210802_jupyter-notebook_tytanic

 

 

 

타이타닉 데이터를 활용한 데이터 분석¶

 

1. 라이브러리 및 데이터¶

In [48]:

import pandas as pd
from sklearn.linear_model import LogisticRegression # 로지스틱 회귀 모델 불러오기
from sklearn.tree import DecisionTreeClassifier #의사결정 나무 모델 불러오기
#사이킷런 - 데이터 학습 모델 라이브러리

In [49]:

train = pd.read_csv('./data/train.csv')
test = pd.read_csv('./data/test.csv')

submission = pd.read_csv('./data/submission.csv')

 

2. 탐색적 자료 분석 (Exploratory Data Analysis(EDA)¶

 

PassengerId : 탑승객의 고유 아이디

Survival : 생존여부(0: 사망, 1: 생존)

Pclass : 등실의 등급(1: 1등급, 2: 2등급, 3: 3등급)

Name : 이름

Sex : 성별

Age : 나이

Sibsp : 함께 탑승한 형제자매, 아내 남편의 수

Parch: 함께 탑승한 부모, 자식의 수

Ticket: 티켓번호

Fare: 티켓의 요금

Cabin: 객실번호

Embarked: 배에 탑승한 위치(C = Cherbourg, Q = Queenstown, S = Southampton)

In [50]:

train.head()

Out[50]:

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

 

SibSp 같이 탄 형제 자매, 아내 남편의 수

Parch 같이 탄 부모, 자식의 수

In [51]:

test.head()

Out[51]:

	PassengerId	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	892	3	Kelly, Mr. James	male	34.5	0	0	330911	7.8292	NaN	Q
1	893	3	Wilkes, Mrs. James (Ellen Needs)	female	47.0	1	0	363272	7.0000	NaN	S
2	894	2	Myles, Mr. Thomas Francis	male	62.0	0	0	240276	9.6875	NaN	Q
3	895	3	Wirz, Mr. Albert	male	27.0	0	0	315154	8.6625	NaN	S
4	896	3	Hirvonen, Mrs. Alexander (Helga E Lindqvist)	female	22.0	1	1	3101298	12.2875	NaN	S

In [52]:

submission.head()

Out[52]:

	PassengerId	Survived
0	892	0
1	893	1
2	894	0
3	895	0
4	896	1

In [53]:

print('train shape :', train.shape)
print('test shape :', test.shape)
print('submission shape :', submission.shape)

 

train shape : (891, 12)
test shape : (418, 11)
submission shape : (418, 2)

 

pd.DataFrame.info()¶

In [54]:

train.info()

 

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 12 columns):
 #   Column       Non-Null Count  Dtype  
---  ------       --------------  -----  
 0   PassengerId  891 non-null    int64  
 1   Survived     891 non-null    int64  
 2   Pclass       891 non-null    int64  
 3   Name         891 non-null    object 
 4   Sex          891 non-null    object 
 5   Age          714 non-null    float64
 6   SibSp        891 non-null    int64  
 7   Parch        891 non-null    int64  
 8   Ticket       891 non-null    object 
 9   Fare         891 non-null    float64
 10  Cabin        204 non-null    object 
 11  Embarked     889 non-null    object 
dtypes: float64(2), int64(5), object(5)
memory usage: 83.7+ KB

 

데이터를 그려보기¶

In [55]:

train.groupby('Sex').mean()

Out[55]:

	PassengerId	Survived	Pclass	Age	SibSp	Parch	Fare
Sex
female	431.028662	0.742038	2.159236	27.915709	0.694268	0.649682	44.479818
male	454.147314	0.188908	2.389948	30.726645	0.429809	0.235702	25.523893

In [56]:

train.groupby('Embarked').mean()

Out[56]:

	PassengerId	Survived	Pclass	Age	SibSp	Parch	Fare
Embarked
C	445.357143	0.553571	1.886905	30.814769	0.386905	0.363095	59.954144
Q	417.896104	0.389610	2.909091	28.089286	0.428571	0.168831	13.276030
S	449.527950	0.336957	2.350932	29.445397	0.571429	0.413043	27.079812

In [103]:

sex_pclass_means = train.groupby(['Sex', 'Pclass']).mean()

In [104]:

sex_pclass_means.Survived.plot(kind = 'bar')

Out[104]:

<AxesSubplot:xlabel='Sex,Pclass'>

 

In [59]:

train.plot(x = 'Age', y='Fare', kind='scatter')

Out[59]:

<AxesSubplot:xlabel='Age', ylabel='Fare'>

 

In [60]:

train.sort_values(by='Fare', ascending=False).head(20)

Out[60]:

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
258	259	1	1	Ward, Miss. Anna	female	35.0	0	0	PC 17755	512.3292	NaN	C
737	738	1	1	Lesurer, Mr. Gustave J	male	35.0	0	0	PC 17755	512.3292	B101	C
679	680	1	1	Cardeza, Mr. Thomas Drake Martinez	male	36.0	0	1	PC 17755	512.3292	B51 B53 B55	C
88	89	1	1	Fortune, Miss. Mabel Helen	female	23.0	3	2	19950	263.0000	C23 C25 C27	S
27	28	0	1	Fortune, Mr. Charles Alexander	male	19.0	3	2	19950	263.0000	C23 C25 C27	S
341	342	1	1	Fortune, Miss. Alice Elizabeth	female	24.0	3	2	19950	263.0000	C23 C25 C27	S
438	439	0	1	Fortune, Mr. Mark	male	64.0	1	4	19950	263.0000	C23 C25 C27	S
311	312	1	1	Ryerson, Miss. Emily Borie	female	18.0	2	2	PC 17608	262.3750	B57 B59 B63 B66	C
742	743	1	1	Ryerson, Miss. Susan Parker "Suzette"	female	21.0	2	2	PC 17608	262.3750	B57 B59 B63 B66	C
118	119	0	1	Baxter, Mr. Quigg Edmond	male	24.0	0	1	PC 17558	247.5208	B58 B60	C
299	300	1	1	Baxter, Mrs. James (Helene DeLaudeniere Chaput)	female	50.0	0	1	PC 17558	247.5208	B58 B60	C
557	558	0	1	Robbins, Mr. Victor	male	NaN	0	0	PC 17757	227.5250	NaN	C
700	701	1	1	Astor, Mrs. John Jacob (Madeleine Talmadge Force)	female	18.0	1	0	PC 17757	227.5250	C62 C64	C
380	381	1	1	Bidois, Miss. Rosalie	female	42.0	0	0	PC 17757	227.5250	NaN	C
716	717	1	1	Endres, Miss. Caroline Louise	female	38.0	0	0	PC 17757	227.5250	C45	C
527	528	0	1	Farthing, Mr. John	male	NaN	0	0	PC 17483	221.7792	C95	S
377	378	0	1	Widener, Mr. Harry Elkins	male	27.0	0	2	113503	211.5000	C82	C
730	731	1	1	Allen, Miss. Elisabeth Walton	female	29.0	0	0	24160	211.3375	B5	S
779	780	1	1	Robert, Mrs. Edward Scott (Elisabeth Walton Mc...	female	43.0	0	1	24160	211.3375	B3	S
689	690	1	1	Madill, Miss. Georgette Alexandra	female	15.0	0	1	24160	211.3375	B5	S

In [67]:

train.head(5)

Out[67]:

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

In [68]:

train.plot(x='Fare', y='Survived', kind='scatter')

Out[68]:

<AxesSubplot:xlabel='Fare', ylabel='Survived'>

 

 

결측치 처리하기¶

In [61]:

train.isna().sum()

Out[61]:

PassengerId      0
Survived         0
Pclass           0
Name             0
Sex              0
Age            177
SibSp            0
Parch            0
Ticket           0
Fare             0
Cabin          687
Embarked         2
dtype: int64

 

중앙값으로 nan을 채운다¶

In [62]:

train.Age.median()

Out[62]:

28.0

In [63]:

train['Age'] = train['Age'].fillna(int(train.Age.median()))

In [64]:

train

Out[64]:

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S
...	...	...	...	...	...	...	...	...	...	...	...	...
886	887	0	2	Montvila, Rev. Juozas	male	27.0	0	0	211536	13.0000	NaN	S
887	888	1	1	Graham, Miss. Margaret Edith	female	19.0	0	0	112053	30.0000	B42	S
888	889	0	3	Johnston, Miss. Catherine Helen "Carrie"	female	28.0	1	2	W./C. 6607	23.4500	NaN	S
889	890	1	1	Behr, Mr. Karl Howell	male	26.0	0	0	111369	30.0000	C148	C
890	891	0	3	Dooley, Mr. Patrick	male	32.0	0	0	370376	7.7500	NaN	Q

891 rows × 12 columns

In [ ]:

In [65]:

train['Embarked'] = train['Embarked'].fillna('S')

 

모델은 수치화 된 자료들만 다룰 수 있다!¶

• 문자열로 된 자료들을 수치화 시키기

In [70]:

train['Sex'] =train['Sex'].map({'male':0, 'female':1})

In [71]:

train

Out[71]:

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	0	22.0	1	0	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	1	38.0	1	0	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	1	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	1	35.0	1	0	113803	53.1000	C123	S
4	5	0	3	Allen, Mr. William Henry	0	35.0	0	0	373450	8.0500	NaN	S
...	...	...	...	...	...	...	...	...	...	...	...	...
886	887	0	2	Montvila, Rev. Juozas	0	27.0	0	0	211536	13.0000	NaN	S
887	888	1	1	Graham, Miss. Margaret Edith	1	19.0	0	0	112053	30.0000	B42	S
888	889	0	3	Johnston, Miss. Catherine Helen "Carrie"	1	28.0	1	2	W./C. 6607	23.4500	NaN	S
889	890	1	1	Behr, Mr. Karl Howell	0	26.0	0	0	111369	30.0000	C148	C
890	891	0	3	Dooley, Mr. Patrick	0	32.0	0	0	370376	7.7500	NaN	Q

891 rows × 12 columns

 

x -> MODEL -> Y

 

변수 선택 및 모델 구축¶

 

feature Engineering & Initial Modeling¶

In [73]:

X_train = train[['Sex', 'Pclass']]
y_train = train['Survived']

In [74]:

X_train

Out[74]:

	Sex	Pclass
0	0	3
1	1	1
2	1	3
3	1	1
4	0	3
...	...	...
886	0	2
887	1	1
888	1	3
889	0	1
890	0	3

891 rows × 2 columns

In [76]:

test['Sex'] = test['Sex'].map({'male':0, 'female':1})

In [77]:

X_test = test[['Sex', 'Pclass']]

X_test

Out[77]:

	Sex	Pclass
0	0	3
1	1	3
2	0	2
3	0	3
4	1	3
...	...	...
413	0	3
414	1	1
415	0	3
416	0	3
417	0	3

418 rows × 2 columns

 

sklearn.linear_model.LogisticRegression()¶

• 로지스틱 회귀 모형
• 0과 1사이의 값을 산출

In [78]:

lr = LogisticRegression()

 

DecisionTreeClassifier¶

• 의사결정 나무 모델

In [80]:

dt = DecisionTreeClassifier()

In [82]:

# 학습이 되었다.
lr.fit(X_train, y_train)

Out[82]:

LogisticRegression()

In [83]:

dt.fit(X_train, y_train)

Out[83]:

DecisionTreeClassifier()

In [84]:

lr.predict(X_test)

Out[84]:

array([0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0,
       1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1,
       1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1,
       1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1,
       1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0,
       0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0,
       1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1,
       0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1,
       1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1,
       0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0,
       1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,
       0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1,
       0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0,
       0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0,
       0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0,
       1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0,
       0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0,
       1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1,
       0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0],
      dtype=int64)

In [85]:

lr.predict_proba(X_test)

Out[85]:

array([[0.89797923, 0.10202077],
       [0.40645631, 0.59354369],
       [0.77524063, 0.22475937],
       [0.89797923, 0.10202077],
       [0.40645631, 0.59354369],
       [0.89797923, 0.10202077],
       [0.40645631, 0.59354369],
       [0.77524063, 0.22475937],
       [0.40645631, 0.59354369],
       [0.89797923, 0.10202077],
       [0.89797923, 0.10202077],
       [0.57476411, 0.42523589],
       ...
       [0.40645631, 0.59354369],
       [0.40645631, 0.59354369],
       [0.40645631, 0.59354369],
       [0.09515218, 0.90484782],
       [0.40645631, 0.59354369],
       [0.89797923, 0.10202077],
       [0.09515218, 0.90484782],
       [0.89797923, 0.10202077],
       [0.89797923, 0.10202077],
       [0.89797923, 0.10202077]])

  

In [87]:

lr.predict_proba(X_test).shape
#하나는 사망확률 / 하나는 생존확률

Out[87]:

(418, 2)

In [92]:

lr_pred = lr.predict_proba(X_test)

In [90]:

type(lr_predict)

Out[90]:

numpy.ndarray

 

TODO. 이게모람¶

In [91]:

lr.predict_proba(X_test)[:, 1] # 생존확률 빼내기

Out[91]:

array([0.10202077, 0.59354369, 0.22475937, 0.10202077, 0.59354369,
       0.10202077, 0.59354369, 0.22475937, 0.59354369, 0.10202077,
       0.10202077, 0.42523589, 0.90484782, 0.22475937, 0.90484782,
       0.78842569, 0.22475937, 0.10202077, 0.59354369, 0.59354369,
       0.42523589, 0.10202077, 0.90484782, 0.42523589, 0.90484782,
       0.10202077, 0.90484782, 0.10202077, 0.42523589, 0.10202077,
       0.22475937, 0.22475937, 0.59354369, 0.59354369, 0.42523589,
       0.10202077, 0.59354369, 0.59354369, 0.10202077, 0.10202077,
       0.10202077, 0.42523589, 0.10202077, 0.78842569, 0.90484782,
       0.10202077, 0.42523589, 0.10202077, 0.90484782, 0.59354369,
       0.42523589, 0.22475937, 0.78842569, 0.90484782, 0.22475937,
       0.10202077, 0.10202077, 0.10202077, 0.10202077, 0.90484782,
       0.10202077, 0.22475937, 0.10202077, 0.59354369, 0.42523589,
       0.78842569, 0.59354369, 0.42523589, 0.42523589, 0.90484782,
        ...
       0.78842569, 0.42523589, 0.10202077, 0.59354369, 0.10202077,
       0.42523589, 0.22475937, 0.10202077, 0.22475937, 0.10202077,
       0.22475937, 0.10202077, 0.10202077, 0.90484782, 0.10202077,
       0.59354369, 0.22475937, 0.59354369, 0.22475937, 0.78842569,
       0.90484782, 0.22475937, 0.22475937, 0.22475937, 0.59354369,
       0.42523589, 0.90484782, 0.10202077, 0.10202077, 0.59354369,
       0.10202077, 0.78842569, 0.78842569, 0.10202077, 0.90484782,
       0.59354369, 0.10202077, 0.59354369, 0.90484782, 0.22475937,
       0.22475937, 0.90484782, 0.42523589, 0.22475937, 0.90484782,
       0.90484782, 0.59354369, 0.22475937, 0.42523589, 0.10202077,
       0.10202077, 0.10202077, 0.59354369, 0.59354369, 0.22475937,
       0.78842569, 0.10202077, 0.22475937, 0.10202077, 0.10202077,
       0.42523589, 0.90484782, 0.10202077, 0.22475937, 0.10202077,
       0.90484782, 0.10202077, 0.90484782, 0.10202077, 0.10202077,
       0.90484782, 0.22475937, 0.90484782, 0.42523589, 0.42523589,
       0.22475937, 0.22475937, 0.42523589, 0.59354369, 0.59354369,
       0.59354369, 0.90484782, 0.59354369, 0.10202077, 0.90484782,
       0.10202077, 0.10202077, 0.10202077])

In [99]:

dt_pred = dt.predict_proba(X_test)[:, 0]

In [100]:

submission['Survived'] = lr_pred
submission

Out[100]:

	PassengerId	Survived
0	892	0.897979
1	893	0.406456
2	894	0.775241
3	895	0.897979
4	896	0.406456
...	...	...
413	1305	0.897979
414	1306	0.095152
415	1307	0.897979
416	1308	0.897979
417	1309	0.897979

418 rows × 2 columns

In [101]:

# TODO. 인덱스는 지운다.
# TODO. 담번엔 TODO 대신에 필기할거는 필기로...
submission.to_csv('logistic_regression_pred.csv', index=False)

In [102]:

submission['Survived'] = dt_pred
submission.to_csv('decision_tree_pred.csv', index=False)

In [98]:

dt_pred

Out[98]:

array([0.13544669, 0.5       , 0.15740741, 0.13544669, 0.5       ,
       0.13544669, 0.5       , 0.15740741, 0.5       , 0.13544669,
       0.13544669, 0.36885246, 0.96808511, 0.15740741, 0.96808511,
       0.92105263, 0.15740741, 0.13544669, 0.5       , 0.5       ,
       0.36885246, 0.13544669, 0.96808511, 0.36885246, 0.96808511,
       0.13544669, 0.96808511, 0.13544669, 0.36885246, 0.13544669,
       0.15740741, 0.15740741, 0.5       , 0.5       , 0.36885246,
       0.13544669, 0.5       , 0.5       , 0.13544669, 0.13544669,
       0.13544669, 0.36885246, 0.13544669, 0.92105263, 0.96808511,
       0.13544669, 0.36885246, 0.13544669, 0.96808511, 0.5       ,
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       0.13544669, 0.15740741, 0.13544669, 0.5       , 0.36885246,
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       0.5       , 0.13544669, 0.5       , 0.36885246, 0.96808511,
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       0.13544669, 0.13544669, 0.13544669, 0.13544669, 0.15740741,
       0.15740741, 0.5       , 0.96808511, 0.5       , 0.96808511,
       0.13544669, 0.13544669, 0.5       , 0.36885246, 0.92105263,
       0.92105263, 0.13544669, 0.96808511, 0.13544669, 0.13544669,
       0.5       , 0.13544669, 0.5       , 0.15740741, 0.13544669,
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       0.13544669, 0.13544669, 0.15740741, 0.5       , 0.13544669,
       0.5       , 0.96808511, 0.36885246, 0.15740741, 0.36885246,
       0.13544669, 0.36885246, 0.13544669, 0.36885246, 0.15740741,
       0.96808511, 0.13544669, 0.13544669, 0.5       , 0.13544669,
       0.13544669, 0.96808511, 0.5       , 0.36885246, 0.5       ,
       0.5       , 0.13544669, 0.92105263, 0.13544669, 0.15740741,
       0.5       , 0.36885246, 0.13544669, 0.96808511, 0.5       ,
       0.13544669, 0.13544669, 0.13544669, 0.13544669, 0.13544669,
       0.92105263, 0.92105263, 0.36885246, 0.92105263, 0.96808511,
       0.15740741, 0.36885246, 0.96808511, 0.13544669, 0.96808511,
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       0.15740741, 0.36885246, 0.13544669, 0.15740741, 0.15740741,
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       0.92105263, 0.15740741, 0.15740741, 0.15740741, 0.15740741,
       0.5       , 0.13544669, 0.5       , 0.5       , 0.5       ,
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       0.36885246, 0.5       , 0.13544669, 0.36885246, 0.13544669,
       0.13544669, 0.92105263, 0.13544669, 0.36885246, 0.13544669,
       0.13544669, 0.15740741, 0.15740741, 0.13544669, 0.5       ,
       0.96808511, 0.36885246, 0.13544669, 0.36885246, 0.5       ,
       0.13544669, 0.13544669, 0.13544669, 0.5       , 0.96808511,
       0.5       , 0.36885246, 0.15740741, 0.13544669, 0.15740741,
       0.13544669, 0.13544669, 0.15740741, 0.36885246, 0.96808511,
       0.13544669, 0.92105263, 0.36885246, 0.15740741, 0.15740741,
       0.92105263, 0.36885246, 0.13544669, 0.5       , 0.13544669,
       0.36885246, 0.15740741, 0.13544669, 0.15740741, 0.13544669,
       0.15740741, 0.13544669, 0.13544669, 0.96808511, 0.13544669,
       0.5       , 0.15740741, 0.5       , 0.15740741, 0.92105263,
       0.96808511, 0.15740741, 0.15740741, 0.15740741, 0.5       ,
       0.36885246, 0.96808511, 0.13544669, 0.13544669, 0.5       ,
       0.13544669, 0.92105263, 0.92105263, 0.13544669, 0.96808511,
       0.5       , 0.13544669, 0.5       , 0.96808511, 0.15740741,
       0.15740741, 0.96808511, 0.36885246, 0.15740741, 0.96808511,
       0.96808511, 0.5       , 0.15740741, 0.36885246, 0.13544669,
       0.13544669, 0.13544669, 0.5       , 0.5       , 0.15740741,
       0.92105263, 0.13544669, 0.15740741, 0.13544669, 0.13544669,
       0.36885246, 0.96808511, 0.13544669, 0.15740741, 0.13544669,
       0.96808511, 0.13544669, 0.96808511, 0.13544669, 0.13544669,
       0.96808511, 0.15740741, 0.96808511, 0.36885246, 0.36885246,
       0.15740741, 0.15740741, 0.36885246, 0.5       , 0.5       ,
       0.5       , 0.96808511, 0.5       , 0.13544669, 0.96808511,
       0.13544669, 0.13544669, 0.13544669])

In [ ]: