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: objectPS：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: int64PS：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
