판다스 pandas (5) - aggregation, groupby

 

정렬, Aggregation 함수, GroupBy 적용¶

 

 

sort_values( )¶

• 주요 파라미터: by='칼럼명', ascending=True(기본 오름차순), inplace=False(호출한 df는 그대로 유지하면서 정렬된 df를 결과로 반환)

 

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 [13]:

titanic_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

 

 

 

In [7]:

titanic_sorted = titanic_df.sort_values(by=['Name'])
titanic_sorted.head(3)

 

Out[7]:

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
845	846	0	3	Abbing, Mr. Anthony	male	42.0	0	0	C.A. 5547	7.55	NaN	S
746	747	0	3	Abbott, Mr. Rossmore Edward	male	16.0	1	1	C.A. 2673	20.25	NaN	S
279	280	1	3	Abbott, Mrs. Stanton (Rosa Hunt)	female	35.0	1	1	C.A. 2673	20.25	NaN	S

 

 

 

In [9]:

titanic_sorted = titanic_df.sort_values(by=['Name', 'Fare'], ascending=False)
titanic_sorted.head(3)

 

Out[9]:

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
868	869	0	3	van Melkebeke, Mr. Philemon	male	NaN	0	0	345777	9.5000	NaN	S
153	154	0	3	van Billiard, Mr. Austin Blyler	male	40.5	0	2	A/5. 851	14.5000	NaN	S
361	362	0	2	del Carlo, Mr. Sebastiano	male	29.0	1	0	SC/PARIS 2167	27.7208	NaN	C

 

 

 

 

 

 

Aggregation 함수 적용¶

• min, max, sum, mean, count와 같은 함수

• df에 count()를 적용하면 모든 칼럼에 count() 결과를 반환, 대신 count는 Null값을 반영하지 않음

• 때문에 Null 값이 있는 칼럼은 count() 결과값이 다름

 

In [14]:

titanic_df.count()

 

Out[14]:

PassengerId    891
Survived       891
Pclass         891
Name           891
Sex            891
Age            714
SibSp          891
Parch          891
Ticket         891
Fare           891
Cabin          204
Embarked       889
dtype: int64

 

 

 

In [15]:

titanic_df[['Age', 'Fare']].mean()

 

Out[15]:

Age     29.699118
Fare    32.204208
dtype: float64

 

 

 

 

 

 

 

groupby( ) 적용¶

• groupby(by='칼럼명')

 

In [20]:

titanic_groupby = titanic_df.groupby(by='Pclass')
titanic_groupby

 

Out[20]:

<pandas.core.groupby.generic.DataFrameGroupBy object at 0x000001F88543DC60>

 

 

 

In [23]:

#반환된 결과에 aggregation 함수 적용하면 대상 칼럼을 제외한 모든 칼럼에 aggregation 함수가 적용됌
#특정 칼럼에 대한 min, max, count 등을 알고싶으면 groupby로 필터링 후 함수 적용
titanic_groupby = titanic_df.groupby(by='Pclass').count()
titanic_groupby

 

Out[23]:

	PassengerId	Survived	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
Pclass
1	216	216	216	216	186	216	216	216	216	176	214
2	184	184	184	184	173	184	184	184	184	16	184
3	491	491	491	491	355	491	491	491	491	12	491

 

 

 

In [24]:

titanic_groupby = titanic_df.groupby(by='Pclass')['PassengerId', 'Survived'].count()
titanic_groupby

 

 

C:\Users\Administrator\AppData\Local\Temp\ipykernel_10184\3732055399.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.
  titanic_groupby = titanic_df.groupby(by='Pclass')['PassengerId', 'Survived'].count()

Out[24]:

	PassengerId	Survived
Pclass
1	216	216
2	184	184
3	491	491

 

 

 

In [26]:

titanic_df.groupby('Pclass')['Age'].agg([min, max])

 

Out[26]:

	min	max
Pclass
1	0.92	80.0
2	0.67	70.0
3	0.42	74.0

 

 

 

In [27]:

#agg( )내에 딕셔너리 형태로 aggregation이 적용될 칼럼들과 함수 입력
agg_format = {'Age':'max', 'SibSp':'sum', 'Fare':'mean'}
titanic_df.groupby('Pclass').agg(agg_format)

 

Out[27]:

	Age	SibSp	Fare
Pclass
1	80.0	90	84.154687
2	70.0	74	20.662183
3	74.0	302	13.675550