資料下載地址：https://query.data.world/s/uikepcpffyo2nhig52xxeevdialfl7
 1.提取資料---電影標題，電影型別，電影導演，電影演員，電影劇情
 2.清洗資料---
      電影劇情使用rake_nltk去除停定詞，對關鍵詞排序。
      電影導演，電影演員去除空格，把姓和名作為一個單詞
 3.把所有的關鍵詞拼接成bag_of_words,計算相似度。
 4.對指定電影進行top10推薦。
 主要的技術點：rate_nltk,sklean中的cosine_similarity,skean中的CountVectorizer
      

#!/usr/bin/Python
# -*- coding: utf-8 -*

import pandas as pd
from rake_nltk import Rake
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.feature_extraction.text import CountVectorizer

pd.set_option('display.max_columns', 100)
df = pd.read_csv('IMDB_Top250Engmovies2_OMDB_Detailed.csv')
print(df.head())
print(df.shape)
df = df[['Title','Genre','Director','Actors','Plot']]
df.head()
# discarding the commas between the actors' full names and getting only the first three names
df['Actors'] = df['Actors'].map(lambda x: x.split(',')[:3])
print("====演員列表====")
print(df['Actors'][:3])

# putting the genres in a list of words
df['Genre'] = df['Genre'].map(lambda x: x.lower().split(','))
print("===型別列表===")
print(df['Genre'][:3])


df['Director'] = df['Director'].map(lambda x: x.split(' '))
print("===導演列表====")
print(df['Director'][:3])

# merging together first and last name for each actor and director, so it's considered as one word
# and there is no mix up between people sharing a first name
for index, row in df.iterrows():
    # 把姓和名變成一個單詞
    row['Actors'] = [x.lower().replace(' ','') for x in row['Actors']]
    # print("變化前")
    # print(row['Director'])
    row['Director'] = ''.join(row['Director']).lower()
    # print("變化後")
    # print(row['Director'])

for index, row in df.iterrows():
    if(index<3):
        print("===演員列表===")
        print(row['Actors'])
        print("===導演列表===")
        print(row['Director'])

# initializing the new column
df['Key_words'] = ""

for index, row in df.iterrows():
    plot = row['Plot']

    # instantiating Rake, by default is uses english stopwords from NLTK
    # and discard all puntuation characters
    r = Rake()

    # extracting the words by passing the text
    r.extract_keywords_from_text(plot)

    # getting the dictionary whith key words and their scores
    key_words_dict_scores = r.get_word_degrees()
    print("===key_words_dict_scores===")
    print(key_words_dict_scores)

    # assigning the key words to the new column
    row['Key_words'] = list(key_words_dict_scores.keys())
    print("===key_words===")
    print(row['Key_words'])

# dropping the Plot column
df.drop(columns=['Plot'], inplace=True)

df.set_index('Title', inplace = True)
print(df.head())

df['bag_of_words'] = ''
print("===df.columns====")
print(df.columns)
columns = df.columns
for index, row in df.iterrows():
    words = ''
    for col in columns:
        if col != 'Director':
            words = words + ' '.join(row[col]) + ' '
        else:
            words = words + row[col] + ' '
        print("====words:====")
        print(words)
    row['bag_of_words'] = words

for index, row in df.iterrows():
    print("===bag_of_words===")
    print(row['bag_of_words'])

df.drop(columns=[col for col in df.columns if col != 'bag_of_words'], inplace=True)

print("===head:===")
print(df.head())

# instantiating and generating the count matrix
count = CountVectorizer()
count_matrix = count.fit_transform(df['bag_of_words'])

# creating a Series for the movie titles so they are associated to an ordered numerical
# list I will use later to match the indexes
indices = pd.Series(df.index)
indices[:5]
print("===indices:===")
print(indices)
# generating the cosine similarity matrix
cosine_sim = cosine_similarity(count_matrix, count_matrix)
# cosine_sim
print("===cosine_sim:===")
print(cosine_sim)


# function that takes in movie title as input and returns the top 10 recommended movies
def recommendations(title, cosine_sim=cosine_sim):
    recommended_movies = []

    # gettin the index of the movie that matches the title
    idx = indices[indices == title].index[0]
    print("===idx:====")
    print(idx)

    # creating a Series with the similarity scores in descending order
    score_series = pd.Series(cosine_sim[idx]).sort_values(ascending=False)
    print("===score_series===")
    print(score_series)

    # getting the indexes of the 10 most similar movies
    top_10_indexes = list(score_series.iloc[1:11].index)

    # populating the list with the titles of the best 10 matching movies
    for i in top_10_indexes:
        recommended_movies.append(list(df.index)[i])

    return recommended_movies

print(recommendations('Fargo'))