DAT8_Coursework/IMDB at master · KIVA7777/DAT8_Coursework · GitHub

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
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
Work for IMDB assignment

impport pandas as pd
import matplotlib.pyplot as plt

pd.read_table('imdb_1000.csv')

imdb_1000 = pd.read_table('imdb_1000.csv', sep=',')

"""
There are 979 rows and 6 columns
"""

imdb_1000.dtypes

"""
star_rating       float64
title              object
content_rating     object
genre              object
duration            int64
actors_list        object
"""

imdb_1000.duration.mean()

"""
mean   120.97957099080695 or an hour and twenty-one minutes
"""
 imdb_1000.sort('duration')

"""
 Freaks is the shortest movie and 64 minutes, Hamlet is the lonest at 242 minutes
"""

imdb_1000.duration.plot(kind='hist', xlim=(0, 250), bins=30, title='histogram of duration')
plt.xlabel('Duration(s)')
plt.ylabel('Movies')

imdb_1000.duration.plot(kind='box')

"""
Part 2
"""

# count how many movies have each of the content ratings

imdb_1000['content_rating'].value_counts()


# use a visualization to display that same data, including a title and x and y labels

imdb_1000['content_rating'].value_counts().plot(kind = 'bar', title = 'Movie Count by Rating')
plt.xlabel('Movie Ratings')
plt.ylabel('# of Movies')

# convert the following content ratings to "UNRATED": NOT RATED, APPROVED, PASSED, GP

imdb_1000.content_rating.replace('PASSED', 'UNRATED', inplace=True)
imdb_1000.content_rating.replace('GP', 'UNRATED', inplace=True)
imdb_1000.content_rating.replace('APPROVED', 'UNRATED', inplace=True)
imdb_1000.content_rating.replace('NOT RATED', 'UNRATED', inplace=True)

imdb_1000['content_rating'].value_counts()
# convert the following content ratings to "NC-17": X, TV-MA
imdb_1000.content_rating.replace('X', 'NC-17', inplace=True)
imdb_1000.content_rating.replace('TV-MA', 'NC-17', inplace=True)

# count the number of missing values in each column

imdb_1000.isnull().sum()
# if there are missing values: examine them, then fill them in with "reasonable" values

imdb_1000[imdb_1000.content_rating.isnull()]
imdb_1000.content_rating.fillna(value='UNRATED', inplace=True)

# calculate the average star rating for movies 2 hours or longer,

imdb_1000[imdb_1000.duration >= 120].star_rating.mean()

"""
7.948898678414082
"""

# and compare that with the average star rating for movies shorter than 2 hours

imdb_1000[imdb_1000.duration <= 120].star_rating.mean()

"""
7.8384898710865345
"""

# use a visualization to detect whether there is a relationship between duration and star rating

imdb_1000.groupby('star_rating').mean().plot(kind='bar')

"""
There is a slight rise among movies that are between movies that are about 2hrs long to 2.5 or 3hrs long
"""

# calculate the average duration for each genre

imdb_1000.drop('star_rating', axis=1).groupby('genre').mean()


# visualize the relationship between content rating and duration

imdb_1000.drop('star_rating', axis=1).groupby('content_rating').mean().plot(kind='bar')

# determine the top rated movie (by star rating) for each genre

imdb_1000.groupby('genre').star_rating.max()

# check if there are multiple movies with the same title, and if so, determine if they are actually duplicates

I tried here
imdb_1000.title.duplicated()

# calculate the average star rating for each genre, but only include genres with at least 10 movies

And here:
imdb_1000[imdb_1000.genre > 10].star_rating.mean()

imdb_1000[imdb_1000.groupby('genre').star_rating.mean(), if x in 'genre' > 10]('this doesn\'t work at all')

imdb_1000.groupby('genre').star_rating.mean()

imdb_1000[imdb_1000.groupby('genre').count() > 10].mean()