数据挖掘 pandas基础入门之查看数据

2024年2月5日 191次阅读 来源: 数据库基础 
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

 

    原文作者：数据库基础
    原文地址: https://my.oschina.net/gain/blog/1812981
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