<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[hellenlee22的博客]]></title><description><![CDATA[]]></description><link>https://blog.youkuaiyun.com/hellenlee22</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; hellenlee22]]></copyright><item><title><![CDATA[pandas中Dataframe索引.ix,.iloc,.loc的使用以及区别]]></title><link>https://blog.youkuaiyun.com/hellenlee22/article/details/107800834</link><guid>https://blog.youkuaiyun.com/hellenlee22/article/details/107800834</guid><author>hellenlee22</author><pubDate>Tue, 04 Aug 2020 21:48:35 +0800</pubDate><description><![CDATA[pandas中Dataframe索引.ix,.iloc,.loc的使用以及区别
https://blog.youkuaiyun.com/qq1483661204/article/details/77587881
Pandas——loc、iloc、ix 函数区别
https://blog.youkuaiyun.com/u012736685/article/details/86610946

]]></description><category></category></item><item><title><![CDATA[【MySQL】如何将csv文件导入到mysql数据库]]></title><link>https://blog.youkuaiyun.com/hellenlee22/article/details/107746057</link><guid>https://blog.youkuaiyun.com/hellenlee22/article/details/107746057</guid><author>hellenlee22</author><pubDate>Sun, 02 Aug 2020 15:59:04 +0800</pubDate><description><![CDATA[【MySQL】如何将csv文件导入到mysql数据库
转自:https://blog.youkuaiyun.com/unsicherheit/article/details/89712797

]]></description><category></category></item><item><title><![CDATA[python-将多个空列添加到pandas DataFrame]]></title><link>https://blog.youkuaiyun.com/hellenlee22/article/details/107721341</link><guid>https://blog.youkuaiyun.com/hellenlee22/article/details/107721341</guid><author>hellenlee22</author><pubDate>Fri, 31 Jul 2020 20:58:03 +0800</pubDate><description><![CDATA[python-将多个空列添加到pandas DataFrame
转自：https://www.itranslater.com/qa/details/2582599586510537728

]]></description><category></category></item><item><title><![CDATA[python-将多个空列添加到pandas DataFrame]]></title><link>https://blog.youkuaiyun.com/hellenlee22/article/details/107663014</link><guid>https://blog.youkuaiyun.com/hellenlee22/article/details/107663014</guid><author>hellenlee22</author><pubDate>Wed, 29 Jul 2020 14:15:16 +0800</pubDate><description><![CDATA[转自：https://www.itranslater.com/qa/details/2582599586510537728
python-将多个空列添加到pandas DataFrame
这可能是一个愚蠢的问题，但是如何将多个空列从列表添加到DataFrame？
我可以：
df[“B”] = None
df[“C”] = None
df[“D”] = None
但是我不能：
df[[“B”, “C”, “D”]] = None
KeyError: “[‘B’ ‘C’ ‘D’] not in index”
p]]></description><category></category></item><item><title><![CDATA[pandas.read_csv() 报错 OSError: Initializing from file failed，报错原因分析和解决方法]]></title><link>https://blog.youkuaiyun.com/hellenlee22/article/details/107645187</link><guid>https://blog.youkuaiyun.com/hellenlee22/article/details/107645187</guid><author>hellenlee22</author><pubDate>Tue, 28 Jul 2020 21:04:59 +0800</pubDate><description><![CDATA[Python版本：Python 3.6
pandas.read_csv() 报错 OSError: Initializing from file failed，一般由两种情况引起：一种是函数参数为路径而非文件名称，另一种是函数参数带有中文。
# -*- coding: utf-8 -*-
"""
Created on Mon Jun  4 09:44:36 2018
@author: wfxu
"""
import pandas as pd
da1=pd.read_csv('F:\\数据源')
da2=pd]]></description><category></category></item><item><title><![CDATA[Sklearn Impute SimpleImputer 处理缺失值]]></title><link>https://blog.youkuaiyun.com/hellenlee22/article/details/107639518</link><guid>https://blog.youkuaiyun.com/hellenlee22/article/details/107639518</guid><author>hellenlee22</author><pubDate>Tue, 28 Jul 2020 16:39:47 +0800</pubDate><description><![CDATA[Sklearn 有专门处理缺失值的模块 sklearn.impute.SimpleImputer，本文将探究如何用 Sklearn 中的预处理模块中的 Impute.SimpleImputer 处理缺失值。
一、模块介绍
官网详解在 part 6.4 Imputation of missing values：https://scikit-learn.org/stable/modules/impute.html
它包含四个重要参数：

二、示例应用
接下来将用实例来讲解如何使用填补缺失值：
1）数据来源及数据]]></description><category></category></item><item><title><![CDATA[Pandas中loc和iloc函数用法详解]]></title><link>https://blog.youkuaiyun.com/hellenlee22/article/details/107583697</link><guid>https://blog.youkuaiyun.com/hellenlee22/article/details/107583697</guid><author>hellenlee22</author><pubDate>Sat, 25 Jul 2020 19:49:18 +0800</pubDate><description><![CDATA[loc函数：通过行索引 “Index” 中的具体值来取行数据（如取"Index"为"A"的行）
iloc函数：通过行号来取行数据（如取第二行的数据）
本文给出loc、iloc常见的五种用法，并附上详细代码。
1. 利用loc、iloc提取行数据
import numpy as np
import pandas as pd
#创建一个Dataframe
data=pd.DataFrame(np.arange(16).reshape(4,4),index=list('abcd'),columns=list('A]]></description><category></category></item><item><title><![CDATA[AttributeError；‘Series‘ object has no attribute ‘split‘解决]]></title><link>https://blog.youkuaiyun.com/hellenlee22/article/details/107565589</link><guid>https://blog.youkuaiyun.com/hellenlee22/article/details/107565589</guid><author>hellenlee22</author><pubDate>Fri, 24 Jul 2020 17:27:08 +0800</pubDate><description><![CDATA[AttributeError；‘Series’ object has no attribute 'split’解决
pandas对字符串做处理
&gt;&gt;&gt; s = pd.Series(["this is good text", "but this is even better"])   
&gt;&gt;&gt; [x for x in s.split()]

如果我们直接对Series中的字符串做切分，就会报错 AttributeError: ‘Series’ object has no a]]></description><category></category></item><item><title><![CDATA[tableau 链接MySQL8.0报错[MySQL][ODBC 8.0(w) Driver]Can‘t connect to MySQL server on ‘192.168.1.3‘ (10061]]></title><link>https://blog.youkuaiyun.com/hellenlee22/article/details/107548570</link><guid>https://blog.youkuaiyun.com/hellenlee22/article/details/107548570</guid><author>hellenlee22</author><pubDate>Thu, 23 Jul 2020 21:46:24 +0800</pubDate><description><![CDATA[tableau 链接MySQL8.0报错[MySQL][ODBC 8.0(w) Driver]Can’t connect to MySQL server on ‘192.168.1.3’ (10061）的解决办法



打开控制面板，右上角的查看方式选择大图标，选择里面的管理工具打开；
在管理工具里面，选择ODBC数据源选项，打开；
在数据源管理程序里，选择用户DNS选项，之后点击右边的添加选项，添加配置驱动程序



在弹出的以下窗口中填入相关的内容，之后点击右下方的test按钮测试，若显示connect]]></description><category></category></item><item><title><![CDATA[DataFrame写入 MySQL（create_engine）]]></title><link>https://blog.youkuaiyun.com/hellenlee22/article/details/107510784</link><guid>https://blog.youkuaiyun.com/hellenlee22/article/details/107510784</guid><author>hellenlee22</author><pubDate>Wed, 22 Jul 2020 15:50:36 +0800</pubDate><description><![CDATA[参考：
Python连接MySQL数据库（pymysql），DataFrame写入 MySQL（create_engine）- Python代码
https://blog.youkuaiyun.com/weixin_40683253/article/details/86741134
Pandas DataFrame数据写入文件和数据库
https://blog.youkuaiyun.com/dta0502/article/details/81876224?utm_source=blogxgwz6




...]]></description><category></category></item><item><title><![CDATA[巴西百货商店olist客户购买商品分析]]></title><link>https://blog.youkuaiyun.com/hellenlee22/article/details/107369744</link><guid>https://blog.youkuaiyun.com/hellenlee22/article/details/107369744</guid><author>hellenlee22</author><pubDate>Wed, 15 Jul 2020 20:33:15 +0800</pubDate><description><![CDATA[#https://zhuanlan.zhihu.com/p/53271588

import pandas as pd
filenamestr=r'C:\Users\Administrator\Desktop\olist_products_dataset.xlsx'
olist=pd.ExcelFile(filenamestr,dtype='str')
olist_product=olist.parse('olist_products_dataset',dtype='str')

olist_product]]></description><category></category></item><item><title><![CDATA[利用python分析泰坦尼克号数据集]]></title><link>https://blog.youkuaiyun.com/hellenlee22/article/details/107363783</link><guid>https://blog.youkuaiyun.com/hellenlee22/article/details/107363783</guid><author>hellenlee22</author><pubDate>Wed, 15 Jul 2020 17:49:55 +0800</pubDate><description><![CDATA[利用python分析泰坦尼克号数据集
参考：https://www.cnblogs.com/mxwbq/p/7106123.html?utm_source=itdadao&amp;utm_medium=referral
Olist巴西电子商务数据分析
参考：https://blog.youkuaiyun.com/xiaocai1python/article/details/104781511
https://www.jianshu.com/p/b5b8be56dfba?utm_campaign=haruki&amp;u]]></description><category></category></item><item><title><![CDATA[jupyter notebook 提示no moudle pymysql的解决办法]]></title><link>https://blog.youkuaiyun.com/hellenlee22/article/details/107331936</link><guid>https://blog.youkuaiyun.com/hellenlee22/article/details/107331936</guid><author>hellenlee22</author><pubDate>Tue, 14 Jul 2020 14:43:01 +0800</pubDate><description><![CDATA[参考:
https://blog.youkuaiyun.com/weixin_41004763/article/details/88073528
https://www.cnblogs.com/jisongxie/p/9892660.html

]]></description><category></category></item><item><title><![CDATA[概率问题的解决办法]]></title><link>https://blog.youkuaiyun.com/hellenlee22/article/details/106761293</link><guid>https://blog.youkuaiyun.com/hellenlee22/article/details/106761293</guid><author>hellenlee22</author><pubDate>Mon, 15 Jun 2020 14:36:03 +0800</pubDate><description><![CDATA[概率问题，是面试数据分析师，算法工程师必考之一。【以下是一些网页截图】
培训了那么多数据求职同学，发现大家普遍薄弱的是概率问题。
由于大多数的概率问题可以由几种不同的思路来解题，所以导致很多同学理解了这题，再思考另一题的时候又卡住了。
而且概率问题，不像曾经做过的project，可以提前做充分的准备。
概率问题，就像高考题目，你永远不知道面试官会问什么，
但是基本思路就这几种，只要融会贯通了，任何题目能可以迎刃而解。
这篇文章总结了几种概率面试问题，
重点是，我们从头到尾就只用同一种方法来解所有的问题！
这]]></description><category></category></item><item><title><![CDATA[R code execution error处理]]></title><link>https://blog.youkuaiyun.com/hellenlee22/article/details/106753953</link><guid>https://blog.youkuaiyun.com/hellenlee22/article/details/106753953</guid><author>hellenlee22</author><pubDate>Sun, 14 Jun 2020 23:27:29 +0800</pubDate><description><![CDATA[R code execution error处理
R code execution error
解决方法：
Ctrl + Shift + F10 to restart your R session
原文链接：https://blog.youkuaiyun.com/u012111465/article/details/89681685

]]></description><category></category></item><item><title><![CDATA[利用python进行数据分析 数据聚合与分组运算groupby 和时间序列操作]]></title><link>https://blog.youkuaiyun.com/hellenlee22/article/details/106139859</link><guid>https://blog.youkuaiyun.com/hellenlee22/article/details/106139859</guid><author>hellenlee22</author><pubDate>Fri, 15 May 2020 13:11:54 +0800</pubDate><description><![CDATA[&gt;&gt;&gt; from pandas import DataFrame,Series
&gt;&gt;&gt; import numpy as np
&gt;&gt;&gt; df=DataFrame({'key1':['a','a','b','b','a'],'key2':['one','two','one','two','one'],'data1':np.random.randn(5),'data2':np.random.randn(5)})


&gt;&gt;&gt; df
  key.]]></description><category></category></item><item><title><![CDATA[pandas: pd.concat 用法]]></title><link>https://blog.youkuaiyun.com/hellenlee22/article/details/106064124</link><guid>https://blog.youkuaiyun.com/hellenlee22/article/details/106064124</guid><author>hellenlee22</author><pubDate>Mon, 11 May 2020 22:20:09 +0800</pubDate><description><![CDATA[Python 3.6.5 (v3.6.5:f59c0932b4, Mar 28 2018, 17:00:18) [MSC v.1900 64 bit (AMD64)] on win32
Type "copyright", "credits" or "license()" for more information.
&gt;&gt;&gt; import pandas as pd
&gt;&gt;&gt; from pandas import Series
&gt;&gt;&gt; import numpy ]]></description><category></category></item><item><title><![CDATA[数据分析师的技能要求是什么？]]></title><link>https://blog.youkuaiyun.com/hellenlee22/article/details/106044843</link><guid>https://blog.youkuaiyun.com/hellenlee22/article/details/106044843</guid><author>hellenlee22</author><pubDate>Sun, 10 May 2020 23:37:36 +0800</pubDate><description><![CDATA[转自:https://zhidao.baidu.com/question/1953122565379821108.html
数据分析师的技能要求是什么？
1、懂业务。从事数据分析工作的前提就会需要懂业务，即熟悉行业知识、公司业务及流程，很好有自己独到的见解，若脱离行业认知和公司业务背景，分析的结果只会是脱了线的风筝，没有太大的使用价值。
2、懂管理。一方面是搭建数据分析框架的要求，比如确定分析思路就需要用到营销、管理等理论知识来指导，如果不熟悉管理理论，就很难搭建数据分析的框架，后续的数据分析也很难进行。另]]></description><category></category></item><item><title><![CDATA[Python操作mongodb的9个步骤]]></title><link>https://blog.youkuaiyun.com/hellenlee22/article/details/106035543</link><guid>https://blog.youkuaiyun.com/hellenlee22/article/details/106035543</guid><author>hellenlee22</author><pubDate>Sun, 10 May 2020 14:04:06 +0800</pubDate><description><![CDATA[一 导入 pymongo
from pymongo import MongoClient
二 连接服务器 端口号 27017
连接MongoDB
连接MongoDB我们需要使用PyMongo库里面的MongoClient，一般来说传入MongoDB的IP及端口即可，第一个参数为地址host，第二个参数为端口port，端口如果不传默认是27017。
conn = MongoClient(“localhost”)
MongoClient(host=‘127.0.0.1’,port=27017)
三 连接数据库
]]></description><category></category></item><item><title><![CDATA[Python匿名函数lambda、filter函数、map函数、reduce函数用法详解]]></title><link>https://blog.youkuaiyun.com/hellenlee22/article/details/106034605</link><guid>https://blog.youkuaiyun.com/hellenlee22/article/details/106034605</guid><author>hellenlee22</author><pubDate>Sun, 10 May 2020 12:36:36 +0800</pubDate><description><![CDATA[&gt;&gt;&gt; range(1,100)
range(1, 100)
&gt;&gt;&gt; x=range(1,100)
&gt;&gt;&gt; for i in range(1,100):
	print (i)

	
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