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pandas知识点（基本功能）

2018年11月27日  | 移动技术网IT编程  | 我要评论

五一假期 2017,2608,维纳吉斯

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

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