13-numpy笔记-莫烦pandas-1

代码

import pandas as pd
import numpy as np

s = pd.Series([1,3,6,np.nan, 44,1])

print('-1-')
print(s)

dates = pd.date_range('20160101', periods=6)
print('-2-')
print(dates)

# index 是行的key; 默认就是数字
df = pd.DataFrame(np.random.randn(6,4), index=dates, columns=['a','b','c','d'])
print('-3-')
print(df)

df1 = pd.DataFrame(np.arange(12).reshape((3,4)))
print('-4-')
print(df1)

df2 = pd.DataFrame({'A':1.,
'B':pd.Timestamp('20130102'),
'C':pd.Series(1,index=list(range(4)), dtype = 'float32'),
'D':np.array([3]*4,dtype='int32'),
'E':pd.Categorical(["test","train","test","train"]),
'F':'foo'})
print('-5-')
print(df2)
print('-6-')
print(df2.dtypes)
print('-7-')
print(df2.index)
print('-8-')
print(df2.columns)
print('-9-')
print(df2.values)

print('-10-')
#只会计算数字串
print(df2.describe())

print('-11-')
print(df2.T)

print('-12-')
# 对 ABCD排序
print(df2.sort_index(axis=1, ascending=False))

print('-13-')
# 对123排序
print(df2.sort_index(axis=0, ascending=False))

print('-14-')
print(df2.sort_values(by='E'))

　　

输出

-1-
0     1.0
1     3.0
2     6.0
3     NaN
4    44.0
5     1.0
dtype: float64
-2-
DatetimeIndex(['2016-01-01', '2016-01-02', '2016-01-03', '2016-01-04',
               '2016-01-05', '2016-01-06'],
              dtype='datetime64[ns]', freq='D')
-3-
                   a         b         c         d
2016-01-01 -0.636080 -0.411646  1.167693 -0.085643
2016-01-02 -0.931738 -0.656105  0.833493  0.866367
2016-01-03 -0.495047 -0.131291 -0.757423 -0.783154
2016-01-04 -0.207423  0.261732  0.300315 -0.674217
2016-01-05  0.241664  0.560630 -0.057852 -0.411710
2016-01-06 -0.964392  0.990477  0.926594  0.388210
-4-
   0  1   2   3
0  0  1   2   3
1  4  5   6   7
2  8  9  10  11
-5-
     A          B    C  D      E    F
0  1.0 2013-01-02  1.0  3   test  foo
1  1.0 2013-01-02  1.0  3  train  foo
2  1.0 2013-01-02  1.0  3   test  foo
3  1.0 2013-01-02  1.0  3  train  foo
-6-
A           float64
B    datetime64[ns]
C           float32
D             int32
E          category
F            object
dtype: object
-7-
Int64Index([0, 1, 2, 3], dtype='int64')
-8-
Index(['A', 'B', 'C', 'D', 'E', 'F'], dtype='object')
-9-
[[1.0 Timestamp('2013-01-02 00:00:00') 1.0 3 'test' 'foo']
 [1.0 Timestamp('2013-01-02 00:00:00') 1.0 3 'train' 'foo']
 [1.0 Timestamp('2013-01-02 00:00:00') 1.0 3 'test' 'foo']
 [1.0 Timestamp('2013-01-02 00:00:00') 1.0 3 'train' 'foo']]
-10-
         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
-11-
                     0         ...                             3
A                    1         ...                             1
B  2013-01-02 00:00:00         ...           2013-01-02 00:00:00
C                    1         ...                             1
D                    3         ...                             3
E                 test         ...                         train
F                  foo         ...                           foo

[6 rows x 4 columns]
-12-
     F      E  D    C          B    A
0  foo   test  3  1.0 2013-01-02  1.0
1  foo  train  3  1.0 2013-01-02  1.0
2  foo   test  3  1.0 2013-01-02  1.0
3  foo  train  3  1.0 2013-01-02  1.0
-13-
     A          B    C  D      E    F
3  1.0 2013-01-02  1.0  3  train  foo
2  1.0 2013-01-02  1.0  3   test  foo
1  1.0 2013-01-02  1.0  3  train  foo
0  1.0 2013-01-02  1.0  3   test  foo
-14-
     A          B    C  D      E    F
0  1.0 2013-01-02  1.0  3   test  foo
2  1.0 2013-01-02  1.0  3   test  foo
1  1.0 2013-01-02  1.0  3  train  foo
3  1.0 2013-01-02  1.0  3  train  foo