1.DataFrame的常用函數： 
(1)np.abs(frame) 絕對值， 
(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


‘‘‘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))

Data Frame的常用函數