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初学pandas与seaborn（三）回归曲线生成以及csv数据导入

2016-12-30 09:42阅读：

http://blog.sina.cn/dpool/blog/u/1259623133

　　看个坐标图、点图的曲线啥的，seaborn还是比较方便的。这里可以用seaborn自带的样板库数据来写，也可以导入外部数据。外部数据是自己编的，点点不够多，不过能体会下pandas调用文件数据并用seaborn显示出来的全过程，对整个数据处理编程应该会有一些启发。上图：
初学pandas与seaborn（三）回归曲线生成以及csv数据导入
自己编的数据点，时间关系数据点少了点，嘿嘿
初学pandas与seaborn（三）回归曲线生成以及csv数据导入
可以发现，刻意构造的相关性被显示出来了
r>好了，这是数据文件：
tips.csv
total_bill,tip,sex,smoker,day,time,size 16.99,1.01,Female,No,Sun,Dinner,2
10.34,1.66,Male,No,Sun,Dinner,3
21.01,3.5,Male,No,Sun,Lunch,3
23.68,3.31,Male,Yes,Sat,Dinner,2
24.59,3.61,Female,No,Sun,Dinner,4
30,5,Female,No,Sat,Dinner,6
29,3,Male,Yes,Sat,Dinner,5
28,3.5,Male,No,Sun,Lunch,6
10,3.31,Male,No,Sun,Dinner,1
9,3.61,Female,Yes,Sat,Dinner,1
64,10,Female,Yes,Sun,Dinner,8
30,4,Male,Yes,Sat,Lunch,5
20,5,Male,Yes,Sun,Dinner,3
24,4,Male,No,Sat,Lunch,4
18,3,Female,No,Sun,Dinner,2
17,1,Female,No,Sat,Dinner,3
16,2,Male,No,Sat,Dinner,2
19,2,Male,No,Sun,Lunch,2
23,4,Male,No,Sun,Dinner,3
22,2,Female,Yes,Sat,Dinner,3
30,1.01,Female,No,Sun,Dinner,5
40,1.66,Male,No,Sun,Dinner,5
33,3.5,Male,No,Sun,Lunch,5
35,18,Male,Yes,Sat,Dinner,6
36,3.61,Female,No,Sun,Dinner,5
38,5,Female,No,Sat,Dinner,5
42,15,Male,Yes,Sat,Dinner,6
44,3.5,Male,No,Sun,Lunch,6
49,3.31,Male,No,Sun,Dinner,5
46,10,Female,Yes,Sat,Dinner,6
47,20,Female,Yes,Sun,Dinner,6
43,11,Male,Yes,Sat,Lunch,7
61,12,Male,Yes,Sun,Dinner,5
51,6,Male,No,Sat,Lunch,8
52,8,Female,No,Sun,Dinner,7
58,8,Female,No,Sat,Dinner,7
55,8,Male,No,Sat,Dinner,8
57,5,Male,No,Sun,Lunch,7
53,6,Male,No,Sun,Dinner,8
62,14,Female,Yes,Sat,Dinner,10

复制到记事本并存为csv扩展名即可，tips.csv存储在python文件相同目录下
接下来是程序本体
# encoding:utf-8 import seaborn as sns
import pandas as pd


tips = pd.read_csv('tips.csv')
print tips
sns.jointplot('total_bill', 'tip', tips, kind='reg')
sns.plt.show()
sns.plt.close() # 用于清空之前的图表。若不清空，那么下一个图表会残留上一个图表的点


sns.lmplot('total_bill', 'tip', tips, col='smoker')
sns.plt.show()

程序分析：
　　我们用pandas库导入了tips.csv文件，然后选择total_bill（总账单）作为横坐标，tip（小费）作为纵坐标，tips对象作为panda数据集合。第一个图生成类型为reg，第二个图我们用是否smoker作为区分，生成了对比图。
　　学习过程中还遇到一些莫名其妙的bug，一开始一个叫tips没有扩展名的文件是这样的……好吧，我醉了，这居然是一个build-in的测试数据，seaborn直接自己自带的？！我居然漏看了红色部分的注释：
# Load one of the data sets that come with seaborn
tips = sns.load_dataset('tips')
print tips

　　看来我惯性思维，认为这个语句一定是导入外部测试数据的。然后就极其傻逼地在网上找啊找。言归正传，我将其打印出来，直接发现有243行数据：
Name: D, dtype: int32
total_bill tip sex smoker day time size
0 16.99 1.01 Female No Sun Dinner 2
1 10.34 1.66 Male No Sun Dinner 3
2 21.01 3.50 Male No Sun Dinner 3
3 23.68 3.31 Male No Sun Dinner 2
4 24.59 3.61 Female No Sun Dinner 4
5 25.29 4.71 Male No Sun Dinner 4
6 8.77 2.00 Male No Sun Dinner 2
7 26.88 3.12 Male No Sun Dinner 4
8 15.04 1.96 Male No Sun Dinner 2
9 14.78 3.23 Male No Sun Dinner 2
10 10.27 1.71 Male No Sun Dinner 2
11 35.26 5.00 Female No Sun Dinner 4
12 15.42 1.57 Male No Sun Dinner 2
13 18.43 3.00 Male No Sun Dinner 4
14 14.83 3.02 Female No Sun Dinner 2
15 21.58 3.92 Male No Sun Dinner 2
16 10.33 1.67 Female No Sun Dinner 3
17 16.29 3.71 Male No Sun Dinner 3
18 16.97 3.50 Female No Sun Dinner 3
19 20.65 3.35 Male No Sat Dinner 3
20 17.92 4.08 Male No Sat Dinner 2
21 20.29 2.75 Female No Sat Dinner 2
22 15.77 2.23 Female No Sat Dinner 2
23 39.42 7.58 Male No Sat Dinner 4
24 19.82 3.18 Male No Sat Dinner 2
25 17.81 2.34 Male No Sat Dinner 4
26 13.37 2.00 Male No Sat Dinner 2
27 12.69 2.00 Male No Sat Dinner 2
28 21.70 4.30 Male No Sat Dinner 2
29 19.65 3.00 Female No Sat Dinner 2
.. ... ... ... ... ... ... ...
214 28.17 6.50 Female Yes Sat Dinner 3
215 12.90 1.10 Female Yes Sat Dinner 2
216 28.15 3.00 Male Yes Sat Dinner 5
217 11.59 1.50 Male Yes Sat Dinner 2
218 7.74 1.44 Male Yes Sat Dinner 2
219 30.14 3.09 Female Yes Sat Dinner 4
220 12.16 2.20 Male Yes Fri Lunch 2
221 13.42 3.48 Female Yes Fri Lunch 2
222 8.58 1.92 Male Yes Fri Lunch 1
223 15.98 3.00 Female No Fri Lunch 3
224 13.42 1.58 Male Yes Fri Lunch 2
225 16.27 2.50 Female Yes Fri Lunch 2
226 10.09 2.00 Female Yes Fri Lunch 2
227 20.45 3.00 Male No Sat Dinner 4
228 13.28 2.72 Male No Sat Dinner 2
229 22.12 2.88 Female Yes Sat Dinner 2
230 24.01 2.00 Male Yes Sat Dinner 4
231 15.69 3.00 Male Yes Sat Dinner 3
232 11.61 3.39 Male No Sat Dinner 2
233 10.77 1.47 Male No Sat Dinner 2
234 15.53 3.00 Male Yes Sat Dinner 2
235 10.07 1.25 Male No Sat Dinner 2
236 12.60 1.00 Male Yes Sat Dinner 2
237 32.83 1.17 Male Yes Sat Dinner 2
238 35.83 4.67 Female No Sat Dinner 3
239 29.03 5.92 Male No Sat Dinner 3
240 27.18 2.00 Female Yes Sat Dinner 2
241 22.67 2.00 Male Yes Sat Dinner 2
242 17.82 1.75 Male No Sat Dinner 2
243 18.78 3.00 Female No Thur Dinner 2
好吧问题其实解决了。如果需要导入自己写的csv，请一定用pandas库。直接用seaborn的load_dataset函数是不行的，会报错：
tips = sns.load_dataset('tips.csv')

pandas.io.common.CParserError: Error tokenizing data. C error: Expected 1 fields in line 13, saw 2
按照stackoverflow提示的方法加了error_bad_lines=False后，
tips = sns.load_dataset('tips.csv', error_bad_lines=False)

又报错：
TypeError: ufunc 'multiply' did not contain a loop with signature matching types dtype('S3') dtype('S3') dtype('S3')
最终解决办法，还是用回pandas库的read_csv方法：
tips = pd.read_csv('tips.csv')

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