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数据可视化库Matplotlib
学习笔记
Matplotlib 是一个0 Python 的 2D绘图库，它以各种硬拷贝格式和跨平台的交互式环境生成出版质量级别的图形。
一、折线图绘制
数据文件’unrate.csv’：这份数据统计了美国从1948年1月到2016年8月每个月的失业率。读取数据：
import pandas as pd
unrate = pd.read_csv('unrate.csv')
我们看到数据格式为’1948/1/1’，可以利用pandas里的to_datetime函数将时间格式转化：
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
导入matplotlib库中pyplot模块，赋别名：
import matplotlib.pyplot as plt
#%matplotlib inline
#Using the different pyplot functions, we can create, customize, and display a plot. For example, we can use 2 functions to :
plt.plot()
plt.show()
上面plt里面没有内容，故什么都没画。取unrate数据前2行，即1948年12个月的失业率，横坐标为Date，纵坐标为Value，绘制折线图：
first_twelve = unrate[0:12]
plt.plot(first_twelve['DATE'], first_twelve['VALUE'])
plt.show()
对于横坐标，不过比较拥挤，可以设置xticks函数的rotation参数改变角度：
#While the y-axis looks fine, the x-axis tick labels are too close together and are unreadable
#We can rotate the x-axis tick labels by 90 degrees so they don't overlap
#We can specify degrees of rotation using a float or integer value.
plt.plot(first_twelve['DATE'], first_twelve['VALUE'])
plt.xticks(rotation=45)
#print help(plt.xticks)
plt.show()
添加x轴说明、y轴说明、标题：
#xlabel(): accepts a string value, which gets set as the x-axis label.
#ylabel(): accepts a string value, which is set as the y-axis label.
#title(): accepts a string value, which is set as the plot title.
plt.plot(first_twelve['DATE'], first_twelve['VALUE'])
plt.xticks(rotation=90)
plt.xlabel('Month')
plt.ylabel('Unemployment Rate')
plt.title('Monthly Unemployment Trends, 1948')
plt.show()
二、子图操作
很多时候，写论文的时候，我们要在一个区域画很多子图。
如下：参数中“3,2”代表创建一个3行2列的图矩阵，第三个参数表示指定图所在位置：
#add_subplot(first,second,index) first means number of Row,second means number of Column.
import matplotlib.pyplot as plt
fig = plt.figure()
ax1 = fig.add_subplot(3,2,1)
ax2 = fig.add_subplot(3,2,2)
ax2 = fig.add_subplot(3,2,6)
plt.show()
利用figsize设置画图域长度和宽度，ax1、ax2添加子图，然后可以分别绘制：
import numpy as np
fig = plt.figure()
#fig = plt.figure(figsize=(3, 3))
ax1 = fig.add_subplot(2,1,1)
ax2 = fig.add_subplot(2,1,2)
ax1.plot(np.random.randint(1,5,5), np.arange(5))
ax2.plot(np.arange(10)*3, np.arange(10))
plt.show()
那么，在同一幅图里画两条折线呢？我们画出1948年和1949年的失业率折线图：
unrate['MONTH'] = unrate['DATE'].dt.month
unrate['MONTH'] = unrate['DATE'].dt.month
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()
写一个循环，画出连续五年失业率变化折线图：
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()
添加图例：
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]
label = str(1948 + i)
plt.plot(subset['MONTH'], subset['VALUE'], c=colors[i], label=label)
plt.legend(loc='best')
#print(help(plt.legend))
plt.show()
添加横、纵坐标轴、标题说明：
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]
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()
三、柱形图
文件’fandango_scores.csv’：不同机构对电影的的评分数据。读取数据：
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])
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
选5个媒体的评分值，设置柱形高度bar_heights即其评分值、柱形位置bar_positions，绘制柱形图：
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() #等价于：fig=plt.figure() ax=fig.add_subplot(111)
ax.bar(bar_positions, bar_heights, 0.5) #0,5表示柱的宽度
plt.show()
对于fig, ax = plt.subplots()的解释：fig, ax = plt.subplots()
等价于：fig=plt.figure() ax=fig.add_subplot(111)
fig是我们对整体图形的控制，比如说我们可以用fig构建figsize，上面并没有用到，ax是我们实际得到的一个画图的轴，在轴上画柱形图就是ax.bar,ax
是我们经常用到的实际操作。
添加横、纵坐标、标题说明：
#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()
将ax.bar(bar_positions, bar_heights, 0.5)中bar_heights改为bar_widths, 绘制横状柱形图：
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()
四、散点图
分析’Fandango_Ratingvalue’和’RT_user_norm’两家媒体对电影评分关系：绘制其散点图：
#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()
以子图方式绘制：
#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()
五、直方图
读入数据’fandango_scores.csv’：
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
统计’Fandango_Ratingvalue’和’IMDB_norm’对电影的评分
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
直方图：
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()
指定直方图一些绘制参数，分别绘制’Distribution of Rotten Tomatoes Ratings’、‘Distribution of Metacritic Ratings’、‘Distribution of IMDB Ratings’
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'], 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()
五、箱线图
用箱线图绘制’Rotten Tomatoes’对电影评分：
fig, ax = plt.subplots()
ax.boxplot(norm_reviews['RT_user_norm'])
ax.set_xticklabels(['Rotten Tomatoes'])
ax.set_ylim(0, 5)
plt.show()
在一个图中绘制多个箱线图：
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()
六、细节设置
读取文件’percent-bachelors-degrees-women-usa.csv’，该文件存储了从1970年到2011年美国Agriculture、Architecture、Art and Performance、Biology等18个专业女生所占比例数据。
读取数据：
import pandas as pd
import matplotlib.pyplot as plt
women_degrees = pd.read_csv('percent-bachelors-degrees-women-usa.csv')
print(women_degrees.head())
print(women_degrees.shape)
Year Agriculture Architecture Art and Performance Biology Business \
0 1970 4.229798 11.921005 59.7 29.088363 9.064439
1 1971 5.452797 12.003106 59.9 29.394403 9.503187
2 1972 7.420710 13.214594 60.4 29.810221 10.558962
3 1973 9.653602 14.791613 60.2 31.147915 12.804602
4 1974 14.074623 17.444688 61.9 32.996183 16.204850
Communications and Journalism Computer Science Education Engineering \
0 35.3 13.6 74.535328 0.8
1 35.5 13.6 74.149204 1.0
2 36.6 14.9 73.554520 1.2
3 38.4 16.4 73.501814 1.6
4 40.5 18.9 73.336811 2.2
English Foreign Languages Health Professions Math and Statistics \
0 65.570923 73.8 77.1 38.0
1 64.556485 73.9 75.5 39.0
2 63.664263 74.6 76.9 40.2
3 62.941502 74.9 77.4 40.9
4 62.413412 75.3 77.9 41.8
Physical Sciences Psychology Public Administration \
0 13.8 44.4 68.4
1 14.9 46.2 65.5
2 14.8 47.6 62.6
3 16.5 50.4 64.3
4 18.2 52.6 66.1
Social Sciences and History
0 36.8
1 36.2
2 36.1
3 36.4
4 37.3
(42, 18)
绘制Biology女生随年份的变化情况：
plt.plot(women_degrees['Year'], women_degrees['Biology'])
plt.show()
绘制1970-2011年Biology专业男女比例对比折线图：
#100-women_degrees means men
plt.plot(women_degrees['Year'], women_degrees['Biology'], c='blue', label='Women')
plt.plot(women_degrees['Year'], 100-women_degrees['Biology'], c='green', label='Men')
plt.legend(loc='upper right')
plt.title('Percentage of Biology Degrees Awarded By Gender')
plt.show()
我们可以设置ax.tick_params中参数，将坐标轴上刻度线去掉：
fig, ax = plt.subplots()
# Add your code here.
fig, ax = plt.subplots()
ax.plot(women_degrees['Year'], women_degrees['Biology'], label='Women')
ax.plot(women_degrees['Year'], 100-women_degrees['Biology'], label='Men')
ax.tick_params(bottom="off", top="off", left="off", right="off")
ax.set_title('Percentage of Biology Degrees Awarded By Gender')
ax.legend(loc="upper right")
plt.show()
D:\Anaconda\lib\site-packages\matplotlib\cbook\deprecation.py:107: MatplotlibDeprecationWarning: Passing one of 'on', 'true', 'off', 'false' as a boolean is deprecated; use an actual boolean (True/False) instead.
warnings.warn(message, mplDeprecation, stacklevel=1)
fig, ax = plt.subplots()
ax.plot(women_degrees['Year'], women_degrees['Biology'], c='blue', label='Women')
ax.plot(women_degrees['Year'], 100-women_degrees['Biology'], c='green', label='Men')
ax.tick_params(bottom="off", top="off", left="off", right="off")
for key,spine in ax.spines.items():
spine.set_visible(False)
# End solution code.
ax.legend(loc='upper right')
plt.show()
D:\Anaconda\lib\site-packages\matplotlib\cbook\deprecation.py:107: MatplotlibDeprecationWarning: Passing one of 'on', 'true', 'off', 'false' as a boolean is deprecated; use an actual boolean (True/False) instead.
warnings.warn(message, mplDeprecation, stacklevel=1)
我们可以利用spine.set_visible(False)将图边框去掉：
major_cats = ['Biology', 'Computer Science', 'Engineering', 'Math and Statistics']
fig = plt.figure(figsize=(12, 12))
for sp in range(0,4):
ax = fig.add_subplot(2,2,sp+1)
ax.plot(women_degrees['Year'], women_degrees[major_cats[sp]], c='blue', label='Women')
ax.plot(women_degrees['Year'], 100-women_degrees[major_cats[sp]], c='green', label='Men')
# Add your code here.
# Calling pyplot.legend() here will add the legend to the last subplot that was created.
plt.legend(loc='upper right')
plt.show()
major_cats = ['Biology', 'Computer Science', 'Engineering', 'Math and Statistics']
fig = plt.figure(figsize=(12, 12))
for sp in range(0,4):
ax = fig.add_subplot(2,2,sp+1)
ax.plot(women_degrees['Year'], women_degrees[major_cats[sp]], c='blue', label='Women')
ax.plot(women_degrees['Year'], 100-women_degrees[major_cats[sp]], c='green', label='Men')
for key,spine in ax.spines.items():
spine.set_visible(False)
ax.set_xlim(1968, 2011)
ax.set_ylim(0,100)
ax.set_title(major_cats[sp])
#ax.tick_params(bottom="off", top="off", left="off", right="off")
# Calling pyplot.legend() here will add the legend to the last subplot that was created.
plt.legend(loc='upper right')
plt.show()
我们可以设置线的颜色，用RGB颜色值，需要注意的一点是 matplotlib.pyplot中，RGB颜色值需要除以255再使用：
#Color
import pandas as pd
import matplotlib.pyplot as plt
women_degrees = pd.read_csv('percent-bachelors-degrees-women-usa.csv')
major_cats = ['Biology', 'Computer Science', 'Engineering', 'Math and Statistics']
cb_dark_blue = (0/255, 107/255, 164/255)
cb_orange = (255/255, 128/255, 14/255)
fig = plt.figure(figsize=(12, 12))
for sp in range(0,4):
ax = fig.add_subplot(2,2,sp+1)
# The color for each line is assigned here.
ax.plot(women_degrees['Year'], women_degrees[major_cats[sp]], c=cb_dark_blue, label='Women')
ax.plot(women_degrees['Year'], 100-women_degrees[major_cats[sp]], c=cb_orange, label='Men')
for key,spine in ax.spines.items():
spine.set_visible(False)
ax.set_xlim(1968, 2011)
ax.set_ylim(0,100)
ax.set_title(major_cats[sp])
#ax.tick_params(bottom="off", top="off", left="off", right="off")
plt.legend(loc='upper right')
plt.show()
我们可以设置线的粗细，图片的整体大小等：
#Setting Line Width
cb_dark_blue = (0/255, 107/255, 164/255)
cb_orange = (255/255, 128/255, 14/255)
fig = plt.figure(figsize=(12, 12))
for sp in range(0,4):
ax = fig.add_subplot(2,2,sp+1)
# Set the line width when specifying how each line should look.
ax.plot(women_degrees['Year'], women_degrees[major_cats[sp]], c=cb_dark_blue, label='Women', linewidth=10)
ax.plot(women_degrees['Year'], 100-women_degrees[major_cats[sp]], c=cb_orange, label='Men', linewidth=10)
for key,spine in ax.spines.items():
spine.set_visible(False)
ax.set_xlim(1968, 2011)
ax.set_ylim(0,100)
ax.set_title(major_cats[sp])
#ax.tick_params(bottom="off", top="off", left="off", right="off")
plt.legend(loc='upper right')
plt.show()
将几个专业的男女比例放在一横排对比：
stem_cats = ['Engineering', 'Computer Science', 'Psychology', 'Biology', 'Physical Sciences', 'Math and Statistics']
fig = plt.figure(figsize=(18, 3))
for sp in range(0,6):
ax = fig.add_subplot(1,6,sp+1)
ax.plot(women_degrees['Year'], women_degrees[stem_cats[sp]], c=cb_dark_blue, label='Women', linewidth=3)
ax.plot(women_degrees['Year'], 100-women_degrees[stem_cats[sp]], c=cb_orange, label='Men', linewidth=3)
for key,spine in ax.spines.items():
spine.set_visible(False)
ax.set_xlim(1968, 2011)
ax.set_ylim(0,100)
ax.set_title(stem_cats[sp])
#ax.tick_params(bottom="off", top="off", left="off", right="off")
plt.legend(loc='upper right')
plt.show()
可以改变标注位置，将标注放在指定为位置，比如线的周围：
fig = plt.figure(figsize=(18, 3))
for sp in range(0,6):
ax = fig.add_subplot(1,6,sp+1)
ax.plot(women_degrees['Year'], women_degrees[stem_cats[sp]], c=cb_dark_blue, label='Women', linewidth=3)
ax.plot(women_degrees['Year'], 100-women_degrees[stem_cats[sp]], c=cb_orange, label='Men', linewidth=3)
for key,spine in ax.spines.items():
spine.set_visible(False)
ax.set_xlim(1968, 2011)
ax.set_ylim(0,100)
ax.set_title(stem_cats[sp])
#ax.tick_params(bottom="off", top="off", left="off", right="off")
plt.legend(loc='upper right')
plt.show()
fig = plt.figure(figsize=(18, 3))
for sp in range(0,6):
ax = fig.add_subplot(1,6,sp+1)
ax.plot(women_degrees['Year'], women_degrees[stem_cats[sp]], c=cb_dark_blue, label='Women', linewidth=3)
ax.plot(women_degrees['Year'], 100-women_degrees[stem_cats[sp]], c=cb_orange, label='Men', linewidth=3)
for key,spine in ax.spines.items():
spine.set_visible(False)
ax.set_xlim(1968, 2011)
ax.set_ylim(0,100)
ax.set_title(stem_cats[sp])
#ax.tick_params(bottom="off", top="off", left="off", right="off")
if sp == 0:
ax.text(2005, 87, 'Men')
ax.text(2002, 8, 'Women')
elif sp == 5:
ax.text(2005, 62, 'Men')
ax.text(2001, 35, 'Women')
plt.show()
End