python 物件 class

class Polygon:

    def __init__(self,*p):

        self.p = p

    def __repr__(self):

        return perimeter()

    def __str__(self):

        return "perimeter is " + "{:>3.2f}".format(self.perimeter())

    def perimeter(self):

        p = self.p

        #計算兩點距離(X1-X2)平方+(Y1-Y2)平方，開根號

        i = len(p) - 1

        d = ((p[0][0] - p[i][0]) ** 2 + (p[0][1] - p[i][1]) ** 2) ** 0.5

        for i in range(1,len(p)):

            d += ((p[i][0] - p[i-1][0]) ** 2 + (p[i][1] - p[i-1][1]) ** 2) ** 0.5

        return d

        

        

triangle = Polygon((0,0), (3,0), (0,4))

diamond = Polygon((-1,0), (0,-1), (1,0), (0,1))

print(triangle.perimeter())

print(diamond.perimeter())

print(triangle)

print(diamond)

python 信賴區間

import numpy as np

import scipy.stats as stats

# 估計 95% 信心水準下，老年人每星期看電視平均時間的信賴區間？

# alpha = 0.05

# 假設樣本平均時間的抽樣分配服從常態分佈: xbar ~ N(mu_xbar, sigma_xbar)

# 抽樣人數 n = 100 ; 樣本平均時間 xbar = 21.2 小時

# 母體看電視時間的標準差 sigma = 8 小時

def Interval_estimation(n, xbar, sigma, alpha):

    # standard error of the mean:

    se = sigma / np.sqrt(n)

    # margin of error:

    # Hint: Z_(0.05/2) for Two-tails

    E = stats.norm.ppf(1 - alpha/2, loc=0, scale=1) * se

    # (1 - alpha)% Confidence Interval of Population mean with Known Variance:

    Interval = [xbar - E, xbar + E]

    return Interval

Interval_estimation(n=100, xbar=21.2, sigma=8, alpha=0.05)

python pi 圓周率 機率 驗證

points = 10000

X = np.random.uniform(-1, 1, points)

Y = np.random.uniform(-1, 1, points)

# 落於圓內的點

inner_index = (X**2 + Y**2) <= 1

# 落於圓外的點

outer_index = ~inner_index

indices = (inner_index, outer_index)

X_inner, Y_inner = X[indices[0]], Y[indices[0]]

X_outer, Y_outer = X[indices[1]], Y[indices[1]]

[(X_inner, Y_inner), (X_outer, Y_outer)], indices

plt.scatter(X_inner, Y_inner,s=1,c='red',marker='o',alpha=0.8,label='C1')

plt.scatter(X_outer, Y_outer,s=1,c='blue',marker='o',alpha=0.8,label='C1')

plt.show()

percent = len(X_inner) / points

print("圈內機率:",percent)

print("圈內面積:",2 * 2 * percent)

print("實際pi:",np.pi)

print("gap:",np.pi - 4 * percent)

asp rs 操作

'=======================

'var categories = ['c1','c2','c3','c4','c5','c6','c7','c8','c9','c10','c11'];

'var data_plan = [10,20,30,40,40,30,30,30,30,30,30];

'var data_act = [11,32,33,34,39,43,33,33,35,33,33];

'=======================

categories = ""

data_plan = ""

data_act = ""

While Not rs.EOF    

    categories = categories & "'" & rs(0) & "'"

    data_plan = data_plan & "," & rs(1)

    data_act = data_act & "," & rs(2)

rs.MoveNext

categories = "[" & mid(categories,2) & "]"

data_plan = "[" & mid(data_plan,2) & "]"

data_act = "[" & mid(data_act,2) & "]"

Wend

python dataframe df

#by欄位計數

df["ITEM_NAME"].value_counts()

#繪圖

df["EXPENSE"].value_counts().plot(kind="bar")

#補值

df.interpolate(method ='linear', limit_direction ='backward', limit = 1)

df合併(欄位) (以下兩種方式)

df1.join(df2)

df = pd.concat([df1, df2], axis=1)

#欄位list分割

df["RecentDelays"].apply(pd.Series)

#分割欄位

new = df.From_To.str.split("_",expand=True)

new.columns = ["From","To"]

df = df.join(new)

#刪除欄位

df.drop("From_To", axis = 1)

#修改欄位名稱

df.rename(columns={'a':'A'})

python 統計 scipy.stats

import numpy as np

arr = np.array([1,3,2,5,4,4,5,453,43,45,43,5])

print("平均:",arr.mean())

print("標準差:",arr.std())

print("變異數:",arr.var())

print(np.sort(arr))

print("中位數:",np.median(arr))

#四分之一位數

np.quantile(e,.25)

#眾數

v = [1,2,3,4,5,3,3,3,3,3,3,3,3,4,4,4,4,4,4,5]

print(np.argmax(np.bincount(v)))

#共變異數 與 相關係數

def cov(X, Y):  # 嘗試手刻共變異數吧

    s = 0

    for i in range(0,len(X)):

        s += (X[i] - X.mean()) * (Y[i] - Y.mean())

    s /= (len(X) - 1)

    return s

def cor(X, Y):  # 嘗試手刻相關係數吧

    s,sx,sy = 0,0,0

    for i in range(0,len(X)):

        s += (X[i] - X.mean()) * (Y[i] - Y.mean())

        sx += (X[i] - X.mean()) ** 2

        sy += (Y[i] - Y.mean()) ** 2

    s /= (sx** 0.5) * (sy** 0.5)

    return s

X = np.array([1,2,3,4,5,6])

Y = np.array([3,5,6,7,8,9])

#使用自訂函式

print(cov(X,Y))

print(cor(X,Y))

#使用Numpy

print(np.cov(X, Y))

print(np.corrcoef(X, Y))

#==========================================================

import scipy.stats as stats

# 請查看 P(X <= -1)，X服從標準常態分佈：

norm_cdf = stats.norm.cdf

print("P(X <= -1)=", norm_cdf(-1, loc=0, scale=1))

# 請畫出標準常態分佈的累積機率函數（CDF），範圍從-3至3：

x = np.linspace(-3, 3, 1000)

norm_cdf = stats.norm.cdf

plt.plot(x, norm_cdf(x, loc=0, scale=1))

plt.arrow(-1, 0, 0, norm_cdf(-1, loc=0, scale=1), head_width=0.02,

          width=0.005, head_length=0.02, color='r')

plt.arrow(-1, norm_cdf(-1, loc=0, scale=1), -3, 0, head_width=0.02,

          width=0.005, head_length=0.02, color='g')

plt.show()

# 請分別查看標準常態分佈的 2.5%, 50%, 97.5% 分位數：

norm_ppf = stats.norm.ppf

print("2.5% 分位數:", norm_ppf(0.025))

print("50% 分位數:", norm_ppf(0.5))

print("97.5% 分位數:", norm_ppf(0.975))

# 請以直方圖（histogram）觀察10000個標準常態分佈的隨機數結果：

norm_rvs = stats.norm.rvs(loc=0, scale=1, size=10000)

n, bins, patches = plt.hist(norm_rvs, 100, density=True, facecolor='green', alpha=0.6)

mu, sigma = 0, 1

x = np.linspace(stats.norm.ppf(0.0001), stats.norm.ppf(0.9999), 101)

y = stats.norm.pdf(bins)

l = plt.plot(bins, y, 'r--', linewidth=2)

plt.xlabel('norm_rvs')

plt.ylabel('Probability')

plt.title(r'$\mathrm{Histogram\ of\ Symmetric\ Distribution:}\ \mu=0,\ \sigma=1$')

plt.show()

#==========================================================

# 隨機抽樣(random sampling):

s0 = np.random.choice([1, 2, 3, 4, 5], 5, replace=False)

# 請利用隨機抽樣的方法，將20個樣本隨機分派至2種不同的群組：

s1 = np.random.choice(2, 20, replace=True)

# 請利用隨機抽樣的方法，從20個樣本中，隨機抽取10個子樣本分派至群組1：

s2 = np.random.choice(20, 10, replace=False)

print(s0)

print(s1)

print(s2)

AIA 20200527 python 小考

#第1題

cx = [1, 3, 5, 7, 2, 1, 8]

def less_than_5(array):

    y = []

    for i in array:

        if i < 5:

            y.append(i)

    return y

print(less_than_5(x))

#第2題

factory1 = {"條件1" :    30, "條件2" : 10, "溫度容許值" : 20}

factory2 = {"條件1" : 48763, "條件3" : 75, "溫度容許值" : 60}

def dict_merge_with_bigger(Dict1, Dict2):

    ND = Dict1

    for key in Dict2:

        if ND.get(key):

            if Dict2[key] > ND[key]:

                ND[key]=Dict2[key]

        else:

            ND[key]=Dict2[key]

    return ND

print(dict_merge_with_bigger(factory1, factory2))

#第3題

#對python物件不熟(待補)

#第4題

def min_max(array):

    min_arr = np.array([])

    max_arr = np.array([])

    for i in array.transpose():

        min_arr = np.append(min_arr,min(i))

        max_arr = np.append(max_arr,max(i))

    return (arr - min_arr) / (max_arr - min_arr)

import numpy as np

arr = np.array(

[[1,3,5],

 [4,5,6],

 [7,8,9]]

)

min_max(arr)

#第5題

def grouped_mean(df, column_name, threshold):

    dict1 = dict(df[df[column_name]<threshold].mean())

    dict2 = dict(df[df[column_name]>=threshold].mean())

    return (dict1,dict2)

import pandas as pd

data = {"height":[150,170,167,158,160],

        "weight":[38,80,59,60,50],

        "salary":[41, 15, 30, 15, 25]}

df = pd.DataFrame(data)

grouped_mean(df, "weight", 60)

指令

sudo systemctl stop dengonban.service

sudo app_v2/install.sh

sudo systemctl enable cloudsqlproxy.service

sudo systemctl start cloudsqlproxy.service

sudo systemctl start dengonban.service

sudo systemctl status dengonban.service

python seaborn 繪圖

import seaborn as sns

import pandas as pd

import matplotlib.pyplot as plt

speed = [4, 4, 7, 7, 8, 9, 10, 10, 10, 11, 11, 12, 12, 12, 12, 13, 13, 13, 13, 14, 14, 14, 14, 15, 15, 15, 16, 16, 17, 17, 17, 18, 18, 18, 18, 19, 19, 19, 20, 20, 20, 20, 20, 22, 23, 24, 24, 24, 24, 25]

dist = [2, 10, 4, 22, 16, 10, 18, 26, 34, 17, 28, 14, 20, 24, 28, 26, 34, 34, 46, 26, 36, 60, 80, 20, 26, 54, 32, 40, 32, 40, 50, 42, 56, 76, 84, 36, 46, 68, 32, 48, 52, 56, 64, 66, 54, 70, 92, 93, 120, 85]

cars_df = pd.DataFrame(

    {"speed": speed,

     "dist": dist

    }

)

# 散佈圖 (Scatter plot)

sns.jointplot(x = "speed", y = "dist", data = cars_df)

# 線圖（Line plot)

sns.factorplot(data = cars_df, x="speed", y="dist", ci = None)

# 長條圖（Bar plot）

sns.countplot(x = "speed", data = cars_df)

normal_samples = np.random.normal(size = 100000) # 生成 100000 組標準常態分配（平均值為 0，標準差為 1 的常態分配）隨機變數

# 直方圖 Histogram

sns.distplot(normal_samples)

# 盒鬚圖 Box plot

sns.boxplot(normal_samples)

import seaborn as sns

ax = sns.scatterplot(x="x1", y="x2", hue="NY", data=df)

ax = sns.boxplot(x="NY", y="x1", data=df)

python matplotlib 繪圖

# 引入模組

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

x = pd.period_range(pd.datetime.now(), periods=200, freq='d')

x = x.to_timestamp().to_pydatetime()

# 產生三組，每組 200 個隨機常態分布元素

y = np.random.randn(200, 3).cumsum(0)

plt.plot(x, y)

plt.show()

#==========

#==========

#線圖

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

y1 = np.array([1, 2, 3])

y2 = np.array([10, 20, 30])

plt.figure(figsize=(8,4))

plt.plot(x,y1,label="line-1",color="red",linewidth=2)

# b--粗體虛線

plt.plot(x,y2,"b--",label="line-2")

plt.xlabel("x label")

plt.ylabel("y label")

plt.title("PyPlot First Example")

#plt.ylim(-1.2,1.2)

plt.legend()

plt.show()

#==========

#==========

# 長條圖

labels = ['Physics', 'Chemistry', 'Literature', 'Peace']

foo_data = [3, 6, 10, 4]

bar_width = 0.5

xlocations = np.array(range(len(foo_data))) + bar_width

plt.bar(xlocations, foo_data, width=bar_width)

plt.title('Stock Price')

plt.show()

# 直方圖

normal_samples = np.random.normal(size=100) # 生成 100 組標準常態分配（平均值為 0，標準差為 1 的常態分配）隨機變數

plt.hist(normal_samples)

plt.show()

#==========

#==========

# 散佈圖 + 迴歸

num_points = 100

gradient = 0.5

x = np.array(range(num_points))

y = np.random.randn(num_points) * 10 + x * gradient

fig, ax = plt.subplots(figsize=(8, 4))

ax.scatter(x, y)

m, c = np.polyfit(x, y, 1) # 使用 Numpy 的 polyfit，參數 1 代表一維，算出 fit 直線斜率

ax.plot(x, m * x + c) # 使用 y = m * x + c 斜率和常數匯出直線

fig.suptitle('Scatter with regression')

plt.show()

#==========

# 圓餅圖

labels = ['A','B','C','D','E']

data = np.random.randint(1, 11, 5) # 生成

x = np.arange(len(data))

plt.pie(data, labels=labels, autopct='%1.1f%%')

plt.show()

#==========

# 盒鬚圖

normal_examples = np.random.normal(size = 100) # 生成 100 組標準常態分配（平均值為 0，標準差為 1 的常態分配）隨機變數

plt.boxplot(normal_examples)

plt.show()

# 把圖儲存，要放在plt.show()之前

plt.savefig("C:/savefig/hist.png")

highcharts formatter

    plotOptions: {

        column: {

            grouping: false,

            shadow: false,

            borderWidth: 0

        },

        line: {

            dataLabels: {

                enabled: true,

//format: '{y:.1f}'

formatter: function() {

return '<b>'+ Highcharts.numberFormat(this.y*100, 2) +'%</b><br/>';

}

            }

}

    },

python LCS (Longest Common SubSequence) 共同字串

#Longest Common SubSequence

import numpy as np

S1="abcdefg"

S2="abzzcdzzefg"

L1 = list(S1)

L2 = list(S2)

#補零作為起始

L1.insert(0,"0")

L2.insert(0,"0")

A = np.zeros((len(L1), len(L2)), dtype=np.int)

#初始值

for i in range(0,len(L1)):

    A[i,0] = -i

for j in range(0,len(L2)):

    A[0,j] = -j

#計算距離矩陣

for i in range(1,len(L1)):

    for j in range(1,len(L2)):

        if L1[i] == L2[j]:

            A[i,j] = A[i-1,j-1] + 2

        else:

            A[i,j] = max(A[i-1,j-1],A[i,j-1],A[i-1,j]) - 1

#回溯字串

i = len(L1)-1

j = len(L2)-1

LCS1 = ""

LCS2 = ""

while i > 0 and j > 0:

    #左上

    if A[i-1,j-1] >= max(A[i,j-1],A[i-1,j]):

        LCS1 = L1[i] + LCS1

        LCS2 = L2[j] + LCS2

        i = i - 1

        j = j - 1

    #左

    elif A[i,j-1] >= A[i-1,j]:

        LCS1 = "-" + LCS1

        LCS2 = L2[j] + LCS2

        j = j - 1

    #上

    else:

        LCS1 = L1[i] + LCS1

        LCS2 = "-" + LCS2

        i = i - 1

while i > 0:

    LCS1 = L1[i] + LCS1

    LCS2 = "-" + LCS2

    i = i - 1

print("輸入的字串")

print(S1)

print(S2)

print("輸出的字串")

print(LCS1)

print(LCS2)

print("共同的字串")

for i in range(0,len(LCS1)):

    if LCS1[i] == LCS2[i]:

        print(LCS1[i],end="")

python numpy

import numpy as np

#array

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

#產生隨機整數亂數(最小值,最大值,個數)

data = np.random.randint(1, 11, 10) # 生成10個1~10的亂數

python Fibonacci 費氏級數

def fib_generator(n):

    L = [0,1]

    if n <= 1:

        return L[n]

    else:

        for i in range(2,n+1):

            f = L[i-2]+L[i-1]

            L.append(f)

        return L

print(fib_generator(10))

python set 集合

s1 = {-3,-2,-1,0,1,2,3,4,5}

s2 = {1,2,3,4,5,6,7,8}

print("交集",s1 & s2)

print("聯集",s1 | s2)

print("差集",s1 - s2)

python 氣泡排序

L = [56, 81, 82, 79, 85, 45, 99, 49, 93, 27, 18, 54, 19, 32]

#氣泡排序 for i in range(0,len(L)-1): for j in range(1,len(L)-i): if L[j-1] > L[j]: #互換 L[j],L[j-1] = L[j-1],L[j] print(L)

python df to json

#force_ascii=False -> 轉中文

json = df.to_json(orient='records',force_ascii=False)

#存檔

f = open('C:/json/news.json','w')

f.write(json)

import json

# Reading data back

with open('C:/json/news.json', 'r') as f:

    data = json.load(f)

#data(list)轉回df

df = pd.DataFrame(data)

python df merge (left outer join)

#left outer join
df = df1.merge(df2, on=['MFG_DAY','EQP_ID'], how='left')

python pandas (list to DataFrame)

import pandas as pd

baby_name = ["A","B","C"]

bavy_birth = [100,200,300]

baby_ds = list(zip(baby_name,bavy_birth))

baby_df = pd.DataFrame(baby_ds,columns=['name','birth'])

baby_df

python 函數 def

def multiply(m, n):

    return m * n

print(multiply(2, 3))

python 字串處理

#####################################

s="0123456789"

print(s[:5])

print(s[:5:2])

s.find("2") #  找字

#####################################

# 印出三角*

for i in range(0,10):

    for j in range(0,i+1):

        print("*",end="") # 不換行

    print("")

#####################################

#Longest Common SubSequence

import numpy as np

S1="abcdefg"

S2="abzzcdzzefg"

L1 = list(S1)

L2 = list(S2)

#補零作為起始

L1.insert(0,"0")

L2.insert(0,"0")

A = np.zeros((len(L1), len(L2)), dtype=np.int)

#初始值

for i in range(0,len(L1)):

    A[i,0] = -i

for j in range(0,len(L2)):

    A[0,j] = -j

#計算距離矩陣

for i in range(1,len(L1)):

    for j in range(1,len(L2)):

        if L1[i] == L2[j]:

            A[i,j] = A[i-1,j-1] + 2

        else:

            A[i,j] = max(A[i-1,j-1],A[i,j-1],A[i-1,j]) - 1

#回溯字串

i = len(L1)-1

j = len(L2)-1

LCS1 = ""

LCS2 = ""

while i > 0 and j > 0:

    #左上

    if A[i-1,j-1] >= max(A[i,j-1],A[i-1,j]):

        LCS1 = L1[i] + LCS1

        LCS2 = L2[j] + LCS2

        i = i - 1

        j = j - 1

    #左

    elif A[i,j-1] >= A[i-1,j]:

        LCS1 = "-" + LCS1

        LCS2 = L2[j] + LCS2

        j = j - 1

    #上

    else:

        LCS1 = L1[i] + LCS1

        LCS2 = "-" + LCS2

        i = i - 1

while i > 0:

    LCS1 = L1[i] + LCS1

    LCS2 = "-" + LCS2

    i = i - 1

print("輸入的字串")

print(S1)

print(S2)

print("輸出的字串")

print(LCS1)

print(LCS2)

print("共同的字串")

for i in range(0,len(LCS1)):

    if LCS1[i] == LCS2[i]:

        print(LCS1[i],end="")

python MySQL

import pyodbc

import pandas as pd

cn = pyodbc.connect('DRIVER={MySQL ODBC 8.0 ANSI Driver};SERVER=localhost;DATABASE=my_db;USER=root;PASSWORD=xxxx;OPTION=3;')

SQL = "select * from expense"

df = pd.read_sql(SQL, cn)

cn.close()

python 判斷是否為數字

def is_number(str):

    try:

        # 因為使用float有一個例外是'NaN'

        if str=='NaN':

            return False

        float(str)

        return True

    except ValueError:

        return False

print(is_number("123"))

str為字串s為字串

str.isalnum() 所有字元都是數字或者字母

str.isalpha() 所有字元都是字母

str.isdigit() 所有字元都是數字

str.isspace() 所有字元都是空白字元、t、n、r

python 日期 週的使用

import datetime

from_date = (datetime.date.today()-datetime.timedelta(days=1)).strftime('%Y-%m-%d')

#週的第一天

#python星期定義為一到日:0~6

#日期寫法:datetime.datetime(2020, 5, 11)

today = datetime.date.today()

#週的第幾天(以0起算)

week_days = (today.weekday()+1) % 7

week_start = today - datetime.timedelta(days=week_days)

print(week_start)

delta = datetime.timedelta(days=7)

MFG_DAY = week_start - delta * 10

for i in range(0,11):

    print((MFG_DAY + delta * i).strftime('%Y/%m/%d'),"第",(MFG_DAY + delta * i).isocalendar()[1],"週")

python 檔案處理

#複製檔案
import shutil
source = "//10.88.39.45/mfg/AI_Picture/weilunwu/test1/aaa.txt"
target = "//10.88.39.45/mfg/AI_Picture/weilunwu/test2/aaa.txt"
shutil.copyfile(source,target)