基于python的旅游系统_基于python的去哪儿网旅游数据分析

20011 基于python的去哪儿网旅游数据分析

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链接：https://pan.baidu.com/s/1tw4Qvtcuwt7ys36M7HvLSg

提取码：1589

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技术

Python + Pandas + Numpy + Pyecharts

功能详情

数据抽取

数据清洗

数据分析

数据展示

主要展示了不同时间不同城市的景区的销售情况。

系统相关截图

景点门票销售额排行

import random

with open('hw.txt', 'w') as f:

f.write('height\tweight\n')

for i in range(100):

height = random.randint(1600, 1900) / 10

weight = (height - 100) * 0.9 + random.randint(-50, 50) / 10

f.write('%.1f\t%.1f\n' % (height, weight))

linear_regression.py:

author = 'DivinerShi'

import numpy as np

import matplotlib.pyplot as plt

def compute_error(b,m,data):

totalError = 0

#Two ways to implement this

#first way

# for i in range(0,len(data)):

# x = data[i,0]

# y = data[i,1]

# totalError += (y-(m*x+b))**2

# print('(%f*%f+%f)=%f,y=%f,loss=%f,totalError=%f' % (m,x,b,(m*x+b),y,(y-(m*x+b))**2,totalError))

#second way

x = data[:,0]

y = data[:,1]

totalError = (y-m*x-b)**2

totalError = np.sum(totalError,axis=0)

return totalError/float(len(data))

def optimizer(data,starting_b,starting_m,learning_rate,num_iter):

b = starting_b

m = starting_m

#gradient descent

for i in range(num_iter):

#update b and m with the new more accurate b and m by performing

# thie gradient step

b,m =compute_gradient(b,m,data,learning_rate)

if i%100==0:

print('iter {0}:error={1}'.format(i,compute_error(b,m,data)))

return [b,m]

def compute_gradient(b_current,m_current,data ,learning_rate):

b_gradient = 0

m_gradient = 0

N = float(len(data))

#Two ways to implement this

#first way

# for i in range(0,len(data)):

# x = data[i,0]

# y = data[i,1]

# #computing partial derivations of our error function

# #b_gradient = -(2/N)*sum((y-(m*x+b))^2)

# #m_gradient = -(2/N)*sum(x*(y-(m*x+b))^2)

# b_gradient += -(2/N)*(y-((m_current*x)+b_current))

# m_gradient += -(2/N) * x * (y-((m_current*x)+b_current))

# # print('m_current=%f,b_current=%f,N=%f,x=%f,y=%f,y-((m_current*x)+b_current)=%f, b_gradient=%f, m_gradient=%f' % (m_current, b_current, N, x, y, y-((m_current*x)+b_current), b_gradient, m_gradient))

#Vectorization implementation

x = data[:,0]

y = data[:,1]

b_gradient = -(2/N)*(y-m_current*x-b_current)

b_gradient = np.sum(b_gradient,axis=0)

m_gradient = -(2/N)*x*(y-m_current*x-b_current)

m_gradient = np.sum(m_gradient,axis=0)

#update our b and m values using out partial derivations

new_b = b_current - (learning_rate * b_gradient)

new_m = m_current - (learning_rate * m_gradient)

return [new_b,new_m]

def Linear_regression():

# get train data

# data =np.loadtxt('data.csv',delimiter=',')

data =np.loadtxt('hw.txt',delimiter='\t',skiprows=True)

#define hyperparamters

#learning_rate is used for update gradient

#defint the number that will iteration

# define y =mx+b

learning_rate = 0.000001

initial_b =0.0

initial_m = 0.0

num_iter = 10000

#train model

#print b m error

print('initial variables:\n initial_b = {0}\n intial_m = {1}\n error of begin = {2} \n'\

.format(initial_b,initial_m,compute_error(initial_b,initial_m,data)))

#optimizing b and m

[b ,m] = optimizer(data,initial_b,initial_m,learning_rate,num_iter)

#print final b m error

print('final formula parmaters:\n b = {1}\n m={2}\n error of end = {3} \n'.format(num_iter,b,m,compute_error(b,m,data)))

#plottting

x = data[:,0]

y = data[:,1]

y_predict = m*x+b

plt.scatter(x, y, color = 'blue')

plt.plot(x,y_predict,'k-', color = 'red', linewidth = 4)

# plt.show()

from sklearn import linear_model

regr = linear_model.LinearRegression()

regr.fit(x.reshape(-1,1), y)

print(regr.coef_, regr.intercept_)

plt.scatter(x, y, color = 'blue')

plt.plot(x, regr.predict(x.reshape(-1,1)), color = 'orange', linewidth = 4)

plt.show()

def lr_by_sklearn():

from sklearn import linear_model

data =np.loadtxt('hw.txt',delimiter='\t',skiprows=True)

x = data[:,0]

y = data[:,1]

regr = linear_model.LinearRegression()

regr.fit(x.reshape(-1,1), y)

print(regr.coef_, regr.intercept_)

# import pandas as pd

# data = pd.read_csv('hw.txt', sep='\t')

# regr.fit(data['height'].values.reshape(-1,1), data['weight'])

# plt.scatter(data['height'], data['weight'], color = 'blue')

# plt.plot(data['height'], regr.predict(data['height'].values.reshape(-1,1)), color = 'orange', linewidth = 4)

# plt.show()

plt.scatter(x, y, color = 'blue')

plt.plot(x, regr.predict(x.reshape(-1,1)), color = 'orange', linewidth = 4)

plt.show()

if name =='main':

Linear_regression()

lr_by_sklearn()

import numpy as np

df2 = df.groupby('addr').agg([np.mean, np.var])

df2['x'] = 1

from math import pi, exp, sqrt

def f(x, mean, var):

return exp(-(x-mean)**2/(2var))/sqrt(2pi*var)

df2['p_x_min']=df2[['min','x']].apply(lambda x: f(x['x'], x['min']['mean'], x['min']['var']), axis=1)

df2['p_x_max']=df2[['max','x']].apply(lambda x: f(x['x'], x['max']['mean'], x['max']['var']), axis=1)

(df2['p_x_min']pcpa['java']).argmax()

方法二：

from numpy import genfromtxt

x = genfromtxt('51.txt', delimiter='\t', skip_header=True, usecols=(2,3))

x.shape

y = genfromtxt('51.txt', delimiter='\t', skip_header=True, usecols=(1), dtype=str)

y.shape

import numpy as np

np.unique(y)

from sklearn.naive_bayes import GaussianNB

clf = GaussianNB()

拟合数据

clf.fit(x, y)

print(clf.predict([[0.8, 1.2]]))

print(clf.predict_proba([[0.8, 1.2]]))

print(clf.predict_log_proba([[0.8, 1.2]]))

import urllib.request

import re

import sqlite3

def get_content(page, key):

url = 'https://search.51job.com/list/010000%2C020000%2C030200%2C040000,000000,0000,00,9,99,' + key + ',2,' + str(page) + '.html'

a = urllib.request.urlopen(url)

html = a.read().decode('gbk')

lst = re.findall(r'(北京|上海|广州|深圳).*?\s+(\d+.?\d?)-(\d+.?\d?)(万|千)/(年|月)', html)

return lst

conn = sqlite3.connect('51.db')

c = conn.cursor()

c.execute('''CREATE TABLE IF NOT EXISTS jobs

(key text, addr text, min float, max float)''')

c.execute('''delete from jobs''')

conn.commit()

with open('51.txt', 'w') as f:

f.write('%s\t%s\t%s\t%s\n' % ('key','addr','min','max'))

for key in ('python', 'java'):

for each in range(1, 11):

for items in get_content(each, key):

min = float(items[1])

max = float(items[2])

if items[3] == "千":

min /= 10

max /= 10

if items[4] == "年":

min /= 12

max /= 12

f.write('%s\t%s\t%s\t%s\n' % (key, items[0], round(min, 2), round(max, 2)))

c.execute("INSERT INTO jobs VALUES (?,?,?,?)", (key, items[0], round(min, 2), round(max, 2)))

conn.commit()

conn.close()

if name == 'main':

lst = get_content(1, 'python')

print(lst)