已信任 Jupyter 服务器: 本地 Python 3: Not Started [4] import pandas as pd import numpy as np df = pd.DataFrame({ 'date':pd.date_range(start='20210714',periods=7,freq='D'), 'a': np.linspace(0,6,7), 'b': np.random.randn(7), 'c': np.random.choice(['Low','Medium','High'],7).tolist(), 'd': np.random.normal(100,10,size=(7)).tolist() }) df date a b c d 0 2021-07-14 0.0 -0.079268 Low 100.637433 1 2021-07-15 1.0 0.231418 High 112.083560 2 2021-07-16 2.0 0.288950 Medium 108.132161 3 2021-07-17 3.0 0.264166 High 90.819338 4 2021-07-18 4.0 -0.750558 Medium 100.886340 5 2021-07-19 5.0 1.173738 Medium 104.307198 6 2021-07-20 6.0 -0.418391 Low 88.523432 [6] # for in 循环的是列 for col in df: print(col) print(df[col]) date 0 2021-07-14 1 2021-07-15 2 2021-07-16 3 2021-07-17 4 2021-07-18 5 2021-07-19 6 2021-07-20 Name: date, dtype: datetime64[ns] a 0 0.0 1 1.0 2 2.0 3 3.0 4 4.0 5 5.0 6 6.0 Name: a, dtype: float64 b 0 -0.079268 1 0.231418 2 0.288950 3 0.264166 4 -0.750558 5 1.173738 6 -0.418391 Name: b, dtype: float64 c 0 Low 1 High 2 Medium 3 High 4 Medium 5 Medium 6 Low Name: c, dtype: object d 0 100.637433 1 112.083560 2 108.132161 3 90.819338 4 100.886340 5 104.307198 6 88.523432 Name: d, dtype: float64 [8] # iteritem 获取列和值 for key,value in df.iteritems(): print(key) print(value) date 0 2021-07-14 1 2021-07-15 2 2021-07-16 3 2021-07-17 4 2021-07-18 5 2021-07-19 6 2021-07-20 Name: date, dtype: datetime64[ns] a 0 0.0 1 1.0 2 2.0 3 3.0 4 4.0 5 5.0 6 6.0 Name: a, dtype: float64 b 0 -0.079268 1 0.231418 2 0.288950 3 0.264166 4 -0.750558 5 1.173738 6 -0.418391 Name: b, dtype: float64 c 0 Low 1 High 2 Medium 3 High 4 Medium 5 Medium 6 Low Name: c, dtype: object d 0 100.637433 1 112.083560 2 108.132161 3 90.819338 4 100.886340 5 104.307198 6 88.523432 Name: d, dtype: float64 [9] # 按行打印，逐行迭代 for key,value in df.iterrows(): print(key) print(value) 0 date 2021-07-14 00:00:00 a 0 b -0.0792684 c Low d 100.637 Name: 0, dtype: object 1 date 2021-07-15 00:00:00 a 1 b 0.231418 c High d 112.084 Name: 1, dtype: object 2 date 2021-07-16 00:00:00 a 2 b 0.28895 c Medium d 108.132 Name: 2, dtype: object 3 date 2021-07-17 00:00:00 a 3 b 0.264166 c High d 90.8193 Name: 3, dtype: object 4 date 2021-07-18 00:00:00 a 4 b -0.750558 c Medium d 100.886 Name: 4, dtype: object 5 date 2021-07-19 00:00:00 a 5 b 1.17374 c Medium d 104.307 Name: 5, dtype: object 6 date 2021-07-20 00:00:00 a 6 b -0.418391 c Low d 88.5234 Name: 6, dtype: object [12] # 以元组形式打印 for row in df.itertuples(): print(row) Pandas(Index=0, date=Timestamp('2021-07-14 00:00:00'), a=0.0, b=-0.07926836478101182, c='Low', d=100.6374326023984) Pandas(Index=1, date=Timestamp('2021-07-15 00:00:00'), a=1.0, b=0.23141819210674755, c='High', d=112.08356043292231) Pandas(Index=2, date=Timestamp('2021-07-16 00:00:00'), a=2.0, b=0.28895002255434654, c='Medium', d=108.13216066430968) Pandas(Index=3, date=Timestamp('2021-07-17 00:00:00'), a=3.0, b=0.26416569787454686, c='High', d=90.81933760723473) Pandas(Index=4, date=Timestamp('2021-07-18 00:00:00'), a=4.0, b=-0.7505580643324384, c='Medium', d=100.88634049762355) Pandas(Index=5, date=Timestamp('2021-07-19 00:00:00'), a=5.0, b=1.1737384361425682, c='Medium', d=104.30719772518808) Pandas(Index=6, date=Timestamp('2021-07-20 00:00:00'), a=6.0, b=-0.41839064630765915, c='Low', d=88.52343226534083) [-]