데이터 셀렉션 및 필터링¶

dataframe뒤의 []는 '칼럼 지정 연산자'

In [1]:

import pandas as pd

titanic_df = pd.read_csv('./titanic_train.csv')
titanic_df

Out[1]:

	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

In [3]:

print('단일 칼럼 데이터 추출: \n', titanic_df['Pclass'].head(3))  #칼럼명 문자
print('\n 여러 칼럼의 데이터 추출: \n', titanic_df[ ['Survived', 'Pclass'] ].head(3))  #칼럼명의 리스트 객체
print('[]안에 숫자 index는 keyerror 오류 발생: \n', titanic_df[0])

단일 칼럼 데이터 추출: 
 0    3
1    1
2    3
Name: Pclass, dtype: int64

 여러 칼럼의 데이터 추출: 
    Survived  Pclass
0         0       3
1         1       1
2         1       3

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
~\anaconda3\envs\test\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance)
   3802             try:
-> 3803                 return self._engine.get_loc(casted_key)
   3804             except KeyError as err:

~\anaconda3\envs\test\lib\site-packages\pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()

~\anaconda3\envs\test\lib\site-packages\pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()

pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()

pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()

KeyError: 0

The above exception was the direct cause of the following exception:

KeyError                                  Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_32016\1187408095.py in <module>
      1 print('단일 칼럼 데이터 추출: \n', titanic_df['Pclass'].head(3))
      2 print('\n 여러 칼럼의 데이터 추출: \n', titanic_df[ ['Survived', 'Pclass'] ].head(3))
----> 3 print('[]안에 숫자 index는 keyerror 오류 발생: \n', titanic_df[0])

~\anaconda3\envs\test\lib\site-packages\pandas\core\frame.py in __getitem__(self, key)
   3803             if self.columns.nlevels > 1:
   3804                 return self._getitem_multilevel(key)
-> 3805             indexer = self.columns.get_loc(key)
   3806             if is_integer(indexer):
   3807                 indexer = [indexer]

~\anaconda3\envs\test\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance)
   3803                 return self._engine.get_loc(casted_key)
   3804             except KeyError as err:
-> 3805                 raise KeyError(key) from err
   3806             except TypeError:
   3807                 # If we have a listlike key, _check_indexing_error will raise

KeyError: 0

In [4]:

#인덱스 형태로 변환 가능한 표현식
titanic_df[0:2]

Out[4]:

	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

In [5]:

#불린 인덱싱 기능
titanic_df[ titanic_df['Pclass'] == 3].head(3)

Out[5]:

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	A/5 21171	7.250	NaN	S
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	STON/O2. 3101282	7.925	NaN	S
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	373450	8.050	NaN	S

iloc[ ] 연산자 >> 위치 기반 인덱싱¶

정숫값 또는 정수형의 슬라이싱, fancy list값을 입력해야 함

열 위치에 -1 입력해서 가장 마지막 열 데이터를 가져오는데 자주 사용

In [6]:

data = { 'Name': ['Chulmin', 'Eunkyung', 'JInwoong', 'Soobeom'],
       'Year': [2011, 2016, 2015, 2015],
       'Gender': ['Male', 'Female', 'Male', 'Male']}
data_df = pd.DataFrame(data, index=['one', 'two', 'three', 'four'])
data_df

Out[6]:

	Name	Year	Gender
one	Chulmin	2011	Male
two	Eunkyung	2016	Female
three	JInwoong	2015	Male
four	Soobeom	2015	Male

In [8]:

data_df.iloc[0, 0]
#iloc에는 칼럼명 입력하면 안됌

Out[8]:

'Chulmin'

In [10]:

data_df.iloc[ 0:2, [0,1]]

Out[10]:

	Name	Year
one	Chulmin	2011
two	Eunkyung	2016

In [11]:

data_df.iloc[ 1:3, [0,1]]

Out[11]:

	Name	Year
two	Eunkyung	2016
three	JInwoong	2015

In [12]:

print('\n 맨 마지막 칼럼 데이터[:, -1]\n', data_df.iloc[:, -1])
print('\n 맨 마지막 칼럼을 제외한 모든 데이터[:, :-1]\n', data_df.iloc[:, :-1])

 맨 마지막 칼럼 데이터[:, -1]
 one        Male
two      Female
three      Male
four       Male
Name: Gender, dtype: object

 맨 마지막 칼럼을 제외한 모든 데이터[:, :-1]
            Name  Year
one     Chulmin  2011
two    Eunkyung  2016
three  JInwoong  2015
four    Soobeom  2015

loc[ ] 연산자 >> 명칭 기반 인덱싱¶

loc[인덱스값, 칼럼명] 으로 추출 >> 인덱스값을 dataframe의 행 위치를 나타내는 고유한 '명칭'으로 생각

loc에 슬라이싱 쓰면 '종료값-1'이 아니라 종료값까지 포함하는 것을 의미, 명칭은 숫자형이 아닐 수 있기 때문에 -1을 할 수 없음

In [13]:

data_df.loc['one', 'Name']

Out[13]:

'Chulmin'

In [14]:

print('위치 기반 iloc slicing \n', data_df.iloc[0:1, 0], '\n')
print('명칭 기반 loc slicing \n', data_df.loc['one':'two', 'Name'])

위치 기반 iloc slicing 
 one    Chulmin
Name: Name, dtype: object 

명칭 기반 loc slicing 
 one     Chulmin
two    Eunkyung
Name: Name, dtype: object

In [15]:

data_df.loc['one':'three', ['Name', 'Gender']]

Out[15]:

	Name	Gender
one	Chulmin	Male
two	Eunkyung	Female
three	JInwoong	Male

In [23]:

data_df.loc[data_df['Year'] >= 2015]

Out[23]:

	Name	Year	Gender
two	Eunkyung	2016	Female
three	JInwoong	2015	Male
four	Soobeom	2015	Male

In [24]:

data_df.loc['three', 'Gender']

Out[24]:

'Male'

불린 인덱싱¶

처음부터 가져올 값을 조건으로 []내에 입력

In [26]:

titanic_boolean = titanic_df[titanic_df['Age'] > 60 ]
print(type(titanic_boolean))
titanic_boolean

<class 'pandas.core.frame.DataFrame'>

Out[26]:

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
33	34	0	2	Wheadon, Mr. Edward H	male	66.0	0	0	C.A. 24579	10.5000	NaN	S
54	55	0	1	Ostby, Mr. Engelhart Cornelius	male	65.0	0	1	113509	61.9792	B30	C
96	97	0	1	Goldschmidt, Mr. George B	male	71.0	0	0	PC 17754	34.6542	A5	C
116	117	0	3	Connors, Mr. Patrick	male	70.5	0	0	370369	7.7500	NaN	Q
170	171	0	1	Van der hoef, Mr. Wyckoff	male	61.0	0	0	111240	33.5000	B19	S
252	253	0	1	Stead, Mr. William Thomas	male	62.0	0	0	113514	26.5500	C87	S
275	276	1	1	Andrews, Miss. Kornelia Theodosia	female	63.0	1	0	13502	77.9583	D7	S
280	281	0	3	Duane, Mr. Frank	male	65.0	0	0	336439	7.7500	NaN	Q
326	327	0	3	Nysveen, Mr. Johan Hansen	male	61.0	0	0	345364	6.2375	NaN	S
438	439	0	1	Fortune, Mr. Mark	male	64.0	1	4	19950	263.0000	C23 C25 C27	S
456	457	0	1	Millet, Mr. Francis Davis	male	65.0	0	0	13509	26.5500	E38	S
483	484	1	3	Turkula, Mrs. (Hedwig)	female	63.0	0	0	4134	9.5875	NaN	S
493	494	0	1	Artagaveytia, Mr. Ramon	male	71.0	0	0	PC 17609	49.5042	NaN	C
545	546	0	1	Nicholson, Mr. Arthur Ernest	male	64.0	0	0	693	26.0000	NaN	S
555	556	0	1	Wright, Mr. George	male	62.0	0	0	113807	26.5500	NaN	S
570	571	1	2	Harris, Mr. George	male	62.0	0	0	S.W./PP 752	10.5000	NaN	S
625	626	0	1	Sutton, Mr. Frederick	male	61.0	0	0	36963	32.3208	D50	S
630	631	1	1	Barkworth, Mr. Algernon Henry Wilson	male	80.0	0	0	27042	30.0000	A23	S
672	673	0	2	Mitchell, Mr. Henry Michael	male	70.0	0	0	C.A. 24580	10.5000	NaN	S
745	746	0	1	Crosby, Capt. Edward Gifford	male	70.0	1	1	WE/P 5735	71.0000	B22	S
829	830	1	1	Stone, Mrs. George Nelson (Martha Evelyn)	female	62.0	0	0	113572	80.0000	B28	NaN
851	852	0	3	Svensson, Mr. Johan	male	74.0	0	0	347060	7.7750	NaN	S

In [28]:

titanic_df[titanic_df['Age'] > 60][['Name', 'Age']]

Out[28]:

	Name	Age
33	Wheadon, Mr. Edward H	66.0
54	Ostby, Mr. Engelhart Cornelius	65.0
96	Goldschmidt, Mr. George B	71.0
116	Connors, Mr. Patrick	70.5
170	Van der hoef, Mr. Wyckoff	61.0
252	Stead, Mr. William Thomas	62.0
275	Andrews, Miss. Kornelia Theodosia	63.0
280	Duane, Mr. Frank	65.0
326	Nysveen, Mr. Johan Hansen	61.0
438	Fortune, Mr. Mark	64.0
456	Millet, Mr. Francis Davis	65.0
483	Turkula, Mrs. (Hedwig)	63.0
493	Artagaveytia, Mr. Ramon	71.0
545	Nicholson, Mr. Arthur Ernest	64.0
555	Wright, Mr. George	62.0
570	Harris, Mr. George	62.0
625	Sutton, Mr. Frederick	61.0
630	Barkworth, Mr. Algernon Henry Wilson	80.0
672	Mitchell, Mr. Henry Michael	70.0
745	Crosby, Capt. Edward Gifford	70.0
829	Stone, Mrs. George Nelson (Martha Evelyn)	62.0
851	Svensson, Mr. Johan	74.0

In [30]:

titanic_df[titanic_df['Pclass'] == 2][['Name', 'Pclass']]

Out[30]:

	Name	Pclass
9	Nasser, Mrs. Nicholas (Adele Achem)	2
15	Hewlett, Mrs. (Mary D Kingcome)	2
17	Williams, Mr. Charles Eugene	2
20	Fynney, Mr. Joseph J	2
21	Beesley, Mr. Lawrence	2
...	...	...
866	Duran y More, Miss. Asuncion	2
874	Abelson, Mrs. Samuel (Hannah Wizosky)	2
880	Shelley, Mrs. William (Imanita Parrish Hall)	2
883	Banfield, Mr. Frederick James	2
886	Montvila, Rev. Juozas	2

184 rows × 2 columns

In [31]:

titanic_df.loc[titanic_df['Age'] > 60][['Name', 'Age']].head(3)

Out[31]:

	Name	Age
33	Wheadon, Mr. Edward H	66.0
54	Ostby, Mr. Engelhart Cornelius	65.0
96	Goldschmidt, Mr. George B	71.0

In [33]:

titanic_df[ (titanic_df['Age'] > 60) & (titanic_df['Pclass'] > 2) ]

Out[33]:

	PassengerId	Survived	Pclass	Name	Sex	Age	Ticket	Fare	Cabin	Embarked
116	117	0	3	Connors, Mr. Patrick	male	70.5	370369	7.7500	NaN	Q
280	281	0	3	Duane, Mr. Frank	male	65.0	336439	7.7500	NaN	Q
326	327	0	3	Nysveen, Mr. Johan Hansen	male	61.0	345364	6.2375	NaN	S
483	484	1	3	Turkula, Mrs. (Hedwig)	female	63.0	4134	9.5875	NaN	S
851	852	0	3	Svensson, Mr. Johan	male	74.0	347060	7.7750	NaN	S

In [39]:

titanic_df[ (titanic_df['Age'] > 60) & (titanic_df['Pclass'] == 1) & (titanic_df['Sex'] == 'female') ]

Out[39]:

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
275	276	1	1	Andrews, Miss. Kornelia Theodosia	female	63.0	1	0	13502	77.9583	D7	S
829	830	1	1	Stone, Mrs. George Nelson (Martha Evelyn)	female	62.0	0	0	113572	80.0000	B28	NaN

In [40]:

# 개별 조건을 변수에 할당하고 다시 결합하는 것도 가능
cond1 = titanic_df['Age'] > 60
cond2 = titanic_df['Pclass'] == 1
cond3 = titanic_df['Sex'] == 'female'
titanic_df[cond1 & cond2 & cond3]

Out[40]:

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
275	276	1	1	Andrews, Miss. Kornelia Theodosia	female	63.0	1	0	13502	77.9583	D7	S
829	830	1	1	Stone, Mrs. George Nelson (Martha Evelyn)	female	62.0	0	0	113572	80.0000	B28	NaN

티스토리

판다스 pandas (4) - indexing, iloc, loc, boolean

판다스 pandas (4) - indexing, iloc, loc, boolean

데이터 셀렉션 및 필터링¶

iloc[ ] 연산자 >> 위치 기반 인덱싱¶

loc[ ] 연산자 >> 명칭 기반 인덱싱¶

불린 인덱싱¶