3.18 2.79
5 334 20.75 27.68 2.70 0.45
6 300 19.80 24.26 3.68 0.46
7 376 25.18 29.20 3.28 3.06
8 406 24.04 33.82 3.71 1.33
9 387 23.37 30.60 3.60 4.78
10 394 23.76 32.11 3.36 2.57
11 98 11.12 4.30 1.41 3.38
12 97 10.69 3.85 1.20 4.61
13 72 10.34 0.29 1.71 6.66
14 81 10.45 2.27 1.27 4.76
15 72 12.39 1.02 1.39 2.72
16 342 5.93 34.24 1.32 4.07
17 357 24.99 27.80 4.22 1.43
18 264 14.21 21.28 5.20 4.09
19 389 25.60 31.14 3.79 1.55
20 466 9.65 29.51 4.75 42.65
21 356 24.94 27.44 3.94 2.22
22 413 29.81 32.34 4.30 0.36
23 327 20.05 27.25 3.79 0.49
24 373 24.48 30.28 3.55 0.68
25 300 22.17 22.35 3.28 2.19
26 318 21.60 24.64 2.91 2.47
27 254 24.26 15.92 3.27 2.77
28 301 24.58 19.72 3.80 6.36
29 368 23.41 30.04 3.66 1.12
... ... ... ... ... ...
8588 389 6.60 0.90 2.00 88.60
8589 91 1.00 0.20 0.76 21.34
8590 68 0.30 0.20 0.29 16.30
8591 465 8.70 15.20 1.85 73.40
8592 401 7.12 5.46 1.22 81.19
8593 429 12.60 9.50 1.71 73.39
8594 73 0.50 0.20 0.52 17.18
8595 179 28.40 5.10 3.50 3.40
8596 377 4.40 7.50 0.80 72.90
8597 280 27.50 17.10 2.40 3.10
8598 688 0.00 77.80 0.40 0.30
8599 884 0.00 100.00 0.00 0.00
8600 279 0.00 0.00 0.00 76.10
8601 257 4.08 19.50 4.74 16.18
8602 319 11.30 2.10 13.90 63.70
8603 356 5.30 2.40 9.90 78.20
8604 62 0.00 0.00 0.22 15.38
8605 179 0.30 0.03 0.08 46.10
8606 181 0.41 0.20 0.35 44.38
8607 287 0.08 0.01 0.07 71.60
8608 365 10.08 3.00 5.70 74.42
8609 351 1.60 0.10 1.86 86.04
8610 350 0.81 0.90 6.80 84.66
8611 370 75.16 1.85 1.00 13.79
8612 73 16.40 0.30 1.40 0.00
8613 305 18.50 25.10 13.40 0.00
8614 111 20.54 0.84 2.97 5.41
8615 269 0.00 0.00 0.86 73.14
8616 90 16.10 1.40 1.30 2.00
8617 89 19.80 0.50 1.20 0.00
Fiber_TD_(g) Sugar_Tot_(g) ... Vit_A_IU Vit_A_RAE Vit_E_(mg) \
0 0.0 0.06 ... 2499.0 684.0 2.32
1 0.0 0.06 ... 2499.0 684.0 2.32
2 0.0 0.00 ... 3069.0 840.0 2.80
3 0.0 0.50 ... 721.0 198.0 0.25
4 0.0 0.51 ... 1080.0 292.0 0.26
5 0.0 0.45 ... 592.0 174.0 0.24
6 0.0 0.46 ... 820.0 241.0 0.21
7 0.0 NaN ... 1054.0 271.0 NaN
8 0.0 0.28 ... 994.0 263.0 0.78
9 0.0 NaN ... 985.0 233.0 NaN
10 0.0 0.52 ... 994.0 264.0 0.28
11 0.0 2.67 ... 140.0 37.0 0.08
12 0.2 2.38 ... 146.0 38.0 0.04
13 0.0 1.85 ... 8.0 2.0 0.01
14 0.0 4.00 ... 225.0 68.0 0.08
15 0.0 2.72 ... 41.0 11.0 0.01
16 0.0 3.21 ... 1343.0 366.0 0.29
17 0.0 1.43 ... 825.0 243.0 0.24
18 0.0 4.09 ... 422.0 125.0 0.18
19 0.0 1.55 ... 913.0 261.0 0.27
20 0.0 NaN ... 1113.0 334.0 NaN
21 0.0 2.22 ... 563.0 165.0 0.24
22 0.0 0.36 ... 948.0 271.0 0.28
23 0.0 0.49 ... 1155.0 340.0 0.23
24 0.0 0.50 ... 769.0 198.0 0.26
25 0.0 1.03 ... 676.0 179.0 0.19
26 0.0 1.01 ... 745.0 197.0 0.21
27 0.0 1.13 ... 481.0 127.0 0.14
28 0.0 2.24 ... 846.0 254.0 0.43
29 0.0 1.12 ... 1012.0 298.0 0.26
... ... ... ... ... ... ...
8588 2.6 1.35 ... 0.0 0.0 0.13
8589 1.6 11.36 ... 5.0 0.0 0.25
8590 1.0 14.66 ... 30.0 2.0 0.02
8591 5.7 0.53 ... 81.0 4.0 3.53
8592 4.2 19.79 ... 2731.0 806.0 1.22
8593 14.2 0.54 ... 147.0 7.0 5.01
8594 2.0 14.87 ... 50.0 3.0 0.79
8595 0.0 1.33 ... 152.0 40.0 0.07
8596 2.1 35.10 ... 2027.0 608.0 0.76
8597 0.0 1.23 ... 517.0 137.0 0.15
8598 0.0 0.30 ... 0.0 0.0 11.79
8599 0.0 0.00 ... 0.0 0.0 14.78
8600 0.1 76.00 ... 0.0 0.0 0.00
8601 0.0 8.21 ... 900.0 45.0 2.15
8602 27.8 35.90 ... 1962.0 98.0 5.55
8603 6.1 0.70 ... 0.0 0.0 0.02
8604 0.1 NaN ... 8.0 NaN NaN
8605 0.8 45.30 ... 3.0 0.0 0.00
8606 2.6 37.75 ... 22.0 1.0 0.23
8607 0.0 24.53 ... 12.0 1.0 0.02
8608 10.1 0.70 ... 0.0 0.0 0.02
8609 0.9 2.90 ... 0.0 0.0 0.05
8610 0.8 0.90 ... 0.0 0.0 0.08
8611 0.6 0.00 ... 0.0 0.0 0.00
8612 0.0 0.00 ... 50.0 15.0 1.00
8613 0.0 0.00 ... 157.0 47.0 2.38
8614 0.0 0.00 ... 5.0 2.0 0.00
8615 0.0 73.20 ... 0.0 0.0 0.00
8616 0.0 0.00 ... 100.0 30.0 5.00
8617 0.0 0.00 ... 100.0 30.0 0.50
Vit_D_mcg Vit_D_IU Vit_K_(mcg) FA_Sat_(g) FA_Mono_(g) FA_poly_(g) \
0 1.5 60.0 7.0 51.368 21.021 3.043
1 1.5 60.0 7.0 50.489 23.426 3.012
2 1.8 73.0 8.6 61.924 28.732 3.694
3 0.5 21.0 2.4 18.669 7.778 0.800
4 0.5 22.0 2.5 18.764 8.598 0.784
5 0.5 20.0 2.3 17.410 8.013 0.826
6 0.4 18.0 2.0 15.259 7.023 0.724
7 NaN NaN NaN 18.584 8.275 0.830
8 0.6 24.0 2.9 19.368 8.428 1.433
9 NaN NaN NaN 19.475 8.671 0.870
10 0.6 24.0 2.7 20.218 9.280 0.953
11 0.1 3.0 0.0 1.718 0.778 0.123
12 0.0 0.0 0.4 2.311 1.036 0.124
13 0.0 0.0 0.0 0.169 0.079 0.003
14 0.0 0.0 0.0 1.235 0.516 0.083
15 0.0 0.0 0.1 0.645 0.291 0.031
16 0.6 25.0 2.9 19.292 8.620 1.437
17 0.5 20.0 2.3 17.572 8.125 0.665
18 0.4 16.0 1.8 14.946 4.623 0.591
19 0.6 23.0 2.6 19.196 8.687 1.654
20 NaN NaN NaN 19.160 7.879 0.938
21 0.5 20.0 2.3 17.614 7.747 0.657
22 0.6 24.0 2.7 18.913 10.043 1.733
23 0.5 20.0 2.3 16.746 8.606 0.495
24 0.6 22.0 2.5 19.066 8.751 0.899
25 0.4 16.0 2.3 13.152 6.573 0.765
26 0.5 18.0 2.5 15.561 7.027 0.778
27 0.3 12.0 1.6 10.114 4.510 0.472
28 0.4 15.0 1.3 11.473 5.104 0.861
29 0.6 22.0 2.5 19.113 8.711 0.661
... ... ... ... ... ... ...
8588 0.0 0.0 0.3 0.185 0.252 0.231
8589 0.0 0.0 0.5 0.072 0.028 0.041
8590 0.0 0.0 0.1 0.058 0.018 0.047
8591 0.0 0.0 0.7 2.837 6.341 5.024
8592 4.6 183.0 3.0 0.600 2.831 1.307
8593 0.0 0.0 15.7 1.415 4.085 3.572
8594 0.0 0.0 5.1 0.030 0.025 0.068
8595 0.1 4.0 0.5 3.304 1.351 0.180
8596 0.0 0.0 13.8 1.500 5.000 0.900
8597 0.3 13.0 1.8 10.867 4.844 0.509
8598 0.0 0.0 24.7 10.784 18.026 45.539
8599 0.0 0.0 21.0 14.367 48.033 33.033
8600 0.0 0.0 0.0 0.000 0.000 0.000
8601 0.0 0.0 36.7 7.996 3.108 7.536
8602 0.0 0.0 584.2 0.555 0.405 1.035
8603 0.0 0.0 0.4 0.984 1.154 0.131
8604 NaN NaN NaN 0.000 0.000 0.000
8605 0.0 0.0 0.2 0.009 0.001 0.008
8606 0.0 0.0 3.9 0.000 0.000 0.000
8607 0.0 0.0 0.0 0.003 0.001 0.009
8608 0.0 0.0 0.5 1.578 1.150 0.130
8609 0.0 0.0 1.1 0.018 0.032 0.050
8610 0.0 0.0 1.7 0.099 0.116 0.433
8611 0.0 0.0 0.0 0.272 0.156 0.810
8612 0.2 8.0 0.1 0.076 0.053 0.102
8613 25.2 1006.0 7.8 7.148 8.320 6.210
8614 0.0 2.0 0.0 0.218 0.082 0.222
8615 0.0 0.0 0.0 0.000 0.000 0.000
8616 0.0 0.0 0.1 0.361 0.259 0.252
8617 0.0 0.0 0.1 0.127 0.088 0.170
Cholestrl_(mg)
0 215.0
1 219.0
2 256.0
3 75.0
4 94.0
5 100.0
6 72.0
7 93.0
8 102.0
9 103.0
10 95.0
11 17.0
12 13.0
13 7.0
14 12.0
15 4.0
16 110.0
17 89.0
18 89.0
19 116.0
20 94.0
21 114.0
22 110.0
23 90.0
24 89.0
25 79.0
26 89.0
27 64.0
28 65.0
29 96.0
... ...
8588 0.0
8589 0.0
8590 0.0
8591 0.0
8592 0.0
8593 0.0
8594 0.0
8595 35.0
8596 0.0
8597 54.0
8598 0.0
8599 0.0
8600 0.0
8601 6.0
8602 0.0
8603 0.0
8604 0.0
8605 0.0
8606 0.0
8607 0.0
8608 0.0
8609 0.0
8610 0.0
8611 0.0
8612 50.0
8613 95.0
8614 41.0
8615 0.0
8616 50.0
8617 50.0
[8618 rows x 36 columns]
print(food_info["Iron_(mg)"][0:10])
#这一列每个值都除以1000
div_1000 = food_info["Iron_(mg)"] / 1000
print(div_1000.loc[0:10])
0 0.02
1 0.16
2 0.00
3 0.31
4 0.43
5 0.50
6 0.33
7 0.64
8 0.16
9 0.21
Name: Iron_(mg), dtype: float64
0 0.00002
1 0.00016
2 0.00000
3 0.00031
4 0.00043
5 0.00050
6 0.00033
7 0.00064
8 0.00016
9 0.00021
10 0.00076
Name: Iron_(mg), dtype: float64
#对应位置的列相乘
water_energy = food_info["Water_(g)"]*food_info["Energ_Kcal"]
print(water_energy[0:10])
iron_grams = food_info["Iron_(mg)"] / 1000
print(food_info.shape)
#增加新的一列
food_info["Iron_(g)"] = iron_grams
print(food_info.shape)
0 11378.79
1 11378.79
2 210.24
3 14970.73
4 15251.81
5 16172.28
6 15540.00
7 14769.28
8 15062.60
9 14570.55
dtype: float64
(8618, 36)
(8618, 37)
weighted_protein = food_info["Protein_(g)"] * 2
weighted_fat = -0.75 * food_info["Lipid_Tot_(g)"]
initial_rating = weighted_protein + weighted_fat
print(weighted_protein[0:10],weighted_fat[0:10],initial_rating[0:10])
0 1.70
1 1.70
2 0.56
3 42.80
4 46.48
5 41.50
6 39.60
7 50.36
8 48.08
9 46.74
Name: Protein_(g), dtype: float64 0 -60.8325
1 -60.8325
2 -74.6100
3 -21.5550
4 -22.2600
5 -20.7600
6 -18.1950
7 -21.9000
8 -25.3650
9 -22.9500
Name: Lipid_Tot_(g), dtype: float64 0 -59.1325
1 -59.1325
2 -74.0500
3 21.2450
4 24.2200
5 20.7400
6 21.4050
7 28.4600
8 22.7150
9 23.7900
dtype: float64
#求某一列的最大值
max_calories = food_info["Energ_Kcal"].max()
print(max_calories)
#将一列都除以最大值
normalized_calories = food_info["Energ_Kcal"] / max_calories
normalized_protein = food_info["Protein_(g)"] / food_info["Protein_(g)"].max()
normalized_fat = food_info["Lipid_Tot_(g)"] / food_info["Lipid_Tot_(g)"].max()
food_info["normalized_protein"] = normalized_protein
food_info["normalized_fat"] = normalized_fat
print(food_info.shape)
print(food_info["normalized_protein"][0:10],food_info["normalized_fat"][0:10])
902
(8618, 39)
0 0.009624
1 0.009624
2 0.003170
3 0.242301
4 0.263134
5 0.234941
6 0.224185
7 0.285100
8 0.272192
9 0.264606
Name: normalized_protein, dtype: float64 0 0.8111
1 0.8111
2 0.9948
3 0.2874
4 0.2968
5 0.2768
6 0.2426
7 0.2920
8 0.3382
9 0.3060
Name: normalized_fat, dtype: float64
#对某一列进行排序，默认从小到大
food_info.sort_values("sodium_(mg)", inplace = True)
print(food_info["sodium_(mg)"])
#加上ascending=False为从大到小
food_info.sort_values("sodium_(mg)", inplace = True, ascending=False)
print(food_info["sodium_(mg)"])
760 0.0
758 0.0
405 0.0
761 0.0
2269 0.0
763 0.0
764 0.0
770 0.0
774 0.0
396 0.0
395 0.0
6827 0.0
394 0.0
393 0.0
391 0.0
390 0.0
787 0.0
788 0.0
2270 0.0
2231 0.0
407 0.0
748 0.0
409 0.0
747 0.0
702 0.0
703 0.0
704 0.0
705 0.0
706 0.0
707 0.0
...
8153 NaN
8155 NaN
8156 NaN
8157 NaN
8158 NaN
8159 NaN
8160 NaN
8161 NaN
8163 NaN
8164 NaN
8165 NaN
8167 NaN
8169 NaN
8170 NaN
8172 NaN
8173 NaN
8174 NaN
8175 NaN
8176 NaN
8177 NaN
8178 NaN
8179 NaN
8180 NaN
8181 NaN
8183 NaN
8184 NaN
8185 NaN
8195 NaN
8251 NaN
8267 NaN
Name: sodium_(mg), dtype: float64
276 38758.0
5814 27360.0
6192 26050.0
1242 26000.0
1245 24000.0
1243 24000.0
1244 23875.0
292 17000.0
1254 11588.0
5811 10600.0
8575 9690.0
291 8068.0
1249 8031.0
5812 7893.0
1292 7851.0
293 7203.0
4472 7027.0
4836 6820.0
1261 6580.0
3747 6008.0
1266 5730.0
4835 5586.0
4834 5493.0
1263 5356.0
1553 5203.0
1552 5053.0
1251 4957.0
1257 4843.0
294 4616.0
8613 4450.0
...
8153 NaN
8155 NaN
8156 NaN
8157 NaN
8158 NaN
8159 NaN
8160 NaN
8161 NaN
8163 NaN
8164 NaN
8165 NaN
8167 NaN
8169 NaN
8170 NaN
8172 NaN
8173 NaN
8174 NaN
8175 NaN
8176 NaN
8177 NaN
8178 NaN
8179 NaN
8180 NaN
8181 NaN
8183 NaN
8184 NaN
8185 NaN
8195 NaN
8251 NaN
8267 NaN
Name: sodium_(mg), dtype: float64
import pandas as pd
import numpy as np
titanic_survival = pd.read_csv("titanic_train.csv")
titanic_survival.head()
#Passengerld 每个人的编号 Survived 只有两个值 0和1 (是否获救) Pclass舱位等级123 Name 乘客姓名
#Sex 性别 Age年龄 SibSp 家人数量 Parch老人孩子数量 Ticket 船票编码 fare 船票价格
#Cabin 船舱编号NaN 缺失值 Embarked 登船地点（码头）
| PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | fare | Cabin | Embarked | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0 | 3 | Braund, Mr. Owen Harris | male | 22.0 | 1 | 0 | A/5 21171 | 7.2500 | NaN | S |
| 1 | 2 | 1 | 1 | Cumings, Mrs. John Bradley (Florence Briggs Th... | female | 38.0 | 1 | 0 | PC 17599 | 71.2833 | C85 | C |
| 2 | 3 | 1 | 3 | Heikkinen, Miss. Laina | female | 26.0 | 0 | 0 | STON/O2. 3101282 | 7.9250 | NaN | S |
| 3 | 4 | 1 | 1 | Futrelle, Mrs. Jacques Heath (Lily May Peel) | female | 35.0 | 1 | 0 | 113803 | 53.1000 | C123 | S |
| 4 | 5 | 0 | 3 | Allen, Mr. William Henry | male | 35.0 | 0 | 0 | 373450 | 8.0500 | NaN | S |
age = titanic_survival["Age"]
#0-10个人的年龄
print(age.loc[0:10])
#isnull()判断是缺失值True
age_is_null = pd.isnull(age)
print(age_is_null)
#作为索引找到行号
age_null_true = age[age_is_null]
print(age_null_true)
#缺失值的数量
age_null_count = len(age_null_true)
print(age_null_count)
0 22.0
1 38.0
2 26.0
3 35.0
4 35.0
5 NaN
6 54.0
7 2.0
8 27.0
9 14.0
10 4.0
Name: Age, dtype: float64
0 False
1 False
2 False
3 False
4 False
5 True
6 False
7 False
8 False
9 False
10 False
11 False
12 False
13 False
14 False
15 False
16 False
17 True
18 False
19 True
20 False
21 False
22 False
23 False
24 False
25 False
26 True
27 False
28 True
29 True
...
861 False
862 False
863 True
864 False
865 False
866 False
867 False
868 True
869 False
870 False
871 False
872 False
873 False
874 False
875 False
876 False
877 False
878 True
879 False
880 False
881 False
882 False
883 False
884 False
885 False
886 False
887 False
888 True
889 False
890 False
Name: Age, dtype: bool
5 NaN
17 NaN
19 NaN
26 NaN
28 NaN
29 NaN
31 NaN
32 NaN
36 NaN
42 NaN
45 NaN
46 NaN
47 NaN
48 NaN
55 NaN
64 NaN
65 NaN
76 NaN
77 NaN
82 NaN
87 NaN
95 NaN
101 NaN
107 NaN
109 NaN
121 NaN
126 NaN
128 NaN
140 NaN
154 NaN
..
718 NaN
727 NaN
732 NaN
738 NaN
739 NaN
740 NaN
760 NaN
766 NaN
768 NaN
773 NaN
776 NaN
778 NaN
783 NaN
790 NaN
792 NaN
793 NaN
815 NaN
825 NaN
826 NaN
828 NaN
832 NaN
837 NaN
839 NaN
846 NaN
849 NaN
859 NaN
863 NaN
868 NaN
878 NaN
888 NaN
Name: Age, dtype: float64
177
#未处理缺失值时计算平均年龄
mean_age = sum(titanic_survival["Age"]) / len(titanic_survival["Age"])
print(mean_age)
nan
#计算非缺失值的平均年龄
good_ages = titanic_survival["Age"][age_is_null == False]
correct_mean_age = sum(good_ages) / len(good_ages)
print(correct_mean_age)
#.mean()函数计算平均值
correct_mean_age = titanic_survival["Age"].mean()
print(correct_mean_age)
29.6991176471
29.69911764705882
#不同船舱等级的船票价格
passenger_classes = [1, 2, 3]
fares_by_class = {}
for this_class in passenger_classes:
#pclass_rows为不同舱别的数据，包含所有列
pclass_rows = titanic_survival[titanic_survival["Pclass"] == this_class]
#pclass_fares为["fare"]列，即不同舱别票价列的数据
pclass_fares = pclass_rows["fare"]
#不同舱别的平均票价
fare_for_class = pclass_fares.mean()
#存入list
fares_by_class[this_class] = fare_for_class
print(fares_by_class)
{1: 84.15468749999992, 2: 20.66218315217391, 3: 13.675550101832997}
#pivot_table参数 index= 索引 values 与**之间的关系 aggfunc= 统计的什么关系
#计算每个舱位的获救概率
passenger_survival = titanic_survival.pivot_table(index="Pclass", values="Survived", aggfunc=np.mean)
print(passenger_survival)
Pclass
1 0.629630
2 0.472826
3 0.242363
Name: Survived, dtype: float64
#计算每个舱位的平均年龄, fun未指定默认为求均值
passenger_age = titanic_survival.pivot_table(index="Pclass", values = "Age")
print(passenger_age)
Pclass
1 38.233441
2 29.877630
3 25.140620
Name: Age, dtype: float64
#计算每个码头中总的船票价格和登船人数
port_stats = titanic_survival.pivot_table(index="Embarked", values=["fare","Survived"], aggfunc=np.sum)
print(port_stats)
fare Survived
Embarked
C 10072.2962 93
Q 1022.2543 30
S 17439.3988 217
#丢弃缺失值 dropna
drop_na_columns = titanic_survival.dropna(axis=1)
new_titanic_survival = titanic_survival.dropna(axis=0, subset=["Age", "Sex"])
print(new_titanic_survival)
PassengerId Survived Pclass \
0 1 0 3
1 2 1 1
2 3 1 3
3 4 1 1
4 5 0 3
6 7 0 1
7 8 0 3
8 9 1 3
9 10 1 2
10 11 1 3
11 12 1 1
12 13 0 3
13 14 0 3
14 15 0 3
15 16 1 2
16 17 0 3
18 19 0 3
20 21 0 2
21 22 1 2
22 23 1 3
23 24 1 1
24 25 0 3
25 26 1 3
27 28 0 1
30 31 0 1
33 34 0 2
34 35 0 1
35 36 0 1
37 38 0 3
38 39 0 3
.. ... ... ...
856 857 1 1
857 858 1 1
858 859 1 3
860 861 0 3
861 862 0 2
862 863 1 1
864 865 0 2
865 866 1 2
866 867 1 2
867 868 0 1
869 870 1 3
870 871 0 3
871 872 1 1
872 873 0 1
873 874 0 3
874 875 1 2
875 876 1 3
876 877 0 3
877 878 0 3
879 880 1 1
880 881 1 2
881 882 0 3
882 883 0 3
883 884 0 2
884 885 0 3
885 886 0 3
886 887 0 2
887 888 1 1
889 890 1 1
890 891 0 3
Name Sex Age SibSp \
0 Braund, Mr. Owen Harris male 22.0 1
1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1
2 Heikkinen, Miss. Laina female 26.0 0
3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1
4 Allen, Mr. William Henry male 35.0 0
6 McCarthy, Mr. Timothy J male 54.0 0
7 Palsson, Master. Gosta Leonard male 2.0 3
8 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.0 0
9 Nasser, Mrs. Nicholas (Adele Achem) female 14.0 1
10 Sandstrom, Miss. Marguerite Rut female 4.0 1
11 Bonnell, Miss. Elizabeth female 58.0 0
12 Saundercock, Mr. William Henry male 20.0 0
13 AndeRSSon, Mr. Anders Johan male 39.0 1
14 Vestrom, Miss. Hulda Amanda Adolfina female 14.0 0
15 Hewlett, Mrs. (Mary D Kingcome) female 55.0 0
16 Rice, Master. Eugene male 2.0 4
18 Vander Planke, Mrs. Julius (Emelia Maria Vande... female 31.0 1
20 Fynney, Mr. Joseph J male 35.0 0
21 Beesley, Mr. LaWrence male 34.0 0
22 McGowan, Miss. Anna "Annie" female 15.0 0
23 Sloper, Mr. William Thompson male 28.0 0
24 Palsson, Miss. Torborg Danira female 8.0 3
25 Asplund, Mrs. Carl Oscar (Selma Augusta Emilia... female 38.0 1
27 Fortune, Mr. Charles Alexander male 19.0 3
30 Uruchurtu, Don. Manuel E male 40.0 0
33 Wheadon, Mr. Edward H male 66.0 0
34 Meyer, Mr. Edgar Joseph male 28.0 1
35 Holverson, Mr. Alexander Oskar male 42.0 1
37 Cann, Mr. Ernest Charles male 21.0 0
38 Vander Planke, Miss. Augusta Maria female 18.0 2
.. ... ... ... ...
856 Wick, Mrs. George Dennick (Mary Hitchcock) female 45.0 1
857 Daly, Mr. Peter Denis male 51.0 0
858 Baclini, Mrs. Solomon (Latifa Qurban) female 24.0 0
860 Hansen, Mr. Claus Peter male 41.0 2
861 Giles, Mr. Frederick Edward male 21.0 1
862 Swift, Mrs. Frederick Joel (Margaret Welles Ba... female 48.0 0
864 Gill, Mr. John William male 24.0 0
865 Bystrom, Mrs. (Karolina) female 42.0 0
866 Duran y More, Miss. Asuncion female 27.0 1
867 Roebling, Mr. Washington Augustus II male 31.0 0
869 Johnson, Master. Harold Theodor male 4.0 1
870 Balkic, Mr. Cerin male 26.0 0
871 Beckwith, Mrs. Richard Leonard (Sallie Monypeny) female 47.0 1
872 Carlsson, Mr. Frans Olof male 33.0 0
873 Vander Cruyssen, Mr. Victor male 47.0 0
874 Abelson, Mrs. Samuel (Hannah Wizosky) female 28.0 1
875 Najib, Miss. Adele Kiamie "Jane" female 15.0 0
876 Gustafsson, Mr. Alfred Ossian male 20.0 0
877 Petroff, Mr. Nedelio male 19.0 0
879 Potter, Mrs. Thomas Jr (Lily Alexenia Wilson) female 56.0 0
880 Shelley, Mrs. William (Imanita Parrish Hall) female 25.0 0
881 Markun, Mr. Johann male 33.0 0
882 Dahlberg, Miss. Gerda Ulrika female 22.0 0
883 Banfield, Mr. Frederick James male 28.0 0
884 Sutehall, Mr. Henry Jr male 25.0 0
885 Rice, Mrs. William (Margaret norton) female 39.0 0
886 Montvila, Rev. Juozas male 27.0 0
887 Graham, Miss. Margaret Edith female 19.0 0
889 Behr, Mr. Karl Howell male 26.0 0
890 Dooley, Mr. Patrick male 32.0 0
Parch Ticket fare Cabin Embarked
0 0 A/5 21171 7.2500 NaN S
1 0 PC 17599 71.2833 C85 C
2 0 STON/O2. 3101282 7.9250 NaN S
3 0 113803 53.1000 C123 S
4 0 373450 8.0500 NaN S
6 0 17463 51.8625 E46 S
7 1 349909 21.0750 NaN S
8 2 347742 11.1333 NaN S
9 0 237736 30.0708 NaN C
10 1 PP 9549 16.7000 G6 S
11 0 113783 26.5500 C103 S
12 0 A/5. 2151 8.0500 NaN S
13 5 347082 31.2750 NaN S
14 0 350406 7.8542 NaN S
15 0 248706 16.0000 NaN S
16 1 382652 29.1250 NaN Q
18 0 345763 18.0000 NaN S
20 0 239865 26.0000 NaN S
21 0 248698 13.0000 D56 S
22 0 330923 8.0292 NaN Q
23 0 113788 35.5000 A6 S
24 1 349909 21.0750 NaN S
25 5 347077 31.3875 NaN S
27 2 19950 263.0000 C23 C25 C27 S
30 0 PC 17601 27.7208 NaN C
33 0 C.A. 24579 10.5000 NaN S
34 0 PC 17604 82.1708 NaN C
35 0 113789 52.0000 NaN S
37 0 A./5. 2152 8.0500 NaN S
38 0 345764 18.0000 NaN S
.. ... ... ... ... ...
856 1 36928 164.8667 NaN S
857 0 113055 26.5500 E17 S
858 3 2666 19.2583 NaN C
860 0 350026 14.1083 NaN S
861 0 28134 11.5000 NaN S
862 0 17466 25.9292 D17 S
864 0 233866 13.0000 NaN S
865 0 236852 13.0000 NaN S
866 0 SC/PARIS 2149 13.8583 NaN C
867 0 PC 17590 50.4958 A24 S
869 1 347742 11.1333 NaN S
870 0 349248 7.8958 NaN S
871 1 11751 52.5542 D35 S
872 0 695 5.0000 B51 B53 B55 S
873 0 345765 9.0000 NaN S
874 0 P/PP 3381 24.0000 NaN C
875 0 2667 7.2250 NaN C
876 0 7534 9.8458 NaN S
877 0 349212 7.8958 NaN S
879 1 11767 83.1583 C50 C
880 1 230433 26.0000 NaN S
881 0 349257 7.8958 NaN S
882 0 7552 10.5167 NaN S
883 0 C.A./sotoN 34068 10.5000 NaN S
884 0 sotoN/OQ 392076 7.0500 NaN S
885 5 382652 29.1250 NaN Q
886 0 211536 13.0000 NaN S
887 0 112053 30.0000 B42 S
889 0 111369 30.0000 C148 C
890 0 370376 7.7500 NaN Q
[714 rows x 12 columns]
#定位到具体的值
row_index_83_age = titanic_survival.loc[83, "Age"]
row_index_1000_pclass = titanic_survival.loc[766, "Pclass"]
print(row_index_83_age, row_index_1000_pclass)
28.0 1
new_titanic_survival = titanic_survival.sort_values("Age", ascending=False)
print(new_titanic_survival[0:10])
#将index值重新排序reset_index, drop=true为原来的删除
titanic_reindexed = new_titanic_survival.reset_index(drop=True)
print(titanic_reindexed[0:10])
PassengerId Survived Pclass Name \
630 631 1 1 Barkworth, Mr. Algernon Henry Wilson
851 852 0 3 svensson, Mr. Johan
493 494 0 1 Artagaveytia, Mr. Ramon
96 97 0 1 Goldschmidt, Mr. George B
116 117 0 3 Connors, Mr. Patrick
672 673 0 2 Mitchell, Mr. Henry Michael
745 746 0 1 Crosby, Capt. Edward Gifford
33 34 0 2 Wheadon, Mr. Edward H
54 55 0 1 Ostby, Mr. Engelhart Cornelius
280 281 0 3 Duane, Mr. Frank
Sex Age SibSp Parch Ticket fare Cabin Embarked
630 male 80.0 0 0 27042 30.0000 A23 S
851 male 74.0 0 0 347060 7.7750 NaN S
493 male 71.0 0 0 PC 17609 49.5042 NaN C
96 male 71.0 0 0 PC 17754 34.6542 A5 C
116 male 70.5 0 0 370369 7.7500 NaN Q
672 male 70.0 0 0 C.A. 24580 10.5000 NaN S
745 male 70.0 1 1 WE/P 5735 71.0000 B22 S
33 male 66.0 0 0 C.A. 24579 10.5000 NaN S
54 male 65.0 0 1 113509 61.9792 B30 C
280 male 65.0 0 0 336439 7.7500 NaN Q
PassengerId Survived Pclass Name Sex \
0 631 1 1 Barkworth, Mr. Algernon Henry Wilson male
1 852 0 3 svensson, Mr. Johan male
2 494 0 1 Artagaveytia, Mr. Ramon male
3 97 0 1 Goldschmidt, Mr. George B male
4 117 0 3 Connors, Mr. Patrick male
5 673 0 2 Mitchell, Mr. Henry Michael male
6 746 0 1 Crosby, Capt. Edward Gifford male
7 34 0 2 Wheadon, Mr. Edward H male
8 55 0 1 Ostby, Mr. Engelhart Cornelius male
9 281 0 3 Duane, Mr. Frank male
Age SibSp Parch Ticket fare Cabin Embarked
0 80.0 0 0 27042 30.0000 A23 S
1 74.0 0 0 347060 7.7750 NaN S
2 71.0 0 0 PC 17609 49.5042 NaN C
3 71.0 0 0 PC 17754 34.6542 A5 C
4 70.5 0 0 370369 7.7500 NaN Q
5 70.0 0 0 C.A. 24580 10.5000 NaN S
6 70.0 1 1 WE/P 5735 71.0000 B22 S
7 66.0 0 0 C.A. 24579 10.5000 NaN S
8 65.0 0 1 113509 61.9792 B30 C
9 65.0 0 0 336439 7.7500 NaN Q
#apply函数，可以自定义函数，参数为函数
#返回第100行数据
def hundredth_row(column):
hundredth_item = column.loc[99]
return hundredth_item
hundredth_row = titanic_survival.apply(hundredth_row)
print(hundredth_row)
PassengerId 100
Survived 0
Pclass 2
Name Kantor, Mr. Sinai
Sex male
Age 34
SibSp 1
Parch 0
Ticket 244367
fare 26
Cabin NaN
Embarked S
dtype: object
#计算每一列缺失值个数
def is_null_count(column):
column_null = pd.isnull(column)
null = column[column_null]
return len(null)
column_null_count = titanic_survival.apply(is_null_count)
print(column_null_count)
PassengerId 0
Survived 0
Pclass 0
Name 0
Sex 0
Age 177
SibSp 0
Parch 0
Ticket 0
fare 0
Cabin 687
Embarked 2
dtype: int64
def which_class(row):
pclass = row['Pclass']
if pclass == 1:
"First Class"
elif pclass == 2:
return "Second Class"
elif pclass == 3:
return "Third Class"
else:
return "UnkNown"
classes = titanic_survival.apply(which_class, axis=1)
print(classes)
0 Third Class
1 None
2 Third Class
3 None
4 Third Class
5 Third Class
6 None
7 Third Class
8 Third Class
9 Second Class
10 Third Class
11 None
12 Third Class
13 Third Class
14 Third Class
15 Second Class
16 Third Class
17 Second Class
18 Third Class
19 Third Class
20 Second Class
21 Second Class
22 Third Class
23 None
24 Third Class
25 Third Class
26 Third Class
27 None
28 Third Class
29 Third Class
...
861 Second Class
862 None
863 Third Class
864 Second Class
865 Second Class
866 Second Class
867 None
868 Third Class
869 Third Class
870 Third Class
871 None
872 None
873 Third Class
874 Second Class
875 Third Class
876 Third Class
877 Third Class
878 Third Class
879 None
880 Second Class
881 Third Class
882 Third Class
883 Second Class
884 Third Class
885 Third Class
886 Second Class
887 None
888 Third Class
889 None
890 Third Class
dtype: object
def is_minor(row):
if row["Age"] < 18:
return True
else:
return False
minors = titanic_survival.apply(is_minor, axis=1)
print(minors)
def generate_age_label(row):
age = row["Age"]
if pd.isnull(age):
return "unkNown"
elif age<18:
return "minor"
else:
return "adult"
age_labels = titanic_survival.apply(generate_age_label, axis=1)
print(age_labels)
0 False
1 False
2 False
3 False
4 False
5 False
6 False
7 True
8 False
9 True
10 True
11 False
12 False
13 False
14 True
15 False
16 True
17 False
18 False
19 False
20 False
21 False
22 True
23 False
24 True
25 False
26 False
27 False
28 False
29 False
...
861 False
862 False
863 False
864 False
865 False
866 False
867 False
868 False
869 True
870 False
871 False
872 False
873 False
874 False
875 True
876 False
877 False
878 False
879 False
880 False
881 False
882 False
883 False
884 False
885 False
886 False
887 False
888 False
889 False
890 False
dtype: bool
0 adult
1 adult
2 adult
3 adult
4 adult
5 unkNown
6 adult
7 minor
8 adult
9 minor
10 minor
11 adult
12 adult
13 adult
14 minor
15 adult
16 minor
17 unkNown
18 adult
19 unkNown
20 adult
21 adult
22 minor
23 adult
24 minor
25 adult
26 unkNown
27 adult
28 unkNown
29 unkNown
...
861 adult
862 adult
863 unkNown
864 adult
865 adult
866 adult
867 adult
868 unkNown
869 minor
870 adult
871 adult
872 adult
873 adult
874 adult
875 minor
876 adult
877 adult
878 unkNown
879 adult
880 adult
881 adult
882 adult
883 adult
884 adult
885 adult
886 adult
887 adult
888 unkNown
889 adult
890 adult
dtype: object
titanic_survival['age_labels'] = age_labels
age_group_survival = titanic_survival.pivot_table(index="age_labels", values="Survived")
print(age_group_survival)
age_labels
adult 0.381032
minor 0.539823
unkNown 0.293785
Name: Survived, dtype: float64
#切分为series结构
import pandas as pd
fandango = pd.read_csv("fandango_score_comparison.csv")
series_film = fandango['FILM']
print(type(series_film))
print(series_film[0:5])
series_rt = fandango['RottenTomatoes']
print(series_rt[0:5])
<class 'pandas.core.series.Series'>
0 Avengers: Age of Ultron (2015)
1 Cinderella (2015)
2 Ant-Man (2015)
3 Do You Believe? (2015)
4 Hot Tub Time Machine 2 (2015)
Name: FILM, dtype: object
0 74
1 85
2 80
3 18
4 14
Name: RottenTomatoes, dtype: int64
#新建series结构
from pandas import Series
#pandas是封装在numpy结构基础之上的
film_names = series_film.values
# print(type(film_names), film_names)
rt_scores = series_rt.values
# print(rt_scores)
#用名字当成索引
series_custom = Series(rt_scores,index=film_names)
series_custom[['Minions (2015)', 'Leviathan (2014)', 'Avengers: Age of Ultron (2015)']]
Minions (2015) 54
Leviathan (2014) 99
Avengers: Age of Ultron (2015) 74
dtype: int64
#排序 按照首字母排序
original_index = series_custom.index.tolist()
# print(original_index)
sorted_index = sorted(original_index)
# print(sorted_index)
sorted_by_index = series_custom.reindex(sorted_index)
# print(sorted_by_index)
original_values = series_custom.values.tolist()
# print(original_index)
sorted_values = sorted(original_values)
# print(sorted_values)
sc3 = series_custom.sort_values()
print(sc3)
Paul Blart: Mall cop 2 (2015) 5
Hitman: Agent 47 (2015) 7
Hot Pursuit (2015) 8
Fantastic Four (2015) 9
Taken 3 (2015) 9
The Boy Next Door (2015) 10
The Loft (2015) 11
Unfinished Business (2015) 11
Mortdecai (2015) 12
Seventh Son (2015) 12
The Vatican Tapes (2015) 13
Sinister 2 (2015) 13
The Lazarus Effect (2015) 14
Hot Tub Time Machine 2 (2015) 14
The gallows (2015) 16
The Gunman (2015) 17
Pixels (2015) 17
Strange Magic (2015) 17
Do You Believe? (2015) 18
Serena (2015) 18
Aloha (2015) 19
Self/less (2015) 20
Little Boy (2015) 20
The Woman In Black 2 Angel of Death (2015) 22
Fifty Shades of Grey (2015) 25
Terminator Genisys (2015) 26
Child 44 (2015) 26
Dark Places (2015) 26
Jupiter Ascending (2015) 26
Annie (2014) 27
...
Ex Machina (2015) 92
Mission: Impossible – Rogue Nation (2015) 92
Spy (2015) 93
The Gift (2015) 93
The Wrecking Crew (2015) 93
Monkey Kingdom (2015) 94
I'll See You In My Dreams (2015) 94
Tangerine (2015) 95
The Diary of a Teenage Girl (2015) 95
Wild Tales (2014) 96
It Follows (2015) 96
Red Army (2015) 96
The Salt of the Earth (2015) 96
What We Do in the Shadows (2015) 96
'71 (2015) 97
About Elly (2015) 97
Two Days, One Night (2014) 97
Amy (2015) 97
Mad Max: Fury Road (2015) 97
Inside Out (2015) 98
Paddington (2015) 98
Mr. Turner (2014) 98
Timbuktu (2015) 99
Shaun the Sheep Movie (2015) 99
Leviathan (2014) 99
Song of the Sea (2014) 99
Phoenix (2015) 99
Selma (2014) 99
Seymour: An Introduction (2015) 100
Gett: The Trial of Viviane Amsalem (2015) 100
Length: 146, dtype: int64
#相加
import numpy as np
print(np.add(series_custom, series_custom))
np.sin(series_custom)
np.max(series_custom)
Avengers: Age of Ultron (2015) 148
Cinderella (2015) 170
Ant-Man (2015) 160
Do You Believe? (2015) 36
Hot Tub Time Machine 2 (2015) 28
The Water Diviner (2015) 126
Irrational Man (2015) 84
Top Five (2014) 172
Shaun the Sheep Movie (2015) 198
love & Mercy (2015) 178
Far From The Madding Crowd (2015) 168
Black Sea (2015) 164
Leviathan (2014) 198
Unbroken (2014) 102
The Imitation Game (2014) 180
Taken 3 (2015) 18
Ted 2 (2015) 92
Southpaw (2015) 118
Night at the Museum: Secret of the Tomb (2014) 100
Pixels (2015) 34
McFarland, USA (2015) 158
Insidious: Chapter 3 (2015) 118
The Man From U.N.C.L.E. (2015) 136
Run All Night (2015) 120
Trainwreck (2015) 170
Selma (2014) 198
Ex Machina (2015) 184
Still Alice (2015) 176
Wild Tales (2014) 192
The End of the Tour (2015) 184
...
Clouds of Sils Maria (2015) 178
Testament of Youth (2015) 162
Infinitely Polar Bear (2015) 160
Phoenix (2015) 198
The Wolfpack (2015) 168
The Stanford Prison Experiment (2015) 168
Tangerine (2015) 190
Magic Mike XXL (2015) 124
Home (2015) 90
The Wedding Ringer (2015) 54
Woman in Gold (2015) 104
The Last Five Years (2015) 120
Mission: Impossible – Rogue Nation (2015) 184
Amy (2015) 194
Jurassic World (2015) 142
Minions (2015) 108
Max (2015) 70
Paul Blart: Mall cop 2 (2015) 10
The Longest Ride (2015) 62
The Lazarus Effect (2015) 28
The Woman In Black 2 Angel of Death (2015) 44
Danny Collins (2015) 154
Spare Parts (2015) 104
Serena (2015) 36
Inside Out (2015) 196
Mr. Holmes (2015) 174
'71 (2015) 194
Two Days, One Night (2014) 194
Gett: The Trial of Viviane Amsalem (2015) 200
Kumiko, The Treasure Hunter (2015) 174
Length: 146, dtype: int64
100
import pandas as pd
fandango = pd.read_csv('fandango_score_comparison.csv')
print(type(fandango))
#将'FILM'当做索引
fandango_films = fandango.set_index('FILM', drop=False)
print(fandango_films.index)
<class 'pandas.core.frame.DataFrame'>
Index(['Avengers: Age of Ultron (2015)', 'Cinderella (2015)', 'Ant-Man (2015)',
'Do You Believe? (2015)', 'Hot Tub Time Machine 2 (2015)',
'The Water Diviner (2015)', 'Irrational Man (2015)', 'Top Five (2014)',
'Shaun the Sheep Movie (2015)', 'love & Mercy (2015)',
...
'The Woman In Black 2 Angel of Death (2015)', 'Danny Collins (2015)',
'Spare Parts (2015)', 'Serena (2015)', 'Inside Out (2015)',
'Mr. Holmes (2015)', ''71 (2015)', 'Two Days, One Night (2014)',
'Gett: The Trial of Viviane Amsalem (2015)',
'Kumiko, The Treasure Hunter (2015)'],
dtype='object', name='FILM', length=146)
fandango_films['Avengers: Age of Ultron (2015)':'Hot Tub Time Machine 2 (2015)']
fandango_films.loc['Avengers: Age of Ultron (2015)':'Hot Tub Time Machine 2 (2015)']
fandango_films.loc['Kumiko, The Treasure Hunter (2015)']
movies = ['Kumiko, The Treasure Hunter (2015)', 'Do You Believe? (2015)','Ant-Man (2015)']
fandango_films.loc[movies]
| FILM | RottenTomatoes | RottenTomatoes_User | Metacritic | Metacritic_User | IMDB | Fandango_Stars | Fandango_ratingvalue | RT_norm | RT_user_norm | ... | IMDB_norm | RT_norm_round | RT_user_norm_round | Metacritic_norm_round | Metacritic_user_norm_round | IMDB_norm_round | Metacritic_user_Vote_count | IMDB_user_Vote_count | Fandango_Votes | Fandango_Difference | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| FILM | |||||||||||||||||||||
| Kumiko, The Treasure Hunter (2015) | Kumiko, The Treasure Hunter (2015) | 87 | 63 | 68 | 6.4 | 6.7 | 3.5 | 3.5 | 4.35 | 3.15 | ... | 3.35 | 4.5 | 3.0 | 3.5 | 3.0 | 3.5 | 19 | 5289 | 41 | 0.0 |
| Do You Believe? (2015) | Do You Believe? (2015) | 18 | 84 | 22 | 4.7 | 5.4 | 5.0 | 4.5 | 0.90 | 4.20 | ... | 2.70 | 1.0 | 4.0 | 1.0 | 2.5 | 2.5 | 31 | 3136 | 1793 | 0.5 |
| Ant-Man (2015) | Ant-Man (2015) | 80 | 90 | 64 | 8.1 | 7.8 | 5.0 | 4.5 | 4.00 | 4.50 | ... | 3.90 | 4.0 | 4.5 | 3.0 | 4.0 | 4.0 | 627 | 103660 | 12055 | 0.5 |
3 rows × 22 columns
#类型转换
import numpy as np
types = fandango_films.dtypes
# print(types)
float_columns = types[types.values == 'float64'].index
float_df = fandango_films[float_columns]
print(float_df)
#lambda 快速定义单行函数 std()计算标准差
deviations = float_df.apply(lambda x:np.std(x))
Metacritic_User IMDB \
FILM
Avengers: Age of Ultron (2015) 7.1 7.8
Cinderella (2015) 7.5 7.1
Ant-Man (2015) 8.1 7.8
Do You Believe? (2015) 4.7 5.4
Hot Tub Time Machine 2 (2015) 3.4 5.1
The Water Diviner (2015) 6.8 7.2
Irrational Man (2015) 7.6 6.9
Top Five (2014) 6.8 6.5
Shaun the Sheep Movie (2015) 8.8 7.4
love & Mercy (2015) 8.5 7.8
Far From The Madding Crowd (2015) 7.5 7.2
Black Sea (2015) 6.6 6.4
Leviathan (2014) 7.2 7.7
Unbroken (2014) 6.5 7.2
The Imitation Game (2014) 8.2 8.1
Taken 3 (2015) 4.6 6.1
Ted 2 (2015) 6.5 6.6
Southpaw (2015) 8.2 7.8
Night at the Museum: Secret of the Tomb (2014) 5.8 6.3
Pixels (2015) 5.3 5.6
McFarland, USA (2015) 7.2 7.5
Insidious: Chapter 3 (2015) 6.9 6.3
The Man From U.N.C.L.E. (2015) 7.9 7.6
Run All Night (2015) 7.3 6.6
Trainwreck (2015) 6.0 6.7
Selma (2014) 7.1 7.5
Ex Machina (2015) 7.9 7.7
Still Alice (2015) 7.8 7.5
Wild Tales (2014) 8.8 8.2
The End of the Tour (2015) 7.5 7.9
... ... ...
Clouds of Sils Maria (2015) 7.1 6.8
Testament of Youth (2015) 7.9 7.3
Infinitely Polar Bear (2015) 7.9 7.2
Phoenix (2015) 8.0 7.2
The Wolfpack (2015) 7.0 7.1
The Stanford Prison Experiment (2015) 8.5 7.1
Tangerine (2015) 7.3 7.4
Magic Mike XXL (2015) 5.4 6.3
Home (2015) 7.3 6.7
The Wedding Ringer (2015) 3.3 6.7
Woman in Gold (2015) 7.2 7.4
The Last Five Years (2015) 6.9 6.0
Mission: Impossible – Rogue Nation (2015) 8.0 7.8
Amy (2015) 8.8 8.0
Jurassic World (2015) 7.0 7.3
Minions (2015) 5.7 6.7
Max (2015) 5.9 7.0
Paul Blart: Mall cop 2 (2015) 2.4 4.3
The Longest Ride (2015) 4.8 7.2
The Lazarus Effect (2015) 4.9 5.2
The Woman In Black 2 Angel of Death (2015) 4.4 4.9
Danny Collins (2015) 7.1 7.1
Spare Parts (2015) 7.1 7.2
Serena (2015) 5.3 5.4
Inside Out (2015) 8.9 8.6
Mr. Holmes (2015) 7.9 7.4
'71 (2015) 7.5 7.2
Two Days, One Night (2014) 8.8 7.4
Gett: The Trial of Viviane Amsalem (2015) 7.3 7.8
Kumiko, The Treasure Hunter (2015) 6.4 6.7
Fandango_Stars \
FILM
Avengers: Age of Ultron (2015) 5.0
Cinderella (2015) 5.0
Ant-Man (2015) 5.0
Do You Believe? (2015) 5.0
Hot Tub Time Machine 2 (2015) 3.5
The Water Diviner (2015) 4.5
Irrational Man (2015) 4.0
Top Five (2014) 4.0
Shaun the Sheep Movie (2015) 4.5
love & Mercy (2015) 4.5
Far From The Madding Crowd (2015) 4.5
Black Sea (2015) 4.0
Leviathan (2014) 4.0
Unbroken (2014) 4.5
The Imitation Game (2014) 5.0
Taken 3 (2015) 4.5
Ted 2 (2015) 4.5
Southpaw (2015) 5.0
Night at the Museum: Secret of the Tomb (2014) 4.5
Pixels (2015) 4.5
McFarland, USA (2015) 5.0
Insidious: Chapter 3 (2015) 4.5
The Man From U.N.C.L.E. (2015) 4.5
Run All Night (2015) 4.5
Trainwreck (2015) 4.5
Selma (2014) 5.0
Ex Machina (2015) 4.5
Still Alice (2015) 4.5
Wild Tales (2014) 4.5
The End of the Tour (2015) 4.5
... ...
Clouds of Sils Maria (2015) 3.5
Testament of Youth (2015) 4.0
Infinitely Polar Bear (2015) 4.0
Phoenix (2015) 3.5
The Wolfpack (2015) 3.5
The Stanford Prison Experiment (2015) 4.0
Tangerine (2015) 4.0
Magic Mike XXL (2015) 4.5
Home (2015) 4.5
The Wedding Ringer (2015) 4.5
Woman in Gold (2015) 4.5
The Last Five Years (2015) 4.5
Mission: Impossible – Rogue Nation (2015) 4.5
Amy (2015) 4.5
Jurassic World (2015) 4.5
Minions (2015) 4.0
Max (2015) 4.5
Paul Blart: Mall cop 2 (2015) 3.5
The Longest Ride (2015) 4.5
The Lazarus Effect (2015) 3.0
The Woman In Black 2 Angel of Death (2015) 3.0
Danny Collins (2015) 4.0
Spare Parts (2015) 4.5
Serena (2015) 3.0
Inside Out (2015) 4.5
Mr. Holmes (2015) 4.0
'71 (2015) 3.5
Two Days, One Night (2014) 3.5
Gett: The Trial of Viviane Amsalem (2015) 3.5
Kumiko, The Treasure Hunter (2015) 3.5
Fandango_ratingvalue RT_norm \
FILM
Avengers: Age of Ultron (2015) 4.5 3.70
Cinderella (2015) 4.5 4.25
Ant-Man (2015) 4.5 4.00
Do You Believe? (2015) 4.5 0.90
Hot Tub Time Machine 2 (2015) 3.0 0.70
The Water Diviner (2015) 4.0 3.15
Irrational Man (2015) 3.5 2.10
Top Five (2014) 3.5 4.30
Shaun the Sheep Movie (2015) 4.0 4.95
love & Mercy (2015) 4.0 4.45
Far From The Madding Crowd (2015) 4.0 4.20
Black Sea (2015) 3.5 4.10
Leviathan (2014) 3.5 4.95
Unbroken (2014) 4.1 2.55
The Imitation Game (2014) 4.6 4.50
Taken 3 (2015) 4.1 0.45
Ted 2 (2015) 4.1 2.30
Southpaw (2015) 4.6 2.95
Night at the Museum: Secret of the Tomb (2014) 4.1 2.50
Pixels (2015) 4.1 0.85
McFarland, USA (2015) 4.6 3.95
Insidious: Chapter 3 (2015) 4.1 2.95
The Man From U.N.C.L.E. (2015) 4.1 3.40
Run All Night (2015) 4.1 3.00
Trainwreck (2015) 4.1 4.25
Selma (2014) 4.6 4.95
Ex Machina (2015) 4.1 4.60
Still Alice (2015) 4.1 4.40
Wild Tales (2014) 4.1 4.80
The End of the Tour (2015) 4.1 4.60
... ... ...
Clouds of Sils Maria (2015) 3.4 4.45
Testament of Youth (2015) 3.9 4.05
Infinitely Polar Bear (2015) 3.9 4.00
Phoenix (2015) 3.4 4.95
The Wolfpack (2015) 3.4 4.20
The Stanford Prison Experiment (2015) 3.9 4.20
Tangerine (2015) 3.9 4.75
Magic Mike XXL (2015) 4.4 3.10
Home (2015) 4.4 2.25
The Wedding Ringer (2015) 4.4 1.35
Woman in Gold (2015) 4.4 2.60
The Last Five Years (2015) 4.4 3.00
Mission: Impossible – Rogue Nation (2015) 4.4 4.60
Amy (2015) 4.4 4.85
Jurassic World (2015) 4.5 3.55
Minions (2015) 4.0 2.70
Max (2015) 4.5 1.75
Paul Blart: Mall cop 2 (2015) 3.5 0.25
The Longest Ride (2015) 4.5 1.55
The Lazarus Effect (2015) 3.0 0.70
The Woman In Black 2 Angel of Death (2015) 3.0 1.10
Danny Collins (2015) 4.0 3.85
Spare Parts (2015) 4.5 2.60
Serena (2015) 3.0 0.90
Inside Out (2015) 4.5 4.90
Mr. Holmes (2015) 4.0 4.35
'71 (2015) 3.5 4.85
Two Days, One Night (2014) 3.5 4.85
Gett: The Trial of Viviane Amsalem (2015) 3.5 5.00
Kumiko, The Treasure Hunter (2015) 3.5 4.35
RT_user_norm Metacritic_norm \
FILM
Avengers: Age of Ultron (2015) 4.30 3.30
Cinderella (2015) 4.00 3.35
Ant-Man (2015) 4.50 3.20
Do You Believe? (2015) 4.20 1.10
Hot Tub Time Machine 2 (2015) 1.40 1.45
The Water Diviner (2015) 3.10 2.50
Irrational Man (2015) 2.65 2.65
Top Five (2014) 3.20 4.05
Shaun the Sheep Movie (2015) 4.10 4.05
love & Mercy (2015) 4.35 4.00
Far From The Madding Crowd (2015) 3.85 3.55
Black Sea (2015) 3.00 3.10
Leviathan (2014) 3.95 4.60
Unbroken (2014) 3.50 2.95
The Imitation Game (2014) 4.60 3.65
Taken 3 (2015) 2.30 1.30
Ted 2 (2015) 2.90 2.40
Southpaw (2015) 4.00 2.85
Night at the Museum: Secret of the Tomb (2014) 2.90 2.35
Pixels (2015) 2.70 1.35
McFarland, USA (2015) 4.45 3.00
Insidious: Chapter 3 (2015) 2.80 2.60
The Man From U.N.C.L.E. (2015) 4.00 2.75
Run All Night (2015) 2.95 2.95
Trainwreck (2015) 3.70 3.75
Selma (2014) 4.30 4.45
Ex Machina (2015) 4.30 3.90
Still Alice (2015) 4.25 3.60
Wild Tales (2014) 4.60 3.85
The End of the Tour (2015) 4.45 4.20
... ... ...
Clouds of Sils Maria (2015) 3.35 3.90
Testament of Youth (2015) 3.95 3.85
Infinitely Polar Bear (2015) 3.80 3.20
Phoenix (2015) 4.05 4.55
The Wolfpack (2015) 3.65 3.75
The Stanford Prison Experiment (2015) 4.35 3.40
Tangerine (2015) 4.30 4.30
Magic Mike XXL (2015) 3.20 3.00
Home (2015) 3.25 2.75
The Wedding Ringer (2015) 3.30 1.75
Woman in Gold (2015) 4.05 2.55
The Last Five Years (2015) 3.00 3.00
Mission: Impossible – Rogue Nation (2015) 4.50 3.75
Amy (2015) 4.55 4.25
Jurassic World (2015) 4.05 2.95
Minions (2015) 2.60 2.80
Max (2015) 3.65 2.35
Paul Blart: Mall cop 2 (2015) 1.80 0.65
The Longest Ride (2015) 3.65 1.65
The Lazarus Effect (2015) 1.15 1.55
The Woman In Black 2 Angel of Death (2015) 1.25 2.10
Danny Collins (2015) 3.75 2.90
Spare Parts (2015) 4.15 2.50
Serena (2015) 1.25 1.80
Inside Out (2015) 4.50 4.70
Mr. Holmes (2015) 3.90 3.35
'71 (2015) 4.10 4.15
Two Days, One Night (2014) 3.90 4.45
Gett: The Trial of Viviane Amsalem (2015) 4.05 4.50
Kumiko, The Treasure Hunter (2015) 3.15 3.40
Metacritic_user_nom \
FILM
Avengers: Age of Ultron (2015) 3.55
Cinderella (2015) 3.75
Ant-Man (2015) 4.05
Do You Believe? (2015) 2.35
Hot Tub Time Machine 2 (2015) 1.70
The Water Diviner (2015) 3.40
Irrational Man (2015) 3.80
Top Five (2014) 3.40
Shaun the Sheep Movie (2015) 4.40
love & Mercy (2015) 4.25
Far From The Madding Crowd (2015) 3.75
Black Sea (2015) 3.30
Leviathan (2014) 3.60
Unbroken (2014) 3.25
The Imitation Game (2014) 4.10
Taken 3 (2015) 2.30
Ted 2 (2015) 3.25
Southpaw (2015) 4.10
Night at the Museum: Secret of the Tomb (2014) 2.90
Pixels (2015) 2.65
McFarland, USA (2015) 3.60
Insidious: Chapter 3 (2015) 3.45
The Man From U.N.C.L.E. (2015) 3.95
Run All Night (2015) 3.65
Trainwreck (2015) 3.00
Selma (2014) 3.55
Ex Machina (2015) 3.95
Still Alice (2015) 3.90
Wild Tales (2014) 4.40
The End of the Tour (2015) 3.75
... ...
Clouds of Sils Maria (2015) 3.55
Testament of Youth (2015) 3.95
Infinitely Polar Bear (2015) 3.95
Phoenix (2015) 4.00
The Wolfpack (2015) 3.50
The Stanford Prison Experiment (2015) 4.25
Tangerine (2015) 3.65
Magic Mike XXL (2015) 2.70
Home (2015) 3.65
The Wedding Ringer (2015) 1.65
Woman in Gold (2015) 3.60
The Last Five Years (2015) 3.45
Mission: Impossible – Rogue Nation (2015) 4.00
Amy (2015) 4.40
Jurassic World (2015) 3.50
Minions (2015) 2.85
Max (2015) 2.95
Paul Blart: Mall cop 2 (2015) 1.20
The Longest Ride (2015) 2.40
The Lazarus Effect (2015) 2.45
The Woman In Black 2 Angel of Death (2015) 2.20
Danny Collins (2015) 3.55
Spare Parts (2015) 3.55
Serena (2015) 2.65
Inside Out (2015) 4.45
Mr. Holmes (2015) 3.95
'71 (2015) 3.75
Two Days, One Night (2014) 4.40
Gett: The Trial of Viviane Amsalem (2015) 3.65
Kumiko, The Treasure Hunter (2015) 3.20
IMDB_norm RT_norm_round \
FILM
Avengers: Age of Ultron (2015) 3.90 3.5
Cinderella (2015) 3.55 4.5
Ant-Man (2015) 3.90 4.0
Do You Believe? (2015) 2.70 1.0
Hot Tub Time Machine 2 (2015) 2.55 0.5
The Water Diviner (2015) 3.60 3.0
Irrational Man (2015) 3.45 2.0
Top Five (2014) 3.25 4.5
Shaun the Sheep Movie (2015) 3.70 5.0
love & Mercy (2015) 3.90 4.5
Far From The Madding Crowd (2015) 3.60 4.0
Black Sea (2015) 3.20 4.0
Leviathan (2014) 3.85 5.0
Unbroken (2014) 3.60 2.5
The Imitation Game (2014) 4.05 4.5
Taken 3 (2015) 3.05 0.5
Ted 2 (2015) 3.30 2.5
Southpaw (2015) 3.90 3.0
Night at the Museum: Secret of the Tomb (2014) 3.15 2.5
Pixels (2015) 2.80 1.0
McFarland, USA (2015) 3.75 4.0
Insidious: Chapter 3 (2015) 3.15 3.0
The Man From U.N.C.L.E. (2015) 3.80 3.5
Run All Night (2015) 3.30 3.0
Trainwreck (2015) 3.35 4.5
Selma (2014) 3.75 5.0
Ex Machina (2015) 3.85 4.5
Still Alice (2015) 3.75 4.5
Wild Tales (2014) 4.10 5.0
The End of the Tour (2015) 3.95 4.5
... ... ...
Clouds of Sils Maria (2015) 3.40 4.5
Testament of Youth (2015) 3.65 4.0
Infinitely Polar Bear (2015) 3.60 4.0
Phoenix (2015) 3.60 5.0
The Wolfpack (2015) 3.55 4.0
The Stanford Prison Experiment (2015) 3.55 4.0
Tangerine (2015) 3.70 5.0
Magic Mike XXL (2015) 3.15 3.0
Home (2015) 3.35 2.5
The Wedding Ringer (2015) 3.35 1.5
Woman in Gold (2015) 3.70 2.5
The Last Five Years (2015) 3.00 3.0
Mission: Impossible – Rogue Nation (2015) 3.90 4.5
Amy (2015) 4.00 5.0
Jurassic World (2015) 3.65 3.5
Minions (2015) 3.35 2.5
Max (2015) 3.50 2.0
Paul Blart: Mall cop 2 (2015) 2.15 0.5
The Longest Ride (2015) 3.60 1.5
The Lazarus Effect (2015) 2.60 0.5
The Woman In Black 2 Angel of Death (2015) 2.45 1.0
Danny Collins (2015) 3.55 4.0
Spare Parts (2015) 3.60 2.5
Serena (2015) 2.70 1.0
Inside Out (2015) 4.30 5.0
Mr. Holmes (2015) 3.70 4.5
'71 (2015) 3.60 5.0
Two Days, One Night (2014) 3.70 5.0
Gett: The Trial of Viviane Amsalem (2015) 3.90 5.0
Kumiko, The Treasure Hunter (2015) 3.35 4.5
RT_user_norm_round \
FILM
Avengers: Age of Ultron (2015) 4.5
Cinderella (2015) 4.0
Ant-Man (2015) 4.5
Do You Believe? (2015) 4.0
Hot Tub Time Machine 2 (2015) 1.5
The Water Diviner (2015) 3.0
Irrational Man (2015) 2.5
Top Five (2014) 3.0
Shaun the Sheep Movie (2015) 4.0
love & Mercy (2015) 4.5
Far From The Madding Crowd (2015) 4.0
Black Sea (2015) 3.0
Leviathan (2014) 4.0
Unbroken (2014) 3.5
The Imitation Game (2014) 4.5
Taken 3 (2015) 2.5
Ted 2 (2015) 3.0
Southpaw (2015) 4.0
Night at the Museum: Secret of the Tomb (2014) 3.0
Pixels (2015) 2.5
McFarland, USA (2015) 4.5
Insidious: Chapter 3 (2015) 3.0
The Man From U.N.C.L.E. (2015) 4.0
Run All Night (2015) 3.0
Trainwreck (2015) 3.5
Selma (2014) 4.5
Ex Machina (2015) 4.5
Still Alice (2015) 4.5
Wild Tales (2014) 4.5
The End of the Tour (2015) 4.5
... ...
Clouds of Sils Maria (2015) 3.5
Testament of Youth (2015) 4.0
Infinitely Polar Bear (2015) 4.0
Phoenix (2015) 4.0
The Wolfpack (2015) 3.5
The Stanford Prison Experiment (2015) 4.5
Tangerine (2015) 4.5
Magic Mike XXL (2015) 3.0
Home (2015) 3.5
The Wedding Ringer (2015) 3.5
Woman in Gold (2015) 4.0
The Last Five Years (2015) 3.0
Mission: Impossible – Rogue Nation (2015) 4.5
Amy (2015) 4.5
Jurassic World (2015) 4.0
Minions (2015) 2.5
Max (2015) 3.5
Paul Blart: Mall cop 2 (2015) 2.0
The Longest Ride (2015) 3.5
The Lazarus Effect (2015) 1.0
The Woman In Black 2 Angel of Death (2015) 1.5
Danny Collins (2015) 4.0
Spare Parts (2015) 4.0
Serena (2015) 1.5
Inside Out (2015) 4.5
Mr. Holmes (2015) 4.0
'71 (2015) 4.0
Two Days, One Night (2014) 4.0
Gett: The Trial of Viviane Amsalem (2015) 4.0
Kumiko, The Treasure Hunter (2015) 3.0
Metacritic_norm_round \
FILM
Avengers: Age of Ultron (2015) 3.5
Cinderella (2015) 3.5
Ant-Man (2015) 3.0
Do You Believe? (2015) 1.0
Hot Tub Time Machine 2 (2015) 1.5
The Water Diviner (2015) 2.5
Irrational Man (2015) 2.5
Top Five (2014) 4.0
Shaun the Sheep Movie (2015) 4.0
love & Mercy (2015) 4.0
Far From The Madding Crowd (2015) 3.5
Black Sea (2015) 3.0
Leviathan (2014) 4.5
Unbroken (2014) 3.0
The Imitation Game (2014) 3.5
Taken 3 (2015) 1.5
Ted 2 (2015) 2.5
Southpaw (2015) 3.0
Night at the Museum: Secret of the Tomb (2014) 2.5
Pixels (2015) 1.5
McFarland, USA (2015) 3.0
Insidious: Chapter 3 (2015) 2.5
The Man From U.N.C.L.E. (2015) 3.0
Run All Night (2015) 3.0
Trainwreck (2015) 4.0
Selma (2014) 4.5
Ex Machina (2015) 4.0
Still Alice (2015) 3.5
Wild Tales (2014) 4.0
The End of the Tour (2015) 4.0
... ...
Clouds of Sils Maria (2015) 4.0
Testament of Youth (2015) 4.0
Infinitely Polar Bear (2015) 3.0
Phoenix (2015) 4.5
The Wolfpack (2015) 4.0
The Stanford Prison Experiment (2015) 3.5
Tangerine (2015) 4.5
Magic Mike XXL (2015) 3.0
Home (2015) 3.0
The Wedding Ringer (2015) 2.0
Woman in Gold (2015) 2.5
The Last Five Years (2015) 3.0
Mission: Impossible – Rogue Nation (2015) 4.0
Amy (2015) 4.5
Jurassic World (2015) 3.0
Minions (2015) 3.0
Max (2015) 2.5
Paul Blart: Mall cop 2 (2015) 0.5
The Longest Ride (2015) 1.5
The Lazarus Effect (2015) 1.5
The Woman In Black 2 Angel of Death (2015) 2.0
Danny Collins (2015) 3.0
Spare Parts (2015) 2.5
Serena (2015) 2.0
Inside Out (2015) 4.5
Mr. Holmes (2015) 3.5
'71 (2015) 4.0
Two Days, One Night (2014) 4.5
Gett: The Trial of Viviane Amsalem (2015) 4.5
Kumiko, The Treasure Hunter (2015) 3.5
Metacritic_user_norm_round \
FILM
Avengers: Age of Ultron (2015) 3.5
Cinderella (2015) 4.0
Ant-Man (2015) 4.0
Do You Believe? (2015) 2.5
Hot Tub Time Machine 2 (2015) 1.5
The Water Diviner (2015) 3.5
Irrational Man (2015) 4.0
Top Five (2014) 3.5
Shaun the Sheep Movie (2015) 4.5
love & Mercy (2015) 4.5
Far From The Madding Crowd (2015) 4.0
Black Sea (2015) 3.5
Leviathan (2014) 3.5
Unbroken (2014) 3.5
The Imitation Game (2014) 4.0
Taken 3 (2015) 2.5
Ted 2 (2015) 3.5
Southpaw (2015) 4.0
Night at the Museum: Secret of the Tomb (2014) 3.0
Pixels (2015) 2.5
McFarland, USA (2015) 3.5
Insidious: Chapter 3 (2015) 3.5
The Man From U.N.C.L.E. (2015) 4.0
Run All Night (2015) 3.5
Trainwreck (2015) 3.0
Selma (2014) 3.5
Ex Machina (2015) 4.0
Still Alice (2015) 4.0
Wild Tales (2014) 4.5
The End of the Tour (2015) 4.0
... ...
Clouds of Sils Maria (2015) 3.5
Testament of Youth (2015) 4.0
Infinitely Polar Bear (2015) 4.0
Phoenix (2015) 4.0
The Wolfpack (2015) 3.5
The Stanford Prison Experiment (2015) 4.5
Tangerine (2015) 3.5
Magic Mike XXL (2015) 2.5
Home (2015) 3.5
The Wedding Ringer (2015) 1.5
Woman in Gold (2015) 3.5
The Last Five Years (2015) 3.5
Mission: Impossible – Rogue Nation (2015) 4.0
Amy (2015) 4.5
Jurassic World (2015) 3.5
Minions (2015) 3.0
Max (2015) 3.0
Paul Blart: Mall cop 2 (2015) 1.0
The Longest Ride (2015) 2.5
The Lazarus Effect (2015) 2.5
The Woman In Black 2 Angel of Death (2015) 2.0
Danny Collins (2015) 3.5
Spare Parts (2015) 3.5
Serena (2015) 2.5
Inside Out (2015) 4.5
Mr. Holmes (2015) 4.0
'71 (2015) 4.0
Two Days, One Night (2014) 4.5
Gett: The Trial of Viviane Amsalem (2015) 3.5
Kumiko, The Treasure Hunter (2015) 3.0
IMDB_norm_round \
FILM
Avengers: Age of Ultron (2015) 4.0
Cinderella (2015) 3.5
Ant-Man (2015) 4.0
Do You Believe? (2015) 2.5
Hot Tub Time Machine 2 (2015) 2.5
The Water Diviner (2015) 3.5
Irrational Man (2015) 3.5
Top Five (2014) 3.5
Shaun the Sheep Movie (2015) 3.5
love & Mercy (2015) 4.0
Far From The Madding Crowd (2015) 3.5
Black Sea (2015) 3.0
Leviathan (2014) 4.0
Unbroken (2014) 3.5
The Imitation Game (2014) 4.0
Taken 3 (2015) 3.0
Ted 2 (2015) 3.5
Southpaw (2015) 4.0
Night at the Museum: Secret of the Tomb (2014) 3.0
Pixels (2015) 3.0
McFarland, USA (2015) 4.0
Insidious: Chapter 3 (2015) 3.0
The Man From U.N.C.L.E. (2015) 4.0
Run All Night (2015) 3.5
Trainwreck (2015) 3.5
Selma (2014) 4.0
Ex Machina (2015) 4.0
Still Alice (2015) 4.0
Wild Tales (2014) 4.0
The End of the Tour (2015) 4.0
... ...
Clouds of Sils Maria (2015) 3.5
Testament of Youth (2015) 3.5
Infinitely Polar Bear (2015) 3.5
Phoenix (2015) 3.5
The Wolfpack (2015) 3.5
The Stanford Prison Experiment (2015) 3.5
Tangerine (2015) 3.5
Magic Mike XXL (2015) 3.0
Home (2015) 3.5
The Wedding Ringer (2015) 3.5
Woman in Gold (2015) 3.5
The Last Five Years (2015) 3.0
Mission: Impossible – Rogue Nation (2015) 4.0
Amy (2015) 4.0
Jurassic World (2015) 3.5
Minions (2015) 3.5
Max (2015) 3.5
Paul Blart: Mall cop 2 (2015) 2.0
The Longest Ride (2015) 3.5
The Lazarus Effect (2015) 2.5
The Woman In Black 2 Angel of Death (2015) 2.5
Danny Collins (2015) 3.5
Spare Parts (2015) 3.5
Serena (2015) 2.5
Inside Out (2015) 4.5
Mr. Holmes (2015) 3.5
'71 (2015) 3.5
Two Days, One Night (2014) 3.5
Gett: The Trial of Viviane Amsalem (2015) 4.0
Kumiko, The Treasure Hunter (2015) 3.5
Fandango_Difference
FILM
Avengers: Age of Ultron (2015) 0.5
Cinderella (2015) 0.5
Ant-Man (2015) 0.5
Do You Believe? (2015) 0.5
Hot Tub Time Machine 2 (2015) 0.5
The Water Diviner (2015) 0.5
Irrational Man (2015) 0.5
Top Five (2014) 0.5
Shaun the Sheep Movie (2015) 0.5
love & Mercy (2015) 0.5
Far From The Madding Crowd (2015) 0.5
Black Sea (2015) 0.5
Leviathan (2014) 0.5
Unbroken (2014) 0.4
The Imitation Game (2014) 0.4
Taken 3 (2015) 0.4
Ted 2 (2015) 0.4
Southpaw (2015) 0.4
Night at the Museum: Secret of the Tomb (2014) 0.4
Pixels (2015) 0.4
McFarland, USA (2015) 0.4
Insidious: Chapter 3 (2015) 0.4
The Man From U.N.C.L.E. (2015) 0.4
Run All Night (2015) 0.4
Trainwreck (2015) 0.4
Selma (2014) 0.4
Ex Machina (2015) 0.4
Still Alice (2015) 0.4
Wild Tales (2014) 0.4
The End of the Tour (2015) 0.4
... ...
Clouds of Sils Maria (2015) 0.1
Testament of Youth (2015) 0.1
Infinitely Polar Bear (2015) 0.1
Phoenix (2015) 0.1
The Wolfpack (2015) 0.1
The Stanford Prison Experiment (2015) 0.1
Tangerine (2015) 0.1
Magic Mike XXL (2015) 0.1
Home (2015) 0.1
The Wedding Ringer (2015) 0.1
Woman in Gold (2015) 0.1
The Last Five Years (2015) 0.1
Mission: Impossible – Rogue Nation (2015) 0.1
Amy (2015) 0.1
Jurassic World (2015) 0.0
Minions (2015) 0.0
Max (2015) 0.0
Paul Blart: Mall cop 2 (2015) 0.0
The Longest Ride (2015) 0.0
The Lazarus Effect (2015) 0.0
The Woman In Black 2 Angel of Death (2015) 0.0
Danny Collins (2015) 0.0
Spare Parts (2015) 0.0
Serena (2015) 0.0
Inside Out (2015) 0.0
Mr. Holmes (2015) 0.0
'71 (2015) 0.0
Two Days, One Night (2014) 0.0
Gett: The Trial of Viviane Amsalem (2015) 0.0
Kumiko, The Treasure Hunter (2015) 0.0
[146 rows x 15 columns]
rt_mt_user = float_df[['RT_user_norm', 'Metacritic_user_nom']]
rt_mt_user.apply(lambda x: np.std(x), axis=1)
print(rt_mt_user)
RT_user_norm \
FILM
Avengers: Age of Ultron (2015) 4.30
Cinderella (2015) 4.00
Ant-Man (2015) 4.50
Do You Believe? (2015) 4.20
Hot Tub Time Machine 2 (2015) 1.40
The Water Diviner (2015) 3.10
Irrational Man (2015) 2.65
Top Five (2014) 3.20
Shaun the Sheep Movie (2015) 4.10
love & Mercy (2015) 4.35
Far From The Madding Crowd (2015) 3.85
Black Sea (2015) 3.00
Leviathan (2014) 3.95
Unbroken (2014) 3.50
The Imitation Game (2014) 4.60
Taken 3 (2015) 2.30
Ted 2 (2015) 2.90
Southpaw (2015) 4.00
Night at the Museum: Secret of the Tomb (2014) 2.90
Pixels (2015) 2.70
McFarland, USA (2015) 4.45
Insidious: Chapter 3 (2015) 2.80
The Man From U.N.C.L.E. (2015) 4.00
Run All Night (2015) 2.95
Trainwreck (2015) 3.70
Selma (2014) 4.30
Ex Machina (2015) 4.30
Still Alice (2015) 4.25
Wild Tales (2014) 4.60
The End of the Tour (2015) 4.45
... ...
Clouds of Sils Maria (2015) 3.35
Testament of Youth (2015) 3.95
Infinitely Polar Bear (2015) 3.80
Phoenix (2015) 4.05
The Wolfpack (2015) 3.65
The Stanford Prison Experiment (2015) 4.35
Tangerine (2015) 4.30
Magic Mike XXL (2015) 3.20
Home (2015) 3.25
The Wedding Ringer (2015) 3.30
Woman in Gold (2015) 4.05
The Last Five Years (2015) 3.00
Mission: Impossible – Rogue Nation (2015) 4.50
Amy (2015) 4.55
Jurassic World (2015) 4.05
Minions (2015) 2.60
Max (2015) 3.65
Paul Blart: Mall cop 2 (2015) 1.80
The Longest Ride (2015) 3.65
The Lazarus Effect (2015) 1.15
The Woman In Black 2 Angel of Death (2015) 1.25
Danny Collins (2015) 3.75
Spare Parts (2015) 4.15
Serena (2015) 1.25
Inside Out (2015) 4.50
Mr. Holmes (2015) 3.90
'71 (2015) 4.10
Two Days, One Night (2014) 3.90
Gett: The Trial of Viviane Amsalem (2015) 4.05
Kumiko, The Treasure Hunter (2015) 3.15
Metacritic_user_nom
FILM
Avengers: Age of Ultron (2015) 3.55
Cinderella (2015) 3.75
Ant-Man (2015) 4.05
Do You Believe? (2015) 2.35
Hot Tub Time Machine 2 (2015) 1.70
The Water Diviner (2015) 3.40
Irrational Man (2015) 3.80
Top Five (2014) 3.40
Shaun the Sheep Movie (2015) 4.40
love & Mercy (2015) 4.25
Far From The Madding Crowd (2015) 3.75
Black Sea (2015) 3.30
Leviathan (2014) 3.60
Unbroken (2014) 3.25
The Imitation Game (2014) 4.10
Taken 3 (2015) 2.30
Ted 2 (2015) 3.25
Southpaw (2015) 4.10
Night at the Museum: Secret of the Tomb (2014) 2.90
Pixels (2015) 2.65
McFarland, USA (2015) 3.60
Insidious: Chapter 3 (2015) 3.45
The Man From U.N.C.L.E. (2015) 3.95
Run All Night (2015) 3.65
Trainwreck (2015) 3.00
Selma (2014) 3.55
Ex Machina (2015) 3.95
Still Alice (2015) 3.90
Wild Tales (2014) 4.40
The End of the Tour (2015) 3.75
... ...
Clouds of Sils Maria (2015) 3.55
Testament of Youth (2015) 3.95
Infinitely Polar Bear (2015) 3.95
Phoenix (2015) 4.00
The Wolfpack (2015) 3.50
The Stanford Prison Experiment (2015) 4.25
Tangerine (2015) 3.65
Magic Mike XXL (2015) 2.70
Home (2015) 3.65
The Wedding Ringer (2015) 1.65
Woman in Gold (2015) 3.60
The Last Five Years (2015) 3.45
Mission: Impossible – Rogue Nation (2015) 4.00
Amy (2015) 4.40
Jurassic World (2015) 3.50
Minions (2015) 2.85
Max (2015) 2.95
Paul Blart: Mall cop 2 (2015) 1.20
The Longest Ride (2015) 2.40
The Lazarus Effect (2015) 2.45
The Woman In Black 2 Angel of Death (2015) 2.20
Danny Collins (2015) 3.55
Spare Parts (2015) 3.55
Serena (2015) 2.65
Inside Out (2015) 4.45
Mr. Holmes (2015) 3.95
'71 (2015) 3.75
Two Days, One Night (2014) 4.40
Gett: The Trial of Viviane Amsalem (2015) 3.65
Kumiko, The Treasure Hunter (2015) 3.20
[146 rows x 2 columns]
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