AI day04(2020 8/3)
Matplotlib
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# 常见的无衬线字体有 Trebuchet MS, Tahoma, Verdana, Arial, Helvetica,SimHei 中文的幼圆、隶书等等
plt.rcParams['font.sans-serif'] = ['FangSong'] # 指定默认字体
plt.rcParams['axes.unicode_minus'] = False # 解决保存图像是负号'-'显示为方块的问题
data = pd.read_csv('data/unrate.csv')
data
DATE | VALUE | |
---|---|---|
0 | 1948-01-01 | 3.4 |
1 | 1948-02-01 | 3.8 |
2 | 1948-03-01 | 4.0 |
3 | 1948-04-01 | 3.9 |
4 | 1948-05-01 | 3.5 |
5 | 1948-06-01 | 3.6 |
6 | 1948-07-01 | 3.6 |
7 | 1948-08-01 | 3.9 |
8 | 1948-09-01 | 3.8 |
9 | 1948-10-01 | 3.7 |
10 | 1948-11-01 | 3.8 |
11 | 1948-12-01 | 4.0 |
12 | 1949-01-01 | 4.3 |
13 | 1949-02-01 | 4.7 |
14 | 1949-03-01 | 5.0 |
15 | 1949-04-01 | 5.3 |
16 | 1949-05-01 | 6.1 |
17 | 1949-06-01 | 6.2 |
18 | 1949-07-01 | 6.7 |
19 | 1949-08-01 | 6.8 |
20 | 1949-09-01 | 6.6 |
21 | 1949-10-01 | 7.9 |
22 | 1949-11-01 | 6.4 |
23 | 1949-12-01 | 6.6 |
24 | 1950-01-01 | 6.5 |
25 | 1950-02-01 | 6.4 |
26 | 1950-03-01 | 6.3 |
27 | 1950-04-01 | 5.8 |
28 | 1950-05-01 | 5.5 |
29 | 1950-06-01 | 5.4 |
... | ... | ... |
794 | 2014-03-01 | 6.7 |
795 | 2014-04-01 | 6.2 |
796 | 2014-05-01 | 6.2 |
797 | 2014-06-01 | 6.1 |
798 | 2014-07-01 | 6.2 |
799 | 2014-08-01 | 6.2 |
800 | 2014-09-01 | 6.0 |
801 | 2014-10-01 | 5.7 |
802 | 2014-11-01 | 5.8 |
803 | 2014-12-01 | 5.6 |
804 | 2015-01-01 | 5.7 |
805 | 2015-02-01 | 5.5 |
806 | 2015-03-01 | 5.5 |
807 | 2015-04-01 | 5.4 |
808 | 2015-05-01 | 5.5 |
809 | 2015-06-01 | 5.3 |
810 | 2015-07-01 | 5.3 |
811 | 2015-08-01 | 5.1 |
812 | 2015-09-01 | 5.1 |
813 | 2015-10-01 | 5.0 |
814 | 2015-11-01 | 5.0 |
815 | 2015-12-01 | 5.0 |
816 | 2016-01-01 | 4.9 |
817 | 2016-02-01 | 4.9 |
818 | 2016-03-01 | 5.0 |
819 | 2016-04-01 | 5.0 |
820 | 2016-05-01 | 4.7 |
821 | 2016-06-01 | 4.9 |
822 | 2016-07-01 | 4.9 |
823 | 2016-08-01 | 4.9 |
824 rows × 2 columns
data.head()
DATE | VALUE | |
---|---|---|
0 | 1948-01-01 | 3.4 |
1 | 1948-02-01 | 3.8 |
2 | 1948-03-01 | 4.0 |
3 | 1948-04-01 | 3.9 |
4 | 1948-05-01 | 3.5 |
unrate = data.copy()
unrate['DATE'] = pd.to_datetime(unrate['DATE'])
unrate.head(12)
DATE | VALUE | |
---|---|---|
0 | 1948-01-01 | 3.4 |
1 | 1948-02-01 | 3.8 |
2 | 1948-03-01 | 4.0 |
3 | 1948-04-01 | 3.9 |
4 | 1948-05-01 | 3.5 |
5 | 1948-06-01 | 3.6 |
6 | 1948-07-01 | 3.6 |
7 | 1948-08-01 | 3.9 |
8 | 1948-09-01 | 3.8 |
9 | 1948-10-01 | 3.7 |
10 | 1948-11-01 | 3.8 |
11 | 1948-12-01 | 4.0 |
plt.plot()
plt.show()
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/font_manager.py:1331: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans(prop.get_family(), self.defaultFamily[fontext]))
first_twelve = unrate[0:12]
first_twelve
DATE | VALUE | |
---|---|---|
0 | 1948-01-01 | 3.4 |
1 | 1948-02-01 | 3.8 |
2 | 1948-03-01 | 4.0 |
3 | 1948-04-01 | 3.9 |
4 | 1948-05-01 | 3.5 |
5 | 1948-06-01 | 3.6 |
6 | 1948-07-01 | 3.6 |
7 | 1948-08-01 | 3.9 |
8 | 1948-09-01 | 3.8 |
9 | 1948-10-01 | 3.7 |
10 | 1948-11-01 | 3.8 |
11 | 1948-12-01 | 4.0 |
# x,y 默认是折线图
plt.plot(first_twelve['DATE'],first_twelve['VALUE'])
plt.show()
plt.plot(first_twelve['DATE'],first_twelve['VALUE'])
# 设置对应轴的角度
plt.xticks(rotation = 45)
plt.show()
plt.plot(first_twelve['DATE'],first_twelve['VALUE'])
# 设置对应轴的角度
plt.xticks(rotation = 45)
# 设置x轴的标签
plt.xlabel('month')
# 设置y轴的标签
plt.ylabel('value')
# 设置图的标题
plt.title('unrate')
plt.show()
# figsize=(10,10) 设置大小为10*10
fig = plt.figure(figsize=(10,10))
ax1 = fig.add_subplot(2,2,1)
ax2 = fig.add_subplot(2,2,2)
ax3 = fig.add_subplot(2,2,4)
ax1.plot(np.random.randint(1,5,5),np.arange(5))
ax2.plot(np.arange(10)*3,np.arange(10))
plt.show()
unrate['MONTH'] = unrate['DATE'].dt.month
print(unrate['MONTH'])
0 1
1 2
2 3
3 4
4 5
5 6
6 7
7 8
8 9
9 10
10 11
11 12
12 1
13 2
14 3
15 4
16 5
17 6
18 7
19 8
20 9
21 10
22 11
23 12
24 1
25 2
26 3
27 4
28 5
29 6..
794 3
795 4
796 5
797 6
798 7
799 8
800 9
801 10
802 11
803 12
804 1
805 2
806 3
807 4
808 5
809 6
810 7
811 8
812 9
813 10
814 11
815 12
816 1
817 2
818 3
819 4
820 5
821 6
822 7
823 8
Name: MONTH, Length: 824, dtype: int64
# 同一图表画多条线
fig = plt.figure(figsize=(5,5))
plt.plot(unrate[0:12]['MONTH'],unrate[0:12]['VALUE'],c='red',label='1948')
plt.plot(unrate[12:24]['MONTH'],unrate[12:24]['VALUE'],c='green',label='1949')
plt.legend(loc='best')
plt.show()
reviews = pd.read_csv('data/fandango_score_comparison.csv')
reviews
FILM | RottenTomatoes | RottenTomatoes_User | Metacritic | Metacritic_User | IMDB | Fandango_Stars | Fandango_Ratingvalue | RT_norm | RT_user_norm | ... | IMDB_norm | RT_norm_round | RT_user_norm_round | Metacritic_norm_round | Metacritic_user_norm_round | IMDB_norm_round | Metacritic_user_vote_count | IMDB_user_vote_count | Fandango_votes | Fandango_Difference | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | Avengers: Age of Ultron (2015) | 74 | 86 | 66 | 7.1 | 7.8 | 5.0 | 4.5 | 3.70 | 4.30 | ... | 3.90 | 3.5 | 4.5 | 3.5 | 3.5 | 4.0 | 1330 | 271107 | 14846 | 0.5 |
1 | Cinderella (2015) | 85 | 80 | 67 | 7.5 | 7.1 | 5.0 | 4.5 | 4.25 | 4.00 | ... | 3.55 | 4.5 | 4.0 | 3.5 | 4.0 | 3.5 | 249 | 65709 | 12640 | 0.5 |
2 | Ant-Man (2015) | 80 | 90 | 64 | 8.1 | 7.8 | 5.0 | 4.5 | 4.00 | 4.50 | ... | 3.90 | 4.0 | 4.5 | 3.0 | 4.0 | 4.0 | 627 | 103660 | 12055 | 0.5 |
3 | Do You Believe? (2015) | 18 | 84 | 22 | 4.7 | 5.4 | 5.0 | 4.5 | 0.90 | 4.20 | ... | 2.70 | 1.0 | 4.0 | 1.0 | 2.5 | 2.5 | 31 | 3136 | 1793 | 0.5 |
4 | Hot Tub Time Machine 2 (2015) | 14 | 28 | 29 | 3.4 | 5.1 | 3.5 | 3.0 | 0.70 | 1.40 | ... | 2.55 | 0.5 | 1.5 | 1.5 | 1.5 | 2.5 | 88 | 19560 | 1021 | 0.5 |
5 | The Water Diviner (2015) | 63 | 62 | 50 | 6.8 | 7.2 | 4.5 | 4.0 | 3.15 | 3.10 | ... | 3.60 | 3.0 | 3.0 | 2.5 | 3.5 | 3.5 | 34 | 39373 | 397 | 0.5 |
6 | Irrational Man (2015) | 42 | 53 | 53 | 7.6 | 6.9 | 4.0 | 3.5 | 2.10 | 2.65 | ... | 3.45 | 2.0 | 2.5 | 2.5 | 4.0 | 3.5 | 17 | 2680 | 252 | 0.5 |
7 | Top Five (2014) | 86 | 64 | 81 | 6.8 | 6.5 | 4.0 | 3.5 | 4.30 | 3.20 | ... | 3.25 | 4.5 | 3.0 | 4.0 | 3.5 | 3.5 | 124 | 16876 | 3223 | 0.5 |
8 | Shaun the Sheep Movie (2015) | 99 | 82 | 81 | 8.8 | 7.4 | 4.5 | 4.0 | 4.95 | 4.10 | ... | 3.70 | 5.0 | 4.0 | 4.0 | 4.5 | 3.5 | 62 | 12227 | 896 | 0.5 |
9 | Love & Mercy (2015) | 89 | 87 | 80 | 8.5 | 7.8 | 4.5 | 4.0 | 4.45 | 4.35 | ... | 3.90 | 4.5 | 4.5 | 4.0 | 4.5 | 4.0 | 54 | 5367 | 864 | 0.5 |
10 | Far From The Madding Crowd (2015) | 84 | 77 | 71 | 7.5 | 7.2 | 4.5 | 4.0 | 4.20 | 3.85 | ... | 3.60 | 4.0 | 4.0 | 3.5 | 4.0 | 3.5 | 35 | 12129 | 804 | 0.5 |
11 | Black Sea (2015) | 82 | 60 | 62 | 6.6 | 6.4 | 4.0 | 3.5 | 4.10 | 3.00 | ... | 3.20 | 4.0 | 3.0 | 3.0 | 3.5 | 3.0 | 37 | 16547 | 218 | 0.5 |
12 | Leviathan (2014) | 99 | 79 | 92 | 7.2 | 7.7 | 4.0 | 3.5 | 4.95 | 3.95 | ... | 3.85 | 5.0 | 4.0 | 4.5 | 3.5 | 4.0 | 145 | 22521 | 64 | 0.5 |
13 | Unbroken (2014) | 51 | 70 | 59 | 6.5 | 7.2 | 4.5 | 4.1 | 2.55 | 3.50 | ... | 3.60 | 2.5 | 3.5 | 3.0 | 3.5 | 3.5 | 218 | 77518 | 9443 | 0.4 |
14 | The Imitation Game (2014) | 90 | 92 | 73 | 8.2 | 8.1 | 5.0 | 4.6 | 4.50 | 4.60 | ... | 4.05 | 4.5 | 4.5 | 3.5 | 4.0 | 4.0 | 566 | 334164 | 8055 | 0.4 |
15 | Taken 3 (2015) | 9 | 46 | 26 | 4.6 | 6.1 | 4.5 | 4.1 | 0.45 | 2.30 | ... | 3.05 | 0.5 | 2.5 | 1.5 | 2.5 | 3.0 | 240 | 104235 | 6757 | 0.4 |
16 | Ted 2 (2015) | 46 | 58 | 48 | 6.5 | 6.6 | 4.5 | 4.1 | 2.30 | 2.90 | ... | 3.30 | 2.5 | 3.0 | 2.5 | 3.5 | 3.5 | 197 | 49102 | 6437 | 0.4 |
17 | Southpaw (2015) | 59 | 80 | 57 | 8.2 | 7.8 | 5.0 | 4.6 | 2.95 | 4.00 | ... | 3.90 | 3.0 | 4.0 | 3.0 | 4.0 | 4.0 | 128 | 23561 | 5597 | 0.4 |
18 | Night at the Museum: Secret of the Tomb (2014) | 50 | 58 | 47 | 5.8 | 6.3 | 4.5 | 4.1 | 2.50 | 2.90 | ... | 3.15 | 2.5 | 3.0 | 2.5 | 3.0 | 3.0 | 103 | 50291 | 5445 | 0.4 |
19 | Pixels (2015) | 17 | 54 | 27 | 5.3 | 5.6 | 4.5 | 4.1 | 0.85 | 2.70 | ... | 2.80 | 1.0 | 2.5 | 1.5 | 2.5 | 3.0 | 246 | 19521 | 3886 | 0.4 |
20 | McFarland, USA (2015) | 79 | 89 | 60 | 7.2 | 7.5 | 5.0 | 4.6 | 3.95 | 4.45 | ... | 3.75 | 4.0 | 4.5 | 3.0 | 3.5 | 4.0 | 59 | 13769 | 3364 | 0.4 |
21 | Insidious: Chapter 3 (2015) | 59 | 56 | 52 | 6.9 | 6.3 | 4.5 | 4.1 | 2.95 | 2.80 | ... | 3.15 | 3.0 | 3.0 | 2.5 | 3.5 | 3.0 | 115 | 25134 | 3276 | 0.4 |
22 | The Man From U.N.C.L.E. (2015) | 68 | 80 | 55 | 7.9 | 7.6 | 4.5 | 4.1 | 3.40 | 4.00 | ... | 3.80 | 3.5 | 4.0 | 3.0 | 4.0 | 4.0 | 144 | 22104 | 2686 | 0.4 |
23 | Run All Night (2015) | 60 | 59 | 59 | 7.3 | 6.6 | 4.5 | 4.1 | 3.00 | 2.95 | ... | 3.30 | 3.0 | 3.0 | 3.0 | 3.5 | 3.5 | 141 | 50438 | 2066 | 0.4 |
24 | Trainwreck (2015) | 85 | 74 | 75 | 6.0 | 6.7 | 4.5 | 4.1 | 4.25 | 3.70 | ... | 3.35 | 4.5 | 3.5 | 4.0 | 3.0 | 3.5 | 169 | 27380 | 8381 | 0.4 |
25 | Selma (2014) | 99 | 86 | 89 | 7.1 | 7.5 | 5.0 | 4.6 | 4.95 | 4.30 | ... | 3.75 | 5.0 | 4.5 | 4.5 | 3.5 | 4.0 | 316 | 45344 | 7025 | 0.4 |
26 | Ex Machina (2015) | 92 | 86 | 78 | 7.9 | 7.7 | 4.5 | 4.1 | 4.60 | 4.30 | ... | 3.85 | 4.5 | 4.5 | 4.0 | 4.0 | 4.0 | 672 | 154499 | 3458 | 0.4 |
27 | Still Alice (2015) | 88 | 85 | 72 | 7.8 | 7.5 | 4.5 | 4.1 | 4.40 | 4.25 | ... | 3.75 | 4.5 | 4.5 | 3.5 | 4.0 | 4.0 | 153 | 57123 | 1258 | 0.4 |
28 | Wild Tales (2014) | 96 | 92 | 77 | 8.8 | 8.2 | 4.5 | 4.1 | 4.80 | 4.60 | ... | 4.10 | 5.0 | 4.5 | 4.0 | 4.5 | 4.0 | 107 | 50285 | 235 | 0.4 |
29 | The End of the Tour (2015) | 92 | 89 | 84 | 7.5 | 7.9 | 4.5 | 4.1 | 4.60 | 4.45 | ... | 3.95 | 4.5 | 4.5 | 4.0 | 4.0 | 4.0 | 19 | 1320 | 121 | 0.4 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
116 | Clouds of Sils Maria (2015) | 89 | 67 | 78 | 7.1 | 6.8 | 3.5 | 3.4 | 4.45 | 3.35 | ... | 3.40 | 4.5 | 3.5 | 4.0 | 3.5 | 3.5 | 36 | 11392 | 162 | 0.1 |
117 | Testament of Youth (2015) | 81 | 79 | 77 | 7.9 | 7.3 | 4.0 | 3.9 | 4.05 | 3.95 | ... | 3.65 | 4.0 | 4.0 | 4.0 | 4.0 | 3.5 | 15 | 5495 | 127 | 0.1 |
118 | Infinitely Polar Bear (2015) | 80 | 76 | 64 | 7.9 | 7.2 | 4.0 | 3.9 | 4.00 | 3.80 | ... | 3.60 | 4.0 | 4.0 | 3.0 | 4.0 | 3.5 | 8 | 1062 | 124 | 0.1 |
119 | Phoenix (2015) | 99 | 81 | 91 | 8.0 | 7.2 | 3.5 | 3.4 | 4.95 | 4.05 | ... | 3.60 | 5.0 | 4.0 | 4.5 | 4.0 | 3.5 | 21 | 3687 | 70 | 0.1 |
120 | The Wolfpack (2015) | 84 | 73 | 75 | 7.0 | 7.1 | 3.5 | 3.4 | 4.20 | 3.65 | ... | 3.55 | 4.0 | 3.5 | 4.0 | 3.5 | 3.5 | 8 | 1488 | 66 | 0.1 |
121 | The Stanford Prison Experiment (2015) | 84 | 87 | 68 | 8.5 | 7.1 | 4.0 | 3.9 | 4.20 | 4.35 | ... | 3.55 | 4.0 | 4.5 | 3.5 | 4.5 | 3.5 | 6 | 950 | 51 | 0.1 |
122 | Tangerine (2015) | 95 | 86 | 86 | 7.3 | 7.4 | 4.0 | 3.9 | 4.75 | 4.30 | ... | 3.70 | 5.0 | 4.5 | 4.5 | 3.5 | 3.5 | 14 | 696 | 36 | 0.1 |
123 | Magic Mike XXL (2015) | 62 | 64 | 60 | 5.4 | 6.3 | 4.5 | 4.4 | 3.10 | 3.20 | ... | 3.15 | 3.0 | 3.0 | 3.0 | 2.5 | 3.0 | 52 | 11937 | 9363 | 0.1 |
124 | Home (2015) | 45 | 65 | 55 | 7.3 | 6.7 | 4.5 | 4.4 | 2.25 | 3.25 | ... | 3.35 | 2.5 | 3.5 | 3.0 | 3.5 | 3.5 | 177 | 41158 | 7705 | 0.1 |
125 | The Wedding Ringer (2015) | 27 | 66 | 35 | 3.3 | 6.7 | 4.5 | 4.4 | 1.35 | 3.30 | ... | 3.35 | 1.5 | 3.5 | 2.0 | 1.5 | 3.5 | 126 | 37292 | 6506 | 0.1 |
126 | Woman in Gold (2015) | 52 | 81 | 51 | 7.2 | 7.4 | 4.5 | 4.4 | 2.60 | 4.05 | ... | 3.70 | 2.5 | 4.0 | 2.5 | 3.5 | 3.5 | 72 | 17957 | 2435 | 0.1 |
127 | The Last Five Years (2015) | 60 | 60 | 60 | 6.9 | 6.0 | 4.5 | 4.4 | 3.00 | 3.00 | ... | 3.00 | 3.0 | 3.0 | 3.0 | 3.5 | 3.0 | 20 | 4110 | 99 | 0.1 |
128 | Mission: Impossible – Rogue Nation (2015) | 92 | 90 | 75 | 8.0 | 7.8 | 4.5 | 4.4 | 4.60 | 4.50 | ... | 3.90 | 4.5 | 4.5 | 4.0 | 4.0 | 4.0 | 362 | 82579 | 8357 | 0.1 |
129 | Amy (2015) | 97 | 91 | 85 | 8.8 | 8.0 | 4.5 | 4.4 | 4.85 | 4.55 | ... | 4.00 | 5.0 | 4.5 | 4.5 | 4.5 | 4.0 | 60 | 5630 | 729 | 0.1 |
130 | Jurassic World (2015) | 71 | 81 | 59 | 7.0 | 7.3 | 4.5 | 4.5 | 3.55 | 4.05 | ... | 3.65 | 3.5 | 4.0 | 3.0 | 3.5 | 3.5 | 1281 | 241807 | 34390 | 0.0 |
131 | Minions (2015) | 54 | 52 | 56 | 5.7 | 6.7 | 4.0 | 4.0 | 2.70 | 2.60 | ... | 3.35 | 2.5 | 2.5 | 3.0 | 3.0 | 3.5 | 204 | 55895 | 14998 | 0.0 |
132 | Max (2015) | 35 | 73 | 47 | 5.9 | 7.0 | 4.5 | 4.5 | 1.75 | 3.65 | ... | 3.50 | 2.0 | 3.5 | 2.5 | 3.0 | 3.5 | 15 | 5444 | 3412 | 0.0 |
133 | Paul Blart: Mall Cop 2 (2015) | 5 | 36 | 13 | 2.4 | 4.3 | 3.5 | 3.5 | 0.25 | 1.80 | ... | 2.15 | 0.5 | 2.0 | 0.5 | 1.0 | 2.0 | 211 | 15004 | 3054 | 0.0 |
134 | The Longest Ride (2015) | 31 | 73 | 33 | 4.8 | 7.2 | 4.5 | 4.5 | 1.55 | 3.65 | ... | 3.60 | 1.5 | 3.5 | 1.5 | 2.5 | 3.5 | 49 | 25214 | 2603 | 0.0 |
135 | The Lazarus Effect (2015) | 14 | 23 | 31 | 4.9 | 5.2 | 3.0 | 3.0 | 0.70 | 1.15 | ... | 2.60 | 0.5 | 1.0 | 1.5 | 2.5 | 2.5 | 62 | 17691 | 1651 | 0.0 |
136 | The Woman In Black 2 Angel of Death (2015) | 22 | 25 | 42 | 4.4 | 4.9 | 3.0 | 3.0 | 1.10 | 1.25 | ... | 2.45 | 1.0 | 1.5 | 2.0 | 2.0 | 2.5 | 55 | 14873 | 1333 | 0.0 |
137 | Danny Collins (2015) | 77 | 75 | 58 | 7.1 | 7.1 | 4.0 | 4.0 | 3.85 | 3.75 | ... | 3.55 | 4.0 | 4.0 | 3.0 | 3.5 | 3.5 | 33 | 11206 | 531 | 0.0 |
138 | Spare Parts (2015) | 52 | 83 | 50 | 7.1 | 7.2 | 4.5 | 4.5 | 2.60 | 4.15 | ... | 3.60 | 2.5 | 4.0 | 2.5 | 3.5 | 3.5 | 7 | 47377 | 450 | 0.0 |
139 | Serena (2015) | 18 | 25 | 36 | 5.3 | 5.4 | 3.0 | 3.0 | 0.90 | 1.25 | ... | 2.70 | 1.0 | 1.5 | 2.0 | 2.5 | 2.5 | 19 | 12165 | 50 | 0.0 |
140 | Inside Out (2015) | 98 | 90 | 94 | 8.9 | 8.6 | 4.5 | 4.5 | 4.90 | 4.50 | ... | 4.30 | 5.0 | 4.5 | 4.5 | 4.5 | 4.5 | 807 | 96252 | 15749 | 0.0 |
141 | Mr. Holmes (2015) | 87 | 78 | 67 | 7.9 | 7.4 | 4.0 | 4.0 | 4.35 | 3.90 | ... | 3.70 | 4.5 | 4.0 | 3.5 | 4.0 | 3.5 | 33 | 7367 | 1348 | 0.0 |
142 | '71 (2015) | 97 | 82 | 83 | 7.5 | 7.2 | 3.5 | 3.5 | 4.85 | 4.10 | ... | 3.60 | 5.0 | 4.0 | 4.0 | 4.0 | 3.5 | 60 | 24116 | 192 | 0.0 |
143 | Two Days, One Night (2014) | 97 | 78 | 89 | 8.8 | 7.4 | 3.5 | 3.5 | 4.85 | 3.90 | ... | 3.70 | 5.0 | 4.0 | 4.5 | 4.5 | 3.5 | 123 | 24345 | 118 | 0.0 |
144 | Gett: The Trial of Viviane Amsalem (2015) | 100 | 81 | 90 | 7.3 | 7.8 | 3.5 | 3.5 | 5.00 | 4.05 | ... | 3.90 | 5.0 | 4.0 | 4.5 | 3.5 | 4.0 | 19 | 1955 | 59 | 0.0 |
145 | Kumiko, The Treasure Hunter (2015) | 87 | 63 | 68 | 6.4 | 6.7 | 3.5 | 3.5 | 4.35 | 3.15 | ... | 3.35 | 4.5 | 3.0 | 3.5 | 3.0 | 3.5 | 19 | 5289 | 41 | 0.0 |
146 rows × 22 columns
cols = ['FILM','RT_user_norm', 'Metacritic_user_nom', 'IMDB_norm', 'Fandango_Ratingvalue', 'Fandango_Stars']
norm_reviews = reviews[cols]
norm_reviews
FILM | RT_user_norm | Metacritic_user_nom | IMDB_norm | Fandango_Ratingvalue | Fandango_Stars | |
---|---|---|---|---|---|---|
0 | Avengers: Age of Ultron (2015) | 4.30 | 3.55 | 3.90 | 4.5 | 5.0 |
1 | Cinderella (2015) | 4.00 | 3.75 | 3.55 | 4.5 | 5.0 |
2 | Ant-Man (2015) | 4.50 | 4.05 | 3.90 | 4.5 | 5.0 |
3 | Do You Believe? (2015) | 4.20 | 2.35 | 2.70 | 4.5 | 5.0 |
4 | Hot Tub Time Machine 2 (2015) | 1.40 | 1.70 | 2.55 | 3.0 | 3.5 |
5 | The Water Diviner (2015) | 3.10 | 3.40 | 3.60 | 4.0 | 4.5 |
6 | Irrational Man (2015) | 2.65 | 3.80 | 3.45 | 3.5 | 4.0 |
7 | Top Five (2014) | 3.20 | 3.40 | 3.25 | 3.5 | 4.0 |
8 | Shaun the Sheep Movie (2015) | 4.10 | 4.40 | 3.70 | 4.0 | 4.5 |
9 | Love & Mercy (2015) | 4.35 | 4.25 | 3.90 | 4.0 | 4.5 |
10 | Far From The Madding Crowd (2015) | 3.85 | 3.75 | 3.60 | 4.0 | 4.5 |
11 | Black Sea (2015) | 3.00 | 3.30 | 3.20 | 3.5 | 4.0 |
12 | Leviathan (2014) | 3.95 | 3.60 | 3.85 | 3.5 | 4.0 |
13 | Unbroken (2014) | 3.50 | 3.25 | 3.60 | 4.1 | 4.5 |
14 | The Imitation Game (2014) | 4.60 | 4.10 | 4.05 | 4.6 | 5.0 |
15 | Taken 3 (2015) | 2.30 | 2.30 | 3.05 | 4.1 | 4.5 |
16 | Ted 2 (2015) | 2.90 | 3.25 | 3.30 | 4.1 | 4.5 |
17 | Southpaw (2015) | 4.00 | 4.10 | 3.90 | 4.6 | 5.0 |
18 | Night at the Museum: Secret of the Tomb (2014) | 2.90 | 2.90 | 3.15 | 4.1 | 4.5 |
19 | Pixels (2015) | 2.70 | 2.65 | 2.80 | 4.1 | 4.5 |
20 | McFarland, USA (2015) | 4.45 | 3.60 | 3.75 | 4.6 | 5.0 |
21 | Insidious: Chapter 3 (2015) | 2.80 | 3.45 | 3.15 | 4.1 | 4.5 |
22 | The Man From U.N.C.L.E. (2015) | 4.00 | 3.95 | 3.80 | 4.1 | 4.5 |
23 | Run All Night (2015) | 2.95 | 3.65 | 3.30 | 4.1 | 4.5 |
24 | Trainwreck (2015) | 3.70 | 3.00 | 3.35 | 4.1 | 4.5 |
25 | Selma (2014) | 4.30 | 3.55 | 3.75 | 4.6 | 5.0 |
26 | Ex Machina (2015) | 4.30 | 3.95 | 3.85 | 4.1 | 4.5 |
27 | Still Alice (2015) | 4.25 | 3.90 | 3.75 | 4.1 | 4.5 |
28 | Wild Tales (2014) | 4.60 | 4.40 | 4.10 | 4.1 | 4.5 |
29 | The End of the Tour (2015) | 4.45 | 3.75 | 3.95 | 4.1 | 4.5 |
... | ... | ... | ... | ... | ... | ... |
116 | Clouds of Sils Maria (2015) | 3.35 | 3.55 | 3.40 | 3.4 | 3.5 |
117 | Testament of Youth (2015) | 3.95 | 3.95 | 3.65 | 3.9 | 4.0 |
118 | Infinitely Polar Bear (2015) | 3.80 | 3.95 | 3.60 | 3.9 | 4.0 |
119 | Phoenix (2015) | 4.05 | 4.00 | 3.60 | 3.4 | 3.5 |
120 | The Wolfpack (2015) | 3.65 | 3.50 | 3.55 | 3.4 | 3.5 |
121 | The Stanford Prison Experiment (2015) | 4.35 | 4.25 | 3.55 | 3.9 | 4.0 |
122 | Tangerine (2015) | 4.30 | 3.65 | 3.70 | 3.9 | 4.0 |
123 | Magic Mike XXL (2015) | 3.20 | 2.70 | 3.15 | 4.4 | 4.5 |
124 | Home (2015) | 3.25 | 3.65 | 3.35 | 4.4 | 4.5 |
125 | The Wedding Ringer (2015) | 3.30 | 1.65 | 3.35 | 4.4 | 4.5 |
126 | Woman in Gold (2015) | 4.05 | 3.60 | 3.70 | 4.4 | 4.5 |
127 | The Last Five Years (2015) | 3.00 | 3.45 | 3.00 | 4.4 | 4.5 |
128 | Mission: Impossible – Rogue Nation (2015) | 4.50 | 4.00 | 3.90 | 4.4 | 4.5 |
129 | Amy (2015) | 4.55 | 4.40 | 4.00 | 4.4 | 4.5 |
130 | Jurassic World (2015) | 4.05 | 3.50 | 3.65 | 4.5 | 4.5 |
131 | Minions (2015) | 2.60 | 2.85 | 3.35 | 4.0 | 4.0 |
132 | Max (2015) | 3.65 | 2.95 | 3.50 | 4.5 | 4.5 |
133 | Paul Blart: Mall Cop 2 (2015) | 1.80 | 1.20 | 2.15 | 3.5 | 3.5 |
134 | The Longest Ride (2015) | 3.65 | 2.40 | 3.60 | 4.5 | 4.5 |
135 | The Lazarus Effect (2015) | 1.15 | 2.45 | 2.60 | 3.0 | 3.0 |
136 | The Woman In Black 2 Angel of Death (2015) | 1.25 | 2.20 | 2.45 | 3.0 | 3.0 |
137 | Danny Collins (2015) | 3.75 | 3.55 | 3.55 | 4.0 | 4.0 |
138 | Spare Parts (2015) | 4.15 | 3.55 | 3.60 | 4.5 | 4.5 |
139 | Serena (2015) | 1.25 | 2.65 | 2.70 | 3.0 | 3.0 |
140 | Inside Out (2015) | 4.50 | 4.45 | 4.30 | 4.5 | 4.5 |
141 | Mr. Holmes (2015) | 3.90 | 3.95 | 3.70 | 4.0 | 4.0 |
142 | '71 (2015) | 4.10 | 3.75 | 3.60 | 3.5 | 3.5 |
143 | Two Days, One Night (2014) | 3.90 | 4.40 | 3.70 | 3.5 | 3.5 |
144 | Gett: The Trial of Viviane Amsalem (2015) | 4.05 | 3.65 | 3.90 | 3.5 | 3.5 |
145 | Kumiko, The Treasure Hunter (2015) | 3.15 | 3.20 | 3.35 | 3.5 | 3.5 |
146 rows × 6 columns
norm_reviews[:1]
FILM | RT_user_norm | Metacritic_user_nom | IMDB_norm | Fandango_Ratingvalue | Fandango_Stars | |
---|---|---|---|---|---|---|
0 | Avengers: Age of Ultron (2015) | 4.3 | 3.55 | 3.9 | 4.5 | 5.0 |
num_cols = ['RT_user_norm','Metacritic_user_nom','IMDB_norm','Fandango_Ratingvalue','Fandango_Stars']
bar_height = norm_reviews.loc[0,num_cols].values
bar_height
array([4.3, 3.55, 3.9, 4.5, 5.0], dtype=object)
bar_positions = np.arange(5) + 0.75
bar_positions
array([0.75, 1.75, 2.75, 3.75, 4.75])
fig,ax = plt.subplots()
# 0.3 为宽度占比
# bar 条形图
ax.bar(bar_positions,bar_height,0.3)
plt.show()
# 散点图
fig,ax = plt.subplots()
ax.scatter(norm_reviews['Fandango_Ratingvalue'],norm_reviews['RT_user_norm'])
ax.set_xlabel('Fandango_Ratingvalue')
ax.set_ylabel('RT_user_norm')
plt.show()
# 柱形图
#画图
fig,ax = plt.subplots()
#hist()表示带有bins结构,默认bins为10个。
#bins:某个变量过多,坐标轴就化不开,用bins化成范围,减少变量数量。
#ax.hist(norm_reviews['Fandango_Ratingvalue'])
#ax.hist(norm_reviews['Fandango_Ratingvalue'],bins=20) #指定bins为20个
#指定bins和指定横坐标区间
ax.hist(norm_reviews['Fandango_Ratingvalue'],range=(4,5),bins=20)
# set_ylim(x,y) 指定y轴的区间
plt.show()
# 箱型图 ----> 发布情况
fig,ax = plt.subplots()
ax.boxplot(norm_reviews['RT_user_norm'])
# 设置x轴的名称
ax.set_xticklabels(['Rotten'])
ax.set_ylim(0,5)
plt.show()
Seaborn
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib as mlp
import numpy as np
%matplotlib inline
def sinplot(flip=1):x = np.linspace(0,14,1000)for i in range(1,7):plt.plot(x,np.sin(x+i*0.5)*(7-i)*flip)
sinplot()
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/font_manager.py:1331: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans(prop.get_family(), self.defaultFamily[fontext]))
# 使用seaborn模板
sns.set()
sinplot()
# 使用seaborn模板
"""五种风格darkgridwhitegriddarkwhiteticks
"""
sns.set_style('whitegrid')
# set_context('') 设置内容风格
sns.set_context('talk')
sinplot()
# 调色板
# set_palette() 设置所有图颜色
# color_palette() 设置图的颜色
# 默认颜色
current_palette = sns.color_palette()
print(current_palette)
sns.palplot(current_palette)
[(0.2980392156862745, 0.4470588235294118, 0.6901960784313725), (0.8666666666666667, 0.5176470588235295, 0.3215686274509804), (0.3333333333333333, 0.6588235294117647, 0.40784313725490196), (0.7686274509803922, 0.3058823529411765, 0.3215686274509804), (0.5058823529411764, 0.4470588235294118, 0.7019607843137254), (0.5764705882352941, 0.47058823529411764, 0.3764705882352941), (0.8549019607843137, 0.5450980392156862, 0.7647058823529411), (0.5490196078431373, 0.5490196078431373, 0.5490196078431373), (0.8, 0.7254901960784313, 0.4549019607843137), (0.39215686274509803, 0.7098039215686275, 0.803921568627451)]
# 第一个参数为颜色空间,第二个参数是返回的颜色个数
sns.palplot(sns.color_palette('hls',12))
# sns.hls_palette(8,l=0.7,s=0.5)
# l 表示亮度
# s 表示饱和度
sns.palplot(sns.hls_palette(8,l=0.7,s=0.5))
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