1.
import pandas as pd web_stats = {'Day':[1,2,3,4,5,6], 'Visitors':[43,34,65,56,29,76], 'Bounce Rate':[65,67,78,65,45,52]} df = pd.DataFrame(web_stats) print(df.head())
输出
Day Visitors Bounce Rate 0 1 43 65 1 2 34 67 2 3 65 78 3 4 56 65 4 5 29 45
2.
import pandas as pd web_stats = {'Day':[1,2,3,4,5,6], 'Visitors':[43,34,65,56,29,76], 'Bounce Rate':[65,67,78,65,45,52]} df = pd.DataFrame(web_stats) #print(df.head()) print(df.tail())
输出:
Day Visitors Bounce Rate 1 2 34 67 2 3 65 78 3 4 56 65 4 5 29 45 5 6 76 52
3.
import pandas as pd web_stats = {'Day':[1,2,3,4,5,6], 'Visitors':[43,34,65,56,29,76], 'Bounce Rate':[65,67,78,65,45,52]} df = pd.DataFrame(web_stats) #print(df.head()) print(df.tail(2))
输出
Day Visitors Bounce Rate 4 5 29 45 5 6 76 52
4.
import pandas as pd import pickle import numpy as np dates=pd.date_range('20180310',periods=6) df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=['A','B','C','D'])#生成6行4列位置 print(df)
输出
A B C D 2018-03-10 0.984919 -0.139348 0.160758 -0.251948 2018-03-11 0.891051 -0.116031 0.491253 0.262518 2018-03-12 -0.922257 0.761505 0.690123 -1.655246 2018-03-13 0.524870 -0.704932 -0.734333 0.619541 2018-03-14 -0.970407 -0.704575 -0.762169 0.829132 2018-03-15 -1.630999 -1.768938 0.744758 -0.521628