Part1: Short form essay
1. Explanation
The story is mainly about what kind of movie will have long-term effect in the movie history and influence and inspire people a lot on earth? Through analyzing the data list of the movies on show recently and observe the characters of the excellent movies on TOP250 Douban Movie list maybe I can tell people the answer.
I choose this particular angle is because that the China’s film market is booming nowadays, we definitely have more films every year but with fewer great ones. By analyzing I want to find what will make an influential and excellent film and what should filmmakers do in the future to produce more great movies for us.
2. A list of the data sources (见Excel表格）
3. Data reduction
Cleaned:
表一：
def pylot_show():
sql = 'select * from douban;'
cur.execute(sql)
rows = cur.fetchall() # 把表中所有字段读取出来
count = [] # 每个分类的数量
category = [] # 分类
for row in rows:
count.append(int(row[2]))
category.append(row[1])
y_pos = np.arange(len(category)) # 定义y轴坐标数
plt.barh(y_pos, count, align='center', alpha=0.4) # alpha图表的填充不透明度(0~1)之间
plt.yticks(y_pos, category) # 在y轴上做分类名的标记
for count, y_pos in zip(count, y_pos):
# 分类个数在图中显示的位置，就是那些数字在柱状图尾部显示的数字 plt.text(count, y_pos, count, horizontalalignment='center', verticalalignment='center', weight='bold')
plt.ylim(+28.0, -1.0) # 可视化范围，相当于规定y轴范围
plt.title(u'电影种类') # 图表的标题
plt.ylabel(u'电影分类') # 图表y轴的标记
plt.subplots_adjust(bottom = 0.15)
plt.xlabel(u'年份') # 图表x轴的标记
Classified：
表二
按电影上映城市分类：
#!-*- coding:utf-8 -*-
import pandas as pd
import numpy as np
import matplotlib.pylab as plt
from matplotlib.font_manager import FontProperties #fontproperties的模块，pyde自动添加的
Movie=pd.read_csv('MovieTop250.csv',encoding='utf-8')
country_iter=(set(x.split(' ')) for x in Movie['Country']) #generator生成器，分解字符串 countries=sorted(set.union(*country_iter)) #Return the union of sets as a new set. #*country_iter:
df=pd.DataFrame(np.zeros((len(Movie),len(countries))),columns=countries)#创建一个
0DataFrame，np.zeros()内为要tuple
for i,gen in enumerate(Movie['Country']):
df.ix[i,gen.split(' ')]=1 #第i条数据的country置为1
num_of_country=df.sum()
num_of_country[1]=num_of_country[0]+num_of_country[1] #中国和中国大陆合并 num_of_country.pop('中国')
num_of_country.sort_values(inplace=True,ascending=False)
f1=plt.figure() for i,gen in enumerate(num_of_country[:10]):
plt.bar(i,gen)
#i为bar的起始横坐标，gen为纵坐标，宽度默认 names=list(num_of_country.index)
plt.xticks(np.arange(10)+0.4,names,fontproperties='SimHei') #在图中显示中文字符要加上fontproperties='SimHei'
plt.ylabel('Movie Number')
plt.title('Douban
Movie\nMovie Distribution by Countries')
plt.savefig('Movie_Distribution_by_Countries.png')
f1.show()
按电影类型分类:
genre_iter=(set(x.split(' ')) for x in Movie['Grenre'])
genre=sorted(set.union(*genre_iter)) frame=pd.DataFrame(np.zeros((len(Movie),len(genre))),columns=genre)
for i,gen in enumerate(Movie['Grenre']):
frame.ix[i,gen.split(' ')]=1
genre_sum=frame.sum()
genre_sum.sort_values(inplace=True,ascending=False)
f2=plt.figure(2)
'''for i,gen in enumerate(genre_sum[:8]):
plt.bar(i,gen)
names=list(genre_sum.index)
plt.xticks(np.arange(8)+0.4,names,fontproperties='SimHei') plt.show()'''
4. Shortcomings
The data is scattered so it takes me a lot of time to classify. And I should compare and find the connection between my different data lists.
Part2: The story
With the development of the economy, people live a better life now. So they begin to transfer from pursuing material life to spiritual enjoyment. Nowadays more and more people will choose to enjoy a great film in their free time, so we also can see from the data that more films are produced and we have more different varieties.
But as the China’s film market is booming, making films just becomes a good way to make money instead of creating excellent artworks. That’s a serious problem which needs us to pay attention to. As we can see in the TOP250 Douban Movie list, most high-score movies are made between 1995-2013. There are 8 movies made in the 1990s of the TOP10. These excellent movies have very profound effect on people all over the world. As watching more great movies, people’s taste has been greatly improved, so they have higher requirement for the movies. But even with high technology nowadays, the filmmakers make fewer great movies. In the age of fast food culture, many films are produced in a rough way and just have plain and vulgar plots. That kind of films，which can reflect the real life and humanity become fewer and fewer. The quality of the films is ragged.
The highest score Chinese movie is Farewell to My Concubine, which is directed by Chen Kaige in 1993. It is a great film about love and belief. The film is very deep and thought-provoking. The performance of the main actors is also very touching. The great success of this movie just shows that we must make movie with respect and love. We must take it as a piece of art and pour our soul into it. That is the key to make a great film.
So after analyzing the data, I really hope that in the future we will have more and more great movies, which involve tradition and innovation and have profound influence in the movie history.
