《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

时间：2022-10-31 14:48:11

写在前面的话：

实例中的所有数据都是在GitHub上下载的，打包下载即可。

地址是：http://github.com/pydata/pydata-book

还有一定要说明的：

我使用的是Python2.7，书中的代码有一些有错误，我使用自己的2.7版本调通。

# 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)

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