2. 用matplotlib做数据可视化

#  读取movie数据集，计算每年的预算中位数，再计算五年滚动均值以平滑数据
 In[32]: movie = pd.read_csv('data/movie.csv')
         med_budget = movie.groupby('title_year')['budget'].median() / 1e6
         med_budget_roll = med_budget.rolling(5, min_periods=1).mean()
         med_budget_roll.tail()
Out[32]: title_year
         2012.0    20.893
         2013.0    19.893
         2014.0    19.100
         2015.0    17.980
         2016.0    17.780
         Name: budget, dtype: float64

#  将数据变为NumPy数组
 In[33]: years = med_budget_roll.index.values
         years[-5:]
Out[33]: array([ 2012.,  2013.,  2014.,  2015.,  2016.])
 In[34]: budget = med_budget_roll.values
         budget[-5:]
Out[34]: array([ 20.893,  19.893,  19.1  ,  17.98 ,  17.78 ])

#  plot方法可以用来画线图
 In[35]: fig, ax = plt.subplots(figsize=(14,4), linewidth=5, edgecolor='.5')
         ax.plot(years, budget, linestyle='--', linewidth=3, color='.2', label='All Movies')
         text_kwargs=dict(fontsize=20, family='cursive')
         ax.set_title('Median Movie Budget', **text_kwargs)
         ax.set_ylabel('Millions of Dollars', **text_kwargs)
Out[35]: Text(0,0.5,'Millions of Dollars')

#  每年的电影产量
 In[36]: movie_count = movie.groupby('title_year')['budget'].count()
         movie_count.tail()
Out[36]: title_year
         2012.0    191
         2013.0    208
         2014.0    221
         2015.0    192
         2016.0     86
         Name: budget, dtype: int64

#  在前图的基础上，将每年的电影产量画成一个柱状图，因为大部分电影都近年的，所以将起始的年设为1970
 In[37]: ct = movie_count.values
         ct_norm = ct / ct.max() * budget.max()
         fifth_year = (years % 5 == 0) & (years >= 1970)
         years_5 = years[fifth_year]
         ct_5 = ct[fifth_year]
         ct_norm_5 = ct_norm[fifth_year]
         ax.bar(years_5, ct_norm_5, 3, facecolor='.5', alpha=.3, label='Movies per Year')
         ax.set_xlim(1968, 2017)
         for x, y, v in zip(years_5, ct_norm_5, ct_5):
             ax.text(x, y + .5, str(v), ha='center')
         ax.legend()
         fig
Out[37]:

#  找到每年的前10部电影的五年滚动中位数
 In[38]: top10 = movie.sort_values('budget', ascending=False) \
                      .groupby('title_year')['budget'] \
                      .apply(lambda x: x.iloc[:10].median() / 1e6)
         top10_roll = top10.rolling(5, min_periods=1).mean()
         top10_roll.tail()
Out[38]: title_year
         2012.0    192.9
         2013.0    195.9
         2014.0    191.7
         2015.0    186.8
         2016.0    189.1
         Name: budget, dtype: float64

#  将上面的数据画到另一张子图中
 In[39]: fig2, ax_array = plt.subplots(2, 1, figsize=(14,6), sharex=True)
         ax1 = ax_array[0]
         ax2 = ax_array[1]
         ax1.plot(years, budget, linestyle='--', linewidth=3, color='.2', label='All Movies')
         ax1.bar(years_5, ct_norm_5, 3, facecolor='.5', alpha=.3, label='Movies per Year')
         ax1.legend(loc='upper left')
         ax1.set_xlim(1968, 2017)
         plt.setp(ax1.get_xticklines(), visible=False)
         for x, y, v in zip(years_5, ct_norm_5, ct_5):
             ax1.text(x, y + .5, str(v), ha='center')
         ax2.plot(years, top10_roll.values, color='.2', label='Top 10 Movies')
         ax2.legend(loc='upper left')
         fig2.tight_layout()
         fig2.suptitle('Median Movie Budget', y=1.02, **text_kwargs)
         fig2.text(0, .6, 'Millions of Dollars', rotation='vertical', ha='center', **text_kwargs)
         import os
         path = os.path.expanduser('~/Desktop/movie_budget.png')
         fig2.savefig(path, bbox_inches='tight')
Out[39]:

原理

 In[40]: med_budget_roll.tail()
Out[40]: title_year
         2012.0    20.893
         2013.0    19.893
         2014.0    19.100
         2015.0    17.980
         2016.0    17.780
         Name: budget, dtype: float64

#  手动确认一下rolling方法
 In[41]: med_budget.loc[2012:2016].mean()
Out[41]: 17.78
 In[42]: med_budget.loc[2011:2015].mean()
Out[42]: 17.98
 In[43]: med_budget.loc[2010:2014].mean()
Out[43]: 19.1

#  必须使用expanduser创建完整路径
 In[44]: os.path.expanduser('~/Desktop/movie_budget.png')
Out[44]: '/Users/Ted/Desktop/movie_budget.png'

更多

 In[45]: cols = ['budget', 'title_year', 'imdb_score', 'movie_title']
         m = movie[cols].dropna()
         #  m = movie[['budget', 'title_year', 'imdb_score', 'movie_title']].dropna()
         m['budget2'] = m['budget'] / 1e6
         np.random.seed(0)
         movie_samp = m.query('title_year >= 2000').sample(100)
         fig, ax = plt.subplots(figsize=(14,6))
         ax.scatter(x='title_year', y='imdb_score', s='budget2', data=movie_samp)
         idx_min = movie_samp['imdb_score'].idxmin()
         idx_max = movie_samp['imdb_score'].idxmax()
         for idx, offset in zip([idx_min, idx_max], [.5, -.5]):
             year = movie_samp.loc[idx, 'title_year']
             score = movie_samp.loc[idx, 'imdb_score']
             title = movie_samp.loc[idx, 'movie_title']
             ax.annotate(xy=(year, score), 
                         xytext=(year + 1, score + offset), 
                         s=title + ' ({})'.format(score),
                         ha='center',
                         size=16,
                         arrowprops=dict(arrowstyle="fancy"))
         ax.set_title('IMDB Score by Year', size=25)
         ax.grid(True)
Out[45]: