Python机器学习&数据分析-关联规则

机器学习课程的笔记整理

一、关联规则前置知识

关联规则

  • 在美国，一些年轻的父亲下班后经常要到超市去买婴儿尿布，超市也因此发现了一个规律，在购买婴儿尿布的年轻父亲们中，有30%～40%的人同时要买一些啤酒。超市随后调整了货架的摆放，把尿布和啤酒放在一起，明显增加了销售额。

python关联规则 python关联规则电影数据集_关联规则

  • 若两个或多个变量的取值之间存在某种规律性，就称为关联
  • 关联规则是寻找在同一个事件中出现的不同项的相关性，比如在一次购买活动中所买不同商品的相关性。
  • “在购买计算机的顾客中，有30％的人也同时购买了打印机”

支持度（support）：一个项集或者规则在所有事务中出现的频率，σ(X):表示项集X的支持度计数

  • 项集X的支持度：s(X)=σ(X)/N
  • 规则X==>Y表示物品集X对物品集Y的支持度，也就是物品集X和物品集Y同时出现的概率
  • 某天共有100个顾客到商场购买物品，其中有30个顾客同时购买了啤酒和尿布，那么上述的关联规则的支持度就是30％

置信度（confidence）：确定Y在包含X的事务中出现的频繁程度。c(X → Y) = σ(X∪Y)/σ(X)

  • p（Y│X）＝p（XY）/p(X)。
  • 置信度反应了关联规则的可信度—购买了项目集X中的商品的顾客同时也购买了Y中商品的可能性有多大
  • 购买薯片的顾客中有50％的人购买了可乐,则置信度为50％

提升度（lift）：物品集A的出现对物品集B的出现概率发生了多大的变化

  • lift（A==>B）=confidence（A==>B）/support(B)=p(B|A)/p(B)
  • 现在有** 1000 ** 个消费者，有** 500** 人购买了茶叶，其中有** 450人同时** 购买了咖啡，另** 50人** 没有。由于** confidence(茶叶=>咖啡)=450/500=90%** ，由此可能会认为喜欢喝茶的人往往喜欢喝咖啡。但如果另外没有购买茶叶的** 500人** ，其中同样有** 450人** 购买了咖啡，同样是很高的** 置信度90%** ,由此，得到不爱喝茶的也爱喝咖啡。这样看来，其实是否购买咖啡，与有没有购买茶叶并没有关联，两者是相互独立的，其** 提升度90%/[(450+450)/1000]=1** 。

由此可见，lift正是弥补了confidence的这一缺陷，if lift=1,X与Y独立，X对Y出现的可能性没有提升作用，其值越大(lift>1),则表明X对Y的提升程度越大，也表明关联性越强。

二、自定义购物数据集的例子

在anaconda命令行下通过

conda install -c conda-forge mlxtend

import pandas as pd
from mlxtend.frequent_patterns import apriori
from mlxtend.frequent_patterns import association_rules

自定义一份购物数据集

data = {"ID":[1,2,3,4,5,6],
       "Onion":[1,0,0,1,1,1],
       "Potato":[1,1,0,1,1,1],
       "Burger":[1,1,0,0,1,1],
       "Milk":[0,1,1,1,0,1],
       "Beer":[0,0,1,0,1,0]}
df = pd.DataFrame(data)
df = df[['ID', 'Onion', 'Potato', 'Burger', 'Milk', 'Beer' ]]
df



ID

Onion

Potato

Burger

Milk

Beer

0

1

1

1

1

0

0

1

2

0

1

1

1

0

2

3

0

0

0

1

1

3

4

1

1

0

1

0

4

5

1

1

1

0

1

5

6

1

1

1

1

0

设置支持度 (support) 来选择频繁项集.

  • 选择最小支持度为50%
  • apriori(df, min_support=0.5, use_colnames=True)
frequent_itemsets = apriori(df[['Onion', 'Potato', 'Burger', 'Milk', 'Beer' ]],min_support=0.5, use_colnames=True)
frequent_itemsets



support

itemsets

0

0.666667

(Onion)

1

0.833333

(Potato)

2

0.666667

(Burger)

3

0.666667

(Milk)

4

0.666667

(Onion, Potato)

5

0.500000

(Onion, Burger)

6

0.666667

(Potato, Burger)

7

0.500000

(Milk, Potato)

8

0.500000

(Onion, Potato, Burger)

计算规则

  • association_rules(df, metric='lift', min_threshold=1)
  • 可以指定不同的衡量标准与最小阈值
rules = association_rules(frequent_itemsets,metric="lift",min_threshold=1)
rules



antecedents

consequents

antecedent support

consequent support

support

confidence

lift

leverage

conviction

0

(Onion)

(Potato)

0.666667

0.833333

0.666667

1.00

1.200

0.111111

inf

1

(Potato)

(Onion)

0.833333

0.666667

0.666667

0.80

1.200

0.111111

1.666667

2

(Onion)

(Burger)

0.666667

0.666667

0.500000

0.75

1.125

0.055556

1.333333

3

(Burger)

(Onion)

0.666667

0.666667

0.500000

0.75

1.125

0.055556

1.333333

4

(Potato)

(Burger)

0.833333

0.666667

0.666667

0.80

1.200

0.111111

1.666667

5

(Burger)

(Potato)

0.666667

0.833333

0.666667

1.00

1.200

0.111111

inf

6

(Onion, Potato)

(Burger)

0.666667

0.666667

0.500000

0.75

1.125

0.055556

1.333333

7

(Onion, Burger)

(Potato)

0.500000

0.833333

0.500000

1.00

1.200

0.083333

inf

8

(Potato, Burger)

(Onion)

0.666667

0.666667

0.500000

0.75

1.125

0.055556

1.333333

9

(Onion)

(Potato, Burger)

0.666667

0.666667

0.500000

0.75

1.125

0.055556

1.333333

10

(Potato)

(Onion, Burger)

0.833333

0.500000

0.500000

0.60

1.200

0.083333

1.250000

11

(Burger)

(Onion, Potato)

0.666667

0.666667

0.500000

0.75

1.125

0.055556

1.333333

rules[ ( rules["lift"] > 1.125) & (rules["confidence"] > 0.8) ]



antecedents

consequents

antecedent support

consequent support

support

confidence

lift

leverage

conviction

0

(Onion)

(Potato)

0.666667

0.833333

0.666667

1.0

1.2

0.111111

inf

5

(Burger)

(Potato)

0.666667

0.833333

0.666667

1.0

1.2

0.111111

inf

7

(Onion, Burger)

(Potato)

0.500000

0.833333

0.500000

1.0

1.2

0.083333

inf

这几条结果就比较有价值了：

  • （洋葱和马铃薯）（汉堡和马铃薯）可以搭配着来卖
  • 如果洋葱和汉堡都在购物篮中, 顾客买马铃薯的可能性也比较高，如果他篮子里面没有，可以推荐一下.

三、模拟实际购物的例子

retail_shopping_basket = {'ID':[1,2,3,4,5,6],
                         'Basket':[['Beer', 'Diaper', 'Pretzels', 'Chips', 'Aspirin'],
                                   ['Diaper', 'Beer', 'Chips', 'Lotion', 'Juice', 'BabyFood', 'Milk'],
                                   ['Soda', 'Chips', 'Milk'],
                                   ['Soup', 'Beer', 'Diaper', 'Milk', 'IceCream'],
                                   ['Soda', 'Coffee', 'Milk', 'Bread'],
                                   ['Beer', 'Chips']
                                  ]
                         }
retail = pd.DataFrame(retail_shopping_basket)
retail = retail[["ID","Basket"]]
pd.options.display.max_colwidth=100
retail



ID

Basket

0

1

[Beer, Diaper, Pretzels, Chips, Aspirin]

1

2

[Diaper, Beer, Chips, Lotion, Juice, BabyFood, Milk]

2

3

[Soda, Chips, Milk]

3

4

[Soup, Beer, Diaper, Milk, IceCream]

4

5

[Soda, Coffee, Milk, Bread]

5

6

[Beer, Chips]

注意:

数据集中都是字符串组成的，需要转换成数值编码

retail_id = retail.drop("Basket",1)
retail_id



ID

0

1

1

2

2

3

3

4

4

5

5

6

retail_Basket = retail.Basket.str.join(",")
retail_Basket
0              Beer,Diaper,Pretzels,Chips,Aspirin
1    Diaper,Beer,Chips,Lotion,Juice,BabyFood,Milk
2                                 Soda,Chips,Milk
3                  Soup,Beer,Diaper,Milk,IceCream
4                          Soda,Coffee,Milk,Bread
5                                      Beer,Chips
Name: Basket, dtype: object
retail_Basket = retail_Basket.str.get_dummies(",")
retail_Basket



Aspirin

BabyFood

Beer

Bread

Chips

Coffee

Diaper

IceCream

Juice

Lotion

Milk

Pretzels

Soda

Soup

0

1

0

1

0

1

0

1

0

0

0

0

1

0

0

1

0

1

1

0

1

0

1

0

1

1

1

0

0

0

2

0

0

0

0

1

0

0

0

0

0

1

0

1

0

3

0

0

1

0

0

0

1

1

0

0

1

0

0

1

4

0

0

0

1

0

1

0

0

0

0

1

0

1

0

5

0

0

1

0

1

0

0

0

0

0

0

0

0

0

retail = retail_id.join(retail_Basket)
retail



ID

Aspirin

BabyFood

Beer

Bread

Chips

Coffee

Diaper

IceCream

Juice

Lotion

Milk

Pretzels

Soda

Soup

0

1

1

0

1

0

1

0

1

0

0

0

0

1

0

0

1

2

0

1

1

0

1

0

1

0

1

1

1

0

0

0

2

3

0

0

0

0

1

0

0

0

0

0

1

0

1

0

3

4

0

0

1

0

0

0

1

1

0

0

1

0

0

1

4

5

0

0

0

1

0

1

0

0

0

0

1

0

1

0

5

6

0

0

1

0

1

0

0

0

0

0

0

0

0

0

frequent_items_2 = apriori(retail.drop("ID",1),use_colnames=True)
frequent_items_2



support

itemsets

0

0.666667

(Beer)

1

0.666667

(Chips)

2

0.500000

(Diaper)

3

0.666667

(Milk)

4

0.500000

(Chips, Beer)

5

0.500000

(Diaper, Beer)

如果光考虑支持度support(X>Y), [Beer, Chips] 和 [Beer, Diaper] 都是很频繁的，哪一种组合更相关呢？

association_rules(frequent_items_2,metric="lift")



antecedents

consequents

antecedent support

consequent support

support

confidence

lift

leverage

conviction

0

(Chips)

(Beer)

0.666667

0.666667

0.5

0.75

1.125

0.055556

1.333333

1

(Beer)

(Chips)

0.666667

0.666667

0.5

0.75

1.125

0.055556

1.333333

2

(Diaper)

(Beer)

0.500000

0.666667

0.5

1.00

1.500

0.166667

inf

3

(Beer)

(Diaper)

0.666667

0.500000

0.5

0.75

1.500

0.166667

2.000000

显然{Diaper, Beer}更相关一些

四、电影题材关联的例子

数据集来源： MovieLens (small)

movies = pd.read_csv("ml-latest-small/movies.csv")
movies.head(10)



movieId

title

genres

0

1

Toy Story (1995)

Adventure|Animation|Children|Comedy|Fantasy

1

2

Jumanji (1995)

Adventure|Children|Fantasy

2

3

Grumpier Old Men (1995)

Comedy|Romance

3

4

Waiting to Exhale (1995)

Comedy|Drama|Romance

4

5

Father of the Bride Part II (1995)

Comedy

5

6

Heat (1995)

Action|Crime|Thriller

6

7

Sabrina (1995)

Comedy|Romance

7

8

Tom and Huck (1995)

Adventure|Children

8

9

Sudden Death (1995)

Action

9

10

GoldenEye (1995)

Action|Adventure|Thriller

数据中包括电影名字与电影类型的标签，第一步还是先转换成one-hot格式

movies_one = movies.drop("genres",1).join(movies.genres.str.get_dummies())
pd.options.display.max_columns=100
movies_one.head()



movieId

title

(no genres listed)

Action

Adventure

Animation

Children

Comedy

Crime

Documentary

Drama

Fantasy

Film-Noir

Horror

IMAX

Musical

Mystery

Romance

Sci-Fi

Thriller

War

Western

0

1

Toy Story (1995)

0

0

1

1

1

1

0

0

0

1

0

0

0

0

0

0

0

0

0

0

1

2

Jumanji (1995)

0

0

1

0

1

0

0

0

0

1

0

0

0

0

0

0

0

0

0

0

2

3

Grumpier Old Men (1995)

0

0

0

0

0

1

0

0

0

0

0

0

0

0

0

1

0

0

0

0

3

4

Waiting to Exhale (1995)

0

0

0

0

0

1

0

0

1

0

0

0

0

0

0

1

0

0

0

0

4

5

Father of the Bride Part II (1995)

0

0

0

0

0

1

0

0

0

0

0

0

0

0

0

0

0

0

0

0

movies_one.shape
(9125, 22)

数据集包括9125部电影，一共有20种不同类型。

movies_one.set_index(["movieId","title"],inplace=True)
movies_one.head()



(no genres listed)

Action

Adventure

Animation

Children

Comedy

Crime

Documentary

Drama

Fantasy

Film-Noir

Horror

IMAX

Musical

Mystery

Romance

Sci-Fi

Thriller

War

Western

movieId

title

1

Toy Story (1995)

0

0

1

1

1

1

0

0

0

1

0

0

0

0

0

0

0

0

0

0

2

Jumanji (1995)

0

0

1

0

1

0

0

0

0

1

0

0

0

0

0

0

0

0

0

0

3

Grumpier Old Men (1995)

0

0

0

0

0

1

0

0

0

0

0

0

0

0

0

1

0

0

0

0

4

Waiting to Exhale (1995)

0

0

0

0

0

1

0

0

1

0

0

0

0

0

0

1

0

0

0

0

5

Father of the Bride Part II (1995)

0

0

0

0

0

1

0

0

0

0

0

0

0

0

0

0

0

0

0

0

frequent_itemsets_movies = apriori(movies_one,use_colnames=True,min_support=0.025)
frequent_itemsets_movies



support

itemsets

0

0.169315

(Action)

1

0.122411

(Adventure)

2

0.048986

(Animation)

3

0.063890

(Children)

4

0.363288

(Comedy)

5

0.120548

(Crime)

6

0.054247

(Documentary)

7

0.478356

(Drama)

8

0.071671

(Fantasy)

9

0.096110

(Horror)

10

0.043178

(Musical)

11

0.059507

(Mystery)

12

0.169315

(Romance)

13

0.086795

(Sci-Fi)

14

0.189479

(Thriller)

15

0.040219

(War)

16

0.058301

(Action, Adventure)

17

0.037589

(Comedy, Action)

18

0.038247

(Action, Crime)

19

0.051178

(Action, Drama)

20

0.040986

(Action, Sci-Fi)

21

0.062904

(Thriller, Action)

22

0.029260

(Children, Adventure)

23

0.036712

(Comedy, Adventure)

24

0.032438

(Drama, Adventure)

25

0.030685

(Fantasy, Adventure)

26

0.027726

(Sci-Fi, Adventure)

27

0.027068

(Children, Animation)

28

0.032877

(Children, Comedy)

29

0.032438

(Comedy, Crime)

30

0.104000

(Comedy, Drama)

31

0.026959

(Fantasy, Comedy)

32

0.090082

(Comedy, Romance)

33

0.067616

(Crime, Drama)

34

0.057863

(Thriller, Crime)

35

0.031671

(Mystery, Drama)

36

0.101260

(Drama, Romance)

37

0.087123

(Thriller, Drama)

38

0.031014

(War, Drama)

39

0.043397

(Horror, Thriller)

40

0.036055

(Thriller, Mystery)

41

0.028932

(Thriller, Sci-Fi)

42

0.035068

(Comedy, Drama, Romance)

43

0.032000

(Crime, Thriller, Drama)

rules_movies = association_rules(frequent_itemsets_movies,metric="lift",min_threshold=1.25)
rules_movies



antecedents

consequents

antecedent support

consequent support

support

confidence

lift

leverage

conviction

0

(Action)

(Adventure)

0.169315

0.122411

0.058301

0.344337

2.812955

0.037575

1.338475

1

(Adventure)

(Action)

0.122411

0.169315

0.058301

0.476276

2.812955

0.037575

1.586111

2

(Action)

(Crime)

0.169315

0.120548

0.038247

0.225890

1.873860

0.017836

1.136081

3

(Crime)

(Action)

0.120548

0.169315

0.038247

0.317273

1.873860

0.017836

1.216716

4

(Action)

(Sci-Fi)

0.169315

0.086795

0.040986

0.242071

2.789015

0.026291

1.204870

5

(Sci-Fi)

(Action)

0.086795

0.169315

0.040986

0.472222

2.789015

0.026291

1.573929

6

(Thriller)

(Action)

0.189479

0.169315

0.062904

0.331984

1.960746

0.030822

1.243510

7

(Action)

(Thriller)

0.169315

0.189479

0.062904

0.371521

1.960746

0.030822

1.289654

8

(Children)

(Adventure)

0.063890

0.122411

0.029260

0.457976

3.741299

0.021439

1.619096

9

(Adventure)

(Children)

0.122411

0.063890

0.029260

0.239033

3.741299

0.021439

1.230158

10

(Fantasy)

(Adventure)

0.071671

0.122411

0.030685

0.428135

3.497518

0.021912

1.534608

11

(Adventure)

(Fantasy)

0.122411

0.071671

0.030685

0.250671

3.497518

0.021912

1.238881

12

(Sci-Fi)

(Adventure)

0.086795

0.122411

0.027726

0.319444

2.609607

0.017101

1.289519

13

(Adventure)

(Sci-Fi)

0.122411

0.086795

0.027726

0.226500

2.609607

0.017101

1.180614

14

(Children)

(Animation)

0.063890

0.048986

0.027068

0.423671

8.648758

0.023939

1.650122

15

(Animation)

(Children)

0.048986

0.063890

0.027068

0.552573

8.648758

0.023939

2.092205

16

(Children)

(Comedy)

0.063890

0.363288

0.032877

0.514580

1.416453

0.009666

1.311672

17

(Comedy)

(Children)

0.363288

0.063890

0.032877

0.090498

1.416453

0.009666

1.029255

18

(Comedy)

(Romance)

0.363288

0.169315

0.090082

0.247964

1.464511

0.028572

1.104581

19

(Romance)

(Comedy)

0.169315

0.363288

0.090082

0.532039

1.464511

0.028572

1.360609

20

(Thriller)

(Crime)

0.189479

0.120548

0.057863

0.305379

2.533256

0.035022

1.266089

21

(Crime)

(Thriller)

0.120548

0.189479

0.057863

0.480000

2.533256

0.035022

1.558693

22

(Drama)

(Romance)

0.478356

0.169315

0.101260

0.211684

1.250236

0.020267

1.053746

23

(Romance)

(Drama)

0.169315

0.478356

0.101260

0.598058

1.250236

0.020267

1.297810

24

(War)

(Drama)

0.040219

0.478356

0.031014

0.771117

1.612015

0.011775

2.279087

25

(Drama)

(War)

0.478356

0.040219

0.031014

0.064834

1.612015

0.011775

1.026321

26

(Horror)

(Thriller)

0.096110

0.189479

0.043397

0.451539

2.383052

0.025186

1.477810

27

(Thriller)

(Horror)

0.189479

0.096110

0.043397

0.229034

2.383052

0.025186

1.172413

28

(Thriller)

(Mystery)

0.189479

0.059507

0.036055

0.190283

3.197672

0.024779

1.161509

29

(Mystery)

(Thriller)

0.059507

0.189479

0.036055

0.605893

3.197672

0.024779

2.056601

30

(Thriller)

(Sci-Fi)

0.189479

0.086795

0.028932

0.152689

1.759206

0.012486

1.077769

31

(Sci-Fi)

(Thriller)

0.086795

0.189479

0.028932

0.333333

1.759206

0.012486

1.215781

32

(Comedy, Drama)

(Romance)

0.104000

0.169315

0.035068

0.337197

1.991536

0.017460

1.253291

33

(Romance)

(Comedy, Drama)

0.169315

0.104000

0.035068

0.207120

1.991536

0.017460

1.130057

34

(Drama, Crime)

(Thriller)

0.067616

0.189479

0.032000

0.473258

2.497673

0.019188

1.538742

35

(Thriller, Drama)

(Crime)

0.087123

0.120548

0.032000

0.367296

3.046884

0.021497

1.389989

36

(Crime)

(Thriller, Drama)

0.120548

0.087123

0.032000

0.265455

3.046884

0.021497

1.242778

37

(Thriller)

(Drama, Crime)

0.189479

0.067616

0.032000

0.168884

2.497673

0.019188

1.121845

rules_movies[(rules_movies.lift>4)].sort_values(by=['lift'], ascending=False)



antecedents

consequents

antecedent support

consequent support

support

confidence

lift

leverage

conviction

14

(Children)

(Animation)

0.063890

0.048986

0.027068

0.423671

8.648758

0.023939

1.650122

15

(Animation)

(Children)

0.048986

0.063890

0.027068

0.552573

8.648758

0.023939

2.092205

Children和Animation 这俩题材是最相关的

movies[(movies.genres.str.contains('Children')) & (~movies.genres.str.contains('Animation'))]



<tr>
  <th>8917</th>
  <td>135266</td>
  <td>Zenon: The Zequel (2001)</td>
  <td>Adventure|Children|Comedy|Sci-Fi</td>
</tr>
<tr>
  <th>8918</th>
  <td>135268</td>
  <td>Zenon: Z3 (2004)</td>
  <td>Adventure|Children|Comedy</td>
</tr>
<tr>
  <th>8960</th>
  <td>139620</td>
  <td>Everything's Gonna Be Great (1998)</td>
  <td>Adventure|Children|Comedy|Drama</td>
</tr>
<tr>
  <th>8967</th>
  <td>140152</td>
  <td>Dreamcatcher (2015)</td>
  <td>Children|Crime|Documentary</td>
</tr>
<tr>
  <th>8981</th>
  <td>140747</td>
  <td>16 Wishes (2010)</td>
  <td>Children|Drama|Fantasy</td>
</tr>
<tr>
  <th>9052</th>
  <td>149354</td>
  <td>Sisters (2015)</td>
  <td>Children|Comedy</td>
</tr>

movieId

title

genres

1

2

Jumanji (1995)

Adventure|Children|Fantasy

7

8

Tom and Huck (1995)

Adventure|Children

26

27

Now and Then (1995)

Children|Drama

32

34

Babe (1995)

Children|Drama

36

38

It Takes Two (1995)

Children|Comedy

51

54

Big Green, The (1995)

Children|Comedy

56

60

Indian in the Cupboard, The (1995)

Adventure|Children|Fantasy

74

80

White Balloon, The (Badkonake sefid) (1995)

Children|Drama

81

87

Dunston Checks In (1996)

Children|Comedy

98

107

Muppet Treasure Island (1996)

Adventure|Children|Comedy|Musical

114

126

NeverEnding Story III, The (1994)

Adventure|Children|Fantasy

125

146

Amazing Panda Adventure, The (1995)

Adventure|Children

137

158

Casper (1995)

Adventure|Children

148

169

Free Willy 2: The Adventure Home (1995)

Adventure|Children|Drama

160

181

Mighty Morphin Power Rangers: The Movie (1995)

Action|Children

210

238

Far From Home: The Adventures of Yellow Dog (1995)

Adventure|Children

213

241

Fluke (1995)

Children|Drama

215

243

Gordy (1995)

Children|Comedy|Fantasy

222

250

Heavyweights (Heavy Weights) (1995)

Children|Comedy

230

258

Kid in King Arthur's Court, A (1995)

Adventure|Children|Comedy|Fantasy|Romance

234

262

Little Princess, A (1995)

Children|Drama

280

314

Secret of Roan Inish, The (1994)

Children|Drama|Fantasy|Mystery

308

343

Baby-Sitters Club, The (1995)

Children

320

355

Flintstones, The (1994)

Children|Comedy|Fantasy

326

362

Jungle Book, The (1994)

Adventure|Children|Romance

338

374

Richie Rich (1994)

Children|Comedy

361

410

Addams Family Values (1993)

Children|Comedy|Fantasy

371

421

Black Beauty (1994)

Adventure|Children|Drama

404

455

Free Willy (1993)

Adventure|Children|Drama

431

484

Lassie (1994)

Adventure|Children

...

...

...

...

7707

83177

Yogi Bear (2010)

Children|Comedy

7735

84312

Home Alone 4 (2002)

Children|Comedy|Crime

7823

87383

Curly Top (1935)

Children|Musical|Romance

7900

89881

Superman and the Mole-Men (1951)

Children|Mystery|Sci-Fi

7929

90866

Hugo (2011)

Children|Drama|Mystery

7935

91094

Muppets, The (2011)

Children|Comedy|Musical

7942

91286

Little Colonel, The (1935)

Children|Comedy|Crime|Drama

7971

91886

Dolphin Tale (2011)

Children|Drama

8096

95740

Adventures of Mary-Kate and Ashley, The: The Case of the United States Navy Adventure (1997)

Children|Musical|Mystery

8199

98441

Rebecca of Sunnybrook Farm (1938)

Children|Comedy|Drama|Musical

8200

98458

Baby Take a Bow (1934)

Children|Comedy|Drama

8377

104074

Percy Jackson: Sea of Monsters (2013)

Adventure|Children|Fantasy

8450

106441

Book Thief, The (2013)

Children|Drama|War

8558

110461

We Are the Best! (Vi är bäst!) (2013)

Children|Comedy|Drama

8592

111659

Maleficent (2014)

Action|Adventure|Children|IMAX

8689

115139

Challenge to Lassie (1949)

Children|Drama

8761

118997

Into the Woods (2014)

Children|Comedy|Fantasy|Musical

8765

119155

Night at the Museum: Secret of the Tomb (2014)

Adventure|Children|Comedy|Fantasy

8766

119655

Seventh Son (2014)

Adventure|Children|Fantasy

8792

122932

Elsa & Fred (2014)

Children|Comedy|Romance

8845

130073

Cinderella (2015)

Children|Drama|Fantasy|Romance

8850

130450

Pan (2015)

Adventure|Children|Fantasy

8871

132046

Tomorrowland (2015)

Action|Adventure|Children|Mystery|Sci-Fi

336 rows × 3 columns