<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[kkbb8811的博客]]></title><description><![CDATA[]]></description><link>https://blog.youkuaiyun.com/kkbb8811</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; kkbb8811]]></copyright><item><title><![CDATA[抓取虎扑王者荣耀板块最近十页帖子，2.5W回帖数据 ，来看看JR们都有哪些有趣的信息]]></title><link>https://blog.youkuaiyun.com/kkbb8811/article/details/79429446</link><guid>https://blog.youkuaiyun.com/kkbb8811/article/details/79429446</guid><author>kkbb8811</author><pubDate>Sat, 03 Mar 2018 12:22:03 +0800</pubDate><description><![CDATA[数据说明：王者荣耀区最近十页的发帖，每个帖子内的第一页用户的个人信息
主题帖：王者荣耀区十页大概是有800个帖子。我抓取了这些帖子的主题，并且对关键词进行了抽取。根据jieba分词包（一个软件包）对这800个主题进行分析后，去除掉一些常用的无实意的词后。得出19个关键字为：王者,英雄,战队,荣耀,单排,KPL,赛季,打野,射手,皮肤,胜率,上分,攻略,春季,裴擒虎,出装,刘邦,大家,主播,大家最]]></description><category></category></item><item><title><![CDATA[基于Python爬虫的大众点评商家评论的文本挖掘]]></title><link>https://blog.youkuaiyun.com/kkbb8811/article/details/79342003</link><guid>https://blog.youkuaiyun.com/kkbb8811/article/details/79342003</guid><author>kkbb8811</author><pubDate>Wed, 21 Feb 2018 10:18:52 +0800</pubDate><description><![CDATA[使用工具
编程语言工具：Python 2.7  R 2 .2.1 excel
浏览器：Google Chrome
数据库： Mongodb

相关算法：情感分析
情感分析（Sentiment Analysis），又被称为倾向性分析、意见挖掘，是通过对带有一定的情感色彩的主观性文本进行处理分析，归纳推理的过程，例如通过用户对产品的性能、价格、便携性等方面的评价分析用户对该产品的情感倾向。]]></description><category></category></item><item><title><![CDATA[利用Python数据分析：数据规整化（五）]]></title><link>https://blog.youkuaiyun.com/kkbb8811/article/details/60574783</link><guid>https://blog.youkuaiyun.com/kkbb8811/article/details/60574783</guid><author>kkbb8811</author><pubDate>Mon, 06 Mar 2017 13:38:44 +0800</pubDate><description><![CDATA[import pandas as pd
from pandas import Series,DataFrame
import numpy as np
import  re
# 计算指标/哑变量（dummy）
df = DataFrame({'key':['b','b','a','c','a','b'],
                'data1':range(6)})
pd.get_dummi]]></description><category></category></item><item><title><![CDATA[利用Python数据分析：数据规整化（四)]]></title><link>https://blog.youkuaiyun.com/kkbb8811/article/details/60476699</link><guid>https://blog.youkuaiyun.com/kkbb8811/article/details/60476699</guid><author>kkbb8811</author><pubDate>Sun, 05 Mar 2017 21:02:09 +0800</pubDate><description><![CDATA[# 移除重复项
data = DataFrame({'k1':['one'] * 3 +['two'] * 4,
                  'k2':[1,1,2,3,3,4,4]})
data
data.duplicated() # 返回一个布尔类型的Series来表示各行是否为重复行  （duplicated adj.	复制出的，复写书的）
data.drop_duplicates(]]></description><category></category></item><item><title><![CDATA[利用Python数据分析：数据规整化（三)]]></title><link>https://blog.youkuaiyun.com/kkbb8811/article/details/60145827</link><guid>https://blog.youkuaiyun.com/kkbb8811/article/details/60145827</guid><author>kkbb8811</author><pubDate>Fri, 03 Mar 2017 19:49:31 +0800</pubDate><description><![CDATA[a = Series([np.nan,2.5,np.nan,3.5,4.5,np.nan],
           index=['f','e','d','c','b','a'])
b = Series(np.arange(len(a),dtype=np.float64),
           index=['f','e','d','c','b','a'])
b[-1] = np.nan
a
b]]></description><category></category></item><item><title><![CDATA[利用Python数据分析：数据规整化（二)]]></title><link>https://blog.youkuaiyun.com/kkbb8811/article/details/60145804</link><guid>https://blog.youkuaiyun.com/kkbb8811/article/details/60145804</guid><author>kkbb8811</author><pubDate>Fri, 03 Mar 2017 19:48:30 +0800</pubDate><description><![CDATA[left1 = DataFrame({'key':['a','b','a','a','b','c'],
                   'value':range(6)})
right1 = DataFrame({'group_val':[3.5,7]},index=['a','b'])
pd.merge(left1,right1,left_on='key',right_index=True]]></description><category></category></item><item><title><![CDATA[利用Python数据分析：数据的规整化（一）]]></title><link>https://blog.youkuaiyun.com/kkbb8811/article/details/60145797</link><guid>https://blog.youkuaiyun.com/kkbb8811/article/details/60145797</guid><author>kkbb8811</author><pubDate>Fri, 03 Mar 2017 19:47:44 +0800</pubDate><description><![CDATA[import pandas as pd
from pandas import Series,DataFrame
import numpy as np
df1 = DataFrame({'key':['b','b','a','c','a','a','b'],
                 'data1':range(7)})
df2 = DataFrame({'key':['a','b','d']]></description><category></category></item><item><title><![CDATA[利用Python数据分析：数据加载、存储与文件格式（一)]]></title><link>https://blog.youkuaiyun.com/kkbb8811/article/details/58630989</link><guid>https://blog.youkuaiyun.com/kkbb8811/article/details/58630989</guid><author>kkbb8811</author><pubDate>Tue, 28 Feb 2017 19:43:24 +0800</pubDate><description><![CDATA[import pandas as pd
from pandas import DataFrame,Series
import sys
import numpy as np
df = pd.read_csv('E:\Python for Data Analysis\pydata-book-master\ch06\ex1.csv')
df
pd.read_table('E:\Python for Da]]></description><category></category></item><item><title><![CDATA[利用Python数据分析：pandas入门（六)]]></title><link>https://blog.youkuaiyun.com/kkbb8811/article/details/58601370</link><guid>https://blog.youkuaiyun.com/kkbb8811/article/details/58601370</guid><author>kkbb8811</author><pubDate>Tue, 28 Feb 2017 15:51:22 +0800</pubDate><description><![CDATA[import pandas as pd
import numpy as np
from pandas import DataFrame,Series
# 使用 Dataframe的列作为索引
frame = DataFrame({'a':range(7),'b':range(7,0,-1),
                   'c':['one','one','one','two','two']]></description><category></category></item><item><title><![CDATA[利用Python数据分析：pandas入门（五)]]></title><link>https://blog.youkuaiyun.com/kkbb8811/article/details/57426571</link><guid>https://blog.youkuaiyun.com/kkbb8811/article/details/57426571</guid><author>kkbb8811</author><pubDate>Sun, 26 Feb 2017 19:07:13 +0800</pubDate><description><![CDATA[from pandas import Series,DataFrame
import pandas as pd
import numpy as np
from numpy import nan as NA
df = DataFrame(np.random.randn(7,3))
df.ix[:4,1] = NA
df.ix[:2,2] = NA
df
df.fillna(0) # 将缺失值填充为0]]></description><category></category></item><item><title><![CDATA[利用Python数据分析：pandas入门（四)]]></title><link>https://blog.youkuaiyun.com/kkbb8811/article/details/56011947</link><guid>https://blog.youkuaiyun.com/kkbb8811/article/details/56011947</guid><author>kkbb8811</author><pubDate>Mon, 20 Feb 2017 13:21:43 +0800</pubDate><description><![CDATA[s1 = Series([7.3,-2.5,3.4,1.5],index=['a','c','d','e'])
s2 = Series([-2.1,3.6,-1.5,4,3.1],index=['a','c','e','f','g'])
s1
s2
s1+s2 # 索引相同的会相加自动对齐，不重叠的索引引入为NA值
df1 = DataFrame(np.arange(9.).reshape((3,]]></description><category></category></item><item><title><![CDATA[利用Python数据分析：pandas入门（三）]]></title><link>https://blog.youkuaiyun.com/kkbb8811/article/details/56011929</link><guid>https://blog.youkuaiyun.com/kkbb8811/article/details/56011929</guid><author>kkbb8811</author><pubDate>Mon, 20 Feb 2017 13:19:17 +0800</pubDate><description><![CDATA[obj  = Series(range(3),index=['a','b','c'])
index = obj.index
index
index[1:]
index[1] = 'd' # index对象是不能被修改的  Index does not support mutable operations
index = pd.Index(np.arange(3))
obj2 = Series([1]]></description><category></category></item><item><title><![CDATA[用Python写网络爬虫系列（三）表单处理]]></title><link>https://blog.youkuaiyun.com/kkbb8811/article/details/55194082</link><guid>https://blog.youkuaiyun.com/kkbb8811/article/details/55194082</guid><author>kkbb8811</author><pubDate>Wed, 15 Feb 2017 12:35:06 +0800</pubDate><description><![CDATA[import  urllib,urllib2
LOGIN_URL = r'http://example.webscraping.com/user/login'
LOGIN_EMAIL = 'kkbb8811@qq.com'
LOGIN_PASSWORD ='qq123456'
data ={'email':LOGIN_EMAIL,'password':LOGIN_PASSWORD}
encoded]]></description><category></category></item><item><title><![CDATA[利用Python数据分析：pandas入门（二）]]></title><link>https://blog.youkuaiyun.com/kkbb8811/article/details/55006463</link><guid>https://blog.youkuaiyun.com/kkbb8811/article/details/55006463</guid><author>kkbb8811</author><pubDate>Sun, 12 Feb 2017 22:02:35 +0800</pubDate><description><![CDATA[import pandas as pd
import numpy as np
from pandas import Series,DataFrame
data = {'state':['Ohio','Ohio','Ohio','Nevada','Nevada'],
        'year':[2000,2001,2002,2001,2002],
        'pop':[1.5,1.7,3]]></description><category></category></item><item><title><![CDATA[利用Python数据分析：pandas入门（一）]]></title><link>https://blog.youkuaiyun.com/kkbb8811/article/details/55006427</link><guid>https://blog.youkuaiyun.com/kkbb8811/article/details/55006427</guid><author>kkbb8811</author><pubDate>Sun, 12 Feb 2017 22:01:40 +0800</pubDate><description><![CDATA[from pandas import Series,DataFrame
import pandas as pd
import numpy as np


# Series:是由数据和数据标签组成的也就是数据+索引 是pandas的基本数据结构
obj = Series([4,7,-5,3])
obj # 因为没有给数组指定索引系统会自动创建一个从0~N-1长度的整数型的索引
obj.values ]]></description><category></category></item><item><title><![CDATA[利用Python数据分析：Numpy基础（七）]]></title><link>https://blog.youkuaiyun.com/kkbb8811/article/details/54943441</link><guid>https://blog.youkuaiyun.com/kkbb8811/article/details/54943441</guid><author>kkbb8811</author><pubDate>Thu, 09 Feb 2017 09:00:23 +0800</pubDate><description><![CDATA[import numpy as np

samples = np.random.normal(size=(4,4)) # 标准正态分布一个4*4的样本数组
samples
#随机漫步
#纯Python的内置模块进行1000步的随机漫步
import  random
position = 0
walk = [position]
steps = 1000
for i in xrange(steps):]]></description><category></category></item><item><title><![CDATA[利用Python数据分析：Numpy基础（六）]]></title><link>https://blog.youkuaiyun.com/kkbb8811/article/details/54925365</link><guid>https://blog.youkuaiyun.com/kkbb8811/article/details/54925365</guid><author>kkbb8811</author><pubDate>Wed, 08 Feb 2017 12:28:42 +0800</pubDate><description><![CDATA[import numpy as np

arr = np.random.randn(8)
arr
arr.sort()
arr
np.sort(arr) #np的顶级方法返回的是副本
arr = np.random.randn(5, 3)
arr
arr.sort(1) #多维数组按照指定的轴进行排序
arr#这里属于就地排序直接在数组上进行操作
#分位数计算：先进行排序而后取对应位置的值
lar]]></description><category></category></item><item><title><![CDATA[利用Python数据分析：Numpy基础（五）]]></title><link>https://blog.youkuaiyun.com/kkbb8811/article/details/54908615</link><guid>https://blog.youkuaiyun.com/kkbb8811/article/details/54908615</guid><author>kkbb8811</author><pubDate>Tue, 07 Feb 2017 13:02:32 +0800</pubDate><description><![CDATA[import numpy as np

points = np.arange(-5, 5, 0.01) # 产生1000个建个相等的点
xs, ys = np.meshgrid(points, points) # meshgrid接受两个一维的数组，并且产生两个二维矩阵
ys
import matplotlib.pyplot as plt
z = np.sqrt(xs**2+ys**2)  # 而]]></description><category></category></item><item><title><![CDATA[利用Python数据分析：Numpy基础（四）]]></title><link>https://blog.youkuaiyun.com/kkbb8811/article/details/54882125</link><guid>https://blog.youkuaiyun.com/kkbb8811/article/details/54882125</guid><author>kkbb8811</author><pubDate>Sun, 05 Feb 2017 17:16:32 +0800</pubDate><description><![CDATA[import numpy as np

arr = np.arange(15).reshape((3,5))
arr
arr.T # 转置返回的是源数据的视图，不仅仅有transpose方法还有一个特殊的T属性，行变成列，列变成行
# np使用 dot来计算矩阵的内积
arr = np.random.randn(6,3)
np.dot(arr.T,arr)
# 对于高维的数组transpose方法]]></description><category></category></item><item><title><![CDATA[利用Python数据分析：Numpy基础（三）]]></title><link>https://blog.youkuaiyun.com/kkbb8811/article/details/54836122</link><guid>https://blog.youkuaiyun.com/kkbb8811/article/details/54836122</guid><author>kkbb8811</author><pubDate>Thu, 02 Feb 2017 15:43:32 +0800</pubDate><description><![CDATA[names = np.array(['Bob', 'Joe', 'Will', 'Bob', 'Will', 'Joe', 'Joe'])
data = np.random.randn(7, 4)  # 七行四列的数组
names
data
names == 'Bob'
data[names == 'Bob']
# 在这里names的长度为7 这个二维数组的长度也为7 于是可以用这个布尔型的数组作]]></description><category></category></item></channel></rss>