Pandas 데이터분석 기초 실습-6

2021. 9. 22. 17:20ㆍ빅데이터 스터디

팬더스기초6

열 정보로 데이터 그룹생성하기¶

group by¶

In [1]:

import pandas as pd

In [2]:

student_list = [
    {'name':'John','major':'computer science','sex':'male'},
    {'name':'Nate','major':'computer science','sex':'male'},
    {'name':'Abraham','major':'Physics','sex':'male'},
    {'name':'Brian','major':'Psychology','sex':'male'},
    {'name':'Janny','major':'Economics','sex':'female'},
    {'name':'Yuna','major':'Economics','sex':'female'},
    {'name':'Jeniffer','major':'computer science','sex':'female'},
    {'name':'Edward','major':'computer science','sex':'male'},
    {'name':'Zara','major':'Psycology','sex':'female'},
    {'name':'Wendy','major':'Economics','sex':'female'},
    {'name':'Sera','major':'Psycology','sex':'female'},
]
df = pd.DataFrame(student_list, columns=['name','major','sex'])
df

Out[2]:

	name	major	sex
0	John	computer science	male
1	Nate	computer science	male
2	Abraham	Physics	male
3	Brian	Psychology	male
4	Janny	Economics	female
5	Yuna	Economics	female
6	Jeniffer	computer science	female
7	Edward	computer science	male
8	Zara	Psycology	female
9	Wendy	Economics	female
10	Sera	Psycology	female

In [21]:

#학과 별로 몇명있는지 알기
groupby_major = df.groupby('major')
groupby_major.groups

Out[21]:

{'Economics': [4, 5, 9], 'Physics': [2], 'Psychology': [3], 'Psycology': [8, 10], 'computer science': [0, 1, 6, 7]}

In [19]:

for name, group in groupby_major:
    print(name+' : '+str(len(group)))
    print(group)
    print()

Economics : 3
    name      major     sex
4  Janny  Economics  female
5   Yuna  Economics  female
9  Wendy  Economics  female

Physics : 1
      name    major   sex
2  Abraham  Physics  male

Psychology : 1
    name       major   sex
3  Brian  Psychology  male

Psycology : 2
    name      major     sex
8   Zara  Psycology  female
10  Sera  Psycology  female

computer science : 4
       name             major     sex
0      John  computer science    male
1      Nate  computer science    male
6  Jeniffer  computer science  female
7    Edward  computer science    male

In [24]:

df_major_cnt =pd.DataFrame( {'count':groupby_major.size()} ).reset_index()
df_major_cnt
#major를 column으로 만들기 .reset_index()

Out[24]:

	major	count
0	Economics	3
1	Physics	1
2	Psychology	1
3	Psycology	2
4	computer science	4

In [25]:

groupby_sex = df.groupby('sex')

In [28]:

for name, group in groupby_sex:
    print(name + ' : '+ str(len(group)))
    print(group)
    print()
#성별로 나눈 그룹

female : 6
        name             major     sex
4      Janny         Economics  female
5       Yuna         Economics  female
6   Jeniffer  computer science  female
8       Zara         Psycology  female
9      Wendy         Economics  female
10      Sera         Psycology  female

male : 5
      name             major   sex
0     John  computer science  male
1     Nate  computer science  male
2  Abraham           Physics  male
3    Brian        Psychology  male
7   Edward  computer science  male

중복 데이터 제거하기¶

drop duplicate¶

In [29]:

student_list =[
    {
        'name':'John',
        'major':'computer science',
        'sex':'male'
    },
    {
        'name':'Nate',
        'major':'computer science',
        'sex':'male'
    },
    {
        'name':'Edward',
        'major':'computer science',
        'sex':'male'
    },
    {
        'name':'Zara',
        'major':'psychology',
        'sex':'female'
    },
    {
        'name':'John',
        'major':'computer science',
        'sex':'male'
    }
]
df = pd.DataFrame(student_list,columns= ['name','major','sex'])
df

Out[29]:

	name	major	sex
0	John	computer science	male
1	Nate	computer science	male
2	Edward	computer science	male
3	Zara	psychology	female
4	John	computer science	male

In [31]:

df.duplicated() #중복여부확인
#John이 중복

Out[31]:

0    False
1    False
2    False
3    False
4     True
dtype: bool

In [32]:

df.drop_duplicates()    #중복제거

Out[32]:

	name	major	sex
0	John	computer science	male
1	Nate	computer science	male
2	Edward	computer science	male
3	Zara	psychology	female

중복값 제거하기¶

by column value¶

In [34]:

student_list = [
    {'name':'John',
    'major':'computer science',
    'sex':'male'},
    {'name':'Nate',
    'major':'computer science',
    'sex':'male'},
    {'name':'Edward',
    'major':'computer science',
    'sex':'male'},
    {'name':'Zara',
    'major':'Psycology',
    'sex':'female'
    },
    {'name':'Wendy',
    'major':'economics',
    'sex':'female'},
    {'name':'Nate',
    'major':None,
    'sex':'male'},
    {'name':'John',
    'major':'economics',
    'sex':'male'}
]
df = pd.DataFrame(student_list,columns = ['name','major','sex'])
df

Out[34]:

	name	major	sex
0	John	computer science	male
1	Nate	computer science	male
2	Edward	computer science	male
3	Zara	Psycology	female
4	Wendy	economics	female
5	Nate	None	male
6	John	economics	male

In [36]:

df.duplicated() #정확하게 일치하는 row값이 없으므로 False만 나온다

Out[36]:

0    False
1    False
2    False
3    False
4    False
5    False
6    False
dtype: bool

In [39]:

df.duplicated(['name']) #이름값이 같을경우 같은 값으로 보겠다

Out[39]:

0    False
1    False
2    False
3    False
4    False
5     True
6     True
dtype: bool

In [40]:

df.drop_duplicates(['name'],keep = 'first') #처음 나오는 값만 유지(default)

Out[40]:

	name	major	sex
0	John	computer science	male
1	Nate	computer science	male
2	Edward	computer science	male
3	Zara	Psycology	female
4	Wendy	economics	female

In [41]:

df.drop_duplicates(['name'],keep = 'last')  #나중에 나오는 값을 유지

Out[41]:

	name	major	sex
2	Edward	computer science	male
3	Zara	Psycology	female
4	Wendy	economics	female
5	Nate	None	male
6	John	economics	male

데이터 프레임 NaN값을 발견하고 변경하기¶

In [44]:

import pandas as pd

school_id_list =[
    {'name':'John','job':'teacher','age':40},
    {'name':'Nate','job':'teacher','age':35},
    {'name':'Yuna','job':'teacher','age':37},
    {'name':'Abraham','job':'student','age':10},
    {'name':'Brian','job':'student','age':12},
    {'name':'Janny','job':'student','age':11},
    {'name':'Nate','job':'teacher','age':None},
    {'name':'John','job':'student','age':None}
]

df = pd.DataFrame(school_id_list,columns = ['name','job','age'])
df

Out[44]:

	name	job	age
0	John	teacher	40.0
1	Nate	teacher	35.0
2	Yuna	teacher	37.0
3	Abraham	student	10.0
4	Brian	student	12.0
5	Janny	student	11.0
6	Nate	teacher	NaN
7	John	student	NaN

In [46]:

df.shape    #8row 3column

Out[46]:

(8, 3)

In [47]:

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 8 entries, 0 to 7
Data columns (total 3 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   name    8 non-null      object 
 1   job     8 non-null      object 
 2   age     6 non-null      float64
dtypes: float64(1), object(2)
memory usage: 320.0+ bytes

In [48]:

df.isna()

Out[48]:

	name	job	age
0	False	False	False
1	False	False	False
2	False	False	False
3	False	False	False
4	False	False	False
5	False	False	False
6	False	False	True
7	False	False	True

In [51]:

df.isnull() #isna와 같은기능

Out[51]:

	name	job	age
0	False	False	False
1	False	False	False
2	False	False	False
3	False	False	False
4	False	False	False
5	False	False	False
6	False	False	True
7	False	False	True

In [52]:

#NaN을 0으로 바꾸기
df.age = df.age.fillna(0)
df

Out[52]:

	name	job	age
0	John	teacher	40.0
1	Nate	teacher	35.0
2	Yuna	teacher	37.0
3	Abraham	student	10.0
4	Brian	student	12.0
5	Janny	student	11.0
6	Nate	teacher	0.0
7	John	student	0.0

In [55]:

school_id_list =[
    {'name':'John','job':'teacher','age':40},
    {'name':'Nate','job':'teacher','age':35},
    {'name':'Yuna','job':'teacher','age':37},
    {'name':'Abraham','job':'student','age':10},
    {'name':'Brian','job':'student','age':12},
    {'name':'Janny','job':'student','age':11},
    {'name':'Nate','job':'teacher','age':None},
    {'name':'John','job':'student','age':None}
]

df = pd.DataFrame(school_id_list,columns = ['name','job','age'])
df

Out[55]:

	name	job	age
0	John	teacher	40.0
1	Nate	teacher	35.0
2	Yuna	teacher	37.0
3	Abraham	student	10.0
4	Brian	student	12.0
5	Janny	student	11.0
6	Nate	teacher	NaN
7	John	student	NaN

In [57]:

#medium 값으로 변경 (추천)
df['age'].fillna(df.groupby('job')['age'].transform('median'),inplace = True)
df

Out[57]:

	name	job	age
0	John	teacher	40.0
1	Nate	teacher	35.0
2	Yuna	teacher	37.0
3	Abraham	student	10.0
4	Brian	student	12.0
5	Janny	student	11.0
6	Nate	teacher	37.0
7	John	student	11.0

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