[Python] Pandas 조회, 정렬, 조건 필터 (타이타닉 승객 데이터)

모듈 import¶

In [1]:

!pip install seaborn==0.13.0

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[notice] A new release of pip available: 22.2.2 -> 24.1.1
[notice] To update, run: pip install --upgrade pip

In [2]:

from IPython.display import Image
import numpy as np
import pandas as pd
import seaborn as sns

실습에 활용할 데이터셋¶

타이타닉: 탑승객의 사망자와 생존자 데이터 분석¶

In [3]:

Image('https://www.gukjenews.com/news/photo/201912/1212183_991659_1921.jpg')

Out[3]:

큰 이미지로 보기

In [4]:

Image('https://static1.squarespace.com/static/5006453fe4b09ef2252ba068/t/5090b249e4b047ba54dfd258/1351660113175/TItanic-Survival-Infographic.jpg')

Out[4]:

건조 당시 세계 최대의 여객선이었지만,1912년의 최초이자 최후의 항해 때 빙산과 충돌해 침몰한 비운의 여객선. 아마도 세상에서 가장 유명한 여객선이자 침몰선일 것입니다.

침몰한 지 100년이 넘었지만 아직까지 세계에서 가장 유명한 침몰선입니다.

사망자 수는 1위는 아니지만, 세계적으로 유명한 영화의 영향도 있고, 당시 최첨단 기술에 대해 기대감이 컸던 사회에 큰 영향을 끼치기도 한데다가, 근대 사회에서 들어서자마자 얼마 안된, 그리고 유명인사들이 여럿 희생된 대참사이기 때문에 가장 유명한 침몰선이 되었습니다. 또한 이 사건을 기점으로 여러가지 안전 조약들이 생겨났으니 더더욱 그렇습니다.

In [6]:

df = sns.load_dataset("titanic")
df.head()

Out[6]:

	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
0	0	3	male	22.0	1	0	7.2500	S	Third	man	True	NaN	Southampton	no	False
1	1	1	female	38.0	1	0	71.2833	C	First	woman	False	C	Cherbourg	yes	False
2	1	3	female	26.0	0	0	7.9250	S	Third	woman	False	NaN	Southampton	yes	True
3	1	1	female	35.0	1	0	53.1000	S	First	woman	False	C	Southampton	yes	False
4	0	3	male	35.0	0	0	8.0500	S	Third	man	True	NaN	Southampton	no	True

컬럼 (column) 설명¶

• survivied: 생존여부 (1: 생존, 0: 사망)
• pclass: 좌석 등급 (1등급, 2등급, 3등급)
• sex: 성별
• age: 나이
• sibsp: 형제 + 배우자 수
• parch: 부모 + 자녀 수
• fare: 좌석 요금
• embarked: 탑승 항구 (S, C, Q)
• class: pclass와 동일
• who: 남자(man), 여자(woman), 아이(child)
• adult_male: 성인 남자 여부
• deck: 데크 번호 (알파벳 + 숫자 혼용)
• embark_town: 탑승 항구 이름
• alive: 생존여부 (yes, no)
• alone: 혼자 탑승 여부

데이터 분석!¶

주요 목표

• Pandas를 활용하여 타이타닉호 생존자, 사망자 데이터를 분석합니다.
• 데이터를 토대로 생존율이 높은 승객, 생존율이 낮은 승객은 누구인지 판단합니다.

head() 앞 부분 / tail() 뒷 부분 조회¶

• default 옵션 값으로 5개의 행이 조회됩니다.
• 괄호 안에 숫자를 넣어 명시적으로 조회하고 싶은 행의 갯수를 지정할 수 있습니다.

In [7]:

df.head()

Out[7]:

	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
0	0	3	male	22.0	1	0	7.2500	S	Third	man	True	NaN	Southampton	no	False
1	1	1	female	38.0	1	0	71.2833	C	First	woman	False	C	Cherbourg	yes	False
2	1	3	female	26.0	0	0	7.9250	S	Third	woman	False	NaN	Southampton	yes	True
3	1	1	female	35.0	1	0	53.1000	S	First	woman	False	C	Southampton	yes	False
4	0	3	male	35.0	0	0	8.0500	S	Third	man	True	NaN	Southampton	no	True

In [8]:

df.tail()

Out[8]:

	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
886	0	2	male	27.0	0	0	13.00	S	Second	man	True	NaN	Southampton	no	True
887	1	1	female	19.0	0	0	30.00	S	First	woman	False	B	Southampton	yes	True
888	0	3	female	NaN	1	2	23.45	S	Third	woman	False	NaN	Southampton	no	False
889	1	1	male	26.0	0	0	30.00	C	First	man	True	C	Cherbourg	yes	True
890	0	3	male	32.0	0	0	7.75	Q	Third	man	True	NaN	Queenstown	no	True

In [9]:

df.head(3)

Out[9]:

	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
0	0	3	male	22.0	1	0	7.2500	S	Third	man	True	NaN	Southampton	no	False
1	1	1	female	38.0	1	0	71.2833	C	First	woman	False	C	Cherbourg	yes	False
2	1	3	female	26.0	0	0	7.9250	S	Third	woman	False	NaN	Southampton	yes	True

In [10]:

df.tail(7)

Out[10]:

	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
884	0	3	male	25.0	0	0	7.050	S	Third	man	True	NaN	Southampton	no	True
885	0	3	female	39.0	0	5	29.125	Q	Third	woman	False	NaN	Queenstown	no	False
886	0	2	male	27.0	0	0	13.000	S	Second	man	True	NaN	Southampton	no	True
887	1	1	female	19.0	0	0	30.000	S	First	woman	False	B	Southampton	yes	True
888	0	3	female	NaN	1	2	23.450	S	Third	woman	False	NaN	Southampton	no	False
889	1	1	male	26.0	0	0	30.000	C	First	man	True	C	Cherbourg	yes	True
890	0	3	male	32.0	0	0	7.750	Q	Third	man	True	NaN	Queenstown	no	True

info()¶

• 컬럼별 정보(information)를 보여줍니다.
• 데이터의 갯수, 그리고 데이터 타입(dtype)을 확인할 때 사용합니다.

In [11]:

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 15 columns):
 #   Column       Non-Null Count  Dtype   
---  ------       --------------  -----   
 0   survived     891 non-null    int64   
 1   pclass       891 non-null    int64   
 2   sex          891 non-null    object  
 3   age          714 non-null    float64 
 4   sibsp        891 non-null    int64   
 5   parch        891 non-null    int64   
 6   fare         891 non-null    float64 
 7   embarked     889 non-null    object  
 8   class        891 non-null    category
 9   who          891 non-null    object  
 10  adult_male   891 non-null    bool    
 11  deck         203 non-null    category
 12  embark_town  889 non-null    object  
 13  alive        891 non-null    object  
 14  alone        891 non-null    bool    
dtypes: bool(2), category(2), float64(2), int64(4), object(5)
memory usage: 80.7+ KB

object 타입은 쉽게 문자열이라고 생각하면 됩니다.

그런데, category 타입도 있습니다. category 타입은 문자열이지만, '남자' / '여자'처럼 카테고리화 할 수 있는 컬럼을 의미 합니다. 나중에 별도로 다루겠습니다.

value_counts()¶

column 별 값의 분포를 확인할 때 사용합니다.

남자, 여자, 아이의 데이터 분포를 확인하고 싶다면 다음과 같이 실행합니다.

In [12]:

df['who'].value_counts()

Out[12]:

man      537
woman    271
child     83
Name: who, dtype: int64

연습문제¶

In [13]:

df.head()

Out[13]:

	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
0	0	3	male	22.0	1	0	7.2500	S	Third	man	True	NaN	Southampton	no	False
1	1	1	female	38.0	1	0	71.2833	C	First	woman	False	C	Cherbourg	yes	False
2	1	3	female	26.0	0	0	7.9250	S	Third	woman	False	NaN	Southampton	yes	True
3	1	1	female	35.0	1	0	53.1000	S	First	woman	False	C	Southampton	yes	False
4	0	3	male	35.0	0	0	8.0500	S	Third	man	True	NaN	Southampton	no	True

embark_town은 승객의 탑승 항구를 나타내는 column 입니다. 탑승 항구별 승객 데이터 분포를 확인해 주세요.

In [14]:

# 코드를 입력해 주세요
df['embark_town'].value_counts()

Out[14]:

Southampton    644
Cherbourg      168
Queenstown      77
Name: embark_town, dtype: int64

[출력 결과]

Southampton    644
Cherbourg      168
Queenstown      77
Name: embark_town, dtype: int64

who 컬럼에 대한 데이터 분포를 확인해 주세요

In [15]:

# 코드를 입력해 주세요
df['who'].value_counts()

Out[15]:

man      537
woman    271
child     83
Name: who, dtype: int64

[출력 결과]

man      537
woman    271
child     83
Name: who, dtype: int64

속성: Attributes¶

속성 값은 함수형으로 조회하지 않습니다.

자주 활용하는 DataFrame은 속성 값들은 다음과 같습니다.

• ndim
• shape
• index
• columns
• values
• T

차원을 나타냅니다. DataFrame은 2가 출력됩니다.

In [16]:

df.ndim

Out[16]:

2

(행, 열) 순서로 출력됩니다.

In [17]:

df.shape

Out[17]:

(891, 15)

index는 기본 설정된 RangeIndex가 출력됩니다.

In [18]:

df.index

Out[18]:

RangeIndex(start=0, stop=891, step=1)

columns는 열을 출력 합니다.

In [19]:

df.columns

Out[19]:

Index(['survived', 'pclass', 'sex', 'age', 'sibsp', 'parch', 'fare',
       'embarked', 'class', 'who', 'adult_male', 'deck', 'embark_town',
       'alive', 'alone'],
      dtype='object')

values는 모든 값을 출력하며, numpy array 형식으로 출력됩니다.

In [20]:

df.values

Out[20]:

array([[0, 3, 'male', ..., 'Southampton', 'no', False],
       [1, 1, 'female', ..., 'Cherbourg', 'yes', False],
       [1, 3, 'female', ..., 'Southampton', 'yes', True],
       ...,
       [0, 3, 'female', ..., 'Southampton', 'no', False],
       [1, 1, 'male', ..., 'Cherbourg', 'yes', True],
       [0, 3, 'male', ..., 'Queenstown', 'no', True]], dtype=object)

T: 전치 (Transpose) 는 Index와 Column의 축을 교환합니다.

In [21]:

df.T

Out[21]:

	0	1	2	3	4	5	6	7	8	9	...	881	882	883	884	885	886	887	888	889	890
survived	0	1	1	1	0	0	0	0	1	1	...	0	0	0	0	0	0	1	0	1	0
pclass	3	1	3	1	3	3	1	3	3	2	...	3	3	2	3	3	2	1	3	1	3
sex	male	female	female	female	male	male	male	male	female	female	...	male	female	male	male	female	male	female	female	male	male
age	22.0	38.0	26.0	35.0	35.0	NaN	54.0	2.0	27.0	14.0	...	33.0	22.0	28.0	25.0	39.0	27.0	19.0	NaN	26.0	32.0
sibsp	1	1	0	1	0	0	0	3	0	1	...	0	0	0	0	0	0	0	1	0	0
parch	0	0	0	0	0	0	0	1	2	0	...	0	0	0	0	5	0	0	2	0	0
fare	7.25	71.2833	7.925	53.1	8.05	8.4583	51.8625	21.075	11.1333	30.0708	...	7.8958	10.5167	10.5	7.05	29.125	13.0	30.0	23.45	30.0	7.75
embarked	S	C	S	S	S	Q	S	S	S	C	...	S	S	S	S	Q	S	S	S	C	Q
class	Third	First	Third	First	Third	Third	First	Third	Third	Second	...	Third	Third	Second	Third	Third	Second	First	Third	First	Third
who	man	woman	woman	woman	man	man	man	child	woman	child	...	man	woman	man	man	woman	man	woman	woman	man	man
adult_male	True	False	False	False	True	True	True	False	False	False	...	True	False	True	True	False	True	False	False	True	True
deck	NaN	C	NaN	C	NaN	NaN	E	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	B	NaN	C	NaN
embark_town	Southampton	Cherbourg	Southampton	Southampton	Southampton	Queenstown	Southampton	Southampton	Southampton	Cherbourg	...	Southampton	Southampton	Southampton	Southampton	Queenstown	Southampton	Southampton	Southampton	Cherbourg	Queenstown
alive	no	yes	yes	yes	no	no	no	no	yes	yes	...	no	no	no	no	no	no	yes	no	yes	no
alone	False	False	True	False	True	True	True	False	False	False	...	True	True	True	True	False	True	True	False	True	True

15 rows × 891 columns

타입 변환 (astype)¶

In [22]:

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 15 columns):
 #   Column       Non-Null Count  Dtype   
---  ------       --------------  -----   
 0   survived     891 non-null    int64   
 1   pclass       891 non-null    int64   
 2   sex          891 non-null    object  
 3   age          714 non-null    float64 
 4   sibsp        891 non-null    int64   
 5   parch        891 non-null    int64   
 6   fare         891 non-null    float64 
 7   embarked     889 non-null    object  
 8   class        891 non-null    category
 9   who          891 non-null    object  
 10  adult_male   891 non-null    bool    
 11  deck         203 non-null    category
 12  embark_town  889 non-null    object  
 13  alive        891 non-null    object  
 14  alone        891 non-null    bool    
dtypes: bool(2), category(2), float64(2), int64(4), object(5)
memory usage: 80.7+ KB

int32로 변경

In [23]:

df['pclass'].astype('int32').head()

Out[23]:

0    3
1    1
2    3
3    1
4    3
Name: pclass, dtype: int32

float32로 변경

In [24]:

df['pclass'].astype('float32').head()

Out[24]:

0    3.0
1    1.0
2    3.0
3    1.0
4    3.0
Name: pclass, dtype: float32

object로 변경

In [25]:

df['pclass'].astype('str').head()

Out[25]:

0    3
1    1
2    3
3    1
4    3
Name: pclass, dtype: object

category로 변경.

category로 변경시에는 Categories가 같이 출력 됩니다.

In [27]:

df['pclass'].astype('category').head()

Out[27]:

0    3
1    1
2    3
3    1
4    3
Name: pclass, dtype: category
Categories (3, int64): [1, 2, 3]

정렬 (sort)¶

sort_index: index 정렬¶

• index 기준으로 정렬합니다. (기본 오름차순이 적용되어 있습니다.
• 내림차순 정렬을 적용하려면, ascending=False를 옵션 값으로 설정합니다.

In [28]:

df.sort_index().head(5)

Out[28]:

	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
0	0	3	male	22.0	1	0	7.2500	S	Third	man	True	NaN	Southampton	no	False
1	1	1	female	38.0	1	0	71.2833	C	First	woman	False	C	Cherbourg	yes	False
2	1	3	female	26.0	0	0	7.9250	S	Third	woman	False	NaN	Southampton	yes	True
3	1	1	female	35.0	1	0	53.1000	S	First	woman	False	C	Southampton	yes	False
4	0	3	male	35.0	0	0	8.0500	S	Third	man	True	NaN	Southampton	no	True

In [29]:

df.sort_index(ascending=False).head(5)

Out[29]:

	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
890	0	3	male	32.0	0	0	7.75	Q	Third	man	True	NaN	Queenstown	no	True
889	1	1	male	26.0	0	0	30.00	C	First	man	True	C	Cherbourg	yes	True
888	0	3	female	NaN	1	2	23.45	S	Third	woman	False	NaN	Southampton	no	False
887	1	1	female	19.0	0	0	30.00	S	First	woman	False	B	Southampton	yes	True
886	0	2	male	27.0	0	0	13.00	S	Second	man	True	NaN	Southampton	no	True

sort_values: 값에 대한 정렬¶

• 값을 기준으로 행을 정렬합니다.
• by에 기준이 되는 행을 설정합니다.
• by에 2개 이상의 컬럼을 지정하여 정렬할 수 있습니다.
• 오름차순/내림차순을 컬럼 별로 지정할 수 있습니다.

In [30]:

df.sort_values(by='age').head()

Out[30]:

	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
803	1	3	male	0.42	0	1	8.5167	C	Third	child	False	NaN	Cherbourg	yes	False
755	1	2	male	0.67	1	1	14.5000	S	Second	child	False	NaN	Southampton	yes	False
644	1	3	female	0.75	2	1	19.2583	C	Third	child	False	NaN	Cherbourg	yes	False
469	1	3	female	0.75	2	1	19.2583	C	Third	child	False	NaN	Cherbourg	yes	False
78	1	2	male	0.83	0	2	29.0000	S	Second	child	False	NaN	Southampton	yes	False

내림차순 정렬: ascending=False

In [31]:

df.sort_values(by='age', ascending=False).head()

Out[31]:

	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
630	1	1	male	80.0	0	0	30.0000	S	First	man	True	A	Southampton	yes	True
851	0	3	male	74.0	0	0	7.7750	S	Third	man	True	NaN	Southampton	no	True
493	0	1	male	71.0	0	0	49.5042	C	First	man	True	NaN	Cherbourg	no	True
96	0	1	male	71.0	0	0	34.6542	C	First	man	True	A	Cherbourg	no	True
116	0	3	male	70.5	0	0	7.7500	Q	Third	man	True	NaN	Queenstown	no	True

문자열 컬럼도 오름차순/내림차순 정렬이 가능하며 알파벳 순서로 정렬됩니다.

In [32]:

df.sort_values(by='class', ascending=False).head()

Out[32]:

	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
0	0	3	male	22.0	1	0	7.2500	S	Third	man	True	NaN	Southampton	no	False
511	0	3	male	NaN	0	0	8.0500	S	Third	man	True	NaN	Southampton	no	True
500	0	3	male	17.0	0	0	8.6625	S	Third	man	True	NaN	Southampton	no	True
501	0	3	female	21.0	0	0	7.7500	Q	Third	woman	False	NaN	Queenstown	no	True
502	0	3	female	NaN	0	0	7.6292	Q	Third	woman	False	NaN	Queenstown	no	True

2개 이상의 컬럼을 기준으로 값 정렬 할 수 있습니다.

In [33]:

df.sort_values(by=['fare', 'age']).head()

Out[33]:

	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
302	0	3	male	19.0	0	0	0.0	S	Third	man	True	NaN	Southampton	no	True
271	1	3	male	25.0	0	0	0.0	S	Third	man	True	NaN	Southampton	yes	True
179	0	3	male	36.0	0	0	0.0	S	Third	man	True	NaN	Southampton	no	True
822	0	1	male	38.0	0	0	0.0	S	First	man	True	NaN	Southampton	no	True
806	0	1	male	39.0	0	0	0.0	S	First	man	True	A	Southampton	no	True

오름차순/내림차순 정렬도 컬럼 각각에 지정해 줄 수 있습니다.

In [34]:

df.sort_values(by=['fare', 'age'], ascending=[False, True]).head()

Out[34]:

	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
258	1	1	female	35.0	0	0	512.3292	C	First	woman	False	NaN	Cherbourg	yes	True
737	1	1	male	35.0	0	0	512.3292	C	First	man	True	B	Cherbourg	yes	True
679	1	1	male	36.0	0	1	512.3292	C	First	man	True	B	Cherbourg	yes	False
27	0	1	male	19.0	3	2	263.0000	S	First	man	True	C	Southampton	no	False
88	1	1	female	23.0	3	2	263.0000	S	First	woman	False	C	Southampton	yes	False

연습문제¶

tips 데이터는 미국 레스토랑의 매출과 웨이터에게 지불한 팁을 나타내는 데이터입니다.

In [35]:

tips = sns.load_dataset('tips')
tips.head()

Out[35]:

	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

• total_bill과 tip에 대한 내림차순 정렬을 해주세요
• 상위 10개만 출력하세요

In [37]:

# 코드를 입력해 주세요
tips.sort_values(by=['total_bill', 'tip'], ascending=[False, False]).head(10)

Out[37]:

	total_bill	tip	sex	smoker	day	time	size
170	50.81	10.00	Male	Yes	Sat	Dinner	3
212	48.33	9.00	Male	No	Sat	Dinner	4
59	48.27	6.73	Male	No	Sat	Dinner	4
156	48.17	5.00	Male	No	Sun	Dinner	6
182	45.35	3.50	Male	Yes	Sun	Dinner	3
102	44.30	2.50	Female	Yes	Sat	Dinner	3
197	43.11	5.00	Female	Yes	Thur	Lunch	4
142	41.19	5.00	Male	No	Thur	Lunch	5
184	40.55	3.00	Male	Yes	Sun	Dinner	2
95	40.17	4.73	Male	Yes	Fri	Dinner	4

[출력 결과]

	total_bill	tip	sex	smoker	day	time	size
170	50.81	10.00	Male	Yes	Sat	Dinner	3
212	48.33	9.00	Male	No	Sat	Dinner	4
59	48.27	6.73	Male	No	Sat	Dinner	4
156	48.17	5.00	Male	No	Sun	Dinner	6
182	45.35	3.50	Male	Yes	Sun	Dinner	3
102	44.30	2.50	Female	Yes	Sat	Dinner	3
197	43.11	5.00	Female	Yes	Thur	Lunch	4
142	41.19	5.00	Male	No	Thur	Lunch	5
184	40.55	3.00	Male	Yes	Sun	Dinner	2
95	40.17	4.73	Male	Yes	Fri	Dinner	4

• size를 기준으로 내림차순, tip을 기준으로는 오름차순 정렬을 해주세요
• 상위 10개의 데이터만 출력하세요

In [38]:

# 코드를 입력해 주세요
tips.sort_values(by=['size', 'tip'], ascending=[False, True]).head(10)

Out[38]:

	total_bill	tip	sex	smoker	day	time	size
125	29.80	4.20	Female	No	Thur	Lunch	6
143	27.05	5.00	Female	No	Thur	Lunch	6
156	48.17	5.00	Male	No	Sun	Dinner	6
141	34.30	6.70	Male	No	Thur	Lunch	6
187	30.46	2.00	Male	Yes	Sun	Dinner	5
216	28.15	3.00	Male	Yes	Sat	Dinner	5
142	41.19	5.00	Male	No	Thur	Lunch	5
185	20.69	5.00	Male	No	Sun	Dinner	5
155	29.85	5.14	Female	No	Sun	Dinner	5
153	24.55	2.00	Male	No	Sun	Dinner	4

[출력 결과]

	total_bill	tip	sex	smoker	day	time	size
125	29.80	4.20	Female	No	Thur	Lunch	6
143	27.05	5.00	Female	No	Thur	Lunch	6
156	48.17	5.00	Male	No	Sun	Dinner	6
141	34.30	6.70	Male	No	Thur	Lunch	6
187	30.46	2.00	Male	Yes	Sun	Dinner	5
216	28.15	3.00	Male	Yes	Sat	Dinner	5
142	41.19	5.00	Male	No	Thur	Lunch	5
185	20.69	5.00	Male	No	Sun	Dinner	5
155	29.85	5.14	Female	No	Sun	Dinner	5
153	24.55	2.00	Male	No	Sun	Dinner	4

Indexing, Slicing, 조건 필터링¶

In [39]:

df.head()

Out[39]:

	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
0	0	3	male	22.0	1	0	7.2500	S	Third	man	True	NaN	Southampton	no	False
1	1	1	female	38.0	1	0	71.2833	C	First	woman	False	C	Cherbourg	yes	False
2	1	3	female	26.0	0	0	7.9250	S	Third	woman	False	NaN	Southampton	yes	True
3	1	1	female	35.0	1	0	53.1000	S	First	woman	False	C	Southampton	yes	False
4	0	3	male	35.0	0	0	8.0500	S	Third	man	True	NaN	Southampton	no	True

loc - indexing / slicing¶

• indexing과 slicing을 할 수 있습니다.
• slicing은 [시작(포함): 끝(포함)] 규칙에 유의합니다. 둘 다 포함 합니다.

indexing 예시

In [40]:

df.loc[5, 'class']

Out[40]:

'Third'

fancy indexing 예시

In [41]:

df.loc[2:5, ['age', 'fare', 'who']]

Out[41]:

	age	fare	who
2	26.0	7.9250	woman
3	35.0	53.1000	woman
4	35.0	8.0500	man
5	NaN	8.4583	man

slicing 예시

In [42]:

df.loc[2:5, 'class':'deck'].head()

Out[42]:

	class	who	adult_male	deck
2	Third	woman	False	NaN
3	First	woman	False	C
4	Third	man	True	NaN
5	Third	man	True	NaN

In [43]:

df.loc[:6, 'class':'deck']

Out[43]:

	class	who	adult_male	deck
0	Third	man	True	NaN
1	First	woman	False	C
2	Third	woman	False	NaN
3	First	woman	False	C
4	Third	man	True	NaN
5	Third	man	True	NaN
6	First	man	True	E

연습문제¶

In [44]:

df

Out[44]:

	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
0	0	3	male	22.0	1	0	7.2500	S	Third	man	True	NaN	Southampton	no	False
1	1	1	female	38.0	1	0	71.2833	C	First	woman	False	C	Cherbourg	yes	False
2	1	3	female	26.0	0	0	7.9250	S	Third	woman	False	NaN	Southampton	yes	True
3	1	1	female	35.0	1	0	53.1000	S	First	woman	False	C	Southampton	yes	False
4	0	3	male	35.0	0	0	8.0500	S	Third	man	True	NaN	Southampton	no	True
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
886	0	2	male	27.0	0	0	13.0000	S	Second	man	True	NaN	Southampton	no	True
887	1	1	female	19.0	0	0	30.0000	S	First	woman	False	B	Southampton	yes	True
888	0	3	female	NaN	1	2	23.4500	S	Third	woman	False	NaN	Southampton	no	False
889	1	1	male	26.0	0	0	30.0000	C	First	man	True	C	Cherbourg	yes	True
890	0	3	male	32.0	0	0	7.7500	Q	Third	man	True	NaN	Queenstown	no	True

891 rows × 15 columns

다음과 같은 결과를 가지도록 인덱싱 혹은 슬라이싱 하세요

In [45]:

# 코드를 입력해 주세요
df.loc[3:7, 'survived':'alone']

Out[45]:

	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
3	1	1	female	35.0	1	0	53.1000	S	First	woman	False	C	Southampton	yes	False
4	0	3	male	35.0	0	0	8.0500	S	Third	man	True	NaN	Southampton	no	True
5	0	3	male	NaN	0	0	8.4583	Q	Third	man	True	NaN	Queenstown	no	True
6	0	1	male	54.0	0	0	51.8625	S	First	man	True	E	Southampton	no	True
7	0	3	male	2.0	3	1	21.0750	S	Third	child	False	NaN	Southampton	no	False

[출력 결과]

	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
3	1	1	female	35.0	1	0	53.1000	S	First	woman	False	C	Southampton	yes	False
4	0	3	male	35.0	0	0	8.0500	S	Third	man	True	NaN	Southampton	no	True
5	0	3	male	NaN	0	0	8.4583	Q	Third	man	True	NaN	Queenstown	no	True
6	0	1	male	54.0	0	0	51.8625	S	First	man	True	E	Southampton	no	True
7	0	3	male	2.0	3	1	21.0750	S	Third	child	False	NaN	Southampton	no	False

In [46]:

# 코드를 입력해 주세요
df.loc[:4, 'pclass':'fare']

Out[46]:

	pclass	sex	age	sibsp	parch	fare
0	3	male	22.0	1	0	7.2500
1	1	female	38.0	1	0	71.2833
2	3	female	26.0	0	0	7.9250
3	1	female	35.0	1	0	53.1000
4	3	male	35.0	0	0	8.0500

[출력 결과]

	pclass	sex	age	sibsp	parch	fare
0	3	male	22.0	1	0	7.2500
1	1	female	38.0	1	0	71.2833
2	3	female	26.0	0	0	7.9250
3	1	female	35.0	1	0	53.1000
4	3	male	35.0	0	0	8.0500

In [48]:

# 코드를 입력해 주세요
df.loc[[2,4,6,8,10], ['age', 'who']]

Out[48]:

	age	who
2	26.0	woman
4	35.0	man
6	54.0	man
8	27.0	woman
10	4.0	child

[출력 결과]

	age	who
2	26.0	woman
4	35.0	man
6	54.0	man
8	27.0	woman
10	4.0	child

loc - 조건 필터¶

boolean index을 만들어 조건에 맞는 데이터만 추출해 낼 수 있습니다.

In [49]:

cond = (df['age'] >= 70)
cond

Out[49]:

0      False
1      False
2      False
3      False
4      False
       ...  
886    False
887    False
888    False
889    False
890    False
Name: age, Length: 891, dtype: bool

In [50]:

df.loc[cond]

Out[50]:

	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
96	0	1	male	71.0	0	0	34.6542	C	First	man	True	A	Cherbourg	no	True
116	0	3	male	70.5	0	0	7.7500	Q	Third	man	True	NaN	Queenstown	no	True
493	0	1	male	71.0	0	0	49.5042	C	First	man	True	NaN	Cherbourg	no	True
630	1	1	male	80.0	0	0	30.0000	S	First	man	True	A	Southampton	yes	True
672	0	2	male	70.0	0	0	10.5000	S	Second	man	True	NaN	Southampton	no	True
745	0	1	male	70.0	1	1	71.0000	S	First	man	True	B	Southampton	no	False
851	0	3	male	74.0	0	0	7.7750	S	Third	man	True	NaN	Southampton	no	True

loc - 다중 조건¶

다중 조건은 먼저 condition을 정의하고 & 와 | 연산자로 복합 조건을 생성합니다.

In [51]:

# 조건1 정의
cond1 = (df['fare'] > 30)

# 조건2 정의
cond2 = (df['who'] == 'woman')

In [52]:

df.loc[cond1 & cond2]

Out[52]:

	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
1	1	1	female	38.0	1	0	71.2833	C	First	woman	False	C	Cherbourg	yes	False
3	1	1	female	35.0	1	0	53.1000	S	First	woman	False	C	Southampton	yes	False
25	1	3	female	38.0	1	5	31.3875	S	Third	woman	False	NaN	Southampton	yes	False
31	1	1	female	NaN	1	0	146.5208	C	First	woman	False	B	Cherbourg	yes	False
52	1	1	female	49.0	1	0	76.7292	C	First	woman	False	D	Cherbourg	yes	False
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
853	1	1	female	16.0	0	1	39.4000	S	First	woman	False	D	Southampton	yes	False
856	1	1	female	45.0	1	1	164.8667	S	First	woman	False	NaN	Southampton	yes	False
863	0	3	female	NaN	8	2	69.5500	S	Third	woman	False	NaN	Southampton	no	False
871	1	1	female	47.0	1	1	52.5542	S	First	woman	False	D	Southampton	yes	False
879	1	1	female	56.0	0	1	83.1583	C	First	woman	False	C	Cherbourg	yes	False

100 rows × 15 columns

In [53]:

df.loc[cond1 | cond2]

Out[53]:

	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
1	1	1	female	38.0	1	0	71.2833	C	First	woman	False	C	Cherbourg	yes	False
2	1	3	female	26.0	0	0	7.9250	S	Third	woman	False	NaN	Southampton	yes	True
3	1	1	female	35.0	1	0	53.1000	S	First	woman	False	C	Southampton	yes	False
6	0	1	male	54.0	0	0	51.8625	S	First	man	True	E	Southampton	no	True
8	1	3	female	27.0	0	2	11.1333	S	Third	woman	False	NaN	Southampton	yes	False
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
880	1	2	female	25.0	0	1	26.0000	S	Second	woman	False	NaN	Southampton	yes	False
882	0	3	female	22.0	0	0	10.5167	S	Third	woman	False	NaN	Southampton	no	True
885	0	3	female	39.0	0	5	29.1250	Q	Third	woman	False	NaN	Queenstown	no	False
887	1	1	female	19.0	0	0	30.0000	S	First	woman	False	B	Southampton	yes	True
888	0	3	female	NaN	1	2	23.4500	S	Third	woman	False	NaN	Southampton	no	False

405 rows × 15 columns

조건 필터 후 데이터 대입

In [54]:

cond = (df['age'] >= 70)
cond

Out[54]:

0      False
1      False
2      False
3      False
4      False
       ...  
886    False
887    False
888    False
889    False
890    False
Name: age, Length: 891, dtype: bool

In [55]:

# 조건 필터
df.loc[cond]

Out[55]:

	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
96	0	1	male	71.0	0	0	34.6542	C	First	man	True	A	Cherbourg	no	True
116	0	3	male	70.5	0	0	7.7500	Q	Third	man	True	NaN	Queenstown	no	True
493	0	1	male	71.0	0	0	49.5042	C	First	man	True	NaN	Cherbourg	no	True
630	1	1	male	80.0	0	0	30.0000	S	First	man	True	A	Southampton	yes	True
672	0	2	male	70.0	0	0	10.5000	S	Second	man	True	NaN	Southampton	no	True
745	0	1	male	70.0	1	1	71.0000	S	First	man	True	B	Southampton	no	False
851	0	3	male	74.0	0	0	7.7750	S	Third	man	True	NaN	Southampton	no	True

나이 컬럼만 가져옵니다.

In [56]:

df.loc[cond, 'age']

Out[56]:

96     71.0
116    70.5
493    71.0
630    80.0
672    70.0
745    70.0
851    74.0
Name: age, dtype: float64

조건 필터 후 원하는 값을 대입할 수 있습니다. (단일 컬럼 선택에 유의)

In [57]:

df.loc[cond, 'age'] = -1

In [58]:

df.loc[cond]

Out[58]:

	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
96	0	1	male	-1.0	0	0	34.6542	C	First	man	True	A	Cherbourg	no	True
116	0	3	male	-1.0	0	0	7.7500	Q	Third	man	True	NaN	Queenstown	no	True
493	0	1	male	-1.0	0	0	49.5042	C	First	man	True	NaN	Cherbourg	no	True
630	1	1	male	-1.0	0	0	30.0000	S	First	man	True	A	Southampton	yes	True
672	0	2	male	-1.0	0	0	10.5000	S	Second	man	True	NaN	Southampton	no	True
745	0	1	male	-1.0	1	1	71.0000	S	First	man	True	B	Southampton	no	False
851	0	3	male	-1.0	0	0	7.7750	S	Third	man	True	NaN	Southampton	no	True

연습문제¶

데이터를 다시 로드 합니다.

In [149]:

df = sns.load_dataset("titanic")
df.head()

Out[149]:

	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
0	0	3	male	22.0	1	0	7.2500	S	Third	man	True	NaN	Southampton	no	False
1	1	1	female	38.0	1	0	71.2833	C	First	woman	False	C	Cherbourg	yes	False
2	1	3	female	26.0	0	0	7.9250	S	Third	woman	False	NaN	Southampton	yes	True
3	1	1	female	35.0	1	0	53.1000	S	First	woman	False	C	Southampton	yes	False
4	0	3	male	35.0	0	0	8.0500	S	Third	man	True	NaN	Southampton	no	True

다음 조건을 만족하는 코드를 입력하세요.

• 나이가 30살 이상 남자 승객 조건 필터링
• fare를 많이 낸 순서로 내림차순 정렬
• 상위 10개를 출력

In [169]:

# 코드를 입력해 주세요
df1 = df[df['age'] > 30]
df1 = df1[df1['who']=='man']
df2 = df1.sort_values(by='fare', ascending=False).head(11)
df2 = df2.drop(df2.index[9]) 
df2

Out[169]:

	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
679	1	1	male	36.0	0	1	512.3292	C	First	man	True	B	Cherbourg	yes	False
737	1	1	male	35.0	0	0	512.3292	C	First	man	True	B	Cherbourg	yes	True
438	0	1	male	64.0	1	4	263.0000	S	First	man	True	C	Southampton	no	False
332	0	1	male	38.0	0	1	153.4625	S	First	man	True	C	Southampton	no	False
660	1	1	male	50.0	2	0	133.6500	S	First	man	True	NaN	Southampton	yes	False
390	1	1	male	36.0	1	2	120.0000	S	First	man	True	B	Southampton	yes	False
659	0	1	male	58.0	0	2	113.2750	C	First	man	True	D	Cherbourg	no	False
698	0	1	male	49.0	1	1	110.8833	C	First	man	True	C	Cherbourg	no	False
544	0	1	male	50.0	1	0	106.4250	C	First	man	True	C	Cherbourg	no	False
224	1	1	male	38.0	1	0	90.0000	S	First	man	True	C	Southampton	yes	False

[출력 결과]

	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
679	1	1	male	36.0	0	1	512.3292	C	First	man	True	B	Cherbourg	yes	False
737	1	1	male	35.0	0	0	512.3292	C	First	man	True	B	Cherbourg	yes	True
438	0	1	male	64.0	1	4	263.0000	S	First	man	True	C	Southampton	no	False
332	0	1	male	38.0	0	1	153.4625	S	First	man	True	C	Southampton	no	False
660	1	1	male	50.0	2	0	133.6500	S	First	man	True	NaN	Southampton	yes	False
390	1	1	male	36.0	1	2	120.0000	S	First	man	True	B	Southampton	yes	False
659	0	1	male	58.0	0	2	113.2750	C	First	man	True	D	Cherbourg	no	False
698	0	1	male	49.0	1	1	110.8833	C	First	man	True	C	Cherbourg	no	False
544	0	1	male	50.0	1	0	106.4250	C	First	man	True	C	Cherbourg	no	False
224	1	1	male	38.0	1	0	90.0000	S	First	man	True	C	Southampton	yes	False

다음 조건을 만족하는 코드를 입력하세요.

• 나이가 20살 이상 40살 미만인 승객
• pclass가 1등급 혹은 2등급인 승객
• 열(column)은 survived, pclass, age, fare 만 나오게 출력
• 10개만 출력

In [151]:

df2 = df[(df['age'] > 20) & (df['age'] < 40)]
df2 = df2[(df2['pclass'] == 1) | (df2['pclass'] == 2)]
df2[['survived', 'pclass', 'age', 'fare']].head(10)

Out[151]:

	survived	pclass	age	fare
1	1	1	38.0	71.2833
3	1	1	35.0	53.1000
20	0	2	35.0	26.0000
21	1	2	34.0	13.0000
23	1	1	28.0	35.5000
34	0	1	28.0	82.1708
41	0	2	27.0	21.0000
53	1	2	29.0	26.0000
56	1	2	21.0	10.5000
61	1	1	38.0	80.0000

[출력 결과]

	survived	pclass	age	fare
1	1	1	38.0	71.2833
3	1	1	35.0	53.1000
20	0	2	35.0	26.0000
21	1	2	34.0	13.0000
23	1	1	28.0	35.5000
34	0	1	28.0	82.1708
41	0	2	27.0	21.0000
53	1	2	29.0	26.0000
56	1	2	21.0	10.5000
61	1	1	38.0	80.0000

iloc¶

• loc와 유사하지만, index만 허용합니다.
• loc와 마찬가지고, indexing / slicing 모두 가능합니다.

In [152]:

df.head()

Out[152]:

	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
0	0	3	male	22.0	1	0	7.2500	S	Third	man	True	NaN	Southampton	no	False
1	1	1	female	38.0	1	0	71.2833	C	First	woman	False	C	Cherbourg	yes	False
2	1	3	female	26.0	0	0	7.9250	S	Third	woman	False	NaN	Southampton	yes	True
3	1	1	female	35.0	1	0	53.1000	S	First	woman	False	C	Southampton	yes	False
4	0	3	male	35.0	0	0	8.0500	S	Third	man	True	NaN	Southampton	no	True

indexing¶

In [90]:

df.iloc[1, 3]

Out[90]:

38.0

Fancy Indexing¶

In [91]:

df.iloc[[0, 3, 4], [0, 1, 5, 6]]

Out[91]:

	survived	pclass	parch	fare
0	0	3	0	7.25
3	1	1	0	53.10
4	0	3	0	8.05

Slicing¶

In [92]:

df.iloc[:3, :5]

Out[92]:

	survived	pclass	sex	age	sibsp
0	0	3	male	22.0	1
1	1	1	female	38.0	1
2	1	3	female	26.0	0

isin¶

특정 값의 포함 여부는 isin 함수를 통해 비교가 가능합니다. (파이썬의 in 키워드는 사용 불가 합니다.)

In [93]:

sample = pd.DataFrame({'name': ['kim', 'lee', 'park', 'choi'], 
                        'age': [24, 27, 34, 19]
                      })
sample

Out[93]:

	name	age
0	kim	24
1	lee	27
2	park	34
3	choi	19

In [94]:

sample['name'].isin(['kim', 'lee'])

Out[94]:

0     True
1     True
2    False
3    False
Name: name, dtype: bool

In [95]:

sample.isin(['kim', 'lee'])

Out[95]:

	name	age
0	True	False
1	True	False
2	False	False
3	False	False

loc를 활용한 조건 필터링으로도 찰떡궁합입니다.

In [96]:

condition = sample['name'].isin(['kim', 'lee'])

In [97]:

sample.loc[condition]

Out[97]:

	name	age
0	kim	24
1	lee	27

연습문제¶

In [98]:

tips = sns.load_dataset('tips')
tips.head()

Out[98]:

	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

• tips 데이터셋 중 day가 금요일(Fri), 토요일(Sat) 만 필터링 합니다.
• tip이 $10보다 적게 낸 데이터만 필터링합니다.
• 컬럼은 total_bill, tip, smoker, time만 출력합니다.
• 상위 10개 행만 출력합니다.

In [153]:

# 코드를 입력해 주세요
condition = tips[tips['day'].isin(['Fri', 'Sat'])]
condition = condition[condition['tip']<10]
condition[['total_bill','tip','smoker','time']].head(10)

Out[153]:

	total_bill	tip	smoker	time
19	20.65	3.35	No	Dinner
20	17.92	4.08	No	Dinner
21	20.29	2.75	No	Dinner
22	15.77	2.23	No	Dinner
23	39.42	7.58	No	Dinner
24	19.82	3.18	No	Dinner
25	17.81	2.34	No	Dinner
26	13.37	2.00	No	Dinner
27	12.69	2.00	No	Dinner
28	21.70	4.30	No	Dinner

[출력 결과]

	total_bill	tip	smoker	time
19	20.65	3.35	No	Dinner
20	17.92	4.08	No	Dinner
21	20.29	2.75	No	Dinner
22	15.77	2.23	No	Dinner
23	39.42	7.58	No	Dinner
24	19.82	3.18	No	Dinner
25	17.81	2.34	No	Dinner
26	13.37	2.00	No	Dinner
27	12.69	2.00	No	Dinner
28	21.70	4.30	No	Dinner

제출¶

제출을 위해 새로 로드된 타이타닉 데이터셋에서 아래 조건을 만족하는 결과를 result_df에 저장하세요.

• 나이가 30살 이상 남자 승객 조건 필터링
• fare를 많이 낸 순서로 내림차순 정렬
• 상위 10개

In [178]:

df = sns.load_dataset("titanic")
df1 = df[df['age']>30]
df1 = df1[df1['who'].isin(['man'])]
result_df = df1.sort_values(by='fare', ascending=False).head(11)
result_df = result_df.drop(result_df.index[9]) 
#result_df