pandas
모양새 변경 및 피봇 팅
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단순 피벗
먼저 pivot 사용해보십시오.
import pandas as pd
import numpy as np
df = pd.DataFrame({'Name':['Mary', 'Josh','Jon','Lucy', 'Jane', 'Sue'],
'Age':[34, 37, 29, 40, 29, 31],
'City':['Boston','New York', 'Chicago', 'Los Angeles', 'Chicago', 'Boston'],
'Position':['Manager','Programmer','Manager','Manager','Programmer', 'Programmer']},
columns=['Name','Position','City','Age'])
print (df)
Name Position City Age
0 Mary Manager Boston 34
1 Josh Programmer New York 37
2 Jon Manager Chicago 29
3 Lucy Manager Los Angeles 40
4 Jane Programmer Chicago 29
5 Sue Programmer Boston 31
print (df.pivot(index='Position', columns='City', values='Age'))
City Boston Chicago Los Angeles New York
Position
Manager 34.0 29.0 40.0 NaN
Programmer 31.0 29.0 NaN 37.0
인덱스를 재설정해야하는 경우 열 이름을 제거하고 NaN 값을 채 웁니다.
#pivoting by numbers - column Age
print (df.pivot(index='Position', columns='City', values='Age')
.reset_index()
.rename_axis(None, axis=1)
.fillna(0))
Position Boston Chicago Los Angeles New York
0 Manager 34.0 29.0 40.0 0.0
1 Programmer 31.0 29.0 0.0 37.0
#pivoting by strings - column Name
print (df.pivot(index='Position', columns='City', values='Name'))
City Boston Chicago Los Angeles New York
Position
Manager Mary Jon Lucy None
Programmer Sue Jane None Josh
집계를 사용하여 피봇 팅
import pandas as pd
import numpy as np
df = pd.DataFrame({'Name':['Mary', 'Jon','Lucy', 'Jane', 'Sue', 'Mary', 'Lucy'],
'Age':[35, 37, 40, 29, 31, 26, 28],
'City':['Boston', 'Chicago', 'Los Angeles', 'Chicago', 'Boston', 'Boston', 'Chicago'],
'Position':['Manager','Manager','Manager','Programmer', 'Programmer','Manager','Manager'],
'Sex':['Female','Male','Female','Female', 'Female','Female','Female']},
columns=['Name','Position','City','Age','Sex'])
print (df)
Name Position City Age Sex
0 Mary Manager Boston 35 Female
1 Jon Manager Chicago 37 Male
2 Lucy Manager Los Angeles 40 Female
3 Jane Programmer Chicago 29 Female
4 Sue Programmer Boston 31 Female
5 Mary Manager Boston 26 Female
6 Lucy Manager Chicago 28 Female
pivot 사용하는 경우 오류가 발생합니다.
print (df.pivot(index='Position', columns='City', values='Age'))
ValueError : 중복 항목이 포함 된 인덱스로 모양을 바꿀 수 없습니다.
함수 집계와 함께 pivot_table 을 사용하십시오.
#default aggfunc is np.mean
print (df.pivot_table(index='Position', columns='City', values='Age'))
City Boston Chicago Los Angeles
Position
Manager 30.5 32.5 40.0
Programmer 31.0 29.0 NaN
print (df.pivot_table(index='Position', columns='City', values='Age', aggfunc=np.mean))
City Boston Chicago Los Angeles
Position
Manager 30.5 32.5 40.0
Programmer 31.0 29.0 NaN
또 다른 기능 :
print (df.pivot_table(index='Position', columns='City', values='Age', aggfunc=sum))
City Boston Chicago Los Angeles
Position
Manager 61.0 65.0 40.0
Programmer 31.0 29.0 NaN
#lost data !!!
print (df.pivot_table(index='Position', columns='City', values='Age', aggfunc='first'))
City Boston Chicago Los Angeles
Position
Manager 35.0 37.0 40.0
Programmer 31.0 29.0 NaN
string 값이있는 열로 집계해야하는 경우 :
print (df.pivot_table(index='Position', columns='City', values='Name'))
DataError : 집계 할 숫자 유형이 없습니다.
다음과 같은 aggragating 함수를 사용할 수 있습니다.
print (df.pivot_table(index='Position', columns='City', values='Name', aggfunc='first'))
City Boston Chicago Los Angeles
Position
Manager Mary Jon Lucy
Programmer Sue Jane None
print (df.pivot_table(index='Position', columns='City', values='Name', aggfunc='last'))
City Boston Chicago Los Angeles
Position
Manager Mary Lucy Lucy
Programmer Sue Jane None
print (df.pivot_table(index='Position', columns='City', values='Name', aggfunc='sum'))
City Boston Chicago Los Angeles
Position
Manager MaryMary JonLucy Lucy
Programmer Sue Jane None
print (df.pivot_table(index='Position', columns='City', values='Name', aggfunc=', '.join))
City Boston Chicago Los Angeles
Position
Manager Mary, Mary Jon, Lucy Lucy
Programmer Sue Jane None
print (df.pivot_table(index='Position', columns='City', values='Name', aggfunc=', '.join, fill_value='-')
.reset_index()
.rename_axis(None, axis=1))
Position Boston Chicago Los Angeles
0 Manager Mary, Mary Jon, Lucy Lucy
1 Programmer Sue Jane -
성에 관한 정보는 아직 사용되지 않았습니다. 열 중 하나를 사용하여 전환하거나 다른 수준으로 추가 할 수 있습니다.
print (df.pivot_table(index='Position', columns=['City','Sex'], values='Age', aggfunc='first'))
City Boston Chicago Los Angeles
Sex Female Female Male Female
Position
Manager 35.0 28.0 37.0 40.0
Programmer 31.0 29.0 NaN NaN
색인, 열 및 값 속성 중 하나에서 여러 열을 지정할 수 있습니다.
print (df.pivot_table(index=['Position','Sex'], columns='City', values='Age', aggfunc='first'))
City Boston Chicago Los Angeles
Position Sex
Manager Female 35.0 28.0 40.0
Male NaN 37.0 NaN
Programmer Female 31.0 29.0 NaN
여러 집계 함수 적용
단일 피벗 중에 여러 기능을 손쉽게 적용 할 수 있습니다.
In [23]: import numpy as np
In [24]: df.pivot_table(index='Position', values='Age', aggfunc=[np.mean, np.std])
Out[24]:
mean std
Position
Manager 34.333333 5.507571
Programmer 32.333333 4.163332
경우에 따라 특정 열에 특정 기능을 적용 할 수 있습니다.
In [35]: df['Random'] = np.random.random(6)
In [36]: df
Out[36]:
Name Position City Age Random
0 Mary Manager Boston 34 0.678577
1 Josh Programmer New York 37 0.973168
2 Jon Manager Chicago 29 0.146668
3 Lucy Manager Los Angeles 40 0.150120
4 Jane Programmer Chicago 29 0.112769
5 Sue Programmer Boston 31 0.185198
For example, find the mean age, and standard deviation of random by Position:
In [37]: df.pivot_table(index='Position', aggfunc={'Age': np.mean, 'Random': np.std})
Out[37]:
Age Random
Position
Manager 34.333333 0.306106
Programmer 32.333333 0.477219
하나의 함수 목록을 전달하여 개별 열에 적용 할 수도 있습니다.
In [38]: df.pivot_table(index='Position', aggfunc={'Age': np.mean, 'Random': [np.mean, np.std]})]
Out[38]:
Age Random
mean mean std
Position
Manager 34.333333 0.325122 0.306106
Programmer 32.333333 0.423712 0.477219
스태킹 및 언 스태킹
import pandas as pd
import numpy as np
np.random.seed(0)
tuples = list(zip(*[['bar', 'bar', 'foo', 'foo', 'qux', 'qux'],
['one', 'two', 'one', 'two','one', 'two']]))
idx = pd.MultiIndex.from_tuples(tuples, names=['first', 'second'])
df = pd.DataFrame(np.random.randn(6, 2), index=idx, columns=['A', 'B'])
print (df)
A B
first second
bar one 1.764052 0.400157
two 0.978738 2.240893
foo one 1.867558 -0.977278
two 0.950088 -0.151357
qux one -0.103219 0.410599
two 0.144044 1.454274
print (df.stack())
first second
bar one A 1.764052
B 0.400157
two A 0.978738
B 2.240893
foo one A 1.867558
B -0.977278
two A 0.950088
B -0.151357
qux one A -0.103219
B 0.410599
two A 0.144044
B 1.454274
dtype: float64
#reset index, rename column name
print (df.stack().reset_index(name='val2').rename(columns={'level_2': 'val1'}))
first second val1 val2
0 bar one A 1.764052
1 bar one B 0.400157
2 bar two A 0.978738
3 bar two B 2.240893
4 foo one A 1.867558
5 foo one B -0.977278
6 foo two A 0.950088
7 foo two B -0.151357
8 qux one A -0.103219
9 qux one B 0.410599
10 qux two A 0.144044
11 qux two B 1.454274
print (df.unstack())
A B
second one two one two
first
bar 1.764052 0.978738 0.400157 2.240893
foo 1.867558 0.950088 -0.977278 -0.151357
qux -0.103219 0.144044 0.410599 1.454274
rename_axis (새 pandas 0.18.0 ) :
#reset index, remove columns names
df1 = df.unstack().reset_index().rename_axis((None,None), axis=1)
#reset MultiIndex in columns with list comprehension
df1.columns = ['_'.join(col).strip('_') for col in df1.columns]
print (df1)
first A_one A_two B_one B_two
0 bar 1.764052 0.978738 0.400157 2.240893
1 foo 1.867558 0.950088 -0.977278 -0.151357
2 qux -0.103219 0.144044 0.410599 1.454274
팬더 0.18.0
#reset index
df1 = df.unstack().reset_index()
#remove columns names
df1.columns.names = (None, None)
#reset MultiIndex in columns with list comprehension
df1.columns = ['_'.join(col).strip('_') for col in df1.columns]
print (df1)
first A_one A_two B_one B_two
0 bar 1.764052 0.978738 0.400157 2.240893
1 foo 1.867558 0.950088 -0.977278 -0.151357
2 qux -0.103219 0.144044 0.410599 1.454274
교차 도표화
import pandas as pd
df = pd.DataFrame({'Sex': ['M', 'M', 'F', 'M', 'F', 'F', 'M', 'M', 'F', 'F'],
'Age': [20, 19, 17, 35, 22, 22, 12, 15, 17, 22],
'Heart Disease': ['Y', 'N', 'Y', 'N', 'N', 'Y', 'N', 'Y', 'N', 'Y']})
df
Age Heart Disease Sex
0 20 Y M
1 19 N M
2 17 Y F
3 35 N M
4 22 N F
5 22 Y F
6 12 N M
7 15 Y M
8 17 N F
9 22 Y F
pd.crosstab(df['Sex'], df['Heart Disease'])
Hearth Disease N Y
Sex
F 2 3
M 3 2
점 표기법 사용 :
pd.crosstab(df.Sex, df.Age)
Age 12 15 17 19 20 22 35
Sex
F 0 0 2 0 0 3 0
M 1 1 0 1 1 0 1
DF 전치 가져 오기 :
pd.crosstab(df.Sex, df.Age).T
Sex F M
Age
12 0 1
15 0 1
17 2 0
19 0 1
20 0 1
22 3 0
35 0 1
여백 또는 cumulatives 받기 :
pd.crosstab(df['Sex'], df['Heart Disease'], margins=True)
Heart Disease N Y All
Sex
F 2 3 5
M 3 2 5
All 5 5 10
누적의 전치를 얻기 :
pd.crosstab(df['Sex'], df['Age'], margins=True).T
Sex F M All
Age
12 0 1 1
15 0 1 1
17 2 0 2
19 0 1 1
20 0 1 1
22 3 0 3
35 0 1 1
All 5 5 10
백분율 얻기 :
pd.crosstab(df["Sex"],df['Heart Disease']).apply(lambda r: r/len(df), axis=1)
Heart Disease N Y
Sex
F 0.2 0.3
M 0.3 0.2
누적되고 100 배 증가 :
df2 = pd.crosstab(df["Age"],df['Sex'], margins=True ).apply(lambda r: r/len(df)*100, axis=1)
df2
Sex F M All
Age
12 0.0 10.0 10.0
15 0.0 10.0 10.0
17 20.0 0.0 20.0
19 0.0 10.0 10.0
20 0.0 10.0 10.0
22 30.0 0.0 30.0
35 0.0 10.0 10.0
All 50.0 50.0 100.0
DF에서 열 제거 (편도) :
df2[["F","M"]]
Sex F M
Age
12 0.0 10.0
15 0.0 10.0
17 20.0 0.0
19 0.0 10.0
20 0.0 10.0
22 30.0 0.0
35 0.0 10.0
All 50.0 50.0
판다 스는 녹아서 길게 갈라진다.
>>> df
ID Year Jan_salary Feb_salary Mar_salary
0 1 2016 4500 4200 4700
1 2 2016 3800 3600 4400
2 3 2016 5500 5200 5300
>>> melted_df = pd.melt(df,id_vars=['ID','Year'],
value_vars=['Jan_salary','Feb_salary','Mar_salary'],
var_name='month',value_name='salary')
>>> melted_df
ID Year month salary
0 1 2016 Jan_salary 4500
1 2 2016 Jan_salary 3800
2 3 2016 Jan_salary 5500
3 1 2016 Feb_salary 4200
4 2 2016 Feb_salary 3600
5 3 2016 Feb_salary 5200
6 1 2016 Mar_salary 4700
7 2 2016 Mar_salary 4400
8 3 2016 Mar_salary 5300
>>> melted_['month'] = melted_['month'].str.replace('_salary','')
>>> import calendar
>>> def mapper(month_abbr):
... # from http://stackoverflow.com/a/3418092/42346
... d = {v: str(k).zfill(2) for k,v in enumerate(calendar.month_abbr)}
... return d[month_abbr]
>>> melted_df['month'] = melted_df['month'].apply(mapper)
>>> melted_df
ID Year month salary
0 1 2016 01 4500
1 2 2016 01 3800
2 3 2016 01 5500
3 1 2016 02 4200
4 2 2016 02 3600
5 3 2016 02 5200
6 1 2016 03 4700
7 2 2016 03 4400
8 3 2016 03 5300
열의 CSV 문자열을 여러 행으로 분할 (모양을 바꾸기), 행당 하나의 요소 사용
import pandas as pd
df = pd.DataFrame([{'var1': 'a,b,c', 'var2': 1, 'var3': 'XX'},
{'var1': 'd,e,f,x,y', 'var2': 2, 'var3': 'ZZ'}])
print(df)
reshaped = \
(df.set_index(df.columns.drop('var1',1).tolist())
.var1.str.split(',', expand=True)
.stack()
.reset_index()
.rename(columns={0:'var1'})
.loc[:, df.columns]
)
print(reshaped)
산출:
var1 var2 var3
0 a,b,c 1 XX
1 d,e,f,x,y 2 ZZ
var1 var2 var3
0 a 1 XX
1 b 1 XX
2 c 1 XX
3 d 2 ZZ
4 e 2 ZZ
5 f 2 ZZ
6 x 2 ZZ
7 y 2 ZZ
Modified text is an extract of the original Stack Overflow Documentation
아래 라이선스 CC BY-SA 3.0
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