πŸ“šνŒŒμ΄μ¬ λ¨Έμ‹ λŸ¬λ‹ νŒλ‹€μŠ€ 데이터뢄석 part2. νŒλ‹€μŠ€ 자료ꡬ쑰 4. μ‚°μˆ μ—°μ‚°

πŸ“„ 211218_4 μ‚°μˆ μ—°μ‚°.ipynb

πŸ«μ‹œλ¦¬μ¦ˆ μ—°μ‚°

πŸ’μ‹œλ¦¬μ¦ˆ vs 숫자

std1=pd.Series({'κ΅­μ–΄':100, 'μ˜μ–΄':80, 'μˆ˜ν•™':90})
std1
κ΅­μ–΄    100
μ˜μ–΄     80
μˆ˜ν•™     90
dtype: int64
pe=student1/100
pe
κ΅­μ–΄    1.0
μ˜μ–΄    0.8
μˆ˜ν•™    0.9
dtype: float64
type(pe)

pandas.core.series.Series

πŸ’μ‹œλ¦¬μ¦ˆ vs μ‹œλ¦¬μ¦ˆ

std2=pd.Series({'μˆ˜ν•™':80, 'κ΅­μ–΄':90, 'μ˜μ–΄':80})
add=std1+std2
sub=std1-std2
mul=std1*std2
div=std1/std2
result=pd.DataFrame([add,sub,mul,div],index=['λ§μ…ˆ','λΊ„μ…ˆ','κ³±μ…ˆ','λ‚˜λˆ„κΈ°'])
result

Untitled

NaN값이 μžˆλŠ” 경우