pandas的常用函数
2017-05-15 23:03
309 查看
1.DataFrame的常用函数: (1)np.abs(frame) 绝对值, (2)apply function, lambda f= lambda x: x.max()-x.min(),frame.apply(f); frame.apply(f,axis = 1) f(x), def f(x): return Series([x.min(),x.max()], index=['min','max']),frame.apply(f) (3) applymap format f= lambda x:'%.2f' %x, frame.applymap(f) 或者 frame['e'].map(format)
2. index 或者 column的排序 行排序:frame.sort_index() 列排序:frame.sort_index(axis=1) 列降序排列:frame.sort_index(axis=1,ascending=False) 通过值进行排序: Series.sort_values() frame.sort_values(by = 'b') frame.sort_values(by = ['a','b']) 3. 排名([code]Series.rank(method='average', ascending=True))的作用与排序的不同之处在于,
他会把对象的 values 替换成名次(从 1 到 n)。这时唯一的问题在于如何处理平级项,方法里的
method参数就是起这个作用的,
他有四个值可选:
average, min, max, first。
Series.rank()
frame.rank(axis=1) 按照columns 进行排序。
4.[/code]
'''function application and mapping''' import numpy as np from pandas import DataFrame , Series frame = DataFrame(np.random.randn(4, 3), columns=list('bde'), index=['Utah', 'Ohio', 'Texas', 'Oregon']) print("frame is \n", frame) print("np.abs(frame) is \n", np.abs(frame)) print("another frequent operation is applying a function on 1D arrays to each column or row.\n DataFrame's apply method does exactly this:") f = lambda x: x.max()-x.min() print("f = lambda x: x.max()-x.min()") print("frame.apply(f):", frame.apply(f)) print("frame.apply(f,axis=1):",frame.apply(f,axis=1)) def f(x): return Series([x.min(), x.max()], index=['min', 'max']) print("frame.apply(f): \n", frame.apply(f)) print("the function pass to apply need not to return a scalar value,it can also return a series with multiple values") format = lambda x: '%.2f' % x print("frame.applymap(format): \n", frame.applymap(format)) print("frame['e'].map(format): \n", frame['e'].map(format))
obj = Series(range(4),index=['d', 'a', 'b', 'c']) print("obj.sort_index: \n", obj.sort_index()) frame = DataFrame(np.arange(8).reshape((2, 4)), index=['three', 'one'], columns= ['d', 'a', 'b', 'c']) print("frame is \n", frame) print("frame.sort_index() \n", frame.sort_index()) print("frame.sort_index(axis=1) \n", frame.sort_index(axis=1)) print("frame.sort_index(axis=1,ascending=False): \n", frame.sort_index(axis=1,ascending=False))
obj= Series([4, 7, -3, 2]) print("obj: \n", obj) print("obj.sort_values(): \n", obj.sort_values()) obj1 = Series([4, np.nan, 7, np.nan, -3, 2]) print("obj1:",obj1) print("obj1.sort_values():\n", obj1.sort_values()) frame1 = DataFrame({'b':[4,7,-3,2],'a':[0,1,0,1]}) print("frame1 is \n",frame1) print("frame1.sort_values(by='b')\n",frame1.sort_values(by='b')) print("frame1.sort_values(by=['a','b'] \n", frame1.sort_values(by=['a','b'])) print("Ranking is closely related to sorting,assigning ranks from one through the number of valid data points in an array") obj2 = Series([7, -5, 7, 4, 2, 0, 4]) print("obj2.rank() is \n", obj2.rank())
obj2 = Series([7, -5, 7, 4, 2, 0, 4]) print("obj2.rank() is \n", obj2.rank()) print("obj2.rank(method='min') \n",obj2.rank(method='min')) print("obj2.rank(method='max') \n",obj2.rank(method = 'max')) print("obj2.rank(method='first' \n",obj2.rank(method = 'first')) print("obj2.rank(method='dense' \n", obj2.rank(method = 'dense')) frame2 = DataFrame({'b':[4.3, 7, -3,2],'a':[0,1,0,1],'c':[-2,5,8,-2.5]}) print("frame2 is \n",frame2) print("frame2.rank(axis=1) \n",frame2.rank(axis=1))
相关文章推荐
- pandas数据处理常用函数demo之缺失值/merge/concact/绘图
- pandas常用函数
- 数据分析处理库Pandas-常用函数
- pandas常用函数使用备忘
- Pandas常用函数小结
- pandas 常用函数
- pandas常用函数
- pytho 4000 n之pandas库学习常用函数
- pandas 常用的函数
- pandas常用功能与函数介绍(结合实例,持续更新)
- pandas常用的数据分析函数(一)
- pandas做数据分析(四):常用函数
- Python-pandas常用函数
- Pandas常用函数入门
- pandas常用函数
- pandas 学习(二)—— pandas 下的常用函数
- Python拓展包:Numpy,pandas...常用函数
- Python之Pandas库常用函数大全(含注释)
- pandas 常用函数大全
- Github上Pandas,Numpy和 Scipy三个库中20个最常用的函数(1)