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pandas知识点（数据结构）

2018年11月22日  | 移动技术网IT编程  | 我要评论

8月节日,视网膜屏是什么意思,大唐猎艳记

1.series
生成一维数组，左边索引，右边值：
in [3]: obj = series([1,2,3,4,5])
in [4]: obj
out[4]:
0    1
1    2
2    3
3    4
4    5
dtype: int64
in [5]: obj.values
out[5]: array([1, 2, 3, 4, 5], dtype=int64)
in [6]: obj.index
out[6]: rangeindex(start=0, stop=5, step=1)

 

创建对各个数据点进行标记的索引：

in [7]: obj2 = series([4,1,9,7], index=["a","c","e","ff"])
in [8]: obj2
out[8]:
a     4
c     1
e     9
ff    7
dtype: int64
in [9]: obj2.index
out[9]: index(['a', 'c', 'e', 'ff'], dtype='object')

 

取一个值或一组值：

in [10]: obj2["c"]
out[10]: 1
in [11]: obj2[["c","e"]]
out[11]:
c    1
e    9
dtype: int64

 

数组运算，会显示索引：

in [12]: obj2[obj2>3]
out[12]:
a     4
e     9
ff    7
dtype: int64

 

series还可以看作有序的字典，很多字典操作可以使用：
in [13]: "c" in obj2
out[13]: true

 

直接用字典创建series：
in [14]: data = {"name":"liu","year":18,"sex":"man"}
in [15]: obj3 = series(data)
in [16]: obj3
out[16]:
name    liu
year     18
sex     man
dtype: object

 

用字典结合列表创建series：
in [17]: list1 = ["name","year","mobile"]
in [18]: obj4 = series(data,index=list1)
in [19]: obj4
out[19]:
name      liu
year       18
mobile    nan
dtype: object

ps：因为data字典中没有mobile所以值为nan

 
检测数据是否缺失：
in [20]: pd.isnull(obj4)
out[20]:
name      false
year      false
mobile     true
dtype: bool
 
in [21]: pd.notnull(obj4)
out[21]:
name       true
year       true
mobile    false
dtype: bool
 
in [22]: obj4.isnull()
out[22]:
name      false
year      false
mobile     true
dtype: bool
 
in [23]: obj4.notnull()
out[23]:
name       true
year       true
mobile    false
dtype: bool

 

series的name属性：
in [7]: obj4.name = "hahaha"
in [8]: obj4.index.name = "state"
in [9]: obj4
out[9]:
state
name      liu
year       18
mobile    nan
name: hahaha, dtype: object

 

2.dataframe
构建dataframe
in [13]: data = {
"state":[1,1,2,1,1],
"year":[2000,2001,2002,2004,2005],
"pop":[1.5,1.7,3.6,2.4,2.9]
}
in [14]: frame = dataframe(data)
in [15]: frame
out[15]:
   state  year  pop
0      1  2000  1.5
1      1  2001  1.7
2      2  2002  3.6
3      1  2004  2.4
4      1  2005  2.9

 

设定行与列的名称，如果数据找不到则产生na值：
in [18]: frame2 = dataframe(
data,
columns=["year","state","pop","debt"],
index=["one","two","three","four","five"]
)
in [19]: frame2
out[19]:
       year  state  pop debt
one    2000      1  1.5  nan
two    2001      1  1.7  nan
three  2002      2  3.6  nan
four   2004      1  2.4  nan
five   2005      1  2.9  nan

 

将dataframe的列获取成为series：
in [7]: frame2.year
out[7]:
one      2000
two      2001
three    2002
four     2004
five     2005
name: year, dtype: int64

ps：返回的索引不变，且name属性被设置了

 

获取行：
in [11]: frame2.loc["three"]
out[11]:
year     2002
state       2
pop       3.6
debt      nan
name: three, dtype: object

 

赋值列：
in [12]: frame2['debt'] = 16.5
in [13]: frame2
out[13]:
       year  state  pop  debt
one    2000      1  1.5  16.5
two    2001      1  1.7  16.5
three  2002      2  3.6  16.5
four   2004      1  2.4  16.5
five   2005      1  2.9  16.5

 

如果赋值列表或数组，长度需要相等；如果赋值series，则精确匹配索引
in [17]: val = series([1.2,1.5,1.7], index=["two","four","five"])
in [18]: frame2['debt'] = val
in [19]: frame2
out[19]:
       year  state  pop  debt
one    2000      1  1.5   nan
two    2001      1  1.7   1.2
three  2002      2  3.6   nan
four   2004      1  2.4   1.5
five   2005      1  2.9   1.7

 

如果列不存在，则创建：
in [21]: frame2["eastern"] = frame2.state == 1
in [22]: frame2
out[22]:
       year  state  pop  debt  eastern
one    2000      1  1.5   nan     true
two    2001      1  1.7   1.2     true
three  2002      2  3.6   nan    false
four   2004      1  2.4   1.5     true
five   2005      1  2.9   1.7     true

 

对于嵌套字典，dataframe会解释为外层为列，内层为行索引：
in [23]: dic = {"name":{"one":"liu","two":"rui"},"year":{"one":"23","two":"22"}}
in [24]: frame3 = dataframe(dic)
in [25]: frame3
out[25]:
    name year
one  liu   23
two  rui   22

 

显示行，列名：
in [26]: frame3.index.name = "index"
in [27]: frame3.columns.name = "state"
in [28]: frame3
out[28]:
state name year
index
one    liu   23
two    rui   22

 

返回二维ndarray形式的数据：
in [29]: frame3.values
out[29]:
array([['liu', '23'],
       ['rui', '22']], dtype=object)

 

3.索引对象 
in [30]: obj = series(range(3),index=["a","b","c"])
in [31]: index = obj.index
in [32]: index
out[32]: index(['a', 'b', 'c'], dtype='object')

 

index对象不可修改的，使得index在多个数据结构中可以共享
in [35]: index = pd.index(np.arange(3))
in [36]: obj2 = series([1.5,0.5,2],index=index)
in [37]: obj2.index is index
out[37]: true

 

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