python 中 read_table 函数

发布时间 2023-05-17 00:02:17作者: 小鲨鱼2018

 

001、

[root@PC1 test3]# ls
a.txt
[root@PC1 test3]# cat a.txt        ## 测试文件
10      2       3       0       3       6
6       12      1       1       5       1
2       2       2       4       2       26
8       3       33      34      5       3
[root@PC1 test3]# python            ## 启用python
Python 3.11.3 (main, May  9 2023, 00:27:08) [GCC 4.8.5 20150623 (Red Hat 4.8.5-44)] on linux
Type "help", "copyright", "credits" or "license" for more information.

 

002、

>>> import os
>>> import pandas as pd       ## 导入包
>>> os.listdir()              ## 列出文件
['a.txt']
>>> a=pd.read_table("a.txt", sep = "\t", header = None)    ## 读入文件
>>> A
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
NameError: name 'A' is not defined. Did you mean: 'a'?
>>> a
    0   1   2   3  4   5
0  10   2   3   0  3   6
1   6  12   1   1  5   1
2   2   2   2   4  2  26
3   8   3  33  34  5   3

 

003、基本属性

>>> a
    0   1   2   3  4   5
0  10   2   3   0  3   6
1   6  12   1   1  5   1
2   2   2   2   4  2  26
3   8   3  33  34  5   3
>>> type(a)
<class 'pandas.core.frame.DataFrame'>
>>> a.head(2)
    0   1  2  3  4  5
0  10   2  3  0  3  6
1   6  12  1  1  5  1
>>> a.tail(1)
   0  1   2   3  4  5
3  8  3  33  34  5  3
>>> a.max()
0    10
1    12
2    33
3    34
4     5
5    26
dtype: int64
>>> a.shape
(4, 6)
>>> a.dtypes
0    int64
1    int64
2    int64
3    int64
4    int64
5    int64
dtype: object
>>> a.describe()
              0          1         2          3     4          5
count   4.00000   4.000000   4.00000   4.000000  4.00   4.000000
mean    6.50000   4.750000   9.75000   9.750000  3.75   9.000000
std     3.41565   4.856267  15.52149  16.255768  1.50  11.518102
min     2.00000   2.000000   1.00000   0.000000  2.00   1.000000
25%     5.00000   2.000000   1.75000   0.750000  2.75   2.500000
50%     7.00000   2.500000   2.50000   2.500000  4.00   4.500000
75%     8.50000   5.250000  10.50000  11.500000  5.00  11.000000
max    10.00000  12.000000  33.00000  34.000000  5.00  26.000000