<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[annex1的博客]]></title><description><![CDATA[]]></description><link>https://blog.youkuaiyun.com/annex1</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; annex1]]></copyright><item><title><![CDATA[STATA 常用模型与命令]]></title><link>https://blog.youkuaiyun.com/annex1/article/details/107858962</link><guid>https://blog.youkuaiyun.com/annex1/article/details/107858962</guid><author>annex1</author><pubDate>Fri, 07 Aug 2020 14:48:01 +0800</pubDate><description><![CDATA[STATA常用命令数据相关生成新数据删除和修改数据改变数据类型条件命令取对数命令输出相关常用回归非线性选择回归logit回归probit回归线性回归OLSHeckman 回归Tobit回归2SLS回归（工具变量法）常用检验异方差检验多重共线性检验：自相关检验：格兰杰因果检验：稳健性检验：内生性检验：
常用命令
数据相关
生成新数据
利用旧数据定义新数据，注意，如果使用的旧数据为两个及以上，只要其中一个为空值，则新数据也为空值（即空值不会自动转化为0）：
gen new_valuename  =  f(val]]></description><category></category></item><item><title><![CDATA[python_matplot_图片批量保存]]></title><link>https://blog.youkuaiyun.com/annex1/article/details/107656395</link><guid>https://blog.youkuaiyun.com/annex1/article/details/107656395</guid><author>annex1</author><pubDate>Wed, 29 Jul 2020 10:11:11 +0800</pubDate><description><![CDATA[python_matplot_图片批量保存
为了选取合适的变量，希望画出每一个变量的数据分布情况
因为有四百多个变量我懒得自己处理
首先读取数据和写表头，都是老生常谈我就不写注释了
import pandas as pd
df = pd.read_excel("filepath\\sample_test1.xlsx")
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif']=['SimHei']
plt.rcParams['axes.u]]></description><category></category></item><item><title><![CDATA[python_matplot_学习日志（scatter）]]></title><link>https://blog.youkuaiyun.com/annex1/article/details/107629753</link><guid>https://blog.youkuaiyun.com/annex1/article/details/107629753</guid><author>annex1</author><pubDate>Tue, 28 Jul 2020 10:48:33 +0800</pubDate><description><![CDATA[python_matplot_学习日志（1）散点图（scatter）
1）导入数据
import pandas as pd

df = pd.read_excel("C:\\Users\\DELL\\Desktop\\sample_test1.xlsx")`


2）创建画布
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif']=['SimHei']
#确保中文的正常显示
plt.rcParams['axes.unicode_minu]]></description><category></category></item></channel></rss>