5. 연산
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작성자 관리자 댓글 0건 조회 3,107회 작성일 20-01-23 20:12본문
5. 연산
색인이 다른 객체를 더하는 산술연산
실습1.
from pandas import Series, DataFrame
import pandas as pd
import numpy as np
s1 = Series([5,6,-1,2], index=['a','c','d','e'])
s2 = Series([3,4,-1,2,7], index=['a','c','e','f','g'])
print(s1+s2)
[결과]
a 8.0
c 10.0
d NaN
e 1.0
f NaN
g NaN
dtype: float64
실습2.
from pandas import Series, DataFrame
import pandas as pd
import numpy as np
df1=DataFrame(np.arange(9).reshape(3,3), columns=list('bcd'),
index=['seoul', 'busan', 'kwangju'])
df2=DataFrame(np.arange(12).reshape(4,3), columns=list('bde'),
index=['incheon','seoul','busan','suwon'])
print(df1)
print(df2)
print(df1+df2)
[결과]
b c d
seoul 0 1 2
busan 3 4 5
kwangju 6 7 8
b d e
incheon 0 1 2
seoul 3 4 5
busan 6 7 8
suwon 9 10 11
b c d e
busan 9.0 NaN 12.0 NaN
incheon NaN NaN NaN NaN
kwangju NaN NaN NaN NaN
seoul 3.0 NaN 6.0 NaN
suwon NaN NaN NaN NaN
실습3.
from pandas import Series, DataFrame
import pandas as pd
import numpy as np
df3 = DataFrame(np.arange(12).reshape(3,4), columns=list('abcd'))
df4 = DataFrame(np.arange(20).reshape(4,5), columns=list('abcde'))
print(df3)
print(df4)
print(df3+df4)
print(df3.add(df4, fill_value=0))
print(df3.reindex(columns=df4.columns, fill_value=0))
[결과]
a b c d
0 0 1 2 3
1 4 5 6 7
2 8 9 10 11
a b c d e
0 0 1 2 3 4
1 5 6 7 8 9
2 10 11 12 13 14
3 15 16 17 18 19
a b c d e
0 0.0 2.0 4.0 6.0 NaN
1 9.0 11.0 13.0 15.0 NaN
2 18.0 20.0 22.0 24.0 NaN
3 NaN NaN NaN NaN NaN
a b c d e
0 0.0 2.0 4.0 6.0 4.0
1 9.0 11.0 13.0 15.0 9.0
2 18.0 20.0 22.0 24.0 14.0
3 15.0 16.0 17.0 18.0 19.0
a b c d e
0 0 1 2 3 0
1 4 5 6 7 0
2 8 9 10 11 0
DataFrame과 Series간의 연산
(Numpy의 브로드캐스팅과 유사하다.)
실습4.
from pandas import Series, DataFrame
import pandas as pd
import numpy as np
arr = np.arange(12.).reshape(3,4)
print(arr)
print(arr[0])
print(arr - arr[0])
[결과]
[[ 0. 1. 2. 3.]
[ 4. 5. 6. 7.]
[ 8. 9. 10. 11.]]
[0. 1. 2. 3.]
[[0. 0. 0. 0.]
[4. 4. 4. 4.]
[8. 8. 8. 8.]]
실습5.
from pandas import Series, DataFrame
import pandas as pd
import numpy as np
df = DataFrame(np.arange(12).reshape(4,3), columns=list('bde'),
index=['seoul', 'kwangju', 'daegu', 'incheon'])
print(df)
s1 = df.iloc[0]
print(s1)
print(df-s1)
[결과]
b d e
seoul 0 1 2
kwangju 3 4 5
daegu 6 7 8
incheon 9 10 11
b 0
d 1
e 2
Name: seoul, dtype: int32
b d e
seoul 0 0 0
kwangju 3 3 3
daegu 6 6 6
incheon 9 9 9
실습6.
from pandas import Series, DataFrame
import pandas as pd
import numpy as np
df = DataFrame(np.arange(12).reshape(4,3), columns=list('bde'),
index=['seoul', 'kwangju', 'daegu', 'incheon'])
print(df)
s2 = Series(range(3), index=list('bef'))
print(s2)
print(df+s2)
[결과]
b d e
seoul 0 1 2
kwangju 3 4 5
daegu 6 7 8
incheon 9 10 11
b 0
e 1
f 2
dtype: int64
b d e f
seoul 0.0 NaN 3.0 NaN
kwangju 3.0 NaN 6.0 NaN
daegu 6.0 NaN 9.0 NaN
incheon 9.0 NaN 12.0 NaN
실습7.
from pandas import Series, DataFrame
import pandas as pd
import numpy as np
df = DataFrame(np.arange(12).reshape(4,3), columns=list('bde'),
index=['seoul', 'kwangju', 'daegu', 'incheon'])
print(df)
s3 = df['d']
print(s3)
print(df+s3)
[결과]
b d e
seoul 0 1 2
kwangju 3 4 5
daegu 6 7 8
incheon 9 10 11
seoul 1
kwangju 4
daegu 7
incheon 10
Name: d, dtype: int32
b d daegu e incheon kwangju seoul
seoul NaN NaN NaN NaN NaN NaN NaN
kwangju NaN NaN NaN NaN NaN NaN NaN
daegu NaN NaN NaN NaN NaN NaN NaN
incheon NaN NaN NaN NaN NaN NaN NaN
실습8.
from pandas import Series, DataFrame
import pandas as pd
import numpy as np
df = DataFrame(np.arange(12).reshape(4,3), columns=list('bde'),
index=['seoul', 'kwangju', 'daegu', 'incheon'])
print(df)
s3 = df['d']
print(s3)
#행에 대한 연산을 수행할 경우에는 함수를 이용한다.
print(df.add(s3, axis=0))
print(df.sub(s3, axis=0))
[결과]
b d e
seoul 0 1 2
kwangju 3 4 5
daegu 6 7 8
incheon 9 10 11
seoul 1
kwangju 4
daegu 7
incheon 10
Name: d, dtype: int32
b d e
seoul 1 2 3
kwangju 7 8 9
daegu 13 14 15
incheon 19 20 21
b d e
seoul -1 0 1
kwangju -1 0 1
daegu -1 0 1
incheon -1 0 1
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