Pandas 1 表格数据类型DataFrame

# -*- encoding:utf-8 -*-
# Copyright (c) 2015 Shiye Inc.
# All rights reserved.
#
# Author: ldq <liangduanqi@shiyejinrong.com>
# Date: 2019/2/12 10:07

import numpy as np
import pandas as pd

dates = pd.date_range("", periods=5)
'''
DatetimeIndex(['2019-01-01', '2019-01-02', '2019-01-03', '2019-01-04',
               '2019-01-05', '2019-01-06'],
              dtype='datetime64[ns]', freq='D')
'''
df = pd.DataFrame(np.random.randn(5, 4), index=dates,
                  columns=["a", "b", "c", "d"])
'''
                   a         b         c         d
2019-01-01 -0.406321 -0.518128 -0.151546  1.438366
2019-01-02 -0.738235  0.400646  1.337277  1.393154
2019-01-03  1.646115 -0.073540  0.644506  0.987226
2019-01-04 -1.270745 -1.333457 -1.571356 -0.051486
2019-01-05 -0.075171  2.424032 -0.274433  1.205959
'''
df1 = pd.DataFrame(np.arange(12).reshape(3, 4))
'''
   0  1   2   3
0  0  1   2   3
1  4  5   6   7
2  8  9  10  11
'''
data2 = {
    "a": 1,
    "b": pd.Timestamp(""),
    "c": pd.Series(1, index=range(4), dtype=np.float64),
    "d": np.array([3] * 4, dtype=np.int32),
    "e": pd.Categorical(["test", "train", "test", "train"]),
    "f": "foo",
    "g": pd.date_range("",periods=4),
}
df2 = pd.DataFrame(data2)
'''
   a          b    c  d      e    f          g
0  1 2019-01-01  1.0  3   test  foo 2002-02-05
1  1 2019-01-01  1.0  3  train  foo 2002-02-06
2  1 2019-01-01  1.0  3   test  foo 2002-02-07
3  1 2019-01-01  1.0  3  train  foo 2002-02-08
'''
columns1 = df2.columns
'''
所有列
Index(['a', 'b', 'c', 'd', 'e', 'f', 'g'], dtype='object')
'''
index1 = df2.index
'''
RangeIndex(start=0, stop=4, step=1)
'''
values1 = df2.values
'''
[[1 Timestamp('2019-01-01 00:00:00') 1.0 3 'test' 'foo'
  Timestamp('2002-02-05 00:00:00')]
 [1 Timestamp('2019-01-01 00:00:00') 1.0 3 'train' 'foo'
  Timestamp('2002-02-06 00:00:00')]
 [1 Timestamp('2019-01-01 00:00:00') 1.0 3 'test' 'foo'
  Timestamp('2002-02-07 00:00:00')]
 [1 Timestamp('2019-01-01 00:00:00') 1.0 3 'train' 'foo'
  Timestamp('2002-02-08 00:00:00')]]
'''
describe1 = df2.describe()
'''
数据简单统计
         a    c    d
count  4.0  4.0  4.0
mean   1.0  1.0  3.0
std    0.0  0.0  0.0
min    1.0  1.0  3.0
25%    1.0  1.0  3.0
50%    1.0  1.0  3.0
75%    1.0  1.0  3.0
max    1.0  1.0  3.0
'''
transpose1 = df2.T
'''
数据翻转
                     0         ...                             3
a                    1         ...                             1
b  2019-01-01 00:00:00         ...           2019-01-01 00:00:00
c                    1         ...                             1
d                    3         ...                             3
e                 test         ...                         train
f                  foo         ...                           foo
g  2002-02-05 00:00:00         ...           2002-02-08 00:00:00

[7 rows x 4 columns]
'''
df2_sort_index = df2.sort_index(axis=0, ascending=False)
'''
对行和列的索引进行排序
   a          b    c  d      e    f          g
3  1 2019-01-01  1.0  3  train  foo 2002-02-08
2  1 2019-01-01  1.0  3   test  foo 2002-02-07
1  1 2019-01-01  1.0  3  train  foo 2002-02-06
0  1 2019-01-01  1.0  3   test  foo 2002-02-05
'''
df2_sort_values = df2.sort_values(by='g', ascending=False)
'''
根据值排序
   a          b    c  d      e    f          g
3  1 2019-01-01  1.0  3  train  foo 2002-02-08
2  1 2019-01-01  1.0  3   test  foo 2002-02-07
1  1 2019-01-01  1.0  3  train  foo 2002-02-06
0  1 2019-01-01  1.0  3   test  foo 2002-02-05
'''