xuefliang

1 、删除anaconda文件 2、修改.bashrc文件 export PATH="/home/xuefliang/anaconda2/bin:$PATH" 中的/home/xuefliang/anaconda2/bin：删除

1、下载prce的makefile，放在SDK中package中 https://github.com/openwrt/packages/tree/master/libs/pcre 2、make menuconfig选中shadowsocks.保存退出再执行make V=s 即可

1、sudo sh ~/Downloads/crouton -r list 2 sudo sh ~/Downloads/crouton -r xenial -t core 3 sudo enter-chroot 4 sudo apt install vim git python bash-completion proxychains software-properties-common sudo enter-chroot 查看 sudo edit-chroot -a 删除 sudo delete-chroot kde

1、sudo sh ~/Downloads/crouton -r list 2 sudo sh ~/Downloads/crouton -r xenial -t core 3 sudo enter-chroot 4 sudo apt install vim git python bash-completion proxychains software-properties-common sudo enter-chroot 查看 sudo edit-chroot -a 删除 sudo delete-chroot kde

1、sudo atp update 2、sudo apt install hplip vim 3、sudo vim  /etc/cups/cupsd.conf 0.0.0.0:631 < Location  / > Order  allow , deny Allow from  @ Local < / Location >   # Restrict access to the admin pages… < Location  / admin > Order  allow , deny Allow from  @ Local < / Location >   # Restrict access to configuration files… < Location  / admin / conf > AuthType  Default Require  user  @ SYSTEM Order  allow , deny Allow from @ Local < / Location > 4、 sudo service cups start 5、 https://你的树莓派ip:631 6、添加 http://你的树莓派ip:631/printers/打印机名

1、raspberry sudo apt-get install xrdp 2、客户端 sudo apt-get install rdesktop  rdesktop 192.168.10.117

# -*- coding: utf-8 -*- import pandas as pd data = {'Country': ['Belgium', 'India', 'Brazil'],            'Capital': ['Brussels', 'New Delhi', 'Brasília'],            'Population': [11190846, 1303171035, 207847528]} df = pd.DataFrame(data, columns=['Country', 'Capital', 'Population']) s = pd.Series([3, -5, 7, 4], index=['a', 'b', 'c', 'd']) df['Country'] #By Position # Select single value by row & column df.iloc[0,0] df.iat[0,0] #By Label #Select single value by row &  column labels df.loc[0,'Country'] df.at[0, 'Country'] # By Label/Position # Select single row of subset of rows df.ix[2] #Select a single column of  subset of columns df.ix[:,'Capital'] # Select rows and columns df.ix[1,'Capital'] #Boolean Indexing # Series s   where value is not >1 s[~(s > 1)] #s where value i