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