Python数据分析与机器学习-Matplot_4

import pandas as pd
import matplotlib.pyplot as plt
reviews = pd.read_csv('fandango_scores.csv')
cols = ['FILM', 'RT_user_norm', 'Metacritic_user_nom', 'IMDB_norm', 'Fandango_Ratingvalue']
norm_reviews = reviews[cols]
print(norm_reviews[:5])

                             FILM  RT_user_norm  Metacritic_user_nom  
0  Avengers: Age of Ultron (2015)           4.3                 3.55   
1               Cinderella (2015)           4.0                 3.75   
2                  Ant-Man (2015)           4.5                 4.05   
3          Do You Believe? (2015)           4.2                 2.35   
4   Hot Tub Time Machine 2 (2015)           1.4                 1.70   

   IMDB_norm  Fandango_Ratingvalue  
0       3.90                   4.5  
1       3.55                   4.5  
2       3.90                   4.5  
3       2.70                   4.5  
4       2.55                   3.0  

fandango_distribution = norm_reviews['Fandango_Ratingvalue'].value_counts()
print(fandango_distribution)
fandango_distribution = fandango_distribution.sort_index()
print('-----------')
print(fandango_distribution)

imdb_distribution = norm_reviews['IMDB_norm'].value_counts()
print(imdb_distribution)
imdb_distribution = imdb_distribution.sort_index()
print("-----------")
print(imdb_distribution)

4.1    16
4.2    12
3.9    12
4.3    11
3.7     9
3.5     9
4.5     9
3.4     9
3.6     8
4.4     7
4.0     7
3.2     5
2.9     5
3.8     5
3.3     4
4.6     4
3.0     4
4.8     3
3.1     3
2.8     2
2.7     2
Name: Fandango_Ratingvalue, dtype: int64
-----------
2.7     2
2.8     2
2.9     5
3.0     4
3.1     3
3.2     5
3.3     4
3.4     9
3.5     9
3.6     8
3.7     9
3.8     5
3.9    12
4.0     7
4.1    16
4.2    12
4.3    11
4.4     7
4.5     9
4.6     4
4.8     3
Name: Fandango_Ratingvalue, dtype: int64
3.60    10
3.30     9
3.15     9
3.90     9
3.70     8
3.45     7
3.55     7
3.35     7
3.75     6
3.20     6
2.75     5
3.65     5
3.50     4
2.70     4
3.05     4
4.10     4
3.25     4
3.85     4
3.80     3
2.95     3
2.60     2
4.20     2
2.45     2
2.30     2
3.95     2
2.80     2
3.00     2
4.00     1
3.10     1
2.00     1
2.50     1
2.85     1
4.05     1
4.15     1
2.20     1
4.30     1
2.55     1
2.15     1
3.40     1
2.90     1
2.10     1
Name: IMDB_norm, dtype: int64
-----------
2.00     1
2.10     1
2.15     1
2.20     1
2.30     2
2.45     2
2.50     1
2.55     1
2.60     2
2.70     4
2.75     5
2.80     2
2.85     1
2.90     1
2.95     3
3.00     2
3.05     4
3.10     1
3.15     9
3.20     6
3.25     4
3.30     9
3.35     7
3.40     1
3.45     7
3.50     4
3.55     7
3.60    10
3.65     5
3.70     8
3.75     6
3.80     3
3.85     4
3.90     9
3.95     2
4.00     1
4.05     1
4.10     4
4.15     1
4.20     2
4.30     1
Name: IMDB_norm, dtype: int64

fig, ax = plt.subplots()
# ax.hist(norm_reviews['Fandango_Ratingvalue'])
# ax.hist(norm_reviews['Fandango_Ratingvalue'],bins=20)
ax.hist(norm_reviews['Fandango_Ratingvalue'], range=(4, 5),bins=20)
plt.show()

fig = plt.figure(figsize=(5,20))
ax1 = fig.add_subplot(4,1,1)
ax2 = fig.add_subplot(4,1,2)
ax3 = fig.add_subplot(4,1,3)
ax4 = fig.add_subplot(4,1,4)
ax1.hist(norm_reviews['Fandango_Ratingvalue'], bins=20, range=(0, 5))
ax1.set_title('Distribution of Fandango Ratings')
ax1.set_ylim(0, 50)

ax2.hist(norm_reviews['RT_user_norm'], bins=20, range=(0, 5))
ax2.set_title('Distribution of Rotten Tomatoes Ratings')
ax2.set_ylim(0, 50)

ax3.hist(norm_reviews['Metacritic_user_nom'], 20, range=(0, 5))
ax3.set_title('Distribution of Metacritic Ratings')
ax3.set_ylim(0, 50)

ax4.hist(norm_reviews['IMDB_norm'], 20, range=(0, 5))
ax4.set_title('Distribution of IMDB Ratings')
ax4.set_ylim(0, 50)

plt.show()

fig, ax = plt.subplots()
ax.boxplot(norm_reviews['RT_user_norm'])
ax.set_xticklabels(['Rotten Tomatoes'])
plt.show()

num_cols = ['RT_user_norm', 'Metacritic_user_nom', 'IMDB_norm', 'Fandango_Ratingvalue']
fig, ax = plt.subplots()
ax.boxplot(norm_reviews[num_cols].values)
ax.set_xticklabels(num_cols,rotation=45)
ax.set_ylim(0,5)
plt.show()