pandas知识点（基本功能）

1.重新索引

如果reindex会根据新索引重新排序，不存在的则引入缺省：

In [3]: obj = Series([4.5,7.2,-5.3,3.6], index=["d","b","a","c"])
In [4]: obj
Out[4]:
d    4.5
b    7.2
a   -5.3
c    3.6
dtype: float64
In [6]: obj2 = obj.reindex(["a","b","c","d","e"])
In [7]: obj2
Out[7]:
a   -5.3
b    7.2
c    3.6
d    4.5
e    NaN
dtype: float64

ffill可以实现前向值填充：

In [8]: obj3 = Series(["blue","purple","yellow"], index=[0,2,4])
In [9]: obj3.reindex(range(6), method="ffill")
Out[9]:
0      blue
1      blue
2    purple
3    purple
4    yellow
5    yellow
dtype: object

2.丢弃指定轴上的项

drop方法返回在指定轴上删除了指定值的新对象：

In [12]: obj = Series(np.arange(5.), index=["a","b","c","d","e"])
In [13]: new_obj = obj.drop("c")
In [14]: new_obj
Out[14]:
a    0.0
b    1.0
d    3.0
e    4.0
dtype: float64

DataFrame可以删除任意轴上的索引值

 

3.索引，选取和过滤

Series的索引可以不止是整数：

In [4]: obj = Series(np.arange(4.), index=["a","b","c","d"])Out[6]:
a    0.0
b    1.0
dtype: float64
In [7]: obj[obj<2]
Out[7]:
a    0.0
b    1.0
dtype: float64

Series切片与普通的python切片不一样，末端也是包含的：

In [8]: obj["b":"c"]
Out[8]:
b    1.0
c    2.0
dtype: float64

DataFrame进行索引：

In [10]: data
Out[10]:
          one  two  three  four
Ohio        0    1      2     3
Colorado    4    5      6     7
Utah        8    9     10    11
New York   12   13     14    15
In [11]: data['two']
Out[11]:
Ohio         1
Colorado     5
Utah         9
New York    13
Name: two, dtype: int32
In [12]: data[:2]
Out[12]:
          one  two  three  four
Ohio        0    1      2     3
Colorado    4    5      6     7

布尔型DataFrame进行索引：

In [13]: data > 5
Out[13]:
            one    two  three   four
Ohio      False  False  False  False
Colorado  False  False   True   True
Utah       True   True   True   True
New York   True   True   True   True

利用ix可以选取行和列的子集：

In [18]: data.ix['Colorado',['two','three']]
Out[18]:
two      5
three    6
Name: Colorado, dtype: int32
In [19]: data.ix[['Colorado','Utah'],[3,0,1]]
Out[19]:
          four  one  two
Colorado     7    4    5
Utah        11    8    9

4.算数运算和数据对齐

对不同索引的对象进行算数运算，如果存在不同的索引，则结果的索引取其并集：

In [20]: s1 = Series([7.3,-2.5,3.4,1.5],index=['a','c','d','e'])
In [21]: s2 = Series([-2.1, 3.6, -1.5, 4, 3.1],index=['a','c','e','f','g'])
In [22]: s1+s2
Out[22]:
a    5.2
c    1.1
d    NaN
e    0.0
f    NaN
g    NaN
dtype: float64

对于DataFrame，对齐操作会同时发生在行和列上：

In [26]: df1
Out[26]:
          b     d     e
Utah    0.0   1.0   2.0
Ohio    3.0   4.0   5.0
Texas   6.0   7.0   8.0
Oregon  9.0  10.0  11.0
In [27]: df2
Out[27]:
            b    c    d
Ohio      0.0  1.0  2.0
Texas     3.0  4.0  5.0
Colorado  6.0  7.0  8.0
In [28]: df1+df2
Out[28]:
            b   c     d   e
Colorado  NaN NaN   NaN NaN
Ohio      3.0 NaN   6.0 NaN
Oregon    NaN NaN   NaN NaN
Texas     9.0 NaN  12.0 NaN
Utah      NaN NaN   NaN NaN

使用add方法相加：

In [30]: df2.add(df1,fill_value=0)
Out[30]:
            b    c     d     e
Colorado  6.0  7.0   8.0   NaN
Ohio      3.0  1.0   6.0   5.0
Oregon    9.0  NaN  10.0  11.0
Texas     9.0  4.0  12.0   8.0
Utah      0.0  NaN   1.0   2.0

5.DataFrame和Series之间的运算：

计算二维数组和某一行的差：

In [31]: arr = np.arange(12.).reshape((3,4))
In [32]: arr
Out[32]:
array([[ 0.,  1.,  2.,  3.],
       [ 4.,  5.,  6.,  7.],
       [ 8.,  9., 10., 11.]])
In [33]: arr - arr[1]
Out[33]:
array([[-4., -4., -4., -4.],
       [ 0.,  0.,  0.,  0.],
       [ 4.,  4.,  4.,  4.]])

DataFrame和Series之间的运算：

In [35]: frame = DataFrame(np.arange(12.).reshape((4,3)),columns=list('bde'),index=['Utah','Ohio','Texas','Oregon'])
In [39]: series = frame.iloc[0]
In [40]: frame
Out[40]:
          b     d     e
Utah    0.0   1.0   2.0
Ohio    3.0   4.0   5.0
Texas   6.0   7.0   8.0
Oregon  9.0  10.0  11.0
In [41]: series
Out[41]:
b    0.0
d    1.0
e    2.0
Name: Utah, dtype: float64
In [43]: frame - series
Out[43]:
          b    d    e
Utah    0.0  0.0  0.0
Ohio    3.0  3.0  3.0
Texas   6.0  6.0  6.0
Oregon  9.0  9.0  9.0

如果某个索引值找不到，则与运算的两个对象会被重新索引以形成并集：

In [45]: frame + series2
Out[45]:
          b   d     e   f
Utah    0.0 NaN   3.0 NaN
Ohio    3.0 NaN   6.0 NaN
Texas   6.0 NaN   9.0 NaN
Oregon  9.0 NaN  12.0 NaN

匹配列并在列上广播：

In [46]: series3 = frame['d']
In [47]: frame.sub(series3, axis=0)
Out[47]:
          b    d    e
Utah   -1.0  0.0  1.0
Ohio   -1.0  0.0  1.0
Texas  -1.0  0.0  1.0
Oregon -1.0  0.0  1.0

6.函数应用和映射

Numpy的ufuncs也可用于操作pandas对象：

In [49]: frame = DataFrame(np.random.randn(4,3), columns=list('bde'),index=['Utah','Ohio','Texas','Oregon'])
In [50]: frame
Out[50]:
               b         d         e
Utah    0.913051 -1.289725 -0.590573
Ohio    1.417612 -1.835357 -0.010755
Texas   0.328839 -0.121878 -1.209583
Oregon  1.315330 -1.026557 -1.777427

In [51]: np.abs(frame)
Out[51]:
               b         d         e
Utah    0.913051  1.289725  0.590573
Ohio    1.417612  1.835357  0.010755
Texas   0.328839  0.121878  1.209583
Oregon  1.315330  1.026557  1.777427
DataFrame的apply方法可以实现将函数应用到由各行或列形成的一维数组上：
In [52]: f = lambda x:x.max() - x.min()
In [53]: frame.apply(f)
Out[53]:
b    1.088773
d    1.713479
e    1.766671
dtype: float64
In [54]: frame.apply(f, axis=1)
Out[54]:
Utah      2.202776
Ohio      3.252969
Texas     1.538421
Oregon    3.092757
dtype: float64

7.排序和排名

sort_index方法可以返回一个已排序的对象

In [57]: obj = Series(range(4), index=['d','a','b','c'])
In [58]: obj
Out[58]:
d    0
a    1
b    2
c    3
dtype: int64
In [59]: obj.sort_index
Out[59]:
<bound method Series.sort_index of d    0
a    1
b    2
c    3
dtype: int64>
In [62]: frame.sort_index()
Out[62]:
               b         d         e
Ohio    1.417612 -1.835357 -0.010755
Oregon  1.315330 -1.026557 -1.777427
Texas   0.328839 -0.121878 -1.209583
Utah    0.913051 -1.289725 -0.590573
In [63]: frame.sort_index(axis=1)
Out[63]:
               b         d         e
Utah    0.913051 -1.289725 -0.590573
Ohio    1.417612 -1.835357 -0.010755
Texas   0.328839 -0.121878 -1.209583
Oregon  1.315330 -1.026557 -1.777427

倒序查看：

In [65]: frame.sort_index(axis=1,ascending=False)
Out[65]:
               e         d         b
Utah   -0.590573 -1.289725  0.913051
Ohio   -0.010755 -1.835357  1.417612
Texas  -1.209583 -0.121878  0.328839
Oregon -1.777427 -1.026557  1.315330

按某一列的值进行排序：

In [67]: frame.sort_values(by='b')
Out[67]:
               b         d         e
Texas   0.328839 -0.121878 -1.209583
Utah    0.913051 -1.289725 -0.590573
Oregon  1.315330 -1.026557 -1.777427
Ohio    1.417612 -1.835357 -0.010755

排名（rank）与排序类似，它会设置一个排名值，并且可以根据某种规则破坏平级关系

In [70]: obj
Out[70]:
0    7
1   -5
2    7
3    4
4    2
5    0
6    4
dtype: int64
In [71]: obj.rank()
Out[71]:
0    6.5
1    1.0
2    6.5
3    4.5
4    3.0
5    2.0
6    4.5
dtype: float64

根据值在原数据中出现的顺序给出排名

In [72]: obj.rank(method='first')
Out[72]:
0    6.0
1    1.0
2    7.0
3    4.0
4    3.0
5    2.0
6    5.0
dtype: float64

8.带有重复值的轴索引

使用is_unique查看值是否唯一

In [73]: obj = Series(range(5),index=['a','a','b','b','c'])
In [74]: obj
Out[74]:
a    0
a    1
b    2
b    3
c    4
dtype: int64
In [75]: obj.index.is_unique
Out[75]: False

对重复索引选取数据：

In [76]: obj['a']
Out[76]:
a    0
a    1
dtype: int64

DataFrame也是同样的道理