由數(shù)組字典構(gòu)造DataFrame

from pandas import Series,DataFrame import numpy as np import pandas as pd data={'state':['ohio','ohio','ohio','Nevada','Nevada'],'year':[2000,2001,2002,2001,2002],'pop':[1.5,1.7,3.6,2.4,2.9]} frame=DataFrame(data) Out[103]: pop state year 0 1.5 ohio 2000 1 1.7 ohio 2001 2 3.6 ohio 2002 3 2.4 Nevada 2001 4 2.9 Nevada 2002

改變序列順序

DataFrame(data,columns=['year','state','pop'])Out[104]: year state pop 0 2000 ohio 1.5 1 2001 ohio 1.7 2 2002 ohio 3.6 3 2001 Nevada 2.4 4 2002 Nevada 2.9

改變標(biāo)簽，增加列沒(méi)有數(shù)據(jù)，顯示NaN

frame2=DataFrame(data,columns=['year','state','pop','debt'], index=['one','two','three','four','five'])frame2 Out[108]: year state pop debt one 2000 ohio 1.5 NaN two 2001 ohio 1.7 NaN three 2002 ohio 3.6 NaN four 2001 Nevada 2.4 NaN five 2002 Nevada 2.9 NaN frame2.columns Out[111]: Index(['year', 'state', 'pop', 'debt'], dtype='object')

frame2.state=frame2[‘state’] 用法相同

frame2.state Out[112]: one ohio two ohio three ohio four Nevada five Nevada Name: state, dtype: objectframe2['state'] Out[113]: one ohio two ohio three ohio four Nevada five Nevada Name: state, dtype: object

debt賦值

frame2['debt']=16.5frame2 Out[115]: year state pop debt one 2000 ohio 1.5 16.5 two 2001 ohio 1.7 16.5 three 2002 ohio 3.6 16.5 four 2001 Nevada 2.4 16.5 five 2002 Nevada 2.9 16.5 frame2['debt']=np.arange(5) frame2.debt=np.arange(5) frame2 Out[120]: year state pop debt one 2000 ohio 1.5 0 two 2001 ohio 1.7 1 three 2002 ohio 3.6 2 four 2001 Nevada 2.4 3 five 2002 Nevada 2.9 4 val=Series([-1.2,-1.5,-1.7],index=['two','four','five'])frame2['debt']=valframe2 Out[127]: year state pop debt one 2000 ohio 1.5 NaN two 2001 ohio 1.7 -1.2 three 2002 ohio 3.6 NaN four 2001 Nevada 2.4 -1.5 five 2002 Nevada 2.9 -1.7 frame2['eastern']=frame2.state=='ohio'frame2 Out[129]: year state pop debt eastern one 2000 ohio 1.5 NaN True two 2001 ohio 1.7 -1.2 True three 2002 ohio 3.6 NaN True four 2001 Nevada 2.4 -1.5 False five 2002 Nevada 2.9 -1.7 False 刪除列 del frame2['eastern']frame2 Out[131]: year state pop debt one 2000 ohio 1.5 NaN two 2001 ohio 1.7 -1.2 three 2002 ohio 3.6 NaN four 2001 Nevada 2.4 -1.5 five 2002 Nevada 2.9 -1.7frame2.columns Out[132]: Index(['year', 'state', 'pop', 'debt'], dtype='object')

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