条形图与散点图

1. 取出一行数据

   import pandas as pd
   reviews = pd.read_csv('fandango_scores.csv')
   cols = ['FILM', 'RT_user_norm', 'Metacritic_user_nom', 'IMDB_norm', 'Fandango_Ratingvalue', 'Fandango_Stars']
   norm_reviews = reviews[cols]
   print(norm_reviews[:1])
   

   运行结果：
   

2. 显示柱形图

   import matplotlib.pyplot as plt
   from numpy import arange
   #The Axes.bar() method has 2 required parameters, left and height. 
   #We use the left parameter to specify the x coordinates of the left sides of the bar. 
   #We use the height parameter to specify the height of each bar
   num_cols = ['RT_user_norm', 'Metacritic_user_nom', 'IMDB_norm', 'Fandango_Ratingvalue', 'Fandango_Stars']
   
   bar_heights = norm_reviews.loc[0, num_cols].values
   #print bar_heights
   bar_positions = arange(5) + 0.75
   #print bar_positions
   fig, ax = plt.subplots()
   ax.bar(bar_positions, bar_heights, 0.5)
   plt.show()
   

   运行结果：
   

3. 设置坐标名称

   #By default, matplotlib sets the x-axis tick labels to the integer values the bars 
   #spanned on the x-axis (from 0 to 6). We only need tick labels on the x-axis where the bars are positioned. 
   #We can use Axes.set_xticks() to change the positions of the ticks to [1, 2, 3, 4, 5]:
   
   num_cols = ['RT_user_norm', 'Metacritic_user_nom', 'IMDB_norm', 'Fandango_Ratingvalue', 'Fandango_Stars']
   bar_heights = norm_reviews.loc[0, num_cols].values
   bar_positions = arange(5) + 0.75
   tick_positions = range(1,6)
   fig, ax = plt.subplots()
   
   ax.bar(bar_positions, bar_heights, 0.5)
   ax.set_xticks(tick_positions)
   ax.set_xticklabels(num_cols, rotation=45)
   
   ax.set_xlabel('Rating Source')
   ax.set_ylabel('Average Rating')
   ax.set_title('Average User Rating For Avengers: Age of Ultron (2015)')
   plt.show()
   

   运行结果：
   

4. 改变图的方向

   import matplotlib.pyplot as plt
   from numpy import arange
   num_cols = ['RT_user_norm', 'Metacritic_user_nom', 'IMDB_norm', 'Fandango_Ratingvalue', 'Fandango_Stars']
   
   bar_widths = norm_reviews.loc[0, num_cols].values
   bar_positions = arange(5) + 0.75
   tick_positions = range(1,6)
   fig, ax = plt.subplots()
   ax.barh(bar_positions, bar_widths, 0.5)
   
   ax.set_yticks(tick_positions)
   ax.set_yticklabels(num_cols)
   ax.set_ylabel('Rating Source')
   ax.set_xlabel('Average Rating')
   ax.set_title('Average User Rating For Avengers: Age of Ultron (2015)')
   plt.show()
   

   运行结果：
   

5. 散点图

   #Let's look at a plot that can help us visualize many points.
   fig, ax = plt.subplots()
   ax.scatter(norm_reviews['Fandango_Ratingvalue'], norm_reviews['RT_user_norm'])
   ax.set_xlabel('Fandango')
   ax.set_ylabel('Rotten Tomatoes')
   plt.show()
   

   运行结果：
   

6. 添加子图

   #Switching Axes
   fig = plt.figure(figsize=(5,10))
   ax1 = fig.add_subplot(2,1,1)
   ax2 = fig.add_subplot(2,1,2)
   ax1.scatter(norm_reviews['Fandango_Ratingvalue'], norm_reviews['RT_user_norm'])
   ax1.set_xlabel('Fandango')
   ax1.set_ylabel('Rotten Tomatoes')
   ax2.scatter(norm_reviews['RT_user_norm'], norm_reviews['Fandango_Ratingvalue'])
   ax2.set_xlabel('Rotten Tomatoes')
   ax2.set_ylabel('Fandango')
   plt.show()
   

   运行结果：