>>> print("hello");print("Tsinghua");
hello
Tsinghua

import keyword
keyword.kwlist

a=b=c=1
a,b,c=1,2,"john"

var1 = 1

del var

>>> s = 'abcdef' 
>>> s[1:5] 
'bcde'

str = 'Hello World!’
print(str) # 输出完整字符串
print(str[0]) # 输出字符串中的第一个字符
print(str[2:5]) # 输出字符串中第三个至第六个之间的字符串
print(str[2:]) # 输出从第三个字符开始的字符串
print(str * 2) # 输出字符串两次
print(str + "TEST") # 输出连接的字符串

>>> letters = ['c','h','e','c','k','i','o']
>>> letters[1:4:2]
['h','c']

tuple = ('tsinghua', 786, 2.23, 'john', 70.2)
tinytuple = (123, 'john')

tuple = ( 'tsinghua', 786 , 2.23, 'john', 70.2 )
list = [ 'tsinghua', 786 , 2.23, 'john', 70.2 ]
tuple[2] = 1000 # 元组中是非法应用
list[2] = 1000 # 列表中是合法应用

dict = {}
dict['one'] = "This is one"
dict[2] = "This is two"
tinydict = {'name': 'Tsinghua', 'code': 6734, 'dept': 'sales'}
dict['one’] # 输出键为'one' 的值
     
dict[2] # 输出键为 2 的值
tinydict # 输出完整的字典
tinydict.keys() # 输出所有键
tinydict.values() # 输出所有值

#!/usr/bin/python
# -*- coding: UTF-8 -*-
a = 10
b = 20
list = [1, 2, 3, 4, 5 ];
if ( a in list ):
	print ("1 - 变量 a 在给定的列表中 list 中")
else:
	print("1 - 变量 a 不在给定的列表中 list 中")
if (b not in list):
	print("2 - 变量 b 不在给定的列表中 list 中")
else:
	print("2 - 变量 b 在给定的列表中 list 中")

a = [1, 2, 3]
>>> b = a
>>> b is a
True
>>> b == a
True
>>> b = a[:]
>>> b is a
False
>>> b == a
True

if 判断条件：
	执行语句……
else：
	执行语句……

if 判断条件1:
	执行语句1……
elif 判断条件2:
	执行语句2……
elif 判断条件3:
	执行语句3……
else:
	执行语句4……

while []:
    []

while[]:
    []
else:
    []

for [] in []:	#for iterating_var in sequence:statements(s)
    []

for [] in []:
    []
else:

for i in range(5): print(i)
for i in range(0, 10, 3) : print(i)
for i in range(-10, -100, -30) : print(i)

a = ['Google', 'Baidu', 'Bing', 'Taobao', 'QQ']
for i in range(len(a)):
	print(i, a[i])

list(range(5))

def functionname( parameters ):
	function_suite
	return [expression]

a = [1, 2, 3]
a = "Tsinghua"

lambda [arg1 [,arg2,.....argn]]:expression

实例 (设置参数 a 加上 10)
x = lambda a : a + 10
print(x(5))

def myfunc(n):
	return lambda a: a * n
mydoubler = myfunc(2)
mytripler = myfunc(3)
print(mydoubler(11))
print(mytripler(11))

class ClassName
	<statement-1>
    <statement-N>
    
class MyClass:
"""一个简单的类实例"""
	i = 12345
	def f(self):
		return 'hello world'
# 实例化类
x = MyClass()
# 访问类的属性和方法
print("MyClass 类的属性 i 为：", x.i)
print("MyClass 类的方法 f 输出为：", x.f())


# MyClass 类的属性 i 为： 12345
# MyClass 类的方法 f 输出为： hello world

def __init__(self):
	self.data = []

class people:
	def __init__(self,name,age):
		self.name=name
		self.age=age
	def __str__(self):
		return ‘这个人的名字是%s,已经有%d岁了！’%(self.name,self.age)

a = people(“孙悟空",999)
print(a)

           
class Complex:
	def __init__(self, realpart, imagpart):
		self.r = realpart
		self.i = imagpart
x = Complex(3.0, -4.5)
print(x.r, x.i)           	

# !/usr/bin/python3
# 类定义
class people:
	# 定义基本属性
	name = ''
	age = 0
	# 定义私有属性,私有属性在类外部无法直接进行访问
	__weight = 0
	# 定义构造方法
	def __init__(self, n, a, w):
		self.name = n
		self.age = a
		self.__weight = w
	def speak(self):
		print("%s 说: 我 %d 岁。" % (self.name, self.age))
        
# 单继承示例
class student(people):
	grade = ''
	def __init__(self, n, a, w, g):
	# 调用父类的构函
		people.__init__(self, n, a, w)
	self.grade = g
	# 覆写父类的方法
	def speak(self):
		print("%s 说: 我 %d 岁了，我在读 %d 年级" %(self.name, self.age, self.grade))

s = student('ken', 10, 60, 3)
s.speak()

class Vector:
	def __init__(self, a, b):
		self.a = a
		self.b = b
	def __str__(self):
		return 'Vector (%d, %d)' % (self.a, self.b)
	def __add__(self, other):
		return Vector(self.a + other.a, self.b + other.b)

v1 = Vector(2, 10)
v2 = Vector(5, -2)
print(v1 + v2)

import random
random.randint(a,b)
#函数返回数字 N ，N 为 a 到 b 之间的数字（a <= N <= b），包含 a 和 b

def swapList(newList):
    newList[0], newList[-1] = newList[-1], newList[0] #同时赋值，分别用原值
    return newList

newList = [1, 2, 3]
print(swapList(newList))

# 引入日历模块
import calendar 
# 输入指定年月 
yy = int(input("输入年份: ")) 
mm = int(input("输入月份: ")) 
# 显示日历 print(calendar.month(yy,mm))


import datetime 
def getYesterday(): 
	yesterday = datetime.date.today() + datetime.timedelta(-1) 
	return yesterday 
# 输出 
print(getYesterday()) 

import time 
print('按下回车开始计时，按下 Ctrl + C 停止计时')
while True: 
	input("") 
	starttime = time.time() 
	print('开始') 
	try: 
		while True: 
			print('计时: ', round(time.time() - starttime, 0), '秒', end="\r") 
			time.sleep(1) 
	except KeyboardInterrupt: 
		print('结束') 
		endtime = time.time() 
		print('总共的时间为:', round(endtime - starttime, 2), 'secs') 
		break

numpy.arry(object, dtype = None, copy = True, order = None, subok = False, ndmin = 0)

import numpy as np
a = np.array([1,2,3])
b = np.array([[1,2],[3,4]])

#生成3行4列零矩阵
np.zero((3,4)) 
#生成3维元素全为1的矩阵
np.ones((2,3,4),dtype = np.int16) 
#初始内容是随机的，取决于内存的状态。默认情况下，创建的数组的dtype是 float64 类型的
np.empty((2,3)) 

>>> np.arange(10,30,5)
array([10,15,20,25])
>>> np.arange(0,2,0.3)
array([0. ,0.3 ,0.6 ,0.9 , 1.2, 1.5, 1.8])

>>> from numpy import pi
>>> np.linspace(0, 2, 9)
array([0, 0.25, 0.5, 0.75, 1. ,1.25 ,1.5, 1.75, 2.])
>>>x = np.linspace(0, 2*pi, 100)
>>>f = np.sin(x)

b[2,3]
b[0:5, 1]	# each row in the second column of b
b[ : ,1]	# equivalent to the previous example
b[1:3, :]	# each column in the second an third row of b

for row in b:
    print(row)
for element in b.flat:
    print(element)

import numpy as np
a = np.array([[1.0,2.0],[3.0,4.0]])
u = np.eye(2) # unit 2x2 matrix; "eye" represents "I"
j = np.array([[0.0,-1.0],[1.0,0.0]])
y = np.array([[5.],[7.]])

a.transpose() # 转置
np.linalg.inv(a) # 求逆
np.trace(a) # 迹
np.linalg.solve(a,y) # 解矩阵方程
np.linage.eig(j) # 特征值和右特征向量

A = np.array([[1,1],[0,1]])
B = np.array([[2,0],[3,4]])

A * B # elementwise product
A @ B # matrix product
A.dot(B) # another matrix product

from scipy.optimize import root
from math import cos
def eqn(x):
	return x + cos(x)
myroot = root(eqn, 0)
print(myroot.x)
print(myroot)

from scipy.optimize import minimize
def eqn(x):
	return x**2 + x + 2
mymin = minimize(eqn, 0, method='BFGS')
print(mymin)

import numpy as np
from scipy.sparse import csr_matrix
arr = np.array([0, 0, 0, 0, 0, 1, 1, 0, 2])
print(csr_matrix(arr))

arr = np.array([[0, 0, 0], [0, 0, 1], [1, 0, 2]])
print(csr_matrix(arr).count_nonzero())

arr = np.array([[0, 0, 0], [0, 0, 1], [1, 0, 2]])
mat = csr_matrix(arr)
mat.eliminate_zeros()
print(mat)

from scipy.interpolate import interp1d
import numpy as np
xs = np.arange(10)
ys = 2*xs + 1
interp_func = interp1d(xs, ys)
newarr = interp_func(np.arange(2.1, 3, 0.1))
print(newarr)

from scipy.interpolate import UnivariateSpline
import numpy as np
xs = np.arange(10)
ys = xs**2 + np.sin(xs) + 1
interp_func = UnivariateSpline(xs, ys)
newarr = interp_func(np.arange(2.1, 3, 0.1))
print(newarr)

from scipy.interpolate import Rbf
import numpy as np
xs = np.arange(10)
ys = xs**2 + np.sin(xs) + 1
interp_func = Rbf(xs, ys)
newarr = interp_func(np.arange(2.1, 3, 0.1))
print(newarr)

import numpy as np
from scipy.stats import normaltest
v = np.random.normal(size=100)
print(normaltest(v))

import numpy as np
from scipy.stats import skew, kurtosis
v = np.random.normal(size=100)
print(skew(v))
print(kurtosis(v)

import numpy as np
import matplotlib.pyplot as plt
X = np.linspace(-np.pi, np.pi, 256, endpoint=True)
C,S = np.cos(X), np.sin(X)
plt.plot(X,C)
plt.plot(X,S)
plt.show()

from pylab import *

figure(figsize=(8,6),dpi=80) #创建一个8*6点的图，设置分辨率为80
subplot(1,1,1) #创建一个新的1*1的子图，接下来的图样绘制在其中的第一块（也是唯一的一块）

X = np.linspace(-np.pi,np.pi,256,endpoint=True)
C,S = np.cos(X),np.sin(X)

plot(X,C,color = "blue", linewidth =1.0, linestyle = "-") #绘制余弦曲线，使用蓝色的、连续的、宽度为1px的线条

plot(X,S,color = "green",linewidth = 1.0, linestyle = "-") #绘制正弦曲线，使用绿色的、连续的、宽度为1px的线条

xlim(-4.0,4.0) #设置横轴的上下限
xticks(np.linspace(-4,4,9,endpoint=True)) #设置横轴记号

ylim(-1.0,1.0) #设置纵轴的上下限
yticks(np.linspace(-1,1,5,endpoint = True)) #设置纵轴记号

# 修改图片边界
xmin ,xmax = X.min(), X.max()
ymin, ymax = Y.min(), Y.max()
dx = (xmax - xmin) * 0.2
dy = (ymax - ymin) * 0.2
xlim(xmin - dx, xmax + dx)
ylim(ymin - dy, ymax + dy)

xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi], [r'$-\pi$', r'$-\pi/2$',r'$0$', r'$+\pi/2$', r'$+\pi$'])
yticks([-1, 0, +1], [r'$-1$', r'$0$', r'$+1$'])

ax = gca() 
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.spines['bottom'].set_position(('data',0))
ax.yaxis.set_ticks_position('left')
ax.spines['left'].set_position(('data',0)) 

plot(X, C, color="blue", linewidth=2.5,linestyle="-", label="cosine")
plot(X, S, color="red", linewidth=2.5,linestyle="-", label="sine")
legend(loc='upper left')

from pylab import *
subplot(2,2,1)
xticks([]), yticks([])
text(0.5,0.5, 'subplot(2,2,1)',ha='center',va='center',size=20,alpha=.5)

subplot(2,2,2)
xticks([]), yticks([])
text(0.5,0.5, 'subplot(2,2,2)',ha='center',va='center',size=20,alpha=.5)

subplot(2,2,3)
xticks([]), yticks([])
text(0.5,0.5, 'subplot(2,2,3)',ha='center',va='center',size=20,alpha=.5)

subplot(2,2,4)
xticks([]), yticks([])
text(0.5,0.5, 'subplot(2,2,4)',ha='center',va='center',size=20,alpha=.5)
# savefig('../figures/subplot-grid.png', dpi=64)
show()

import matplotlib.pyplot as plt
plt.figure(1) # the first figure
plt.subplot(211) # the first subplot in the first figure
plt.plot([1, 2, 3])
plt.subplot(212) # the second subplot in the first figure
plt.plot([4, 5, 6])

plt.figure(2) # a second figure
plt.plot([4, 5, 6]) # creates a subplot(111) by default

plt.figure(1) # figure 1 current; subplot(212) still current
plt.subplot(211) # make subplot(211) in figure1 current
plt.title('Easy as 1, 2, 3') # subplot 211 title
plt.show()

# scatter
from pylab import *
n = 1024
X = np.random.normal(0,1,n)
Y = np.random.normal(0,1,n)
scatter(X,Y)
show()
# bar
from pylab import *
n = 12 X = np.arange(n)
Y1 = (1-X/float(n)) * np.random.uniform(0.5,1.0,n)
Y2 = (1-X/float(n)) * np.random.uniform(0.5,1.0,n)
bar(X, +Y1, facecolor='#9999ff', edgecolor='white’)
bar(X, -Y2, facecolor='#ff9999', edgecolor='white’)
for x,y in zip(X,Y1):
text(x+0.4, y+0.05, '%.2f' % y, ha='center', va= 'bottom’)
ylim(-1.25,+1.25)
show()
# polar
from pylab import *
axes([0,0,1,1])
N = 20
theta = np.arange(0.0, 2*np.pi, 2*np.pi/N)
radii = 10*np.random.rand(N)
width = np.pi/4*np.random.rand(N)
bars = bar(theta, radii, width=width, bottom=0.0)
for r,bar in zip(radii, bars):
bar.set_facecolor( cm.jet(r/10.))
bar.set_alpha(0.5)
show()

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import NullFormatter # useful for `logit` scale
# Fixing random state for reproducibility
np.random.seed(19680801)
# make up some data in the interval ]0, 1[
y = np.random.normal(loc=0.5, scale=0.4, size=1000)
y = y[(y > 0) & (y < 1)]
y.sort()
x = np.arange(len(y))
# plot with various axes scales
plt.figure(1)

# linear
plt.subplot(221)
plt.plot(x, y)
plt.yscale('linear')
plt.title('linear')
plt.grid(True)
# plt.show()
# log
plt.subplot(222)
plt.plot(x, y)
plt.yscale('log')
plt.title('log')
plt.grid(True)
# plt.show()
# symmetric log
plt.subplot(223)
plt.plot(x, y - y.mean())
plt.yscale('symlog')
plt.title('symlog')
plt.grid(True)
# logit
plt.subplot(224)
plt.plot(x, y)
plt.yscale('logit')
plt.title('logit')
plt.grid(True)
# Format the minor tick labels of the y-axis into empty strings with
# `NullFormatter`, to avoid cumbering the axis with too many labels.
plt.gca().yaxis.set_minor_formatter(NullFormatter())
# Adjust the subplot layout, because the logit one may take more space
# than usual, due to y-tick labels like "1 - 10^{-3}"
plt.subplots_adjust(top=0.92, bottom=0.08, left=0.10, right=0.95, hspace=0.25,
wspace=0.35)
plt.show()

import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
th = np.linspace(0, 2*np.pi, 128)
def demo(sty):
	mpl.style.use(sty)
	fig, ax = plt.subplots(figsize=(3, 3))
	ax.set_title('style: {!r}'.format(sty), color='C0')
	ax.plot(th, np.cos(th), 'C1', label='C1')
	ax.plot(th, np.sin(th), 'C2', label='C2')
	ax.legend()
demo('default')
demo('seaborn')
plt.show()

import pandas as pd

pandas.Series( data, index, dtype, name, copy)

import pandas as pd
a = [1, 2, 3]
myvar = pd.Series(a)
print(myvar)

pandas.DataFrame( data, index, columns, dtype, copy)

import pandas as pd
data = {"calories": [420, 380, 390],"duration": [50, 40, 45]}
# 数据载入到 DataFrame 对象
df = pd.DataFrame(data)
# 返回第一行
print(df.loc[0])
# 返回第二行
print(df.loc[1])

import pandas as pd
df = pd.read_csv('nba.csv')
print(df.to_string())