人工智能学习笔记——可视化库matplotlib

源数据（前12）



数据预处理（DATE格式转换）：
import pandas as pd
unrate = pd.read_csv("UNRATE.csv")
unrate["DATE"] = pd.to_datetime(unrate["DATE"])
print(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绘制折线图：import matplotlib.pyplot as plt
plt.plot()
plt.show()



firts_twelve = unrate = unrate[0:12]
plt.plot(firts_twelve["DATE"],firts_twelve["VALUE"])
plt.show()



x坐标标注旋转
plt.xticks(rotation = 45)



坐标轴与标题标注
plt.xlabel("Month") 
plt.ylabel("Unemployment Rate")
plt.title("Monthly Unemployment Trend,1948")



子图操作：



fig = plt.figure()#新建绘图区域
ax1 = fig.add_subplot(4,3,1)
ax2 = fig.add_subplot(4,3,2)
ax3 = fig.add_subplot(4,3,6)



fig = plt.figure(figsize=(6,6))#指定画图区域大小
ax1 = fig.add_subplot(2,1,1)
ax2 = fig.add_subplot(2,1,2)
ax1.plot(np.arange(5),np.random.randint(1,5,5))
ax2.plot(np.arange(10),np.arange(10)*3)
plt.show()



同一坐标系下绘制多条线：
fig = plt.figure(figsize=(6,3))

plt.plot(unrate[0:12]['MONTH'], unrate[0:12]['VALUE'], c='red')
plt.plot(unrate[12:24]['MONTH'], unrate[12:24]['VALUE'], c='blue')

plt.show()



fig = plt.figure(figsize=(10,6))
colors = ['red', 'blue', 'green', 'orange', 'black']
for i in range(5):
    start_index = i*12
    end_index = (i+1)*12
    subset = unrate[start_index:end_index]
    plt.plot(subset['MONTH'], subset['VALUE'], c=colors[i])
    
plt.show()



曲线标签：
fig = plt.figure(figsize=(5,3))
colors = ['red', 'blue', 'green', 'orange', 'black']
for i in range(5):
    start_index = i*12
    end_index = (i+1)*12
    subset = unrate[start_index:end_index]
    label = str(1948 + i)
    plt.plot(subset['MONTH'], subset['VALUE'], c=colors[i], label=label)
plt.legend(loc='best')#标签定位
#print (help(plt.legend))
plt.show()
loc=  best
upper right
upper left
lower left
lower right
right
center left
center right
lower center
upper center
center
完整折线图：
fig = plt.figure(figsize=(5,3))
colors = ['red', 'blue', 'green', 'orange', 'black']
for i in range(5):
    start_index = i*12
    end_index = (i+1)*12
    subset = unrate[start_index:end_index]
    label = str(1948 + i)
    plt.plot(subset['MONTH'], subset['VALUE'], c=colors[i], label=label)
plt.legend(loc='upper left')
plt.xlabel('Month, Integer')
plt.ylabel('Unemployment Rate, Percent')
plt.title('Monthly Unemployment Trends, 1948-1952')

plt.show()
绘制条形图：
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])

FILM  RT_user_norm  Metacritic_user_nom  \
0  Avengers: Age of Ultron (2015)           4.3                 3.55

IMDB_norm  Fandango_Ratingvalue  Fandango_Stars
0        3.9                   4.5             5.0

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.ix[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()
[4.3 3.55 3.9 4.5 5.0]
[0.75 1.75 2.75 3.75 4.75]


横向条形图：
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.ix[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()



散点图：
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()



柱形图：
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? (
b301
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()
fandango_distribution = fandango_distribution.sort_index()

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

print(fandango_distribution)
print(imdb_distribution)

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
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'])#绘制柱形图
#ax.hist(norm_reviews['Fandango_Ratingvalue'],bins=20)#规定20条
ax.hist(norm_reviews['Fandango_Ratingvalue'], range=(4, 5),bins=20)#4到5范围内20条
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)#y轴范围
plt.show()



箱型图：
fig, ax = plt.subplots()
ax.boxplot(norm_reviews['RT_user_norm'].values)
ax.set_xticklabels(['Rotten Tomatoes'])
ax.set_ylim(0, 5)
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=90)
ax.set_ylim(0,5)
plt.show()



去坐标锯齿：
fig, ax = plt.subplots()
# Add your code here.
fig, ax = plt.subplots()
ax.tick_params(bottom="off", top="off", left="off", right="off")
plt.show()



去边框：
fig, ax = plt.subplots()
# Add your code here.
fig, ax = plt.subplots()
for key,spine in ax.spines.items():
    spine.set_visible(False)
plt.show()



RGB颜色通道：
cb_dark_blue = (0/255, 107/255, 164/255)

线宽：
ax.plot(women_degrees['Year'], women_degrees[major_cats[sp]], c=cb_dark_blue, label='Women', linewidth=10)

曲线标注：
ax.text(2005, 87, 'Men')
ax.text(2002, 8, 'Women')