Seaborn 시각화 연습_Heatmap,Pairplot,jointplot,tsplot

 

Pinkwink님 블로그를 보고 연습하였다.

https://pinkwink.kr/986?category=522424

pairplot 이랑 jointplot은 정말 유용하게 사용 할 수 있을거 같다.

[Seaborn 연재] pairplot, jointplot, tsplot 익히기

 

 

In [12]:

import numpy as np
import seaborn as sns
from matplotlib import pyplot as plt

In [17]:

x = np.random.rand( 5, 12)
x

Out[17]:

array([[0.03324251, 0.88104451, 0.27053807, 0.8320188 , 0.49092681,
        0.75255923, 0.15278282, 0.73902481, 0.86908686, 0.45668774,
        0.31050087, 0.15716642],
       [0.67982771, 0.59736936, 0.45207916, 0.59698116, 0.55910991,
        0.3271498 , 0.77314427, 0.30645617, 0.80380316, 0.79532911,
        0.42675504, 0.17486688],
       [0.86124541, 0.21337203, 0.16131834, 0.05187921, 0.19474029,
        0.30777195, 0.36514702, 0.29470368, 0.5188736 , 0.85080587,
        0.46255703, 0.04595159],
       [0.31246925, 0.38037254, 0.04014281, 0.8491799 , 0.72682537,
        0.01165201, 0.9666183 , 0.65619396, 0.39759107, 0.16876977,
        0.96138433, 0.83583517],
       [0.66950724, 0.12716263, 0.41842518, 0.09924881, 0.48370869,
        0.76010372, 0.60450044, 0.13803113, 0.00321277, 0.38555838,
        0.51358915, 0.50555199]])

 

Heatmap¶

In [18]:

sns.heatmap(x, vmin=0, vmax=1)

Out[18]:

<matplotlib.axes._subplots.AxesSubplot at 0x1a37f9078c8>

 

In [15]:

flights= sns.load_dataset("flights")
flights.head()

Out[15]:

	year	month	passengers
0	1949	January	112
1	1949	February	118
2	1949	March	132
3	1949	April	129
4	1949	May	121

In [16]:

flights = flights.pivot( "month", "year", "passengers" )
flights.head(5)

Out[16]:

year	1949	1950	1951	1952	1953	1954	1955	1956	1957	1958	1959	1960
month
January	112	115	145	171	196	204	242	284	315	340	360	417
February	118	126	150	180	196	188	233	277	301	318	342	391
March	132	141	178	193	236	235	267	317	356	362	406	419
April	129	135	163	181	235	227	269	313	348	348	396	461
May	121	125	172	183	229	234	270	318	355	363	420	472

In [11]:

plt.figure(figsize = (10, 8))
sns.heatmap(flights, annot = True, fmt = "d")
plt.show()

 

 

Pairplot¶

In [19]:

sns.set(style = "ticks",color_codes=True)
iris = sns.load_dataset("iris")
iris.head(5)

Out[19]:

	sepal_length	sepal_width	petal_length	petal_width	species
0	5.1	3.5	1.4	0.2	setosa
1	4.9	3.0	1.4	0.2	setosa
2	4.7	3.2	1.3	0.2	setosa
3	4.6	3.1	1.5	0.2	setosa
4	5.0	3.6	1.4	0.2	setosa

In [27]:

sns.pairplot(iris, hue = "species", palette ="husl" )

Out[27]:

<seaborn.axisgrid.PairGrid at 0x1a305042788>

 

 

Jointplot¶

In [28]:

tips = sns.load_dataset("tips")

tips.head(5)

Out[28]:

	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

In [30]:

sns.set_style("whitegrid")

sns.jointplot(x="total_bill", y = "tip", data = tips)
plt.show()
#jointplot은 scatter 플롯을 보이면서도 또 각 축에 히스토그램을 보여준다.

 

In [32]:

iris = sns.load_dataset("iris")
sns.jointplot("sepal_width", "petal_length", data = iris, kind ="kde", color ="r")

Out[32]:

<seaborn.axisgrid.JointGrid at 0x1a307134908>

 

 

Tsplot¶

In [37]:

x = np.linspace( 0 , 15, 31)

data = np.sin(x) + np.random.rand(10,31) + np.random.randn(10, 1)
data

sns.tsplot(data = data, err_style= "boot_traces", n_boot = 200)

 

C:\Users\swcheon\Anaconda3\lib\site-packages\seaborn\timeseries.py:183: UserWarning: The `tsplot` function is deprecated and will be removed in a future release. Please update your code to use the new `lineplot` function.
  warnings.warn(msg, UserWarning)

Out[37]:

<matplotlib.axes._subplots.AxesSubplot at 0x1a307395388>

 

In [ ]: