Basic Dataframe

import pandas as pd

Basic Dataframe Handling

# read_csv 결과를 df에 저장
df = pd.read_csv("train.csv") 

# shape (행,열) 
df.shape 

(891, 12)

# size (크기 = 행 X 열)
df.size

10692

# numerical description
df.describe()

	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

# What data is incomplete
df.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

# random data sample
df.sample()

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
849	850	1	1	Goldenberg, Mrs. Samuel L (Edwiga Grabowska)	female	NaN	1	0	17453	89.1042	C92	C

df.columns

Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',
       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],
      dtype='object')

Accessing columns

조회

• 배열과 비슷하게 인덱스로 접근 가능: df[Column 이름]
• df.Column 이름 으로도 접근 가능

df['Sex']

0        male
1      female
2      female
3      female
4        male
        ...  
886      male
887    female
888    female
889      male
890      male
Name: Sex, Length: 891, dtype: object

df.Cabin

0       NaN
1       C85
2       NaN
3      C123
4       NaN
       ... 
886     NaN
887     B42
888     NaN
889    C148
890     NaN
Name: Cabin, Length: 891, dtype: object

수정

• 아래와 같이 해당 Column의 모든 값들을 특정 값으로 update 할 수 있다.

df['Cabin'] = 0

df['Cabin']

0      0
1      0
2      0
3      0
4      0
      ..
886    0
887    0
888    0
889    0
890    0
Name: Cabin, Length: 891, dtype: int64

삭제

• del 혹은 drop을 통해 해당 column을 삭제할 수 있다.

del df['Cabin'] # df.drop("Cabin", axis = 1, inplace = True)

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 11 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  Embarked     889 non-null    object 
dtypes: float64(2), int64(5), object(4)
memory usage: 76.7+ KB

Crosstab

• 빈도표
• dataframe의 column을 parameter로 넘겨주면 빈도표 생성
• pandas.crosstab(기준 데이터, 빈도 데이터)

Crosstab 구조

• pandas.crosstab(index, columns, rownames, colnames, margins, normalize)

# 성별로 Survived 비교
pd.crosstab(df["Sex"], df["Survived"])

Survived	0	1
Sex
female	81	233
male	468	109

# 행 이름, 열 이름 부여
pd.crosstab(df["Sex"], df["Survived"], rownames=['row'], colnames=['col'])

col	0	1
row
female	81	233
male	468	109

df.columns

Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',
       'Parch', 'Ticket', 'Fare', 'Embarked'],
      dtype='object')

• 아래와 같이 기준 데이터 및 빈도 데이터를 여러개 설정할 수 있다.
• margins = True로 설정하면 총합계를 나타낸다.

pd.crosstab([df.Sex, df.Age], df.Survived, margins=True)

	Survived	0	1	All
Sex	Age
female	0.75	0	2	2
	1.0	0	2	2
	2.0	4	2	6
	3.0	1	1	2
	4.0	0	5	5
...	...	...	...	...
male	70.5	1	0	1
	71.0	2	0	2
	74.0	1	0	1
	80.0	0	1	1
All		424	290	714

146 rows × 3 columns

pd.crosstab(df.Sex, [df.Survived, df.Pclass])

Survived	0			1
Pclass	1	2	3	1	2	3
Sex
female	3	6	72	91	70	72
male	77	91	300	45	17	47

• 만들어진 crosstab에 접근 가능

ct = pd.crosstab(df["Pclass"], df["Survived"])
ct.head()

Survived	0	1
Pclass
1	80	136
2	97	87
3	372	119

ct.loc[(1,0)]

80