绘图和可视化 《利用Python进行数据分析》第8章 读书笔记

绘图和可视化回归 第八章

代码下载链接

import matplotlib.pyplot as plt
import numpy as np
from numpy.random import randn
plt.plot(np.arange(10))
plt.show()

Figure和Subplot

fig=plt.figure()
ax1=fig.add_subplot(2,2,1)
ax2=fig.add_subplot(2,2,2)
ax3=fig.add_subplot(2,2,3)

plt.plot(np.random.randn(50).cumsum(),'k--')

[

_=ax1.hist(np.random.randn(100),bins=20,color='k',alpha=0.3)
ax2.scatter(np.arange(30),np.arange(30)+3*np.random.randn(30))
plt.show()

fig,axes=plt.subplots(2,3)
axes

array([[

调整subplot周围的间距

# subplots_adjust(left=None,bottom=None,right=None,top=None,wpace=None,hspace=None)
fig,axes=plt.subplots(2,2,sharex=True,sharey=True)
for i in range(2):
    for j in range(2):
        axes[i,j].hist(np.random.randn(500),bins=50,color='k',alpha=0.5)

plt.subplots_adjust(wspace=0,hspace=0)
plt.show()

颜色、标记和线型

ax.plot(x,y,'g--')
ax.plot(x,y,linestyle='--',color='g')

plt.plot(np.random.randn(30).cumsum(),'ko--')
plt.show()

#还可以写成更明确的方式
plt.plot(np.random.randn(30).cumsum(),color='k',linestyle='dashed',marker='o')

[

#在线形图中，非实际数据点默认是按线型方式插值的。可以通过drawstyle选项修改
data=np.random.randn(30).cumsum()
plt.plot(data,'ko--',label='Default')

[

plt.plot(data,'k-',drawstyle='steps-post',label='steps-post')
plt.legend(loc='best')
plt.show()

刻度、标签和图例

设置标题、轴标签、刻度以及刻度标签

fig=plt.figure()
ax=fig.add_subplot(1,1,1)
ax.plot(randn(1000).cumsum())

[

#要修改X轴的刻度，最简单的办法就是使用set_xticks和set_xticklabels
ticks=ax.set_xticks([0,250,500,750,1000])
labels=ax.set_xticklabels(['one','two','three','four','five'],rotation=30,fontsize='small')
ax.set_xlabel('Stages')
ax.set_title('My first matplotlib plot')
plt.show()

添加图例

fig=plt.figure()
ax=fig.add_subplot(1,1,1)
ax.plot(randn(1000).cumsum(),'k',label='one')
ax.plot(randn(1000).cumsum(),'k--',label='two')
ax.plot(randn(1000).cumsum(),'k.',label='three')
ax.legend(loc='best')
plt.show()

注解以及在Subplot上绘图

#text可以将文本绘制在图表的指定坐标（x,y)
ax.text(x,y,'Hello world',family='monospace',fontsize=10)

#在图表中添加一个图形，需要创建一个块对象shp,然后通过ax.add_patch(shp)将其添加到subplot中
fig=plt.figure()
ax=fig.add_subplot(1,1,1)
rect=plt.Rectangle((0.2,0.75),0.4,0.15,color='k',alpha=0.3)
circ=plt.Circle((0.7,0.2),0.15,color='b',alpha=0.3)
pgon=plt.Polygon([[0.15,0.15],[0.35,0.4],[0.2,0.6]],color='g',alpha=0.5)
ax.add_patch(rect)
ax.add_patch(circ)
ax.add_patch(pgon)
plt.show()

将图标保存到文件

plt.savefig('figpath.png',dpi=400,bbox_inches='tight')
#savefig并非一定要写入磁盘，也可以写入任何文件型的对象，比如StringIO
from io import StringIO
plt.savefig(buffer)
plot_data=buffer.getvaule()
#这对在Web上提供动态生成的图片是很实用的

matplotlib配置

#将全局的图形默认大小设置为10x10
plt.rc('figure',figsize=(10,10))
#将配置写成字典
font_opinions={'family':'monospace','weight':'bold','size':'samll'}
plt.rc('font',**font_options)

pandas中的绘图函数

提醒，关于这部分内容参考最新的pandas在线文档是最好的学习方式

线型图

#Series和DataFrame都有一个用于生成各类图表的plot方法，默认情况下，它们所生成的是线型图
from pandas import Series,DataFrame
s=Series(randn(10).cumsum(),index=np.arange(0,100,10))
s.plot()
plt.show()

#该Series的索引值会被传给matplotlib,并用以绘制X轴。可以通过use_index=False禁用。X轴的刻度和界限可以通过xticks和xlim选项进行调节
#Y轴就用yticks和ylim
#pandas的大部分糊涂方法都有一个可选的ax参数，它可以是一个matplotlib的subplot对象，能使你在网络布局中更为灵活地处理subplot的位置
#DataFrame的plot方法会在一个subplot中为各列绘制一条线，并自动创建图例
df=DataFrame(randn(10,4).cumsum(0),columns=['A','B','C','D'],index=np.arange(0,100,10))
df.plot()
plt.show()

柱状图

在生成线型图的代码中加上kind=’bar’(垂直柱状图）或kind=’barh’(水平柱状图）即可生成柱状图，这时，Series和DataFrame的索引会被用作X（bar）或Y（barh）刻度

fig,axes=plt.subplots(2,1)
data=Series(np.random.rand(16),index=list('abcdefghijklmnop'))
data.plot(kind='bar',ax=axes[0],color='k',alpha=0.7)
data.plot(kind='barh',ax=axes[1],color='k',alpha=0.7)
plt.show()

#对于DataFrame,柱状图会将每一行的值分为一组
df=DataFrame(np.random.rand(6,4),index=['one','two','three','four','five','six'],columns=['A','B','C','D'])
df

	A	B	C	D
one	0.605969	0.392503	0.159506	0.689187
two	0.706356	0.548750	0.489465	0.886399
three	0.539584	0.598980	0.482615	0.478261
four	0.277114	0.683394	0.407497	0.671090
five	0.201349	0.797898	0.454740	0.355270
six	0.113781	0.288068	0.597394	0.130346

df.plot(kind='bar')
plt.show()

df.plot(kind='barh',stacked=True)
plt.show()

#柱状图还有一个非常不错的用户：利用value_counts图形化显示Series中各值出现频率，比如s.value_counts().plot(kind='bar')
#小栗子
import pandas as pd
tips=pd.read_csv('ch08/tips.csv')
party_counts=pd.crosstab(tips.day,tips['size'])#如果通过tips.size,取到的是一整列的和
party_counts

size	1	2	3	4	5	6
day
Fri	1	16	1	1	0	0
Sat	2	53	18	13	1	0
Sun	0	39	15	18	3	1
Thur	1	48	4	5	1	3

party_counts=party_counts.ix[:,2:5]
#然后进行规格化，使得各行的和为1（必须转成浮点数）
party_pcts=party_counts.div(party_counts.sum(1).astype(float),axis=0)
party_pcts

size	2	3	4	5
day
Fri	0.888889	0.055556	0.055556	0.000000
Sat	0.623529	0.211765	0.152941	0.011765
Sun	0.520000	0.200000	0.240000	0.040000
Thur	0.827586	0.068966	0.086207	0.017241

party_pcts.plot(kind='bar',stacked=True)
plt.show()

#通过该数据集可以看出，聚会规模则周末会变大

直方图和密度图

tips['tip_pct']=tips['tip']/tips['total_bill']
tips['tip_pct'].hist(bins=50)
plt.show()

#密度图 kind='kde'
tips['tip_pct'].plot(kind='kde')
plt.show()

#接下来看一个由两个不同的标准正态分布组成的双峰分布
comp1=np.random.normal(0,1,size=200)#N(0,1)
comp2=np.random.normal(10,2,size=200)# (10,4)
values=Series(np.concatenate([comp1,comp2]))
values.hist(bins=100,alpha=0.3,color='k',normed=True)
values.plot(kind='kde',style='k--')
plt.show()

散布图

scatterplot观察两个一维数组序列之间关系的有效手段

macro=pd.read_csv('ch08/macrodata.csv')
data=macro[['cpi','m1','tbilrate','unemp']]
#选择其中几列，计算对数差
trans_data=np.log(data).diff().dropna()
trans_data[-5:]

	cpi	m1	tbilrate	unemp
198	-0.007904	0.045361	-0.396881	0.105361
199	-0.021979	0.066753	-2.277267	0.139762
200	0.002340	0.010286	0.606136	0.160343
201	0.008419	0.037461	-0.200671	0.127339
202	0.008894	0.012202	-0.405465	0.042560

plt.scatter(trans_data['m1'],trans_data['unemp'])
plt.title('Cahnges in log %s vs. log %s '%('m1','unemp'))
plt.show()

pd.scatter_matrix(trans_data,diagonal='kde',alpha=0.3)
plt.show()

绘制地图：图形化显示海地地震危机数据

import pandas as pd
data=pd.read_csv('ch08/Haiti.csv')
data.head()

	Serial	INCIDENT TITLE	INCIDENT DATE	LOCATION	DESCRIPTION	CATEGORY	LATITUDE	LONGITUDE	APPROVED	VERIFIED
0	4052	* URGENT * Type O blood donations needed in #J…	05/07/2010 17:26	Jacmel, Haiti	Birthing Clinic in Jacmel #Haiti urgently need…	1. Urgences | Emergency, 3. Public Health,	18.233333	-72.533333	YES	NO
1	4051	Food-Aid sent to Fondwa, Haiti	28/06/2010 23:06	fondwa	Please help food-aid.org deliver more food to …	1. Urgences | Emergency, 2. Urgences logistiqu…	50.226029	5.729886	NO	NO
2	4050	how haiti is right now and how it was during t…	24/06/2010 16:21	centrie	i feel so bad for you i know i am supposed to …	2. Urgences logistiques | Vital Lines, 8. Autr…	22.278381	114.174287	NO	NO
3	4049	Lost person	20/06/2010 21:59	Genoca	We are family members of Juan Antonio Zuniga O…	1. Urgences | Emergency,	44.407062	8.933989	NO	NO
4	4042	Citi Soleil school	18/05/2010 16:26	Citi Soleil, Haiti	We are working with Haitian (NGO) -The Christi…	1. Urgences | Emergency,	18.571084	-72.334671	YES	NO

data[['INCIDENT DATE','LATITUDE','LONGITUDE']][:10]

	INCIDENT DATE	LATITUDE	LONGITUDE
0	05/07/2010 17:26	18.233333	-72.533333
1	28/06/2010 23:06	50.226029	5.729886
2	24/06/2010 16:21	22.278381	114.174287
3	20/06/2010 21:59	44.407062	8.933989
4	18/05/2010 16:26	18.571084	-72.334671
5	26/04/2010 13:14	18.593707	-72.310079
6	26/04/2010 14:19	18.482800	-73.638800
7	26/04/2010 14:27	18.415000	-73.195000
8	15/03/2010 10:58	18.517443	-72.236841
9	15/03/2010 11:00	18.547790	-72.410010

#CATEGORY字段含有一组以逗号分隔的代码，这些代码表示消息的类型
data['CATEGORY'][:6]

0 1. Urgences | Emergency, 3. Public Health, 1 1. Urgences | Emergency, 2. Urgences logistiqu… 2 2. Urgences logistiques | Vital Lines, 8. Autr… 3 1. Urgences | Emergency, 4 1. Urgences | Emergency, 5 5e. Communication lines down, Name: CATEGORY, dtype: object

data.describe()

	Serial	LATITUDE	LONGITUDE
count	3593.000000	3593.000000	3593.000000
mean	2080.277484	18.611495	-72.322680
std	1171.100360	0.738572	3.650776
min	4.000000	18.041313	-74.452757
25%	1074.000000	18.524070	-72.417500
50%	2163.000000	18.539269	-72.335000
75%	3088.000000	18.561820	-72.293570
max	4052.000000	50.226029	114.174287

#清除错误位置信息并移除缺失分类信息
data=data[(data.LATITUDE>18)&(data.LATITUDE<20)&(data.LONGITUDE>-75)
&(data.LONGITUDE<-70)&(data.CATEGORY.notnull())]

def to_cat_list(catstr):
    stripped=(x.strip() for x in catstr.split(','))
    return [x for x in stripped if x]
def get_all_categories(cat_series):
    cat_sets=(set(to_cat_list(x)) for x in cat_series)
    return sorted(set.union(*cat_sets))
def get_english(cat):
    code,names=cat.split('.')
    if '|' in names:
        names=names.split('|')[1]
    return code,names.strip()    
get_english('2.  Urgences logistique |Vital Lines')

(‘2’, ‘Vital Lines’)

#做一个将编码跟名称映射起来的字典，我们用编码进行分析
all_cats=get_all_categories(data.CATEGORY)
#生成器表达式
english_mapping=dict(get_english(x) for x in all_cats)
english_mapping['2a']

‘Food Shortage’

english_mapping['6c']

‘Earthquake and aftershocks’

#抽取出唯一的分类编码，构造一个权零DataFrame
from pandas import DataFrame
def get_code(seq):
    return [x.split('.')[0] for x in seq if x]
all_codes=get_code(all_cats)
code_index=pd.Index(np.unique(all_codes))
dummy_frame=DataFrame(np.zeros((len(data),len(code_index))),index=data.index,columns=code_index)
dummy_frame.head()

	1	1a	1b	1c	1d	2	2a	2b	2c	2d	…	7c	7d	7g	7h	8	8a	8c	8d	8e	8f
0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	…	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
4	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	…	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
5	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	…	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
6	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	…	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
7	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	…	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

5 rows × 45 columns

#将各行中适当的项设置为1，然后再与data进行连接
for row,cat in zip(data.index,data.CATEGORY):
    codes=get_code(to_cat_list(cat))
    dummy_frame.ix[row][codes]=1     
data=data.join(dummy_frame.add_prefix('category_'))
data.head()

	Serial	INCIDENT TITLE	INCIDENT DATE	LOCATION	DESCRIPTION	CATEGORY	LATITUDE	LONGITUDE	APPROVED	VERIFIED	…	category_7c	category_7d	category_7g	category_7h	category_8	category_8a	category_8c	category_8d	category_8e	category_8f
0	4052	* URGENT * Type O blood donations needed in #J…	05/07/2010 17:26	Jacmel, Haiti	Birthing Clinic in Jacmel #Haiti urgently need…	1. Urgences | Emergency, 3. Public Health,	18.233333	-72.533333	YES	NO	…	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
4	4042	Citi Soleil school	18/05/2010 16:26	Citi Soleil, Haiti	We are working with Haitian (NGO) -The Christi…	1. Urgences | Emergency,	18.571084	-72.334671	YES	NO	…	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
5	4041	Radio Commerce in Sarthe	26/04/2010 13:14	Radio Commerce Shelter, Sarthe	i’m Louinel from Sarthe. I’d to know what can …	5e. Communication lines down,	18.593707	-72.310079	YES	NO	…	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
6	4040	Contaminated water in Baraderes.	26/04/2010 14:19	Marc near Baraderes	How do we treat water in areas without Pipe?\t…	4. Menaces | Security Threats, 4e. Assainissem…	18.482800	-73.638800	YES	NO	…	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
7	4039	Violence at "arcahaie bas Saint-Ard"	26/04/2010 14:27	unable to find "arcahaie bas Saint-Ard&qu…	Goodnight at (arcahaie bas Saint-Ard) 2 young …	4. Menaces | Security Threats,	18.415000	-73.195000	YES	NO	…	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

5 rows × 55 columns

from mpl_toolkits.basemap import Basemap
def basic_haiti_map(ax=None,lllat=17.25,urlat=20.25,lllon=-75.0,urlon=-71.0):
    #创建极球面投影Basemap实例
    m=Basemap(ax=ax,projection='stere',lon_0=(urlon+lllon)/2,
              lat_0=(urlat+lllat)/2,llcrnrlat=lllat,
              urcrnrlat=urlat,llcrnrlon=lllon,urcrnrlon=urlon,resolution='f')
    #绘制海岸线，州界、国界以及地图边界
    m.drawcoastlines()
    m.drawstates()
    m.drawcounties()
    return m
#对于每一个分类，在数据集中找到对应的坐标，并在适当的subplot中绘制一个Basemap,转换坐标，然后通过Basemap的plot方法绘制点
fig,axes=plt.subplots(nrows=2,ncols=2,figsize=(12,10))
fig.subplots_adjust(hspace=0.05,wspace=0.05)
to_plot=['2a','1','3c','7a']
lllat=17.25
urlat=20.25
lllon=-75
urlon=-71
for code,ax in zip(to_plot,axes.flat):
    m=basic_haiti_map(ax,lllat=lllat,urlat=urlat,lllon=lllon,urlon=urlon)
    cat_data=data[data['category_%s' % code]==1]
    #计算地图的投影坐标
    x,y=m(list(cat_data.LONGITUDE),list(cat_data.LATITUDE))
    m.plot(x,y,'k.',alpha=0.5)
    ax.set_title('%s:%s' % (code,english_mapping[code]))
plt.show()

最后的地图由于软件原因没有显示出来，读者可以参考原书的相关章节