写在前面
经过前面几天对Numpy和Pandas的学习,我感觉我变秃了,也变强了
对于学习,我们都知道仅仅Input是没有任何效果的,在掌握了基础知识后,还需要Output
这次我到国外的Grouplens网站找来一份百万电影数据,你可以点击我进行下载
我们通过这份数据就可以简单的进行数据分析,筛选出前100的热门电影
话不多说,我们直接开肝
欢迎大家访问我的个人博客一起学习,共同进步http://syjun.vip
导入第三方库和所需文件
import pandas as pd
unames = ['user_id','gender','age','occupation','zip']
users = pd.read_table('file/users.dat',
sep='::',header=None,
names=unames)
users.head()
用户数据
代码结果
|
user_id |
gender |
age |
occupation |
zip |
| 0 |
1 |
F |
1 |
10 |
48067 |
| 1 |
2 |
M |
56 |
16 |
70072 |
| 2 |
3 |
M |
25 |
15 |
55117 |
| 3 |
4 |
M |
45 |
7 |
02460 |
| 4 |
5 |
M |
25 |
20 |
55455 |
评分数据
rating_names = ['user_id','movie_id','rating','timestamp']
ratings = pd.read_table('file/ratings.dat',
sep='::',header=None,
names = rating_names)
ratings.head()
代码结果
|
user_id |
movie_id |
rating |
timestamp |
| 0 |
1 |
1193 |
5 |
978300760 |
| 1 |
1 |
661 |
3 |
978302109 |
| 2 |
1 |
914 |
3 |
978301968 |
| 3 |
1 |
3408 |
4 |
978300275 |
| 4 |
1 |
2355 |
5 |
978824291 |
电影数据
movie_names = ['movie_id','title','genres']
movies = pd.read_table('file/movies.dat',sep='::',
header=None,names=movie_names)
movies.head()
代码结果
|
movie_id |
title |
genres |
| 0 |
1 |
Toy Story (1995) |
Animation\Children's\Comedy |
| 1 |
2 |
Jumanji (1995) |
Animation\Children's\Comedy |
| 2 |
3 |
Grumpier Old Men (1995) |
Comedy\Romance |
| 3 |
4 |
Waiting to Exhale (1995) |
Comedy\Drama |
| 4 |
5 |
Father of the Bride Part II (1995) |
Comedy |
引入三个文件后,使用merge()函数将三个表合并在一起
data = pd.merge(pd.merge(users,ratings),movies)
data.head()
代码结果
小试牛刀
在正式开始之前,我们先做几个小的练习题
分析某部电影男女平均评分
- 这里我们以《One Flew Over the Cuckoo's Nest (1975)"》为例
#筛选出关于这部电影的所有数据
one_movie = data[data.title == "One Flew Over the Cuckoo's Nest (1975)" ]
#使用groupby()函数按照gender这一列分组
one_movie_grop = one_movie.groupby('gender')
#使用DataFrameGroupBy 对象中mean()函数求平均值,并选出rating这一列
one_movie_grop.mean()['rating']
#代码结果
gender
F 4.310811
M 4.418423
Name: rating, dtype: float64
分析所有电影男女平均评分
- 这时我们就可以想到使用pivot_table(),很简单的就能得出结果
rating_group = data.pivot_table(values='rating',
index='title',
columns='gender',
aggfunc='mean')
rating_group.head()
代码结果
| gender |
F |
M |
| title |
| $1,000,000 Duck (1971) |
3.375000 |
2.761905 |
| 'Night Mother (1986) |
3.388889 |
3.352941 |
| 'Til There Was You (1997) |
2.675676 |
2.733333 |
| 'burbs, The (1989) |
2.793478 |
2.962085 |
| ...And Justice for All (1979) |
3.828571 |
3.689024 |
求出男女评分的差值
rating_group['diff'] = rating_group.F - rating_group.M
rating_group.head()
代码结果
| gender |
F |
M |
diff |
| title |
| $1,000,000 Duck (1971) |
3.375000 |
2.761905 |
0.613095 |
| 'Night Mother (1986) |
3.388889 |
3.352941 |
0.035948 |
| 'Til There Was You (1997) |
2.675676 |
2.733333 |
-0.057658 |
| 'burbs, The (1989) |
2.793478 |
2.962085 |
-0.168607 |
| ...And Justice for All (1979) |
3.828571 |
3.689024 |
0.139547 |
查找出现次数最多的前十电影
ratings_by_title = data.groupby('title').size()
ratings_by_title.sort_values(ascending = False).head(10)
#代码结果
title
American Beauty (1999) 3428
Star Wars: Episode IV - A New Hope (1977) 2991
Star Wars: Episode V - The Empire Strikes Back (1980) 2990
Star Wars: Episode VI - Return of the Jedi (1983) 2883
Jurassic Park (1993) 2672
Saving Private Ryan (1998) 2653
Terminator 2: Judgment Day (1991) 2649
Matrix, The (1999) 2590
Back to the Future (1985) 2583
Silence of the Lambs, The (1991) 2578
dtype: int64
查找平均评分最高的前二十电影
mean_ratings = data.pivot_table(values = 'rating',index='title',aggfunc='mean')
mean_ratings.sort_values(by='rating',ascending = False).head(20)
#代码结果
rating
title
Ulysses (Ulisse) (1954) 5.000000
Lured (1947) 5.000000
Follow the Bitch (1998) 5.000000
Bittersweet Motel (2000) 5.000000
Song of Freedom (1936) 5.000000
One Little Indian (1973) 5.000000
Smashing Time (1967) 5.000000
Schlafes Bruder (Brother of Sleep) (1995) 5.000000
Gate of Heavenly Peace, The (1995) 5.000000
Baby, The (1973) 5.000000
I Am Cuba (Soy Cuba/Ya Kuba) (1964) 4.800000
Lamerica (1994) 4.750000
Apple, The (Sib) (1998) 4.666667
Sanjuro (1962) 4.608696
Seven Samurai (The Magnificent Seven) (Shichinin no samurai) (1954) 4.560510
Shawshank Redemption, The (1994) 4.554558
Godfather, The (1972) 4.524966
Close Shave, A (1995) 4.520548
Usual Suspects, The (1995) 4.517106
Schindler's List (1993) 4.510417
由于评分前二十名的电影很有可能出现,虽然评分很高,但是看的人却很少,不信我们验证一下
利用ratings_by_title,将前二十名的电影名作为索引,查看电影出现的次数
ratings_by_title.loc[top_20_score.index]
#代码结果
title
Ulysses (Ulisse) (1954) 1
Lured (1947) 1
Follow the Bitch (1998) 1
Bittersweet Motel (2000) 1
Song of Freedom (1936) 1
One Little Indian (1973) 1
Smashing Time (1967) 2
Schlafes Bruder (Brother of Sleep) (1995) 1
Gate of Heavenly Peace, The (1995) 3
Baby, The (1973) 1
I Am Cuba (Soy Cuba/Ya Kuba) (1964) 5
Lamerica (1994) 8
Apple, The (Sib) (1998) 9
Sanjuro (1962) 69
Seven Samurai (The Magnificent Seven) (Shichinin no samurai) (1954) 628
Shawshank Redemption, The (1994) 2227
Godfather, The (1972) 2223
Close Shave, A (1995) 657
Usual Suspects, The (1995) 1783
Schindler's List (1993) 2304
dtype: int64
正片开始
#通过筛选条件:出现次数超过1000,选出热门电影
hot_movies = ratings_by_title[ratings_by_title >1000]
#利用mean_ratings,将hot_movies作为索引,找出平均评分,出现次数最多的电影
hot_mocies_rating = mean_ratings.loc[hot_movies.index]
#最后得出最好看的前100部电影
top_100_good_movies = hot_mocies_rating.sort_values(
ascending = False,by = 'title').head(100)
top_100_good_movies.sort_values(ascending = False,by = 'rating')
#代码结果
rating
title
Shawshank Redemption, The (1994) 4.554558
Usual Suspects, The (1995) 4.517106
Schindler's List (1993) 4.510417
Raiders of the Lost Ark (1981) 4.477725
Rear Window (1954) 4.476190
... ...
Mission: Impossible 2 (2000) 3.195735
Twister (1996) 3.173874
Starship Troopers (1997) 3.133276
Lost World: Jurassic Park, The (1997) 3.036653
Mars Attacks! (1996) 2.900372
100 rows × 1 columns
|
rating |
| title |
| Shawshank Redemption, The (1994) |
4.554558 |
| Usual Suspects, The (1995) |
4.517106 |
| Schindler's List (1993) |
4.510417 |
| Raiders of the Lost Ark (1981) |
4.477725 |
| Rear Window (1954) |
4.476190 |
| ... |
... |
| Mission: Impossible 2 (2000) |
3.195735 |
| Twister (1996) |
3.173874 |
| Starship Troopers (1997) |
3.133276 |
| Lost World: Jurassic Park, The (1997) |
3.036653 |
| Mars Attacks! (1996) |
2.900372 |
100 rows × 1 columns
世界因代码而改变 Peace Out
发表于 2020-9-29 11:00
|
发表于 2020-9-29 13:24
以后有谁想转码农就把你的作品给他看看，让他死了这条心
