i = 3
print(1 < i < 3)  # False
print(1 < i <= 3)  # True

from operator import *

def calculator(a, b, k):
    return {
        '+': add,
        '-': sub,
        '*': mul,
        '/': truediv,
        '**': pow
    }[k](a, b)

calculator(1, 2, '+')  # 3
calculator(3, 4, '**')  # 81

from operator import (add, sub)

def add_or_sub(a, b, oper):
    return (add if oper == '+' else sub)(a, b)

add_or_sub(1, 2, '-')  # -1

def str_byte_len(mystr):
    return (len(mystr.encode('utf-8')))

str_byte_len('i love python')  # 13(个字节)
str_byte_len('字符')  # 6(个字节)

def search_n(s, c, n):
    size = 0
    for i, x in enumerate(s):
        if x == c:
            size += 1
        if size == n:
            return i
    return -1

print(search_n("fdasadfadf", "a", 3))# 结果为7，正确
print(search_n("fdasadfadf", "a", 30))# 结果为-1，正确

def score_mean(lst):
    lst.sort()
    lst2=lst[1:(len(lst)-1)]
    return round((sum(lst2)/len(lst2)),2)

score_mean([9.1, 9.0,8.1, 9.7, 19,8.2, 8.6,9.8]) # 9.07

def swap(a, b):
    return b, a

swap(1, 0)  # (0,1)

def binarySearch(arr, left, right, x):
    while left <= right:
        mid = int(left + (right - left) / 2); # 找到中间位置。求中点写成(left+right)/2更容易溢出，所以不建议这样写

        # 检查x是否出现在位置mid
        if arr[mid] == x:
            print('found %d 在索引位置%d 处' %(x,mid))
            return mid

            # 假如x更大，则不可能出现在左半部分
        elif arr[mid] < x:
            left = mid + 1 #搜索区间变为[mid+1,right]
            print('区间缩小为[%d,%d]' %(mid+1,right))

        elif x<arr[mid]:
            right = mid - 1 #搜索区间变为[left,mid-1]
            print('区间缩小为[%d,%d]' %(left,mid-1))

    return -1

x,y = mgrid[0:5,0:5]
list(map(lambda xe,ye: [(ex,ey) for ex, ey in zip(xe, ye)], x,y))
[[(0, 0), (0, 1), (0, 2), (0, 3), (0, 4)],
 [(1, 0), (1, 1), (1, 2), (1, 3), (1, 4)],
 [(2, 0), (2, 1), (2, 2), (2, 3), (2, 4)],
 [(3, 0), (3, 1), (3, 2), (3, 3), (3, 4)],
 [(4, 0), (4, 1), (4, 2), (4, 3), (4, 4)]]

for i in range(1,10):
    for j in range(1,i+1):
        print('{0}*{1}={2}'.format(j,i,j*i),end="\t")
    print()

from collections.abc import *

def flatten(input_arr, output_arr=None):
    if output_arr is None:
        output_arr = []
    for ele in input_arr:
        if isinstance(ele, Iterable): # 判断ele是否可迭代
            flatten(ele, output_arr)  # 尾数递归
        else:
            output_arr.append(ele)    # 产生结果
    return output_arr

flatten([[1,2,3],[4,5]], [6,7]) # [6, 7, 1, 2, 3, 4, 5]

from math import ceil

def divide(lst, size):
    if size <= 0:
        return [lst]
    return [lst[i * size:(i+1)*size] for i in range(0, ceil(len(lst) / size))]

r = divide([1, 3, 5, 7, 9], 2) # [[1, 3], [5, 7], [9]]

def fibonacci(n):
    if n <= 1:
        return [1]
    fib = [1, 1]
    while len(fib) < n:
        fib.append(fib[len(fib) - 1] + fib[len(fib) - 2])
    return fib

fibonacci(5)  # [1, 1, 2, 3, 5]

def filter_false(lst):
    return list(filter(bool, lst))

filter_false([None, 0, False, '', [], 'ok', [1, 2]])# ['ok', [1, 2]]

def head(lst):
    return lst[0] if len(lst) > 0 else None

head([])  # None
head([3, 4, 1])  # 3

def tail(lst):
    return lst[-1] if len(lst) > 0 else None
    
print(tail([]))  # None
print(tail([3, 4, 1]))  # 1

from collections.abc import Iterable

def cast_iterable(val):
    return val if isinstance(val, Iterable) else [val]

cast_iterable('foo')# foo
cast_iterable(12)# [12]
cast_iterable({'foo': 12})# {'foo': 12}

def max_length(*lst):
    return max(*lst, key=lambda v: len(v))

r = max_length([1, 2, 3], [4, 5, 6, 7], [8])# [4, 5, 6, 7]

def max_frequency(lst):
    return max(lst, default='列表为空', key=lambda v: lst.count(v))

lst = [1, 3, 3, 2, 1, 1, 2]
max_frequency(lst) # 1 

def max_lists(*lst):
    return max(max(*lst, key=lambda v: max(v)))

max_lists([1, 2, 3], [6, 7, 8], [4, 5]) # 8

def min_lists(*lst):
    return min(min(*lst, key=lambda v: max(v)))

min_lists([1, 2, 3], [6, 7, 8], [4, 5]) # 1

def has_duplicates(lst):
    return len(lst) == len(set(lst))

x = [1, 1, 2, 2, 3, 2, 3, 4, 5, 6]
y = [1, 2, 3, 4, 5]
has_duplicates(x)  # False
has_duplicates(y)  # True

from collections import Counter

def find_all_duplicates(lst):
    c = Counter(lst)
    return list(filter(lambda k: c[k] > 1, c))

find_all_duplicates([1, 2, 2, 3, 3, 3])  # [2,3]

def reverse(lst):
    return lst[::-1]

reverse([1, -2, 3, 4, 1, 2])# [2, 1, 4, 3, -2, 1]

def rang(start, stop, n):
    start,stop,n = float('%.2f' % start), float('%.2f' % stop),int('%.d' % n)
    step = (stop-start)/n
    lst = [start]
    while n > 0:
        start,n = start+step,n-1
        lst.append(round((start), 2))
    return lst

rang(1, 8, 10) # [1.0, 1.7, 2.4, 3.1, 3.8, 4.5, 5.2, 5.9, 6.6, 7.3, 8.0]

#对列表按条件分组
def bif_by(lst, Fn):
    return [ [x for x in lst if Fn(x)],[x for x in lst if not Fn(x)]]

records = [25,89,31,34] 
bif_by(records, lambda x: x<80) # [[25, 31, 34], [89]]

def max_pairs(dic):
    if len(dic) == 0:
        return dic
    max_val = max(map(lambda v: v[1], dic.items()))
    return [item for item in dic.items() if item[1] == max_val]

max_pairs({'a': -10, 'b': 5, 'c': 3, 'd': 5})# [('b', 5), ('d', 5)]

def min_pairs(dic):
    if len(dic) == 0:
        return []
    min_val = min(map(lambda v: v[1], dic.items()))
    return [item for item in dic.items() if item[1] == min_val]


min_pairs({}) # []

r = min_pairs({'a': -10, 'b': 5, 'c': 3, 'd': 5})
print(r)  # [('b', 5), ('d', 5)]

def merge_dict2(dic1, dic2):
    return {**dic1, **dic2}  # python3.5后支持的一行代码实现合并字典

merge_dict({'a': 1, 'b': 2}, {'c': 3})  # {'a': 1, 'b': 2, 'c': 3}

from heapq import nlargest

# 返回字典d前n个最大值对应的键
def topn_dict(d, n):
    return nlargest(n, d, key=lambda k: d[k])

topn_dict({'a': 10, 'b': 8, 'c': 9, 'd': 10}, 3)  # ['a', 'd', 'c']

d={'a':-10,'b':5, 'c':3,'d':5}
min(d.items(),key=lambda x:x[1]) #('a', -10)

from collections import Counter
# 检查两个字符串是否 相同字母异序词，简称：互为变位词
def anagram(str1, str2):
    return Counter(str1) == Counter(str2)

anagram('eleven+two', 'twelve+one')  # True 这是一对神奇的变位词
anagram('eleven', 'twelve')  # False

import os

def find_file(work_dir,extension='jpg'):
    lst = []
    for filename in os.listdir(work_dir):
        print(filename)
        splits = os.path.splitext(filename)
        ext = splits[1] # 拿到扩展名
        if ext == '.'+extension:
            lst.append(filename)
    return lst

find_file('.','md') # 返回所有目录下的md文件

import re
def camel(s):
    s = re.sub(r"(\s|_|-)+", " ", s).title().replace(" ", "")
    return s[0].lower() + s[1:]

# 批量转化
def batch_camel(slist):
    return [camel(s) for s in slist]

batch_camel(['student_id', 'student\tname', 'student-add']) #['studentId', 'studentName', 'studentAdd']

import hashlib
import pandas as pd
from wordcloud import WordCloud
geo_data=pd.read_excel(r"../data/geo_data.xlsx")
words = ','.join(x for x in geo_data['city'] if x != []) #筛选出非空列表值
wc = WordCloud(
    background_color="green", #背景颜色"green"绿色
    max_words=100, #显示最大词数
    font_path='./fonts/simhei.ttf', #显示中文
    min_font_size=5,
    max_font_size=100,
    width=500  #图幅宽度
    )
x = wc.generate(words)
x.to_file('../data/geo_data.png')

def fibonacci(n):
    a, b = 1, 1
    for _ in range(n):
        yield a
        a, b = b, a + b

list(fibonacci(5))  # [1, 1, 2, 3, 5]

from math import ceil

def divide_iter(lst, n):
    if n <= 0:
        yield lst
        return
    i, div = 0, ceil(len(lst) / n)
    while i < n:
        yield lst[i * div: (i + 1) * div]
        i += 1

list(divide_iter([1, 2, 3, 4, 5], 0))  # [[1, 2, 3, 4, 5]]
list(divide_iter([1, 2, 3, 4, 5], 2))  # [[1, 2, 3], [4, 5]]

#多层列表展开成单层列表
a=[1,2,[3,4,[5,6],7],8,["python",6],9]
def function(lst):
    for i in lst:
        if type(i)==list:
            yield from function(i)
        else:
            yield i
print(list(function(a))) # [1, 2, 3, 4, 5, 6, 7, 8, 'python', 6, 9]

import numpy as np
from keras.models import Sequential
from keras.layers import Dense

data = np.random.random((1000, 1000))
labels = np.random.randint(2, size=(1000, 1))
model = Sequential()
model.add(Dense(32,
                activation='relu',
                input_dim=100))
model.add(Dense(1, activation='sigmoid'))
model.compile(optimize='rmsprop', loss='binary_crossentropy',
              metrics=['accuracy'])
model.fit(data, labels, epochs=10, batch_size=32)
predictions = model.predict(data)

import smtplib
from email import (header)
from email.mime import (text, application, multipart)
import time

def sender_mail():
    smt_p = smtplib.SMTP()
    smt_p.connect(host='smtp.qq.com', port=25)
    sender, password = '113097485@qq.com', "**************"
    smt_p.login(sender, password)
    receiver_addresses, count_num = [
        'guozhennianhua@163.com', 'xiaoxiazi99@163.com'], 1
    for email_address in receiver_addresses:
        try:
            msg = multipart.MIMEMultipart()
            msg['From'] = "zhenguo"
            msg['To'] = email_address
            msg['subject'] = header.Header('这是邮件主题通知', 'utf-8')
            msg.attach(text.MIMEText(
                '这是一封测试邮件，请勿回复本邮件~', 'plain', 'utf-8'))
            smt_p.sendmail(sender, email_address, msg.as_string())
            time.sleep(10)
            print('第%d次发送给%s' % (count_num, email_address))
            count_num = count_num + 1
        except Exception as e:
            print('第%d次给%s发送邮件异常' % (count_num, email_address))
            continue
    smt_p.quit()
    
sender_mail()