《利用Python进行数据分析》笔记---第6章数据加载、存储与文件格式

    # coding: utf-8
    from pandas import Series, DataFrame
    import pandas as pd
    import numpy as np
    
    df = pd.read_csv('D:\Source Code\pydata-book-master\ch06\ex1.csv')
    df
    pd.read_table('D:\Source Code\pydata-book-master\ch06\ex1.csv', sep=',')
    
    pd.read_csv('D:\Source Code\pydata-book-master\ch06\ex2.csv', header=None)
    pd.read_csv('D:\Source Code\pydata-book-master\ch06\ex2.csv', names=['a','b','c','d','message'])
    names=['a','b','c','d','message']
    pd.read_csv('D:\Source Code\pydata-book-master\ch06\ex2.csv', names=names, index_col = 'message')
    
    parsed = pd.read_csv('D:\Source Code\pydata-book-master\ch06\csv_mindex.csv', index_col = ['key1','key2'])
    
    list(open('D:\Source Code\pydata-book-master\ch06\ex3.txt'))
    result = pd.read_table('D:\Source Code\pydata-book-master\ch06\ex3.txt', sep='\s+')
    result
    
    pd.read_csv('D:\Source Code\pydata-book-master\ch06\ex4.csv', skiprows=[0,2,3])
    
    result = pd.read_csv('D:\Source Code\pydata-book-master\ch06\ex5.csv')
    result
    pd.isnull(result)
    result = pd.read_csv('D:\Source Code\pydata-book-master\ch06\ex5.csv', na_values=['NULL'])
    result
    
    sentinels = {'message':['foo','NA'],'something':['two']}
    pd.read_csv('D:\Source Code\pydata-book-master\ch06\ex5.csv',na_values = sentinels)
    
    result = pd.read_csv('D:\Source Code\pydata-book-master\ch06\ex6.csv')
    result
    pd.read_csv('D:\Source Code\pydata-book-master\ch06\ex6.csv', nrows=5)
    chunker = pd.read_csv('D:\Source Code\pydata-book-master\ch06\ex6.csv', chunksize=1000)
    chunker
    tot = Series([])
    for piece in chunker:
        tot = tot.add(piece['key'].value_counts(), fill_value=0)
    tot = tot.order(ascending=False)
    tot[:10]
    
    data = pd.read_csv('D:\Source Code\pydata-book-master\ch06\ex5.csv')
    data
    data.to_csv('D:\out.csv')
    pd.read_csv('D:\out.csv')
    
    import sys
    data.to_csv(sys.stdout, sep='|')
    data.to_csv(sys.stdout, na_rep='NULL')
    data.to_csv(sys.stdout, index=False, header=False)
    data.to_csv(sys.stdout, index=False, cols=['a','b','c'])
    
    dates = pd.date_range('1/1/2000',periods=7)
    ts = Series(np.arange(7),index=dates)
    ts.to_csv('D:\out.csv')
    Series.from_csv('D:\out.csv', parse_dates=True)
    
    import csv
    f = open('D:\Source Code\pydata-book-master\ch06\ex7.csv')
    reader = csv.reader(f)
    for line in reader:
        print line
    lines = list(csv.reader(open('D:\Source Code\pydata-book-master\ch06\ex7.csv')))
    header,values = line[0],lines[1:]
    data_dict = {h:v for h, v in zip(header,zip(*values))}
    data_dict
    
    import json
    obj = """{"names":"www0","places":["aa","bb","cc","dd"],"pet":null,"siblings":[{"name":"wang","age":25,"pet":"Zuko"},{"name":"zhang","age":33,"pet":"Cisco"}]}"""
    result = json.loads(obj)
    result
    asjson = json.dumps(result)
    asjson
    siblings = DataFrame(result['siblings'],columns=['name','age'])
    siblings
    
    from lxml.html import parse
    from urllib2 import urlopen
    parsed = parse(urlopen('http://finance.yahoo.com/q/op?s=AAPL+Options'))
    doc = parsed.getroot()
    
    from lxml import objectify
    path = 'D:\Source Code\pydata-book-master\ch06\mta_perf\Performance_MNR.xml'
    parsed = objectify.parse(open(path))
    root = parsed.getroot()
    data = []
    for elt in root.INDICATOR:
        el_data = {}
        for child in elt.getchildren():
            el_data[child.tag] = child.pyval
        data.append(el_data)
    perf = DataFrame(data)
    perf
    
    frame = pd.read_csv('D:\Source Code\pydata-book-master\ch06\ex1.csv')
    frame
    frame.save('D:\Source Code\pydata-book-master\ch06\\aa')
    frame.load('D:\Source Code\pydata-book-master\ch06\\aa')
    
    import requests
    url = 'http://gc.ditu.aliyun.com/regeocoding?l=39.938133,116.395739&type=001'
    resp = requests.get(url)
    resp
    data = json.loads(resp.text)
[/code]