Pandas数据特征分析
学习笔记手札及单元小结数据的排序Pandas库的数据排序
数据的基本统计分析数据的累计统计分析累计统计分析函数
数据的相关分析实例：房价增幅与M2增幅的相关性
学习笔记手札及单元小结
数据的排序
Pandas库的数据排序
.sort_index()方法在指定轴上根据索引进行排序，默认升序 .sort_index(axis=0, ascending=True)
import pandas
as pd
import numpy
as np
b
= pd
.DataFrame
(np
.arange
(20).reshape
(4,5),index
=['c','a','d','b'])
b
Out
[4]:
0 1 2 3 4
c
0 1 2 3 4
a
5 6 7 8 9
d
10 11 12 13 14
b
15 16 17 18 19
b
.sort_index
()
Out
[5]:
0 1 2 3 4
a
5 6 7 8 9
b
15 16 17 18 19
c
0 1 2 3 4
d
10 11 12 13 14
b
.sort_index
(ascending
=False)
Out
[6]:
0 1 2 3 4
d
10 11 12 13 14
c
0 1 2 3 4
b
15 16 17 18 19
a
5 6 7 8 9
c
=b
.sort_index
(axis
=1,ascending
=False)
c
Out
[8]:
4 3 2 1 0
c
4 3 2 1 0
a
9 8 7 6 5
d
14 13 12 11 10
b
19 18 17 16 15
c
= c
.sort_index
()
c
Out
[10]:
4 3 2 1 0
a
9 8 7 6 5
b
19 18 17 16 15
c
4 3 2 1 0
d
14 13 12 11 10
.sort_values()方法在指定轴上根据数值进行排序，默认升序 Series.sort_values(axis=0,ascending=True) DataFrame.sort_values(by,axis=0,ascending=True) by:axis轴上的某个索引或索引列表
import pandas
as pd
import numpy
as np
b
= pd
.DataFrame
(np
.arange
(20).reshape
(4,5),index
=['c','a','d','b'])
b
Out
[4]:
0 1 2 3 4
c
0 1 2 3 4
a
5 6 7 8 9
d
10 11 12 13 14
b
15 16 17 18 19
c
= b
.sort_values
(2,ascending
=False)
c
Out
[6]:
0 1 2 3 4
b
15 16 17 18 19
d
10 11 12 13 14
a
5 6 7 8 9
c
0 1 2 3 4
c
= c
.sort_values
('a',axis
=1,ascending
=False)
c
Out
[8]:
4 3 2 1 0
b
19 18 17 16 15
d
14 13 12 11 10
a
9 8 7 6 5
c
4 3 2 1 0
NaN统一放到排序末尾
import pandas
as pd
import numpy
as np
a
= pd
.DataFrame
(np
.arange
(12).reshape
(3,4),index
=['a','b','c'])
a
Out
[4]:
0 1 2 3
a
0 1 2 3
b
4 5 6 7
c
8 9 10 11
b
= pd
.DataFrame
(np
.arange
(20).reshape
(4,5),index
=['c','a','b','d'])
b
Out
[6]:
0 1 2 3 4
c
0 1 2 3 4
a
5 6 7 8 9
b
10 11 12 13 14
d
15 16 17 18 19
c
=a
+ b
c
Out
[8]:
0 1 2 3 4
a
5.0 7.0 9.0 11.0 NaN
b
14.0 16.0 18.0 20.0 NaN
c
8.0 10.0 12.0 14.0 NaN
d NaN NaN NaN NaN NaN
c
.sort_values
(2,ascending
= False)
Out
[9]:
0 1 2 3 4
b
14.0 16.0 18.0 20.0 NaN
c
8.0 10.0 12.0 14.0 NaN
a
5.0 7.0 9.0 11.0 NaN
d NaN NaN NaN NaN NaN
c
.sort_values
(2,ascending
(2,ascending
= True))
---------------------------------------------------------------------------
NameError Traceback
(most recent call last
)
<ipython
-input-10-7fe7e363a39a> in <module
>
----> 1 c
.sort_values
(2,ascending
(2,ascending
= True))
NameError
: name
'ascending' is not defined
c
.sort_values
(2,ascending
= True)
Out
[11]:
0 1 2 3 4
a
5.0 7.0 9.0 11.0 NaN
c
8.0 10.0 12.0 14.0 NaN
b
14.0 16.0 18.0 20.0 NaN
d NaN NaN NaN NaN NaN
数据的基本统计分析
import pandas
as pd
a
= pd
.Series
([9,8,7,6],index
=['a','b','c','d'])
a
Out
[3]:
a
9
b
8
c
7
d
6
dtype
: int64
a
.describe
()
Out
[4]:
count
4.000000
mean
7.500000
std
1.290994
min 6.000000
25% 6.750000
50% 7.500000
75% 8.250000
max 9.000000
dtype
: float64
type(a
.describe
())
Out
[5]: pandas
.core
.series
.Series
a
.describa
()['count']
---------------------------------------------------------------------------
AttributeError Traceback
(most recent call last
)
<ipython
-input-6-30160ee76872> in <module
>
----> 1 a
.describa
()['count']
~\anaconda3\lib\site
-packages\pandas\core\generic
.py
in __getattr__
(self
, name
)
5272 if self
._info_axis
._can_hold_identifiers_and_holds_name
(name
):
5273 return self
[name
]
-> 5274 return object.__getattribute__
(self
, name
)
5275
5276 def __setattr__(self
, name
: str, value
) -> None:
AttributeError
: 'Series' object has no attribute
'describa'
a
.describe
()['count']
Out
[7]: 4.0
a
.describe
()['max']
Out
[8]: 9.0
数据的累计统计分析
import pandas
as pd
import numpy
as np
b
= pd
.DataFrame
(np
.arange
(20).reshape
(4,5),index
=['c','a','d','b'])
b
Out
[4]:
0 1 2 3 4
c
0 1 2 3 4
a
5 6 7 8 9
d
10 11 12 13 14
b
15 16 17 18 19
b
.cumsum
()
Out
[5]:
0 1 2 3 4
c
0 1 2 3 4
a
5 7 9 11 13
d
15 18 21 24 27
b
30 34 38 42 46
b
.cumprod
()
Out
[6]:
0 1 2 3 4
c
0 1 2 3 4
a
0 6 14 24 36
d
0 66 168 312 504
b
0 1056 2856 5616 9576
b
.cummin
()
Out
[7]:
0 1 2 3 4
c
0 1 2 3 4
a
0 1 2 3 4
d
0 1 2 3 4
b
0 1 2 3 4
b
.cummax
()
Out
[8]:
0 1 2 3 4
c
0 1 2 3 4
a
5 6 7 8 9
d
10 11 12 13 14
b
15 16 17 18 19
累计统计分析函数
import pandas
as pd
import numpy
as np
b
= pd
.DataFrame
(np
.arange
(20).reshape
(4,5),index
=['c','a','b','d'])
b
Out
[4]:
0 1 2 3 4
c
0 1 2 3 4
a
5 6 7 8 9
b
10 11 12 13 14
d
15 16 17 18 19
b
.rolling
(2).sum()
Out
[5]:
0 1 2 3 4
c NaN NaN NaN NaN NaN
a
5.0 7.0 9.0 11.0 13.0
b
15.0 17.0 19.0 21.0 23.0
d
25.0 27.0 29.0 31.0 33.0
b
.rolling
(3).sum()
Out
[6]:
0 1 2 3 4
c NaN NaN NaN NaN NaN
a NaN NaN NaN NaN NaN
b
15.0 18.0 21.0 24.0 27.0
d
30.0 33.0 36.0 39.0 42.0
数据的相关分析
实例：房价增幅与M2增幅的相关性
import pandas
as pd
import numpy
as np
b
= pd
.DataFrame
(np
.arange
(20).reshape
(4,5),index
=['c','a','b','d'])
b
Out
[4]:
0 1 2 3 4
c
0 1 2 3 4
a
5 6 7 8 9
b
10 11 12 13 14
d
15 16 17 18 19
b
.rolling
(2).sum()
Out
[5]:
0 1 2 3 4
c NaN NaN NaN NaN NaN
a
5.0 7.0 9.0 11.0 13.0
b
15.0 17.0 19.0 21.0 23.0
d
25.0 27.0 29.0 31.0 33.0
b
.rolling
(3).sum()
Out
[6]:
0 1 2 3 4
c NaN NaN NaN NaN NaN
a NaN NaN NaN NaN NaN
b
15.0 18.0 21.0 24.0 27.0
d
30.0 33.0 36.0 39.0 42.0
import pandas
as pd
hprice
= pd
.Series
([3.04,22.93,12.75,22.6,12.33],index
=['2008','2009','2010','2011','2012'])
m2
= pd
.Series
([8.18,18.38,9.13,7.82,6.69],index
=['2008','2009','2010','2011','2012'])
hprice
.corr
(m2
)
Out
[10]: 0.5239439145220387
