멀티켐퍼스 2주차-3/Pandas 기본문법과 데이터셋 다루기

2024. 1. 4. 17:25ㆍData Science/Study 자료

 

 

 

날짜 자동 생성

pandas_data_range()함수의 freq 옵션

 

Time series / date functionality — pandas 2.1.4 documentation

Time series / date functionality pandas contains extensive capabilities and features for working with time series data for all domains. Using the NumPy datetime64 and timedelta64 dtypes, pandas has consolidated a large number of features from other Python

pandas.pydata.org

https://pandas.pydata.org/docs/user_guide/timeseries.html#timeseries-offset-aliases

 

Time series / date functionality — pandas 2.1.4 documentation

Time series / date functionality pandas contains extensive capabilities and features for working with time series data for all domains. Using the NumPy datetime64 and timedelta64 dtypes, pandas has consolidated a large number of features from other Python

pandas.pydata.org

샘플 데이터셋의 종류를 알아보고  titanic 데이터 셋을 불러와보았다.

 

DataFrame에서 필터링 

- 조건식을 통해서 필터링이 된다

- 불린을 이용하여 필터링 후 True값만 출력이 되도록 하는 작업

 

위와 동일한 방법으로 loc 명령어를 사용하여 'embark_town'이 'Southampton'인 데이터만 추출을 해보았다. 

 

핵심 코드는

titanic.loc[titanic['칼럼명'] == '항목명', 인덱스번호]

인덱스번호 대신 ':'을 쓰면 전체 인덱스를 선택할 수 있다

다중 조건문 추출 

and 연산자의 경우     tips.loc[(조건1) & (조건2),:] 
or 연산자의 경우      tips.loc[(조건1) | (조건2),:] 

다중 조건문 Tip

 

 .loc[

         

,:]

사전형태와 같이 써주면 실수를 방지할 수 있음

 

예시)

tips.loc[
(tips['tip'] > 2.998279) & 
    (tips['time'] == 'Dinner')&
    (tips['size'] == 3) & 
    (tips['sex'] == 'Male') & 
    (tips['day'] == 'Sat')

, :] #이러한 형태가 안헷갈림

 

 

 

 

 공부 코드

import pandas as pd
import numpy as np
In [ ]:
pd.__version__
Out[ ]:
'2.1.4'

pandas 데이터 구조

  • Series 데이터 구조 1차원 데이터 : 컬럼의 갯수가 1개인 데이터
  • DataFrame 데이터 구조 컬럼 갯수가 여러개인 데이터
In [ ]:
s1 = pd.Series([10, 20, 30, 40, 50])
s1 #가장 왼쪽 인덱스 타입은 정수로 처리
Out[ ]:
0    10
1    20
2    30
3    40
4    50
dtype: int64
In [ ]:
s1.index
Out[ ]:
RangeIndex(start=0, stop=5, step=1)
In [ ]:
s1.values #넘파이 배열
Out[ ]:
array([10, 20, 30, 40, 50])
In [ ]:
s2 = pd.Series(['a','b','c',1,2,3])
s2 #object로 처리 간단하게 말하면 문자로 처리
Out[ ]:
0    a
1    b
2    c
3    1
4    2
5    3
dtype: object
In [ ]:
s3 = pd.Series([np.nan,10,30]) # nan은 결측치(missing value)
s3
Out[ ]:
0     NaN
1    10.0
2    30.0
dtype: float64
In [ ]:
index_date = ['2018-10-07','2018-10-10']  # 인덱스에 데이터를 넣는것이 가능
s4 = pd.Series([200, 195],index = index_date)# 명령어
s4
Out[ ]:
2018-10-07    200
2018-10-10    195
dtype: int64
In [ ]:
index_date = ['2018-10-08','2018-10-10']  # 인덱스에 데이터를 넣는것이 가능
s4 = pd.Series([200, 195],index = index_date)# 명령어
s4

#코드상으로는 중복이 가능하지만 인덱스의 기본원칙은 충복이 되면 안됨
Out[ ]:
2018-10-08    200
2018-10-10    195
dtype: int64
In [ ]:
data_dict = {
    '국어' : 100,
    '영어' : 95
}

s5 = pd.Series(data_dict)
s5
Out[ ]:
국어    100
영어     95
dtype: int64

날짜 데이터

In [ ]:
pd.date_range(start = '2024/01/01', end='2024.01.07')  #/와.구분
Out[ ]:
DatetimeIndex(['2024-01-01', '2024-01-02', '2024-01-03', '2024-01-04',
               '2024-01-05', '2024-01-06', '2024-01-07'],
              dtype='datetime64[ns]', freq='D')
In [ ]:
pd.date_range(start = '2024/01/01', end='2024.01.07')  #/와.구분
Out[ ]:
DatetimeIndex(['2024-01-01', '2024-01-02', '2024-01-03', '2024-01-04',
               '2024-01-05', '2024-01-06', '2024-01-07'],
              dtype='datetime64[ns]', freq='D')
In [ ]:
pd.date_range(start = '2024/01/01', periods = 7) #일단위 기간
Out[ ]:
DatetimeIndex(['2024-01-01', '2024-01-02', '2024-01-03', '2024-01-04',
               '2024-01-05', '2024-01-06', '2024-01-07'],
              dtype='datetime64[ns]', freq='D')
In [ ]:
pd.date_range(start = '2024/01/01', periods = 4,freq = '2D')
Out[ ]:
DatetimeIndex(['2024-01-01', '2024-01-03', '2024-01-05', '2024-01-07'], dtype='datetime64[ns]', freq='2D')
In [ ]:
pd.date_range(start = '2024/01/01 08:00', periods = 4,freq = 'H')
Out[ ]:
DatetimeIndex(['2024-01-01 08:00:00', '2024-01-01 09:00:00',
               '2024-01-01 10:00:00', '2024-01-01 11:00:00'],
              dtype='datetime64[ns]', freq='H')

DataFrame을 활용한 데이터 생성

In [ ]:
import seaborn as sns #시각화 라이브러리(통게시각화)
sns.__version__
Out[ ]:
'0.12.2'
In [ ]:
sns.get_dataset_names() # 샘플데이터셋 종류
Out[ ]:
['anagrams',
 'anscombe',
 'attention',
 'brain_networks',
 'car_crashes',
 'diamonds',
 'dots',
 'dowjones',
 'exercise',
 'flights',
 'fmri',
 'geyser',
 'glue',
 'healthexp',
 'iris',
 'mpg',
 'penguins',
 'planets',
 'seaice',
 'taxis',
 'tips',
 'titanic']
In [ ]:
iris = sns.load_dataset('iris')
iris
Out[ ]:
sepal_lengthsepal_widthpetal_lengthpetal_widthspecies01234...145146147148149
5.1 3.5 1.4 0.2 setosa
4.9 3.0 1.4 0.2 setosa
4.7 3.2 1.3 0.2 setosa
4.6 3.1 1.5 0.2 setosa
5.0 3.6 1.4 0.2 setosa
... ... ... ... ...
6.7 3.0 5.2 2.3 virginica
6.3 2.5 5.0 1.9 virginica
6.5 3.0 5.2 2.0 virginica
6.2 3.4 5.4 2.3 virginica
5.9 3.0 5.1 1.8 virginica

150 rows × 5 columns

In [ ]:
#247p 참고
#249p 딕셔너리 스타일 좋아함
In [ ]:
table_data = {
    '연도' : [2015, 2016, 2016, 2017, 2017],
    '지사' : ['한국', '한국', '미국', '한국', '미국'],
    '고객수' : [200,250,450,300,500]
}
In [ ]:
data = pd.DataFrame(table_data)
In [ ]:
table_data
Out[ ]:
{'연도': [2015, 2016, 2016, 2017, 2017],
 '지사': ['한국', '한국', '미국', '한국', '미국'],
 '고객수': [200, 250, 450, 300, 500]}
In [ ]:
data.index
Out[ ]:
RangeIndex(start=0, stop=5, step=1)
In [ ]:
data.values #차원확인 .shape
Out[ ]:
array([[2015, '한국', 200],
       [2016, '한국', 250],
       [2016, '미국', 450],
       [2017, '한국', 300],
       [2017, '미국', 500]], dtype=object)
  • 데이터 가공 시,numpy 메서드와 pandas 메서드 조합을 해서 처리하는 경우 많음
    • vectorization으로 처리/ 파이썬 기초문법(for-loop) 대신
    • 속도가 매우빠름 (gpt처리도 괜찮음)
In [ ]:
data.columns
Out[ ]:
Index(['연도', '지사', '고객수'], dtype='object')
In [ ]:
titanic = sns.load_dataset('titanic')
In [ ]:
titanic
Out[ ]:
survivedpclasssexagesibspparchfareembarkedclasswhoadult_maledeckembark_townalivealone01234...886887888889890
0 3 male 22.0 1 0 7.2500 S Third man True NaN Southampton no False
1 1 female 38.0 1 0 71.2833 C First woman False C Cherbourg yes False
1 3 female 26.0 0 0 7.9250 S Third woman False NaN Southampton yes True
1 1 female 35.0 1 0 53.1000 S First woman False C Southampton yes False
0 3 male 35.0 0 0 8.0500 S Third man True NaN Southampton no True
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
0 2 male 27.0 0 0 13.0000 S Second man True NaN Southampton no True
1 1 female 19.0 0 0 30.0000 S First woman False B Southampton yes True
0 3 female NaN 1 2 23.4500 S Third woman False NaN Southampton no False
1 1 male 26.0 0 0 30.0000 C First man True C Cherbourg yes True
0 3 male 32.0 0 0 7.7500 Q Third man True NaN Queenstown no True

891 rows × 15 columns

In [ ]:
titanic.head()
Out[ ]:
survivedpclasssexagesibspparchfareembarkedclasswhoadult_maledeckembark_townalivealone01234
0 3 male 22.0 1 0 7.2500 S Third man True NaN Southampton no False
1 1 female 38.0 1 0 71.2833 C First woman False C Cherbourg yes False
1 3 female 26.0 0 0 7.9250 S Third woman False NaN Southampton yes True
1 1 female 35.0 1 0 53.1000 S First woman False C Southampton yes False
0 3 male 35.0 0 0 8.0500 S Third man True NaN Southampton no True

DataFrame에서 열 선택

In [ ]:
age = titanic['age']
In [ ]:
age.head()
Out[ ]:
0    22.0
1    38.0
2    26.0
3    35.0
4    35.0
Name: age, dtype: float64
In [ ]:
type(titanic['age'])
Out[ ]:
pandas.core.series.Series
In [ ]:
age_sex = titanic[['age','sex']]
In [ ]:
age_sex
Out[ ]:
agesex01234...886887888889890
22.0 male
38.0 female
26.0 female
35.0 female
35.0 male
... ...
27.0 male
19.0 female
NaN female
26.0 male
32.0 male

891 rows × 2 columns

In [ ]:
type(titanic[['age','sex']])
Out[ ]:
pandas.core.frame.DataFrame
In [ ]:
titanic[['age','sex']].shape
Out[ ]:
(891, 2)

DataFrame에서 행 필터링

  • 조건식을 통해서 필터링이 됨
In [ ]:
above_35 = titanic[titanic['age'] >35] #불린을 이용하여 필터링 후 True에 속한 데이터 값만 추출
In [ ]:
above_35.head()
Out[ ]:
survivedpclasssexagesibspparchfareembarkedclasswhoadult_maledeckembark_townalivealone16111315
1 1 female 38.0 1 0 71.2833 C First woman False C Cherbourg yes False
0 1 male 54.0 0 0 51.8625 S First man True E Southampton no True
1 1 female 58.0 0 0 26.5500 S First woman False C Southampton yes True
0 3 male 39.0 1 5 31.2750 S Third man True NaN Southampton no False
1 2 female 55.0 0 0 16.0000 S Second woman False NaN Southampton yes True
In [ ]:
above_35.shape
Out[ ]:
(217, 15)
In [ ]:
# 객실 2,3만 보고싶음
In [ ]:
class_23 = titanic[titanic['pclass'].isin([2, 3])]
class_23.head()
Out[ ]:
survivedpclasssexagesibspparchfareembarkedclasswhoadult_maledeckembark_townalivealone02457
0 3 male 22.0 1 0 7.2500 S Third man True NaN Southampton no False
1 3 female 26.0 0 0 7.9250 S Third woman False NaN Southampton yes True
0 3 male 35.0 0 0 8.0500 S Third man True NaN Southampton no True
0 3 male NaN 0 0 8.4583 Q Third man True NaN Queenstown no True
0 3 male 2.0 3 1 21.0750 S Third child False NaN Southampton no False
In [ ]:
class_23 = titanic[(titanic["pclass"] == 2) | (titanic["pclass"] == 3)]
class_23.head()
Out[ ]:
survivedpclasssexagesibspparchfareembarkedclasswhoadult_maledeckembark_townalivealone02457
0 3 male 22.0 1 0 7.2500 S Third man True NaN Southampton no False
1 3 female 26.0 0 0 7.9250 S Third woman False NaN Southampton yes True
0 3 male 35.0 0 0 8.0500 S Third man True NaN Southampton no True
0 3 male NaN 0 0 8.4583 Q Third man True NaN Queenstown no True
0 3 male 2.0 3 1 21.0750 S Third child False NaN Southampton no False
In [ ]:
#.isin() 명령어는 or연산자(|)와 같은 역할을 한다
In [ ]:
#나이가 적힌 데이터만 추출
In [ ]:
titanic['age'].notna() #나이에 결측값이 있는 사람을 필터링
Out[ ]:
0       True
1       True
2       True
3       True
4       True
       ...  
886     True
887     True
888    False
889     True
890     True
Name: age, Length: 891, dtype: bool
In [ ]:
titanic[titanic['age'].notna()]  #불린값으로 받은 데이터자료
Out[ ]:
survivedpclasssexagesibspparchfareembarkedclasswhoadult_maledeckembark_townalivealone01234...885886887889890
0 3 male 22.0 1 0 7.2500 S Third man True NaN Southampton no False
1 1 female 38.0 1 0 71.2833 C First woman False C Cherbourg yes False
1 3 female 26.0 0 0 7.9250 S Third woman False NaN Southampton yes True
1 1 female 35.0 1 0 53.1000 S First woman False C Southampton yes False
0 3 male 35.0 0 0 8.0500 S Third man True NaN Southampton no True
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
0 3 female 39.0 0 5 29.1250 Q Third woman False NaN Queenstown no False
0 2 male 27.0 0 0 13.0000 S Second man True NaN Southampton no True
1 1 female 19.0 0 0 30.0000 S First woman False B Southampton yes True
1 1 male 26.0 0 0 30.0000 C First man True C Cherbourg yes True
0 3 male 32.0 0 0 7.7500 Q Third man True NaN Queenstown no True

714 rows × 15 columns

In [ ]:
age_no_na = titanic[titanic['age'].notna()]
age_no_na.head()
Out[ ]:
survivedpclasssexagesibspparchfareembarkedclasswhoadult_maledeckembark_townalivealone01234
0 3 male 22.0 1 0 7.2500 S Third man True NaN Southampton no False
1 1 female 38.0 1 0 71.2833 C First woman False C Cherbourg yes False
1 3 female 26.0 0 0 7.9250 S Third woman False NaN Southampton yes True
1 1 female 35.0 1 0 53.1000 S First woman False C Southampton yes False
0 3 male 35.0 0 0 8.0500 S Third man True NaN Southampton no True
In [ ]:
age_no_na.shape #나이 결측값이 있는 항목을 삭제한 데이터프레임
Out[ ]:
(714, 15)

특정 행과 열을 선택해보기

  • .loc[인덱스의 명칭 , 칼럼의 명칭]
  • .iloc[인덱스번호,칼럼번호]
In [ ]:
adult_name = titanic.loc[titanic['age'] > 35, 'embark_town']
adult_name.head()
Out[ ]:
1       Cherbourg
6     Southampton
11    Southampton
13    Southampton
15    Southampton
Name: embark_town, dtype: object
In [ ]:
titanic.iloc[0:3,3] = 'anonymous'  # 0부터 2까지의 인덱스, 3번째 칼럼에서 의 값을 anonymous로 바꿈
titanic.head()

/var/folders/dk/vy49vgx92_3d76t7s3b_x5c40000gn/T/ipykernel_92221/253640465.py:1: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value 'anonymous' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.
  titanic.iloc[0:3,3] = 'anonymous'
Out[ ]:
survivedpclasssexagesibspparchfareembarkedclasswhoadult_maledeckembark_townalivealone01234
0 3 male anonymous 1 0 7.2500 S Third man True NaN Southampton no False
1 1 female anonymous 1 0 71.2833 C First woman False C Cherbourg yes False
1 3 female anonymous 0 0 7.9250 S Third woman False NaN Southampton yes True
1 1 female 35.0 1 0 53.1000 S First woman False C Southampton yes False
0 3 male 35.0 0 0 8.0500 S Third man True NaN Southampton no True

데이터 연산

In [ ]:
import seaborn as sns
s1 = pd.Series([1,2,3,4,5])
s2 = pd.Series([10,20,30,40,50])
In [ ]:
s1+s2 #시리즈의 연산이 가능(-,*,/)
Out[ ]:
0    11
1    22
2    33
3    44
4    55
dtype: int64
In [ ]:
#리스트와 numpy의 배열과 다르게, 서로 크기가 달라도 연산이 가능함

s1 = pd.Series([1,2,3,4])
s2 = pd.Series([10,20,30,40,50])

s1+s2
Out[ ]:
0    11.0
1    22.0
2    33.0
3    44.0
4     NaN
dtype: float64
In [ ]:
#단 결과값은 NaN 결측치로 표기가 된다
In [ ]:
table_data1 = {'A': [1, 2, 3, 4, 5],
              'B': [10, 20, 30, 40, 50],
              'C': [100, 200, 300, 400, 500]}
df1 = pd.DataFrame(table_data1)
df1
Out[ ]:
ABC01234
1 10 100
2 20 200
3 30 300
4 40 400
5 50 500
In [ ]:
table_data2 = {'A': [6, 7, 8],
              'B': [60, 70, 80],
              'C': [600, 700, 800]}
df2 = pd.DataFrame(table_data2)
df2
Out[ ]:
ABC012
6 60 600
7 70 700
8 80 800
In [ ]:
df1 + df2
Out[ ]:
ABC01234
7.0 70.0 700.0
9.0 90.0 900.0
11.0 110.0 1100.0
NaN NaN NaN
NaN NaN NaN
In [ ]:
df1 - df2
Out[ ]:
ABC01234
-5.0 -50.0 -500.0
-5.0 -50.0 -500.0
-5.0 -50.0 -500.0
NaN NaN NaN
NaN NaN NaN
In [ ]:
df1 * df2
Out[ ]:
ABC01234
6.0 600.0 60000.0
14.0 1400.0 140000.0
24.0 2400.0 240000.0
NaN NaN NaN
NaN NaN NaN
In [ ]:
df1 / df2
Out[ ]:
ABC01234
0.166667 0.166667 0.166667
0.285714 0.285714 0.285714
0.375000 0.375000 0.375000
NaN NaN NaN
NaN NaN NaN
In [ ]:
table_data3 = {'봄':  [256.5, 264.3, 215.9, 223.2, 312.8],
              '여름': [770.6, 567.5, 599.8, 387.1, 446.2],
              '가을': [363.5, 231.2, 293.1, 247.7, 381.6],
              '겨울': [139.3, 59.9, 76.9, 109.1, 108.1]}

columns_list = ['봄', '여름', '가을', '겨울']
index_list = ['2012', '2013', '2014', '2015', '2016']

df3 = pd.DataFrame(table_data3, columns = columns_list, index = index_list)
df3
Out[ ]:
봄여름가을겨울20122013201420152016
256.5 770.6 363.5 139.3
264.3 567.5 231.2 59.9
215.9 599.8 293.1 76.9
223.2 387.1 247.7 109.1
312.8 446.2 381.6 108.1
In [ ]:
df3.mean()
Out[ ]:
봄     254.54
여름    554.24
가을    303.42
겨울     98.66
dtype: float64
In [ ]:
df3.max()
Out[ ]:
봄     312.8
여름    770.6
가을    381.6
겨울    139.3
dtype: float64
In [ ]:
df3.min()
Out[ ]:
봄     215.9
여름    387.1
가을    231.2
겨울     59.9
dtype: float64
In [ ]:
df3.mean(axis=1) #행으로 평균을 냄  axis=1
Out[ ]:
2012    382.475
2013    280.725
2014    296.425
2015    241.775
2016    312.175
dtype: float64
In [ ]:
df3.max(axis=1)
Out[ ]:
2012    770.6
2013    567.5
2014    599.8
2015    387.1
2016    446.2
dtype: float64
In [ ]:
df3.std(axis=1)  #행단위 표준편차
Out[ ]:
2012    274.472128
2013    211.128782
2014    221.150739
2015    114.166760
2016    146.548658
dtype: float64

해석하는 능력을 키워야함

  • 평균을 중심으로 표준편차는 폭을 나타냄 즉 2012년도는 편차가 여름에비해 다른계절이 큼
  • 2015년은 폭이 작아 여름과 비교하면 편차가 작음
  • 데이터의 분포가 상상이 되야함(평균과 표준편차를보고)
In [ ]:
df3.describe()
Out[ ]:
봄여름가을겨울countmeanstdmin25%50%75%max
5.000000 5.000000 5.000000 5.000000
254.540000 554.240000 303.420000 98.660000
38.628267 148.888895 67.358496 30.925523
215.900000 387.100000 231.200000 59.900000
223.200000 446.200000 247.700000 76.900000
256.500000 567.500000 293.100000 108.100000
264.300000 599.800000 363.500000 109.100000
312.800000 770.600000 381.600000 139.300000
In [ ]:
df3.T.describe()
Out[ ]:
20122013201420152016countmeanstdmin25%50%75%max
4.000000 4.000000 4.000000 4.00000 4.000000
382.475000 280.725000 296.425000 241.77500 312.175000
274.472128 211.128782 221.150739 114.16676 146.548658
139.300000 59.900000 76.900000 109.10000 108.100000
227.200000 188.375000 181.150000 194.67500 261.625000
310.000000 247.750000 254.500000 235.45000 347.200000
465.275000 340.100000 369.775000 282.55000 397.750000
770.600000 567.500000 599.800000 387.10000 446.200000
In [ ]:
KTX_data = {'경부선 KTX': [39060, 39896, 42005, 43621, 41702, 41266, 32427],
            '호남선 KTX': [7313, 6967, 6873, 6626, 8675, 10622, 9228],
            '경전선 KTX': [3627, 4168, 4088, 4424, 4606, 4984, 5570],
            '전라선 KTX': [309, 1771, 1954, 2244, 3146, 3945, 5766],
            '동해선 KTX': [np.nan,np.nan, np.nan, np.nan, 2395, 3786, 6667]}
index_list = ['2011', '2012', '2013', '2014', '2015', '2016', '2017']

df_KTX = pd.DataFrame(KTX_data, index = index_list)
df_KTX
Out[ ]:
경부선 KTX호남선 KTX경전선 KTX전라선 KTX동해선 KTX2011201220132014201520162017
39060 7313 3627 309 NaN
39896 6967 4168 1771 NaN
42005 6873 4088 1954 NaN
43621 6626 4424 2244 NaN
41702 8675 4606 3146 2395.0
41266 10622 4984 3945 3786.0
32427 9228 5570 5766 6667.0
In [ ]:
#p.259
df_KTX.head(3)
Out[ ]:
경부선 KTX호남선 KTX경전선 KTX전라선 KTX동해선 KTX201120122013
39060 7313 3627 309 NaN
39896 6967 4168 1771 NaN
42005 6873 4088 1954 NaN
In [ ]:
df_KTX.tail(3)
Out[ ]:
경부선 KTX호남선 KTX경전선 KTX전라선 KTX동해선 KTX201520162017
41702 8675 4606 3146 2395.0
41266 10622 4984 3945 3786.0
32427 9228 5570 5766 6667.0

행 선택 (행 추출)

  • slicing과 동일한 문법으로 추출 가능
In [ ]:
df_KTX[0:2]  #인덱스 순번
Out[ ]:
경부선 KTX호남선 KTX경전선 KTX전라선 KTX동해선 KTX20112012
39060 7313 3627 309 NaN
39896 6967 4168 1771 NaN
In [ ]:
df_KTX[2:5]
Out[ ]:
경부선 KTX호남선 KTX경전선 KTX전라선 KTX동해선 KTX201320142015
42005 6873 4088 1954 NaN
43621 6626 4424 2244 NaN
41702 8675 4606 3146 2395.0

loc vs iloc를 활용한 열 추출, 행 추출

  • 두 가지의 차이 기억하기
In [ ]:
#df_KTX,loc[행,열]
#df_KTX.loc['2013':'2016']
Out[ ]:
경부선 KTX호남선 KTX경전선 KTX전라선 KTX동해선 KTX2013201420152016
42005 6873 4088 1954 NaN
43621 6626 4424 2244 NaN
41702 8675 4606 3146 2395.0
41266 10622 4984 3945 3786.0
In [ ]:
df_KTX.loc['2013':'2016','경부선 KTX']
Out[ ]:
2013    42005
2014    43621
2015    41702
2016    41266
Name: 경부선 KTX, dtype: int64
In [ ]:
df_KTX.loc['2013':'2016',['경부선 KTX','호남선 KTX']]
Out[ ]:
경부선 KTX호남선 KTX2013201420152016
42005 6873
43621 6626
41702 8675
41266 10622
In [ ]:
df_KTX.loc['2013':'2016',['호남선 KTX']]
Out[ ]:
호남선 KTX2013201420152016
6873
6626
8675
10622
In [ ]:
#외울때 df.loc[행조건식, [컬럼명]]
In [ ]:
iris = sns.load_dataset('iris')
In [ ]:
iris
Out[ ]:
sepal_lengthsepal_widthpetal_lengthpetal_widthspecies01234...145146147148149
5.1 3.5 1.4 0.2 setosa
4.9 3.0 1.4 0.2 setosa
4.7 3.2 1.3 0.2 setosa
4.6 3.1 1.5 0.2 setosa
5.0 3.6 1.4 0.2 setosa
... ... ... ... ...
6.7 3.0 5.2 2.3 virginica
6.3 2.5 5.0 1.9 virginica
6.5 3.0 5.2 2.0 virginica
6.2 3.4 5.4 2.3 virginica
5.9 3.0 5.1 1.8 virginica

150 rows × 5 columns

In [ ]:
#sepal_length의 길이가 5cm 이상인 것만 조회
In [ ]:
iris.loc[iris['sepal_length']>=7, :]
Out[ ]:
sepal_lengthsepal_widthpetal_lengthpetal_widthspecies50102105107109117118122125129130131135
7.0 3.2 4.7 1.4 versicolor
7.1 3.0 5.9 2.1 virginica
7.6 3.0 6.6 2.1 virginica
7.3 2.9 6.3 1.8 virginica
7.2 3.6 6.1 2.5 virginica
7.7 3.8 6.7 2.2 virginica
7.7 2.6 6.9 2.3 virginica
7.7 2.8 6.7 2.0 virginica
7.2 3.2 6.0 1.8 virginica
7.2 3.0 5.8 1.6 virginica
7.4 2.8 6.1 1.9 virginica
7.9 3.8 6.4 2.0 virginica
7.7 3.0 6.1 2.3 virginica
In [ ]:
#species가 virginica인 것만 조회
In [ ]:
iris.loc[iris['species']== 'virginica', :]
Out[ ]:
sepal_lengthsepal_widthpetal_lengthpetal_widthspecies100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149
6.3 3.3 6.0 2.5 virginica
5.8 2.7 5.1 1.9 virginica
7.1 3.0 5.9 2.1 virginica
6.3 2.9 5.6 1.8 virginica
6.5 3.0 5.8 2.2 virginica
7.6 3.0 6.6 2.1 virginica
4.9 2.5 4.5 1.7 virginica
7.3 2.9 6.3 1.8 virginica
6.7 2.5 5.8 1.8 virginica
7.2 3.6 6.1 2.5 virginica
6.5 3.2 5.1 2.0 virginica
6.4 2.7 5.3 1.9 virginica
6.8 3.0 5.5 2.1 virginica
5.7 2.5 5.0 2.0 virginica
5.8 2.8 5.1 2.4 virginica
6.4 3.2 5.3 2.3 virginica
6.5 3.0 5.5 1.8 virginica
7.7 3.8 6.7 2.2 virginica
7.7 2.6 6.9 2.3 virginica
6.0 2.2 5.0 1.5 virginica
6.9 3.2 5.7 2.3 virginica
5.6 2.8 4.9 2.0 virginica
7.7 2.8 6.7 2.0 virginica
6.3 2.7 4.9 1.8 virginica
6.7 3.3 5.7 2.1 virginica
7.2 3.2 6.0 1.8 virginica
6.2 2.8 4.8 1.8 virginica
6.1 3.0 4.9 1.8 virginica
6.4 2.8 5.6 2.1 virginica
7.2 3.0 5.8 1.6 virginica
7.4 2.8 6.1 1.9 virginica
7.9 3.8 6.4 2.0 virginica
6.4 2.8 5.6 2.2 virginica
6.3 2.8 5.1 1.5 virginica
6.1 2.6 5.6 1.4 virginica
7.7 3.0 6.1 2.3 virginica
6.3 3.4 5.6 2.4 virginica
6.4 3.1 5.5 1.8 virginica
6.0 3.0 4.8 1.8 virginica
6.9 3.1 5.4 2.1 virginica
6.7 3.1 5.6 2.4 virginica
6.9 3.1 5.1 2.3 virginica
5.8 2.7 5.1 1.9 virginica
6.8 3.2 5.9 2.3 virginica
6.7 3.3 5.7 2.5 virginica
6.7 3.0 5.2 2.3 virginica
6.3 2.5 5.0 1.9 virginica
6.5 3.0 5.2 2.0 virginica
6.2 3.4 5.4 2.3 virginica
5.9 3.0 5.1 1.8 virginica
In [ ]:
titanic = sns.load_dataset('titanic')
titanic.head()
Out[ ]:
survivedpclasssexagesibspparchfareembarkedclasswhoadult_maledeckembark_townalivealone01234
0 3 male 22.0 1 0 7.2500 S Third man True NaN Southampton no False
1 1 female 38.0 1 0 71.2833 C First woman False C Cherbourg yes False
1 3 female 26.0 0 0 7.9250 S Third woman False NaN Southampton yes True
1 1 female 35.0 1 0 53.1000 S First woman False C Southampton yes False
0 3 male 35.0 0 0 8.0500 S Third man True NaN Southampton no True
In [ ]:
ti_data = titanic.loc[titanic['embark_town']=='Southampton',:]
In [ ]:
ti_data['age'].mean()
Out[ ]:
29.44539711191336
In [ ]:
ti_data['age']>= 29.44539711191336
Out[ ]:
0      False
2      False
3       True
4       True
6       True
       ...  
883    False
884    False
886    False
887    False
888    False
Name: age, Length: 644, dtype: bool
In [ ]:
ti_data.loc[ti_data['age']>= 29.44539711191336,:]
Out[ ]:
survivedpclasssexagesibspparchfareembarkedclasswhoadult_maledeckembark_townalivealone3461113...867871872873881
1 1 female 35.0 1 0 53.1000 S First woman False C Southampton yes False
0 3 male 35.0 0 0 8.0500 S Third man True NaN Southampton no True
0 1 male 54.0 0 0 51.8625 S First man True E Southampton no True
1 1 female 58.0 0 0 26.5500 S First woman False C Southampton yes True
0 3 male 39.0 1 5 31.2750 S Third man True NaN Southampton no False
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
0 1 male 31.0 0 0 50.4958 S First man True A Southampton no True
1 1 female 47.0 1 1 52.5542 S First woman False D Southampton yes False
0 1 male 33.0 0 0 5.0000 S First man True B Southampton no True
0 3 male 47.0 0 0 9.0000 S Third man True NaN Southampton no True
0 3 male 33.0 0 0 7.8958 S Third man True NaN Southampton no True

252 rows × 15 columns

다중조건문 추출

In [ ]:
# total_bill의 중간값 이하인것만 조회
#day가 Sun인 데이터만 조회
#tip의 평균보다 크면서, time이 Dinner인 데이터 조회
#and     tips.loc[(조건1) & (조건2),:]
#or      tips.loc[(조건1) | (조건2),:]
tips = sns.load_dataset('tips')
tips.head()
Out[ ]:
total_billtipsexsmokerdaytimesize01234
16.99 1.01 Female No Sun Dinner 2
10.34 1.66 Male No Sun Dinner 3
21.01 3.50 Male No Sun Dinner 3
23.68 3.31 Male No Sun Dinner 2
24.59 3.61 Female No Sun Dinner 4
In [ ]:
tips.describe()
Out[ ]:
total_billtipsizecountmeanstdmin25%50%75%max
244.000000 244.000000 244.000000
19.785943 2.998279 2.569672
8.902412 1.383638 0.951100
3.070000 1.000000 1.000000
13.347500 2.000000 2.000000
17.795000 2.900000 2.000000
24.127500 3.562500 3.000000
50.810000 10.000000 6.000000
In [ ]:
# total_bill의 중간값 이하인것만 조회
tips['total_bill'] <= 17.795000
Out[ ]:
0       True
1       True
2      False
3      False
4      False
       ...  
239    False
240    False
241    False
242    False
243    False
Name: total_bill, Length: 244, dtype: bool
In [ ]:
tips.loc[tips['total_bill'] <= 17.795000, :]  # tips['total_bill'].median()으로 중간값 대체 가능
Out[ ]:
total_billtipsexsmokerdaytimesize01689...232233234235236
16.99 1.01 Female No Sun Dinner 2
10.34 1.66 Male No Sun Dinner 3
8.77 2.00 Male No Sun Dinner 2
15.04 1.96 Male No Sun Dinner 2
14.78 3.23 Male No Sun Dinner 2
... ... ... ... ... ... ...
11.61 3.39 Male No Sat Dinner 2
10.77 1.47 Male No Sat Dinner 2
15.53 3.00 Male Yes Sat Dinner 2
10.07 1.25 Male No Sat Dinner 2
12.60 1.00 Male Yes Sat Dinner 2

122 rows × 7 columns

In [ ]:
#day가 Sun인 데이터만 조회
tips.loc[tips['day'] == 'Sun', :]
Out[ ]:
total_billtipsexsmokerdaytimesize01234...186187188189190
16.99 1.01 Female No Sun Dinner 2
10.34 1.66 Male No Sun Dinner 3
21.01 3.50 Male No Sun Dinner 3
23.68 3.31 Male No Sun Dinner 2
24.59 3.61 Female No Sun Dinner 4
... ... ... ... ... ... ...
20.90 3.50 Female Yes Sun Dinner 3
30.46 2.00 Male Yes Sun Dinner 5
18.15 3.50 Female Yes Sun Dinner 3
23.10 4.00 Male Yes Sun Dinner 3
15.69 1.50 Male Yes Sun Dinner 2

76 rows × 7 columns

In [ ]:
tips.loc[
    (tips['tip'] > 2.998279) &
    (tips['time'] == 'Dinner')&
    (tips['size'] == 3) &
    (tips['sex'] == 'Male') &
    (tips['day'] == 'Sat')

, :] #이러한 형태가 안헷갈림
Out[ ]:
total_billtipsexsmokerdaytimesize19353965170206231239
20.65 3.35 Male No Sat Dinner 3
24.06 3.60 Male No Sat Dinner 3
31.27 5.00 Male No Sat Dinner 3
20.08 3.15 Male No Sat Dinner 3
50.81 10.00 Male Yes Sat Dinner 3
26.59 3.41 Male Yes Sat Dinner 3
15.69 3.00 Male Yes Sat Dinner 3
29.03 5.92 Male No Sat Dinner 3

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