韩国三级第一页,色欲av伊人

1.本課程導學
2.pandas庫的介紹
3.pandas庫的Serious類型
4.pandas庫的DataFrame類型
5.pandas庫的數據類型操作
6.pandas庫的數據類型運算
7.單元小結
[網頁鏈接【Python數據分析與展示】.MOOC. 北京理工大學
https://www.bilibili.com/video/av10101509/?from=search&seid=8584212945516406240#page=35)

最近更新：2018-01-29

1.本課程導學

2.pandas庫的介紹

2.1Pandas庫的引用

Pandas庫小測
左邊0-19是索引,右邊是值

import pandas as pd

d=pd.Series(range(20))

d
Out[15]: 
0      0
1      1
2      2
3      3
4      4
5      5
6      6
7      7
8      8
9      9
10    10
11    11
12    12
13    13
14    14
15    15
16    16
17    17
18    18
19    19
dtype: int64

d.cumsum()
Out[16]: 
0       0
1       1
2       3
3       6
4      10
5      15
6      21
7      28
8      36
9      45
10     55
11     66
12     78
13     91
14    105
15    120
16    136
17    153
18    171
19    190
dtype: int64

2.2Pandas庫的理解

3.pandas庫的Serious類型

3.1Serious類型

import pandas as pd
a=pd.Series([9,8,7,6])
a
Out[19]: 
0    9
1    8
2    7
3    6
dtype: int64

自定義索引

import pandas as pd

a=pd.Series([9,8,7,6],index=["a","b","c","d"])

a
Out[22]: 
a    9
b    8
c    7
d    6
dtype: int64

從標量值創(chuàng)建
注意:圖片上寫著不可以省略index,實際是可以省略,a=pd.Series(25,["a","b","c"])

import pandas as pd

a=pd.Series(25,index=["a","b","c"])

a
Out[25]: 
a    25
b    25
c    25
dtype: int64

從字典類型創(chuàng)建

import pandas as pd
a=pd.Series({"a":9,"b":8,"c":7})
a
Out[30]: 
a    9
b    8
c    7
dtype: int64

import pandas as pd

e=pd.Series({"a":9,"b":8,"c":7},index=["c","a","b","d"])

e
Out[33]: 
c    7.0
a    9.0
b    8.0
d    NaN
dtype: float64

從ndarray類型創(chuàng)建

import pandas as pd

n=pd.Series(np.arange(5))

n
Out[36]: 
0    0
1    1
2    2
3    3
4    4
dtype: int32

import pandas as pd
m=pd.Series(np.arange(5),index=np.arange(9,4,-1))
m
Out[41]: 
9    0
8    1
7    2
6    3
5    4
dtype: int32

Serious類型總結

3.2Serious類型的基本操作

Serious類型包括index和values兩部分

import pandas as pd

b=pd.Series([9,8,7,6],["a","b","c","d"])

b.index
Out[44]: Index(['a', 'b', 'c', 'd'], dtype='object')

b.values
Out[45]: array([9, 8, 7, 6], dtype=int64)

b["b"]
Out[46]: 8

b[1]
Out[47]: 8

b[["c","d",0]]
Out[49]: 
c    7.0
d    6.0
0    NaN
dtype: float64

b[["c","d","a"]]
Out[50]: 
c    7
d    6
a    9
dtype: int64

Serious類型的操作類似ndarray類型

import pandas as pd
b=pd.Series([9,8,7,6],["a","b","c","d"])

b
Out[52]: 
a    9
b    8
c    7
d    6
dtype: int64

b[3]
Out[53]: 6

b[:3]
Out[54]: 
a    9
b    8
c    7
dtype: int64

b[b>b.median()]
Out[55]: 
a    9
b    8
dtype: int64

np.exp(b)
Out[56]: 
a    8103.083928
b    2980.957987
c    1096.633158
d     403.428793
dtype: float64

Serious類型的操作類似Python字典類型
0是否在自定義的索引中

import pandas as pd

b=pd.Series([9,8,7,6],["a","b","c","d"])

b["b"]
Out[59]: 8

"c" in b
Out[60]: True

0 in b
Out[61]: False

b.get("f",100)
Out[62]: 100
#在b中提取索引"f"'對應的值100,如果對應的值不存在就返回100.

3.3Serious類型對齊操作

import pandas as pd

a=pd.Series([1,2,3],["c","d","e"])

b=pd.Series([9,8,7,6],["a","b","c","d"])

a+b
Out[69]: 
a    NaN
b    NaN
c    8.0
d    8.0
e    NaN
dtype: float64

3.4Serious類型的name屬性

import pandas as pd
b=pd.Series([9,8,7,6],["a","b","c","d"])
b.name

b.name="Serious 對象"
b.index.name="索引列"
b
Out[76]: 
索引列
a    9
b    8
c    7
d    6
Name: Serious 對象, dtype: int64

3.5Serious類型的修改

import pandas as pd

b=pd.Series([9,8,7,6],["a","b","c","d"])

b["a"]=15

b.name="Serious"

b
Out[81]: 
a    15
b     8
c     7
d     6
Name: Serious, dtype: int64

b.name="New Serious"

b["b","c"]=20

b
Out[84]: 
a    15
b    20
c    20
d     6
Name: New Serious, dtype: int64

3.6Serious類型的總結

4.pandas庫的DataFrame類型

4.1DataFram類型

二維ndarray對象

import pandas as pd
import numpy as np
d=pd.DataFrame(np.arange(10).reshape(2,5))

d
Out[88]: 
   0  1  2  3  4
0  0  1  2  3  4
1  5  6  7  8  9

由一維ndarray/列表/字典/元組或Serious構成的字典
1)從一維ndarray對象字典創(chuàng)建

import pandas as pd

dt={"one":pd.Series(([1,2,3]),index=["a","b","c"]),"two":pd.Series(([9,8,7,6]),index=["a","b","c","d"])}

d=pd.DataFrame(dt)

d
Out[92]: 
   one  two
a  1.0    9
b  2.0    8
c  3.0    7
d  NaN    6

pd.DataFrame(dt,index=["b","c","d"],columns=["two","three"])
Out[93]: 
   two three
b    8   NaN
c    7   NaN
d    6   NaN

2)從列表類型的字典創(chuàng)建

import numpy as np
d1={"one":[1,2,3,4],"two":[9,8,7,6]}
d=pd.DataFrame(d1,index=["a","b","c","d"])

d
Out[100]: 
   one  two
a    1    9
b    2    8
c    3    7
d    4    6