电影数据探索分析

探索电影数据集

在这个项目中，使用 NumPy、Pandas、matplotlib、seaborn 库中的函数，来对电影数据集进行探索。

下载数据集：
TMDb电影数据

数据集各列名称的含义：

列名称	id	imdb_id	popularity	budget	revenue	original_title	cast	homepage	director	tagline	keywords	overview	runtime	genres	production_companies	release_date	vote_count	vote_average	release_year	budget_adj	revenue_adj
含义	编号	IMDB 编号	知名度	预算	票房	名称	主演	网站	导演	宣传词	关键词	简介	时常	类别	发行公司	发行日期	投票总数	投票均值	发行年份	预算（调整后）	票房（调整后）

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第一节 数据的导入与处理

任务1.1： 导入库以及数据

1. 载入需要的库 NumPy、Pandas、matplotlib、seaborn。
2. 利用 Pandas 库，读取 tmdb-movies.csv 中的数据，保存为 movie_data。

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import time

%matplotlib inline
%timeit

movie_data = pd.read_csv('C:/Users/Administrator/Documents/Explore_Movie_Dataset/Explore Movie Dataset/tmdb-movies.csv')

---

**任务1.2: ** 了解数据

movie_data.head(2)

	id	imdb_id	popularity	budget	revenue	original_title	cast	homepage	director	tagline	...	overview	runtime	genres	production_companies	release_date	vote_count	vote_average	release_year	budget_adj	revenue_adj
0	135397	tt0369610	32.985763	150000000	1513528810	Jurassic World	Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...	http://www.jurassicworld.com/	Colin Trevorrow	The park is open.	...	Twenty-two years after the events of Jurassic ...	124	Action|Adventure|Science Fiction|Thriller	Universal Studios|Amblin Entertainment|Legenda...	6/9/15	5562	6.5	2015	1.379999e+08	1.392446e+09
1	76341	tt1392190	28.419936	150000000	378436354	Mad Max: Fury Road	Tom Hardy|Charlize Theron|Hugh Keays-Byrne|Nic...	http://www.madmaxmovie.com/	George Miller	What a Lovely Day.	...	An apocalyptic story set in the furthest reach...	120	Action|Adventure|Science Fiction|Thriller	Village Roadshow Pictures|Kennedy Miller Produ...	5/13/15	6185	7.1	2015	1.379999e+08	3.481613e+08

2 rows × 21 columns

movie_data.dtypes

id                        int64
imdb_id                  object
popularity              float64
budget                    int64
revenue                   int64
original_title           object
cast                     object
homepage                 object
director                 object
tagline                  object
keywords                 object
overview                 object
runtime                   int64
genres                   object
production_companies     object
release_date             object
vote_count                int64
vote_average            float64
release_year              int64
budget_adj              float64
revenue_adj             float64
dtype: object

movie_data.isnull().sum()

id                         0
imdb_id                   10
popularity                 0
budget                     0
revenue                    0
original_title             0
cast                      76
homepage                7930
director                  44
tagline                 2824
keywords                1493
overview                   4
runtime                    0
genres                    23
production_companies    1030
release_date               0
vote_count                 0
vote_average               0
release_year               0
budget_adj                 0
revenue_adj                0
dtype: int64

movie_data.shape

(10866, 21)

movie_data.describe()

	id	popularity	budget	revenue	runtime	vote_count	vote_average	release_year	budget_adj	revenue_adj
count	10866.000000	10866.000000	1.086600e+04	1.086600e+04	10866.000000	10866.000000	10866.000000	10866.000000	1.086600e+04	1.086600e+04
mean	66064.177434	0.646441	1.462570e+07	3.982332e+07	102.070863	217.389748	5.974922	2001.322658	1.755104e+07	5.136436e+07
std	92130.136561	1.000185	3.091321e+07	1.170035e+08	31.381405	575.619058	0.935142	12.812941	3.430616e+07	1.446325e+08
min	5.000000	0.000065	0.000000e+00	0.000000e+00	0.000000	10.000000	1.500000	1960.000000	0.000000e+00	0.000000e+00
25%	10596.250000	0.207583	0.000000e+00	0.000000e+00	90.000000	17.000000	5.400000	1995.000000	0.000000e+00	0.000000e+00
50%	20669.000000	0.383856	0.000000e+00	0.000000e+00	99.000000	38.000000	6.000000	2006.000000	0.000000e+00	0.000000e+00
75%	75610.000000	0.713817	1.500000e+07	2.400000e+07	111.000000	145.750000	6.600000	2011.000000	2.085325e+07	3.369710e+07
max	417859.000000	32.985763	4.250000e+08	2.781506e+09	900.000000	9767.000000	9.200000	2015.000000	4.250000e+08	2.827124e+09

---

**任务1.3: ** 清理数据

在真实的工作场景中，数据处理往往是最为费时费力的环节。但是幸运的是，我们提供给大家的 tmdb 数据集非常的「干净」，不需要大家做特别多的数据清洗以及处理工作。在这一步中，你的核心的工作主要是对数据表中的空值进行处理。你可以使用 .fillna() 来填补空值，当然也可以使用 .dropna() 来丢弃数据表中包含空值的某些行或者列。

任务：使用适当的方法来清理空值，并将得到的数据保存。

movie_data_nonul = movie_data.dropna(axis=1).copy()

---

---

第二节 根据指定要求读取数据

相比 Excel 等数据分析软件，Pandas 的一大特长在于，能够轻松地基于复杂的逻辑选择合适的数据。因此，如何根据指定的要求，从数据表当获取适当的数据，是使用 Pandas 中非常重要的技能，也是本节重点考察大家的内容。

---

**任务2.1: ** 简单读取

1. 读取数据表中名为 id、popularity、budget、runtime、vote_average 列的数据。
2. 读取数据表中前1～20行以及48、49行的数据。
3. 读取数据表中第50～60行的 popularity 那一列的数据。

要求：每一个语句只能用一行代码实现。

movie_data_nonul.id.head()
movie_data_nonul['popularity']
movie_data_nonul['vote_average'].head()

0    6.5
1    7.1
2    6.3
3    7.5
4    7.3
Name: vote_average, dtype: float64

movie_data_nonul[:20]
movie_data_nonul[48:50]

	id	popularity	budget	revenue	original_title	runtime	release_date	vote_count	vote_average	release_year	budget_adj	revenue_adj
48	265208	2.932340	30000000	0	Wild Card	92	1/14/15	481	5.3	2015	2.759999e+07	0.000000e+00
49	254320	2.885126	4000000	9064511	The Lobster	118	10/8/15	638	6.6	2015	3.679998e+06	8.339346e+06

movie_data_nonul[50:61]['popularity']

50    2.883233
51    2.814802
52    2.798017
53    2.793297
54    2.614499
55    2.584264
56    2.578919
57    2.575711
58    2.557859
59    2.550747
60    2.487849
Name: popularity, dtype: float64

---

**任务2.2: **逻辑读取（Logical Indexing）

1. 读取数据表中 popularity 大于5 的所有数据。
2. 读取数据表中 popularity 大于5 的所有数据且发行年份在1996年之后的所有数据。

提示：Pandas 中的逻辑运算符如 &、|，分别代表且以及或。

要求：请使用 Logical Indexing实现。

movie_data_nonul[movie_data_nonul['popularity'] > 5]

	id	popularity	budget	revenue	original_title	runtime	release_date	vote_count	vote_average	release_year	budget_adj	revenue_adj
0	135397	32.985763	150000000	1513528810	Jurassic World	124	6/9/15	5562	6.5	2015	1.379999e+08	1.392446e+09
1	76341	28.419936	150000000	378436354	Mad Max: Fury Road	120	5/13/15	6185	7.1	2015	1.379999e+08	3.481613e+08
2	262500	13.112507	110000000	295238201	Insurgent	119	3/18/15	2480	6.3	2015	1.012000e+08	2.716190e+08
3	140607	11.173104	200000000	2068178225	Star Wars: The Force Awakens	136	12/15/15	5292	7.5	2015	1.839999e+08	1.902723e+09
4	168259	9.335014	190000000	1506249360	Furious 7	137	4/1/15	2947	7.3	2015	1.747999e+08	1.385749e+09
5	281957	9.110700	135000000	532950503	The Revenant	156	12/25/15	3929	7.2	2015	1.241999e+08	4.903142e+08
6	87101	8.654359	155000000	440603537	Terminator Genisys	125	6/23/15	2598	5.8	2015	1.425999e+08	4.053551e+08
7	286217	7.667400	108000000	595380321	The Martian	141	9/30/15	4572	7.6	2015	9.935996e+07	5.477497e+08
8	211672	7.404165	74000000	1156730962	Minions	91	6/17/15	2893	6.5	2015	6.807997e+07	1.064192e+09
9	150540	6.326804	175000000	853708609	Inside Out	94	6/9/15	3935	8.0	2015	1.609999e+08	7.854116e+08
10	206647	6.200282	245000000	880674609	Spectre	148	10/26/15	3254	6.2	2015	2.253999e+08	8.102203e+08
11	76757	6.189369	176000003	183987723	Jupiter Ascending	124	2/4/15	1937	5.2	2015	1.619199e+08	1.692686e+08
12	264660	6.118847	15000000	36869414	Ex Machina	108	1/21/15	2854	7.6	2015	1.379999e+07	3.391985e+07
13	257344	5.984995	88000000	243637091	Pixels	105	7/16/15	1575	5.8	2015	8.095996e+07	2.241460e+08
14	99861	5.944927	280000000	1405035767	Avengers: Age of Ultron	141	4/22/15	4304	7.4	2015	2.575999e+08	1.292632e+09
15	273248	5.898400	44000000	155760117	The Hateful Eight	167	12/25/15	2389	7.4	2015	4.047998e+07	1.432992e+08
16	260346	5.749758	48000000	325771424	Taken 3	109	1/1/15	1578	6.1	2015	4.415998e+07	2.997096e+08
17	102899	5.573184	130000000	518602163	Ant-Man	115	7/14/15	3779	7.0	2015	1.195999e+08	4.771138e+08
18	150689	5.556818	95000000	542351353	Cinderella	112	3/12/15	1495	6.8	2015	8.739996e+07	4.989630e+08
19	131634	5.476958	160000000	650523427	The Hunger Games: Mockingjay - Part 2	136	11/18/15	2380	6.5	2015	1.471999e+08	5.984813e+08
20	158852	5.462138	190000000	209035668	Tomorrowland	130	5/19/15	1899	6.2	2015	1.747999e+08	1.923127e+08
21	307081	5.337064	30000000	91709827	Southpaw	123	6/15/15	1386	7.3	2015	2.759999e+07	8.437300e+07
629	157336	24.949134	165000000	621752480	Interstellar	169	11/5/14	6498	8.0	2014	1.519800e+08	5.726906e+08
630	118340	14.311205	170000000	773312399	Guardians of the Galaxy	121	7/30/14	5612	7.9	2014	1.565855e+08	7.122911e+08
631	100402	12.971027	170000000	714766572	Captain America: The Winter Soldier	136	3/20/14	3848	7.6	2014	1.565855e+08	6.583651e+08
632	245891	11.422751	20000000	78739897	John Wick	101	10/22/14	2712	7.0	2014	1.842182e+07	7.252661e+07
633	131631	10.739009	125000000	752100229	The Hunger Games: Mockingjay - Part 1	123	11/18/14	3590	6.6	2014	1.151364e+08	6.927528e+08
634	122917	10.174599	250000000	955119788	The Hobbit: The Battle of the Five Armies	144	12/10/14	3110	7.1	2014	2.302728e+08	8.797523e+08
635	177572	8.691294	165000000	652105443	Big Hero 6	102	10/24/14	4185	7.8	2014	1.519800e+08	6.006485e+08
636	205596	8.110711	14000000	233555708	The Imitation Game	113	11/14/14	3478	8.0	2014	1.289527e+07	2.151261e+08
...	...	...	...	...	...	...	...	...	...	...	...	...
2876	10681	5.678119	180000000	521311860	WALL·E	98	6/22/08	4209	7.6	2008	1.823016e+08	5.279777e+08
3371	161337	8.411577	0	0	Underworld: Endless War	18	10/19/11	21	5.9	2011	0.000000e+00	0.000000e+00
3372	1771	7.959228	140000000	370569774	Captain America: The First Avenger	124	7/22/11	5025	6.5	2011	1.357157e+08	3.592296e+08
3373	64690	5.903353	15000000	76175166	Drive	100	1/10/11	2347	7.3	2011	1.454097e+07	7.384406e+07
3374	12445	5.711315	125000000	1327817822	Harry Potter and the Deathly Hallows: Part 2	130	7/7/11	3750	7.7	2011	1.211748e+08	1.287184e+09
3911	121	8.095275	79000000	926287400	The Lord of the Rings: The Two Towers	179	12/18/02	5114	7.8	2002	9.576865e+07	1.122902e+09
3912	672	6.012584	100000000	876688482	Harry Potter and the Chamber of Secrets	161	11/13/02	3458	7.2	2002	1.212261e+08	1.062776e+09
4177	680	8.093754	8000000	213928762	Pulp Fiction	154	10/14/94	5343	8.1	1994	1.176889e+07	3.147131e+08
4178	278	7.192039	25000000	28341469	The Shawshank Redemption	142	9/10/94	5754	8.4	1994	3.677779e+07	4.169346e+07
4179	13	6.715966	55000000	677945399	Forrest Gump	142	7/6/94	4856	8.1	1994	8.091114e+07	9.973333e+08
4361	24428	7.637767	220000000	1519557910	The Avengers	143	4/25/12	8903	7.3	2012	2.089437e+08	1.443191e+09
4362	52520	7.031452	70000000	132400000	Underworld: Awakening	88	1/19/12	1426	6.0	2012	6.648210e+07	1.257461e+08
4363	49026	6.591277	250000000	1081041287	The Dark Knight Rises	165	7/16/12	6723	7.5	2012	2.374361e+08	1.026713e+09
4364	68718	5.944518	100000000	425368238	Django Unchained	165	12/25/12	7375	7.7	2012	9.497443e+07	4.039911e+08
4365	37724	5.603587	200000000	1108561013	Skyfall	143	10/25/12	6137	6.8	2012	1.899489e+08	1.052849e+09
4949	122	7.122455	94000000	1118888979	The Lord of the Rings: The Return of the King	201	12/1/03	5636	7.9	2003	1.114231e+08	1.326278e+09
4950	277	6.887883	22000000	95708457	Underworld	121	9/19/03	1708	6.5	2003	2.607776e+07	1.134483e+08
4951	22	6.864067	140000000	655011224	Pirates of the Caribbean: The Curse of the Bla...	143	7/9/03	4223	7.3	2003	1.659494e+08	7.764193e+08
4952	24	6.174132	30000000	180949000	Kill Bill: Vol. 1	111	10/10/03	2932	7.6	2003	3.556058e+07	2.144884e+08
5230	13590	6.668990	0	0	Eddie Izzard: Glorious	99	11/17/97	11	5.5	1997	0.000000e+00	0.000000e+00
5422	109445	6.112766	150000000	1274219009	Frozen	102	11/27/13	3369	7.5	2013	1.404050e+08	1.192711e+09
5423	49047	5.242753	105000000	716392705	Gravity	91	9/27/13	3775	7.4	2013	9.828350e+07	6.705675e+08
5424	76338	5.111900	170000000	479765000	Thor: The Dark World	112	10/29/13	3025	6.8	2013	1.591257e+08	4.490760e+08
6081	105	6.095293	19000000	381109762	Back to the Future	116	7/3/85	3785	7.8	1985	3.851615e+07	7.725728e+08
6190	674	5.939927	150000000	895921036	Harry Potter and the Goblet of Fire	157	11/5/05	3406	7.3	2005	1.674845e+08	1.000353e+09
6191	272	5.400826	150000000	374218673	Batman Begins	140	6/14/05	4914	7.3	2005	1.674845e+08	4.178388e+08
6554	834	5.838503	50000000	111340801	Underworld: Evolution	106	1/12/06	1015	6.3	2006	5.408346e+07	1.204339e+08
6962	673	5.827781	130000000	789804554	Harry Potter and the Prisoner of Azkaban	141	5/31/04	3550	7.4	2004	1.500779e+08	9.117862e+08
7269	238	5.738034	6000000	245066411	The Godfather	175	3/15/72	3970	8.3	1972	3.128737e+07	1.277914e+09
7309	1891	5.488441	18000000	538400000	The Empire Strikes Back	124	1/1/80	3954	8.0	1980	4.762866e+07	1.424626e+09

85 rows × 12 columns

movie_data_nonul[(movie_data_nonul['popularity'] > 5) & (movie_data_nonul['release_year'] > 1996)]

	id	popularity	budget	revenue	original_title	runtime	release_date	vote_count	vote_average	release_year	budget_adj	revenue_adj
0	135397	32.985763	150000000	1513528810	Jurassic World	124	6/9/15	5562	6.5	2015	1.379999e+08	1.392446e+09
1	76341	28.419936	150000000	378436354	Mad Max: Fury Road	120	5/13/15	6185	7.1	2015	1.379999e+08	3.481613e+08
2	262500	13.112507	110000000	295238201	Insurgent	119	3/18/15	2480	6.3	2015	1.012000e+08	2.716190e+08
3	140607	11.173104	200000000	2068178225	Star Wars: The Force Awakens	136	12/15/15	5292	7.5	2015	1.839999e+08	1.902723e+09
4	168259	9.335014	190000000	1506249360	Furious 7	137	4/1/15	2947	7.3	2015	1.747999e+08	1.385749e+09
5	281957	9.110700	135000000	532950503	The Revenant	156	12/25/15	3929	7.2	2015	1.241999e+08	4.903142e+08
6	87101	8.654359	155000000	440603537	Terminator Genisys	125	6/23/15	2598	5.8	2015	1.425999e+08	4.053551e+08
7	286217	7.667400	108000000	595380321	The Martian	141	9/30/15	4572	7.6	2015	9.935996e+07	5.477497e+08
8	211672	7.404165	74000000	1156730962	Minions	91	6/17/15	2893	6.5	2015	6.807997e+07	1.064192e+09
9	150540	6.326804	175000000	853708609	Inside Out	94	6/9/15	3935	8.0	2015	1.609999e+08	7.854116e+08
10	206647	6.200282	245000000	880674609	Spectre	148	10/26/15	3254	6.2	2015	2.253999e+08	8.102203e+08
11	76757	6.189369	176000003	183987723	Jupiter Ascending	124	2/4/15	1937	5.2	2015	1.619199e+08	1.692686e+08
12	264660	6.118847	15000000	36869414	Ex Machina	108	1/21/15	2854	7.6	2015	1.379999e+07	3.391985e+07
13	257344	5.984995	88000000	243637091	Pixels	105	7/16/15	1575	5.8	2015	8.095996e+07	2.241460e+08
14	99861	5.944927	280000000	1405035767	Avengers: Age of Ultron	141	4/22/15	4304	7.4	2015	2.575999e+08	1.292632e+09
15	273248	5.898400	44000000	155760117	The Hateful Eight	167	12/25/15	2389	7.4	2015	4.047998e+07	1.432992e+08
16	260346	5.749758	48000000	325771424	Taken 3	109	1/1/15	1578	6.1	2015	4.415998e+07	2.997096e+08
17	102899	5.573184	130000000	518602163	Ant-Man	115	7/14/15	3779	7.0	2015	1.195999e+08	4.771138e+08
18	150689	5.556818	95000000	542351353	Cinderella	112	3/12/15	1495	6.8	2015	8.739996e+07	4.989630e+08
19	131634	5.476958	160000000	650523427	The Hunger Games: Mockingjay - Part 2	136	11/18/15	2380	6.5	2015	1.471999e+08	5.984813e+08
20	158852	5.462138	190000000	209035668	Tomorrowland	130	5/19/15	1899	6.2	2015	1.747999e+08	1.923127e+08
21	307081	5.337064	30000000	91709827	Southpaw	123	6/15/15	1386	7.3	2015	2.759999e+07	8.437300e+07
629	157336	24.949134	165000000	621752480	Interstellar	169	11/5/14	6498	8.0	2014	1.519800e+08	5.726906e+08
630	118340	14.311205	170000000	773312399	Guardians of the Galaxy	121	7/30/14	5612	7.9	2014	1.565855e+08	7.122911e+08
631	100402	12.971027	170000000	714766572	Captain America: The Winter Soldier	136	3/20/14	3848	7.6	2014	1.565855e+08	6.583651e+08
632	245891	11.422751	20000000	78739897	John Wick	101	10/22/14	2712	7.0	2014	1.842182e+07	7.252661e+07
633	131631	10.739009	125000000	752100229	The Hunger Games: Mockingjay - Part 1	123	11/18/14	3590	6.6	2014	1.151364e+08	6.927528e+08
634	122917	10.174599	250000000	955119788	The Hobbit: The Battle of the Five Armies	144	12/10/14	3110	7.1	2014	2.302728e+08	8.797523e+08
635	177572	8.691294	165000000	652105443	Big Hero 6	102	10/24/14	4185	7.8	2014	1.519800e+08	6.006485e+08
636	205596	8.110711	14000000	233555708	The Imitation Game	113	11/14/14	3478	8.0	2014	1.289527e+07	2.151261e+08
...	...	...	...	...	...	...	...	...	...	...	...	...
1922	44214	5.293180	13000000	327803731	Black Swan	108	12/2/10	2597	7.1	2010	1.300000e+07	3.278037e+08
2409	550	8.947905	63000000	100853753	Fight Club	139	10/14/99	5923	8.1	1999	8.247033e+07	1.320229e+08
2410	603	7.753899	63000000	463517383	The Matrix	136	3/30/99	6351	7.8	1999	8.247033e+07	6.067687e+08
2633	120	8.575419	93000000	871368364	The Lord of the Rings: The Fellowship of the Ring	178	12/18/01	6079	7.8	2001	1.145284e+08	1.073080e+09
2634	671	8.021423	125000000	976475550	Harry Potter and the Philosopher's Stone	152	11/16/01	4265	7.2	2001	1.539360e+08	1.202518e+09
2875	155	8.466668	185000000	1001921825	The Dark Knight	152	7/16/08	8432	8.1	2008	1.873655e+08	1.014733e+09
2876	10681	5.678119	180000000	521311860	WALL·E	98	6/22/08	4209	7.6	2008	1.823016e+08	5.279777e+08
3371	161337	8.411577	0	0	Underworld: Endless War	18	10/19/11	21	5.9	2011	0.000000e+00	0.000000e+00
3372	1771	7.959228	140000000	370569774	Captain America: The First Avenger	124	7/22/11	5025	6.5	2011	1.357157e+08	3.592296e+08
3373	64690	5.903353	15000000	76175166	Drive	100	1/10/11	2347	7.3	2011	1.454097e+07	7.384406e+07
3374	12445	5.711315	125000000	1327817822	Harry Potter and the Deathly Hallows: Part 2	130	7/7/11	3750	7.7	2011	1.211748e+08	1.287184e+09
3911	121	8.095275	79000000	926287400	The Lord of the Rings: The Two Towers	179	12/18/02	5114	7.8	2002	9.576865e+07	1.122902e+09
3912	672	6.012584	100000000	876688482	Harry Potter and the Chamber of Secrets	161	11/13/02	3458	7.2	2002	1.212261e+08	1.062776e+09
4361	24428	7.637767	220000000	1519557910	The Avengers	143	4/25/12	8903	7.3	2012	2.089437e+08	1.443191e+09
4362	52520	7.031452	70000000	132400000	Underworld: Awakening	88	1/19/12	1426	6.0	2012	6.648210e+07	1.257461e+08
4363	49026	6.591277	250000000	1081041287	The Dark Knight Rises	165	7/16/12	6723	7.5	2012	2.374361e+08	1.026713e+09
4364	68718	5.944518	100000000	425368238	Django Unchained	165	12/25/12	7375	7.7	2012	9.497443e+07	4.039911e+08
4365	37724	5.603587	200000000	1108561013	Skyfall	143	10/25/12	6137	6.8	2012	1.899489e+08	1.052849e+09
4949	122	7.122455	94000000	1118888979	The Lord of the Rings: The Return of the King	201	12/1/03	5636	7.9	2003	1.114231e+08	1.326278e+09
4950	277	6.887883	22000000	95708457	Underworld	121	9/19/03	1708	6.5	2003	2.607776e+07	1.134483e+08
4951	22	6.864067	140000000	655011224	Pirates of the Caribbean: The Curse of the Bla...	143	7/9/03	4223	7.3	2003	1.659494e+08	7.764193e+08
4952	24	6.174132	30000000	180949000	Kill Bill: Vol. 1	111	10/10/03	2932	7.6	2003	3.556058e+07	2.144884e+08
5230	13590	6.668990	0	0	Eddie Izzard: Glorious	99	11/17/97	11	5.5	1997	0.000000e+00	0.000000e+00
5422	109445	6.112766	150000000	1274219009	Frozen	102	11/27/13	3369	7.5	2013	1.404050e+08	1.192711e+09
5423	49047	5.242753	105000000	716392705	Gravity	91	9/27/13	3775	7.4	2013	9.828350e+07	6.705675e+08
5424	76338	5.111900	170000000	479765000	Thor: The Dark World	112	10/29/13	3025	6.8	2013	1.591257e+08	4.490760e+08
6190	674	5.939927	150000000	895921036	Harry Potter and the Goblet of Fire	157	11/5/05	3406	7.3	2005	1.674845e+08	1.000353e+09
6191	272	5.400826	150000000	374218673	Batman Begins	140	6/14/05	4914	7.3	2005	1.674845e+08	4.178388e+08
6554	834	5.838503	50000000	111340801	Underworld: Evolution	106	1/12/06	1015	6.3	2006	5.408346e+07	1.204339e+08
6962	673	5.827781	130000000	789804554	Harry Potter and the Prisoner of Azkaban	141	5/31/04	3550	7.4	2004	1.500779e+08	9.117862e+08

78 rows × 12 columns

---

**任务2.3: **分组读取

movie_data_nonul.groupby('release_year').agg({'revenue': np.mean}).head()

	revenue
release_year
1960	4.531406e+06
1961	1.089420e+07
1962	6.736870e+06
1963	5.511911e+06
1964	8.118614e+06

movie_data.dropna().groupby('director').agg({'popularity': np.mean}).sort_values(by='popularity', ascending=False).head()

	popularity
director
Colin Trevorrow	32.985763
George Miller	14.675428
Joe Russo|Anthony Russo	12.971027
Chad Stahelski|David Leitch	11.422751
Don Hall|Chris Williams	8.691294

---

---

第三节 绘图与可视化

可视化的目标	可以使用的图像
表示某一属性数据的分布	饼图、直方图、散点图
表示某一属性数据随着某一个变量变化	条形图、折线图、热力图
比较多个属性的数据之间的关系	散点图、小提琴图、堆积条形图、堆积折线图

**任务3.1：**对 popularity 最高的20名电影绘制其 popularity 值。

 movie_top20_popularity = movie_data.loc[movie_data.sort_values(by='popularity', ascending=False)[:20].index]

base_color = sns.color_palette()[9]
sns.barplot(data=movie_top20_popularity, x='popularity', y='original_title', color=base_color)
sns.title = ('movie_top20_popularity')

---

**任务3.2：**分析电影净利润（票房-成本）随着年份变化的情况，并简单进行分析。

movie_data['profit'] = movie_data['revenue'] - movie_data['budget']
movies_profit = movie_data[['release_year', 'profit'] ]
profit_mean = movie_data.groupby('release_year').agg({'profit': np.sum})

sns.title = ('movie_profit')
base_color = sns.color_palette()[9]
bin_edges = np.arange(movie_data.release_year.min(), movie_data.release_year.max()+1)
plt.xlabel('release_year')
plt.ylabel('profit')

plt.errorbar(data=profit_mean, x=profit_mean.index,y=profit_mean, color=base_color)

sns.title = ('movie_profit')
base_color = sns.color_palette()[0]
plt.xlabel('year')
plt.ylabel('profit')

xbin_edges = np.arange(1960, 2016, 1)
xbin_centers = (xbin_edges)[:-1]

data_xbins = pd.cut(movie_data['release_year'], xbin_edges, right = False, include_lowest = True)
y_sum = movie_data['profit'].groupby(data_xbins).sum()
y_sems = movie_data['profit'].groupby(data_xbins).sem()

plt.errorbar(x = xbin_centers, y = y_sum, yerr = y_sems, color = base_color)

---

**任务3.3：**选择最多产的10位导演（电影数量最多的），绘制他们排行前3的三部电影的票房情况，并简要进行分析。

movie_data[['director', 'original_title', 'revenue']].head()

	director	original_title	revenue
0	Colin Trevorrow	Jurassic World	1513528810
1	George Miller	Mad Max: Fury Road	378436354
2	Robert Schwentke	Insurgent	295238201
3	J.J. Abrams	Star Wars: The Force Awakens	2068178225
4	James Wan	Furious 7	1506249360

director_info = movie_data[['director', 'original_title']].\
                groupby('director').count()['original_title'].nlargest(10).index
director_info

Index(['Woody Allen', 'Clint Eastwood', 'Martin Scorsese', 'Steven Spielberg',
       'Ridley Scott', 'Ron Howard', 'Steven Soderbergh', 'Joel Schumacher',
       'Brian De Palma', 'Barry Levinson'],
      dtype='object', name='director')

analysis = pd.DataFrame()
for i in director_info:
    data = movie_data.loc[movie_data['director'] == i][['director', 'original_title',
                                         'revenue','revenue_adj']].nlargest(3, 'revenue_adj')
    analysis = analysis.append(data)

analysis

	director	original_title	revenue	revenue_adj
3429	Woody Allen	Midnight in Paris	151119219	1.464947e+08
1332	Woody Allen	Annie Hall	38251425	1.376203e+08
7835	Woody Allen	Manhattan	39946780	1.200223e+08
657	Clint Eastwood	American Sniper	542307423	4.995145e+08
2888	Clint Eastwood	Gran Torino	269958228	2.734101e+08
8092	Clint Eastwood	The Bridges of Madison County	182016617	2.604597e+08
5428	Martin Scorsese	The Wolf of Wall Street	392000694	3.669257e+08
6563	Martin Scorsese	The Departed	289847354	3.135189e+08
1927	Martin Scorsese	Shutter Island	294804195	2.948042e+08
9806	Steven Spielberg	Jaws	470654000	1.907006e+09
8889	Steven Spielberg	E.T. the Extra-Terrestrial	792910554	1.791694e+09
10223	Steven Spielberg	Jurassic Park	920100000	1.388863e+09
8661	Ridley Scott	Gladiator	457640427	5.795065e+08
7	Ridley Scott	The Martian	595380321	5.477497e+08
2778	Ridley Scott	Hannibal	351692268	4.331048e+08
6558	Ron Howard	The Da Vinci Code	758239851	8.201647e+08
8076	Ron Howard	Apollo 13	355237933	5.083337e+08
8663	Ron Howard	How the Grinch Stole Christmas	345141403	4.370498e+08
2641	Steven Soderbergh	Ocean's Eleven	450717150	5.550528e+08
6980	Steven Soderbergh	Ocean's Twelve	362744280	4.187685e+08
7425	Steven Soderbergh	Ocean's Thirteen	311312624	3.273977e+08
8082	Joel Schumacher	Batman Forever	336529144	4.815621e+08
5236	Joel Schumacher	Batman & Robin	238207122	3.235949e+08
8471	Joel Schumacher	A Time to Kill	152266007	2.116828e+08
8458	Brian De Palma	Mission: Impossible	457696359	6.362971e+08
9610	Brian De Palma	The Untouchables	76270454	1.463691e+08
7988	Brian De Palma	Scarface	65884703	1.442422e+08
9454	Barry Levinson	Rain Man	354825435	6.542594e+08
4209	Barry Levinson	Disclosure	214015089	3.148401e+08
9611	Barry Levinson	Good Morning, Vietnam	123922370	2.378170e+08

fig = plt.figure(figsize=(15, 6)) 
ax = sns.barplot(data=analysis, x='original_title', y='revenue_adj', hue='director', dodge=False, palette="Set2")
plt.xticks(rotation = 90)
plt.ylabel('Revenue')
plt.xlabel('Original Title')

Text(0.5,0,'Original Title')

---

**[选做]任务3.4：**分析1968年~2015年六月电影的数量的变化。

movie_data_summary = movie_data[['id', 'release_year']].groupby('release_year')['id'].agg(len)

fig = plt.figure(figsize=(15, 6)) 
movie_data_summary[:5]
plt.errorbar(data=profit_mean, x=movie_data_summary.index,y=movie_data_summary.values)

movies_public_number = movie_data[['id', 'release_year']]
public_number = movie_data.groupby('release_year').agg({'id': np.count_nonzero})

sns.title = ('movie_number')
base_color = sns.color_palette()[2]
bin_edges = np.arange(movies_public_number['release_year'].min(), movies_public_number['release_year'].max()+1)
plt.xlabel('release_year')
plt.ylabel('number')

plt.errorbar(data=public_number , x=public_number.index,y=public_number, color=base_color)

---

**任务3.5：**分析1968年~2015年六月电影 Comedy 和 Drama 两类电影的数量的变化。

movie_year = np.arange(1968, 2016)
movie_df = movie_data[movie_data['release_year'].isin(movie_year)][['release_year', 'genres']]
movie_df.dropna(inplace=True)

Comedy_trendy = movie_df[movie_df['genres'].str.contains(r'Comedy')]
Comedy_sum = Comedy_trendy.groupby('release_year').agg(len)
Comedy_sum.head(2)

	genres
release_year
1968	9
1969	12

Drama_trendy = movie_df[movie_df['genres'].str.contains(r'Drama')]
Dram_sum = Drama_trendy.groupby('release_year').agg(len)
Dram_sum.rename(columns={'genres': 'D_genres'}, inplace=True)
Dram_sum.head(2)

	D_genres
release_year
1968	20
1969	13

trendy_cd= Comedy_sum.join(Dram_sum)
trendy_cd.head(2)

	genres	D_genres
release_year
1968	9	20
1969	12	13

fig = plt.figure(figsize=(15, 6)) 

plt.xticks(rotation = 90)
base_color = sns.color_palette()[1]
bin_edges = np.arange(trendy_cd.index.min(), trendy_cd.index.max()+1)
plt.xlabel('release_year')
plt.ylabel('number')

plt.errorbar(data=trendy_cd, x=trendy_cd.index,y=trendy_cd['genres'], color=base_color)
plt.errorbar(data=trendy_cd, x=trendy_cd.index,y=trendy_cd['D_genres'])

# 拆分电影类型
df = movie_data
df_genres = df.drop('genres', axis=1).join(df['genres'].str.split('|', expand=True) \
                               .stack().reset_index(level=1, drop=True).rename('genres'))
fig, axes = plt.subplots(2, 1, figsize=(20, 10))

# 为了易于辨认, 只展示部分电影类型
top5_genres = df_genres['genres'].value_counts().nlargest(5).index
btm5_genres = df_genres['genres'].value_counts().nsmallest(5).index

# 作出中位数参考线
median_cir = df_genres.groupby('release_year')['genres'].value_counts().unstack().mean(axis=1)
median_cir.plot(ax=axes[0], ls='--', label='mean', legend=True)
median_cir.plot(ax=axes[1], ls='--', label='mean', legend=True)

# 按年份作图
vis_params = {'grid': True, 'marker': 'o', 'markersize': 2, 'linewidth': 1}
df_genres[df_genres['genres'].isin(top5_genres)] \
                 .groupby('release_year')['genres'].value_counts().unstack().fillna(0) \
                 .plot(ax=axes[0], title='circulation over years of top 5 genres', **vis_params)
df_genres[df_genres['genres'].isin(btm5_genres)] \
                 .groupby('release_year')['genres'].value_counts().unstack().fillna(0) \
                 .plot(ax=axes[1], title='circulation over years of bottom 5 genres', **vis_params)

plt.tight_layout()

data = df
kw_expand = data['keywords'].str.split('|', expand=True).stack().reset_index(level=1, drop=True).rename('keywords')
df_kw_rev = data[[factor]].join(kw_expand)
word_dict = df_kw_rev.groupby('keywords')[factor].sum().to_dict()

# create wordcloud
params = {'mode': 'RGBA', 
          'background_color': 'rgba(255, 255, 255, 0)', 
          'colormap': 'Spectral'}
wordcloud = WordCloud(width=1200, height=800, **params)
wordcloud.generate_from_frequencies(word_dict)

# plot
plt.figure(figsize=(15, 10))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis('off')

(-0.5, 1199.5, 799.5, -0.5)

df_genres = movie_data.drop('genres', axis=1).join(movie_data['genres'].str.split('|', expand=True).stack().reset_index(level=1, drop=True).rename('genres'))

# 筛选条件1 年份
sel_year = df_genres['release_year'].between(1968, 2015)

# 筛选条件2 六月
sel_June = pd.to_datetime(df_genres['release_date']).dt.month == 6

# 筛选条件3 类型
sel_genre = df_genres['genres'].isin(['Drama', 'Comedy'])

# 筛选数据并作图(参考逻辑读取部分)
plt.figure(figsize=[18, 5])
sns.countplot(data=df_genres[sel_year&sel_June&sel_genre], x='release_year', hue='genres')
plt.xticks(rotation=90)

(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
        17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,
        34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47]),
 )