Seabornの基本操作をJupyter Labで書いてみた #Python

第２４回

Seaborn

import seaborn as sns
%matplotlib inline

import pandas as pd
df = pd.read_csv('train.csv')
df = df.dropna(subset=['Age'])
sns.distplot(df['Age'])

<matplotlib.axes._subplots.AxesSubplot at 0x7fcbcdd7e3d0>

sns.set()
sns.distplot(df['Age'],bins=50)

<matplotlib.axes._subplots.AxesSubplot at 0x7fcbcdd05850>

sns.jointplot()で２変数の分布を見る

sns.jointplot(x='Age', y='Fare', data=df)

<seaborn.axisgrid.JointGrid at 0x7fcbcdbe31d0>

sns.jointplot(x='Age', y='Fare', data=df, kind='hex')

<seaborn.axisgrid.JointGrid at 0x7fcbcdb2b890>

(重要)sns.pairplot()で複数のカラムの分布を一発で表示

sns.pairplot(df[['Age', 'Fare', 'Pclass', 'Survived']], hue='Survived', kind='scatter', plot_kws={'alpha': 0.5})

/opt/anaconda3/lib/python3.7/site-packages/statsmodels/nonparametric/kde.py:487: RuntimeWarning: invalid value encountered in true_divide
  binned = fast_linbin(X, a, b, gridsize) / (delta * nobs)
/opt/anaconda3/lib/python3.7/site-packages/statsmodels/nonparametric/kdetools.py:34: RuntimeWarning: invalid value encountered in double_scalars
  FAC1 = 2*(np.pi*bw/RANGE)**2





<seaborn.axisgrid.PairGrid at 0x7fcbcda34990>

第２５回

sns.barplot() 「棒グラフ」を作る

import numpy as np
import pandas as pd
import seaborn as sns
%matplotlib inline

df = pd.read_csv('train.csv')
df.head()

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	STON/O2. 3101282	7.9250	NaN	S
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	113803	53.1000	C123	S
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	373450	8.0500	NaN	S

sns.barplot(x='Survived', y='Age', data=df) #平均値

<matplotlib.axes._subplots.AxesSubplot at 0x7fcbcdd7e390>

sns.barplot(x='Survived', y='Age', data=df, estimator=np.median) #中央値

<matplotlib.axes._subplots.AxesSubplot at 0x7fcbcce3a190>

sns.countplot() データ数を比較

sns.countplot(x='Sex', data=df, hue='Survived')

<matplotlib.axes._subplots.AxesSubplot at 0x7fcbccfb0c10>

sns.boxplot() カテゴリ別に値を比較する

sns.boxplot(x='Pclass', y='Age', data=df)

<matplotlib.axes._subplots.AxesSubplot at 0x7fcbccd82750>

sns.boxplot(x='Pclass', y='Age', data=df, hue='Survived')

<matplotlib.axes._subplots.AxesSubplot at 0x7fcbcc2a4ed0>

sns.violonplot() データの分析を可視化できる

sns.violinplot(x='Pclass', y='Age', data=df)

<matplotlib.axes._subplots.AxesSubplot at 0x7fcbcc1e3ad0>

sns.violinplot(x='Pclass', y='Age', data=df, hue=('Survived'))

<matplotlib.axes._subplots.AxesSubplot at 0x7fcbcc107210>

sns.swarmplot() 本当の分布を確認できる

sns.swarmplot(x='Pclass', y='Age', data=df)

<matplotlib.axes._subplots.AxesSubplot at 0x7fcbcc19a710>

sns.swarmplot(x='Pclass', y='Age', data=df, size=4, hue='Survived')

<matplotlib.axes._subplots.AxesSubplot at 0x7fcbc6e9fe50>

第２６回

Heatmapを描写する

df.corr()で相関関係を作る

import pandas as pd
df = pd.read_csv('train.csv')
corr = df.corr()
corr

	PassengerId	Survived	Pclass	Age	SibSp	Parch	Fare
PassengerId	1.000000	-0.005007	-0.035144	0.036847	-0.057527	-0.001652	0.012658
Survived	-0.005007	1.000000	-0.338481	-0.077221	-0.035322	0.081629	0.257307
Pclass	-0.035144	-0.338481	1.000000	-0.369226	0.083081	0.018443	-0.549500
Age	0.036847	-0.077221	-0.369226	1.000000	-0.308247	-0.189119	0.096067
SibSp	-0.057527	-0.035322	0.083081	-0.308247	1.000000	0.414838	0.159651
Parch	-0.001652	0.081629	0.018443	-0.189119	0.414838	1.000000	0.216225
Fare	0.012658	0.257307	-0.549500	0.096067	0.159651	0.216225	1.000000

sns.heatmap()でHeatmapをplot

sns.heatmap(corr)

<matplotlib.axes._subplots.AxesSubplot at 0x7fcbc6df9850>

sns.heatmap(corr, cmap='coolwarm', annot=True)

<matplotlib.axes._subplots.AxesSubplot at 0x7fcbcd0b3290>

データを俯瞰するときに役立つsns.heatmap()

flights = sns.load_dataset('flights')
print(len(flights))
flights.head()

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

# pivot_tableを作成
flights_pivot = flights.pivot_table(index='month', columns='year', values='passengers') 
flights_pivot

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
June	135	149	178	218	243	264	315	374	422	435	472	535
July	148	170	199	230	264	302	364	413	465	491	548	622
August	148	170	199	242	272	293	347	405	467	505	559	606
September	136	158	184	209	237	259	312	355	404	404	463	508
October	119	133	162	191	211	229	274	306	347	359	407	461
November	104	114	146	172	180	203	237	271	305	310	362	390
December	118	140	166	194	201	229	278	306	336	337	405	432

sns.heatmap(flights_pivot)

<matplotlib.axes._subplots.AxesSubplot at 0x7fcbc5baabd0>

第２7回

sns.set()で基本styleを変更

context引数で用途を指定

import pandas as pd
import seaborn as sns
%matplotlib inline

df = pd.read_csv('train.csv')

sns.set(context=('poster'))
df = df.dropna(subset=['Age'])
sns.distplot(df['Age'])

<matplotlib.axes._subplots.AxesSubplot at 0x7fcbc568f690>

style引数でグラフ全体のstyleを指定

sns.set_style(style='whitegrid') #背景色を変更
sns.distplot(df['Age'])

<matplotlib.axes._subplots.AxesSubplot at 0x7fcbc563d850>

palette引数で色を指定

sns.set(palette='bright')
sns.violinplot(x='Pclass', y='Age', data=df)

<matplotlib.axes._subplots.AxesSubplot at 0x7fcbc5472850>

sns.despine()で軸や枠を落とす

sns.set(palette='bright' ,style='ticks') ##style='ticks'でplotに軸
sns.violinplot(x='Pclass', y='Age', data=df)

<matplotlib.axes._subplots.AxesSubplot at 0x7fcbc53ed810>

sns.set(palette='bright' )
sns.violinplot(x='Pclass', y='Age', data=df)
sns.despine()

matplotlibと同様pltモジュールでいろいろなことができる

import matplotlib.pyplot as plt
plt.figure(figsize=(10, 5))
sns.distplot(df['Age'])

<matplotlib.axes._subplots.AxesSubplot at 0x7fcbc52d8750>

sns.distplot(df['Age'])
plt.savefig('seaborn_sample.png')