2주차 DAY9 FLASK EDA chap 1

Exploratory Data Analysis ¶

탐색적 데이터 분석을 통해 데이터를 통달해봅시다.with Titanic Data

1. 분석의 목적과 변수 확인
2. 데이터 전체적으로 살펴보기
3. 데이터의 개별 속성 파악하기

0. 라이브러리 준비 ¶

In [3]:

                                
                                # 라이브러리 불러오기

                                import
                                numpy
                                as
                                np
                                import
                                pandas
                                as
                                pd
                                import
                                matplotlib.pyplot
                                as
                                plt
                                import
                                seaborn
                                as
                                sns

                                %
                                matplotlib
                                 inline
                            

In [6]:

                                
                                # 데이터 불러오기

                                titanic_df
                                =
                                pd
                                .
                                read_csv
                                (
                                "./train.csv"
                                )
                                titanic_df
                            

Out[6]:

	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	NaN	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

1. 분석의 목적과 변수 확인 ¶

• 타이타닉 호에서 생존한 생존자들은 어떤 사람들일까?

In [8]:

                                
                                titanic_df
                                .
                                head
                                (
                                5
                                )
                            

Out[8]:

	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 [9]:

                                
                                ## 각 Column의 데이터 타입 확인하기
                                titanic_df
                                .
                                dtypes
                            

Out[9]:

PassengerId      int64
                        Survived         int64
                        Pclass           int64
                        Name            object
                        Sex             object
                        Age            float64
                        SibSp            int64
                        Parch            int64
                        Ticket          object
                        Fare           float64
                        Cabin           object
                        Embarked        object
                        dtype: object

2. 데이터 전체적으로 살펴보기 ¶

In [10]:

                                
                                ## 데이터 전체 정볼르 얻는 함수: .describe()

                                titanic_df
                                .
                                describe
                                ()
                                # 수치형 데이터에 대한 요약만을 제공!
                            

Out[10]:

	PassengerId	Survived	Pclass	Age	SibSp	Parch	Fare
count	891.000000	891.000000	891.000000	714.000000	891.000000	891.000000	891.000000
mean	446.000000	0.383838	2.308642	29.699118	0.523008	0.381594	32.204208
std	257.353842	0.486592	0.836071	14.526497	1.102743	0.806057	49.693429
min	1.000000	0.000000	1.000000	0.420000	0.000000	0.000000	0.000000
25%	223.500000	0.000000	2.000000	20.125000	0.000000	0.000000	7.910400
50%	446.000000	0.000000	3.000000	28.000000	0.000000	0.000000	14.454200
75%	668.500000	1.000000	3.000000	38.000000	1.000000	0.000000	31.000000
max	891.000000	1.000000	3.000000	80.000000	8.000000	6.000000	512.329200

In [12]:

                                
                                ## 상관계수 확인!

                                titanic_df
                                .
                                corr
                                ()
                                # Correlation is Not Causation

                                # 상관성: A up, B up, ...
                                # 인과성: A -> B 종속관계가 있는거 
                            

Out[12]:

	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

In [14]:

                                
                                ## 결측지 확인

                                titanic_df
                                .
                                isnull
                                ()
                                .
                                sum
                                ()
                                # Age,Cabin, Embarked에서 결측치 발견!
                            

Out[14]:

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

In [ ]: