项目一

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings(‘ignore‘)

from bokeh.plotting import figure, show, output_file
from bokeh.models import ColumnDataSource

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（1）加载数据
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import os
os.chdir(r‘C:\Users\Administrator\Desktop\python数据分析\课程资料\【非常重要】python数据分析_项目资料\项目07城市餐饮店铺选址分析‘)
df1 = pd.read_excel(‘上海餐饮数据.xlsx‘, sheetname=0, header=0)

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（2）计算口味、客单价、性价比指标
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data1 = df1[[‘类别‘,‘口味‘,‘环境‘,‘服务‘,‘人均消费‘]]
data1.dropna(inplace=True)
data1 = data1[(data1[‘口味‘] > 0) & (data1[‘人均消费‘] > 0)]
data1[‘性价比‘] = (data1[‘口味‘] + data1[‘环境‘] + data1[‘服务‘]) / data1[‘人均消费‘]
              
#数据清洗 + 性价比计算

def f1():
    fig,axes = plt.subplots(1,3,figsize = (10,4))
    data1.boxplot(column = [‘口味‘], ax = axes[0])
    data1.boxplot(column = [‘人均消费‘], ax = axes[1])
    data1.boxplot(column = [‘性价比‘], ax = axes[2])      
#创建函数f1,制作箱型图，查看异常值；
              
def f2(data, col):
    q1 = data[col].quantile(q = 0.25)
    q3 = data[col].quantile(q = 0.75)
    iqr = q3-q1
    t1 = q1 - 3*iqr
    t2 = q3 + 3*iqr
    return data[(data[col] > t1) & (data[col] < t2)][[‘类别‘, col]] #要筛选出一个单独的数据
#创建函数f2，清除异常值；

data_kw = f2(data1, ‘口味‘)
data_rj = f2(data1,‘人均消费‘)
data_xjb = f2(data1,‘性价比‘)
def f3(data, col):
    col_name = col + ‘_norm‘
    data_gp = data.groupby(‘类别‘).mean()
    data_gp[col_name] = (data_gp[col] - data_gp[col].min()) / (data_gp[col].max()-data_gp[col].min())
    data_gp.sort_values(by = col_name, inplace = True, ascending = False)
    return data_gp
#创建函数f3，标准化指标并排序

data_kw_score = f3(data_kw, ‘口味‘)
data_rj_score = f3(data_rj, ‘人均消费‘)
data_xjb_score = f3(data_xjb, ‘性价比‘)
#指标标准化得分

data_final_q1 = pd.merge(data_kw_score, data_rj_score, left_index = True, right_index = True)
data_final_q1 = pd.merge(data_final_q1, data_xjb_score, left_index = True,right_index = True)
#数据合并


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绘制图表，辅助分析
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from bokeh.layouts import gridplot

output_file(‘project07_h1.html‘)

data_final_q1[‘size‘] = data_final_q1[‘口味_norm‘] * 40
data_final_q1.index.name = ‘type‘
data_final_q1.columns = [‘kw‘, ‘kw_norm‘,‘price‘, ‘price_norm‘, ‘xjb‘, ‘xjb_norm‘, ‘size‘]
#将列名改为英文

source = ColumnDataSource(data_final_q1)
#创建数据


result = figure(plot_width = 800, plot_height = 300, title = ‘餐饮类型得分‘,
                x_axis_label = ‘人均消费‘, y_axis_label = ‘性价比得分‘)
result.circle(x = ‘price‘, y = ‘xjb_norm‘, source = source,
              line_color = ‘black‘, line_dash = [6,4], fill_alpha = 0.6, size = ‘size‘)
#散点图

data_type = data_final_q1.index.tolist()
kw = figure(plot_width = 800, plot_height = 300, title= ‘口味得分‘,
            x_range = data_type)
kw.vbar(x = ‘type‘, top = ‘kw_norm‘, source = source,
        width = 0.8, alpha = 0.7, color = ‘red‘)
#柱状图1

price = figure(plot_width = 800, plot_height = 300, title= ‘人均消费得分‘,
            x_range = data_type)
price.vbar(x = ‘type‘, top = ‘price_norm‘, source = source,
        width = 0.8, alpha = 0.7, color = ‘green‘)
#柱状图2

p = gridplot([[result], [kw], [price]]) #把3个并排放一块
show(p)

print(‘finish‘)