函数一览表：
#coding=gbk
#pandas数据读写
#1csv和文本文件的读取
import numpy as np
import pandas as pd
mydata=pd.read_csv('D:\data\mydata.csv') #读取数据
print(mydata)
# color object name others
# 0 red book haha 1
# 1 white pen NaN 4
# 2 yellow notebook this 12
# 3 blue desk smith 5
# 4 black door jerry 7
mydata1=pd.read_table('D:\data\mydata.csv',sep=',') #csv也是文本格式，但是需要指定 分隔符，
print(mydata1)
# color object name others
# 0 red book haha 1
# 1 white pen NaN 4
# 2 yellow notebook this 12
# 3 blue desk smith 5
# 4 black door jerry 7
#自定义表头
mydata2=pd.read_csv('D:\data\mydata1.csv')
print(mydata2)
# red book haha 1
# 0 white pen NaN 4
# 1 yellow notebook this 12
# 2 blue desk smith 5
# 3 black door jerry 7
mydata3=pd.read_csv('D:\data\mydata1.csv',names=['color','object','name','others'])
print(mydata3)
# color object name others #添加了表头
# 0 red book haha 1
# 1 white pen NaN 4
# 2 yellow notebook this 12
# 3 blue desk smith 5
# 4 black door jerry 7
#读取文本文件
data1=pd.read_table('D:\data\data1.txt',sep='\s*') #正则表达式为分隔符 空格\s
print(data1)
# blue black white
# 0 1 2 3
# 1 4 5 6
# 2 7 8 9
#skiprows用法
data2=pd.read_table('D:\data\data2.txt')
print(data2)
# hahhahah -----
# 0 -----------aha---
# 1 fenge----------
# 2 blue,black,white,yellow
# 3 hahhahah -----
# 4 1,2,3,4
# 5 5,6,7,8
# 6 1,22,33,44
# 7 6,8,9,10
data3=pd.read_table('D:\data\data2.txt',sep=',',skiprows=[0,1,2,4]) #删除对应的行
print(data3)
# blue black white yellow
# 0 1 2 3 4
# 1 5 6 7 8
# 2 1 22 33 44
# 3 6 8 9 10
#读取梯txt文件部分数据
data4=pd.read_table('D:\data\data1.txt',sep='\s*',skiprows=[1],nrows=2) #skiprows定义起始行
print(data4)
# blue black white
# 0 4 5 6
# 1 7 8 9
#在csv文件中写入数据
mydata2=pd.read_csv('D:\data\mydata1.csv')
print(mydata2)
# red book haha 1
# 0 white pen NaN 4
# 1 yellow notebook this 12
# 2 blue desk smith 5
# 3 black door jerry 7
m=mydata2.to_csv('D:\data\data2.csv')
print(m)
#对于读取excel的xls同理，使用pd.read_excel();pd.to_excel()
#使用pandas对象实现序列化
frame=pd.DataFrame(np.arange(16).reshape(4,4),index=['up','down','left','right'])
print(frame)
frame.to_pickle('D:\data\datapickle.pkl') #保存到对应的文件路径当中
frame1=pd.read_pickle('D:\data\datapickle.pkl') #读取pkl格式的数据
print(frame1)
# 0 1 2 3
# up 0 1 2 3
# down 4 5 6 7
# left 8 9 10 11
# right 12 13 14 15