数据挖掘 pandas基础入门之查看数据
2018-05-15 16:55
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import pandas import numpy # 通过传递一个 numpyarray,时间索引以及列标签来创建一个DataFrame: dates = pandas.date_range("20180509", periods=6) df = pandas.DataFrame(numpy.random.randn(6, 4), index=dates, columns=list('ABCD')) print("时间索引以及列标签来创建一个DataFrame:", df, sep="\n") # 查看DataFrame中头部和尾部的行 print("头部行: ", df.head(), sep="\n") # 不给定head()参数时,默认除最后一行都是头部 print("尾部行: ", df.tail(), sep="\n") # 不给定tail()参数时,默认除第一行都是尾部 print("头部行第一行: ", df.head(1), sep="\n") print("尾部行最后一行: ", df.tail(1), sep="\n") # 显示索引、列和底层的numpy数据 print("索引:", df.index, sep="\n") print("列:", df.columns, sep="\n") print("值:", df.values, sep="\n") # describe()函数对于数据的快速统计汇总 print("数据统计:", df.describe(), sep="\n") # 对数据的转置 print("对数据的转置: ", df.T, sep="\n") # 按轴进行排序 print("按轴进行排序: ", df.sort_index(axis=0, ascending=False), sep="\n") # ascending 是否自增 # 按值进行排序 print("按值进行排序: ", df.sort_values(by='B'), sep="\n")
"E:\Python 3.6.2\python.exe" F:/PycharmProjects/test.py 时间索引以及列标签来创建一个DataFrame: A B C D 2018-05-09 -1.900068 -0.208794 -0.523035 1.240455 2018-05-10 1.512279 -2.283494 0.608609 1.027053 2018-05-11 -3.320670 -0.260807 0.508715 0.662909 2018-05-12 0.338343 -1.735734 1.500790 -0.959845 2018-05-13 1.990765 0.214486 -1.244937 -0.258515 2018-05-14 -1.044454 0.360775 -0.657407 -0.593493 头部行: A B C D 2018-05-09 -1.900068 -0.208794 -0.523035 1.240455 2018-05-10 1.512279 -2.283494 0.608609 1.027053 2018-05-11 -3.320670 -0.260807 0.508715 0.662909 2018-05-12 0.338343 -1.735734 1.500790 -0.959845 2018-05-13 1.990765 0.214486 -1.244937 -0.258515 尾部行: A B C D 2018-05-10 1.512279 -2.283494 0.608609 1.027053 2018-05-11 -3.320670 -0.260807 0.508715 0.662909 2018-05-12 0.338343 -1.735734 1.500790 -0.959845 2018-05-13 1.990765 0.214486 -1.244937 -0.258515 2018-05-14 -1.044454 0.360775 -0.657407 -0.593493 头部行第一行: A B C D 2018-05-09 -1.900068 -0.208794 -0.523035 1.240455 尾部行最后一行: A B C D 2018-05-14 -1.044454 0.360775 -0.657407 -0.593493 索引: DatetimeIndex(['2018-05-09', '2018-05-10', '2018-05-11', '2018-05-12', '2018-05-13', '2018-05-14'], dtype='datetime64[ns]', freq='D') 列: Index(['A', 'B', 'C', 'D'], dtype='object') 值: [[-1.90006837 -0.20879388 -0.52303491 1.24045481] [ 1.51227925 -2.28349377 0.60860861 1.02705302] [-3.32067045 -0.26080686 0.50871488 0.6629095 ] [ 0.33834299 -1.73573353 1.5007895 -0.95984505] [ 1.99076464 0.21448643 -1.24493715 -0.25851535] [-1.04445367 0.36077537 -0.65740657 -0.59349347]] 数据统计: A B C D count 6.000000 6.000000 6.000000 6.000000 mean -0.403968 -0.652261 0.032122 0.186427 std 2.054919 1.091991 1.013014 0.912672 min -3.320670 -2.283494 -1.244937 -0.959845 25% -1.686165 -1.367002 -0.623814 -0.509749 50% -0.353055 -0.234800 -0.007160 0.202197 75% 1.218795 0.108666 0.583635 0.936017 max 1.990765 0.360775 1.500790 1.240455 对数据的转置: 2018-05-09 2018-05-10 2018-05-11 2018-05-12 2018-05-13 2018-05-14 A -1.900068 1.512279 -3.320670 0.338343 1.990765 -1.044454 B -0.208794 -2.283494 -0.260807 -1.735734 0.214486 0.360775 C -0.523035 0.608609 0.508715 1.500790 -1.244937 -0.657407 D 1.240455 1.027053 0.662909 -0.959845 -0.258515 -0.593493 按轴进行排序: A B C D 2018-05-14 -1.044454 0.360775 -0.657407 -0.593493 2018-05-13 1.990765 0.214486 -1.244937 -0.258515 2018-05-12 0.338343 -1.735734 1.500790 -0.959845 2018-05-11 -3.320670 -0.260807 0.508715 0.662909 2018-05-10 1.512279 -2.283494 0.608609 1.027053 2018-05-09 -1.900068 -0.208794 -0.523035 1.240455 按值进行排序: A B C D 2018-05-10 1.512279 -2.283494 0.608609 1.027053 2018-05-12 0.338343 -1.735734 1.500790 -0.959845 2018-05-11 -3.320670 -0.260807 0.508715 0.662909 2018-05-09 -1.900068 -0.208794 -0.523035 1.240455 2018-05-13 1.990765 0.214486 -1.244937 -0.258515 2018-05-14 -1.044454 0.360775 -0.657407 -0.593493 Process finished with exit code 0
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