[Python]데이터 시각화, 연관성 분석 heat map, pairplot 그리기

데이터는 Kaggle에 있는 bostan marathon 데이터를 참고했다.

 

https://www.kaggle.com/rojour/boston-results

Finishers Boston Marathon 2015, 2016 & 2017

 

 

In [12]:

#tistory 관련 코드(필요없음)
from IPython.core.display import display, HTML
display(HTML("<style>.container {width:90% !important;}</style>"))

heatmap¶

correlation http://seaborn.pydata.org/generated/seaborn.heatmap.html

In [1]:

import pandas as pd

In [2]:

marathon_2017 = pd.read_csv("C://Users//User//Desktop//boston-results/marathon_results_2017.csv")

In [10]:

marathon_2017.head()

Out[10]:

	Unnamed: 0	Bib	Name	Age	M/F	City	State	Country	Citizen	Unnamed: 9	...	25K	30K	35K	40K	Pace	Proj Time	Official Time	Overall	Gender	Division
0	0	11	Kirui, Geoffrey	24	M	Keringet	NaN	KEN	NaN	NaN	...	1:16:59	1:33:01	1:48:19	2:02:53	0:04:57	-	2:09:37	1	1	1
1	1	17	Rupp, Galen	30	M	Portland	OR	USA	NaN	NaN	...	1:16:59	1:33:01	1:48:19	2:03:14	0:04:58	-	2:09:58	2	2	2
2	2	23	Osako, Suguru	25	M	Machida-City	NaN	JPN	NaN	NaN	...	1:17:00	1:33:01	1:48:31	2:03:38	0:04:59	-	2:10:28	3	3	3
3	3	21	Biwott, Shadrack	32	M	Mammoth Lakes	CA	USA	NaN	NaN	...	1:17:00	1:33:01	1:48:58	2:04:35	0:05:03	-	2:12:08	4	4	4
4	4	9	Chebet, Wilson	31	M	Marakwet	NaN	KEN	NaN	NaN	...	1:16:59	1:33:01	1:48:41	2:05:00	0:05:04	-	2:12:35	5	5	5

5 rows × 25 columns

In [3]:

marathon_2017.corr()

Out[3]:

	Unnamed: 0	Age	Overall	Gender	Division
Unnamed: 0	1.000000	0.259623	1.000000	0.902079	0.449813
Age	0.259623	1.000000	0.259626	0.368879	-0.579735
Overall	1.000000	0.259626	1.000000	0.902077	0.449812
Gender	0.902079	0.368879	0.902077	1.000000	0.403068
Division	0.449813	-0.579735	0.449812	0.403068	1.000000

In [4]:

import seaborn as sns

In [8]:

sns.heatmap(marathon_2017.corr(),annot=True)

Out[8]:

<matplotlib.axes._subplots.AxesSubplot at 0x211329c91d0>

Scatterplot¶

http://seaborn.pydata.org/generated/seaborn.pairplot.html

In [11]:

sns.pairplot(marathon_2017,hue="M/F")

Out[11]:

<seaborn.axisgrid.PairGrid at 0x211339f3668>