This commit is contained in:
parent
c76dd65c7c
commit
7531432845
10
kano/main.py
10
kano/main.py
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@ -13,7 +13,6 @@ import pandas
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from utils.pandas_extension import save_as_workbook
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print("1 打开并读取Excel文件...", end="")
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try:
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dataset = pandas.read_excel(io="KANO模型客户调研问卷.xlsx", sheet_name="问卷结果")
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@ -60,9 +59,13 @@ for column in range(columns):
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continue
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# 功能名称
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feature = re.search(
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if not (
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match := re.search(
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pattern="【(?P<feature>.*?)】", string=dataset.columns[column]
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).group("feature")
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)
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):
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raise RuntimeError(f"功能名称未匹配到")
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feature = match.group("feature")
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# 生成某功能的选项分布
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distribution = pandas.crosstab(
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@ -178,5 +181,4 @@ kano = (
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)
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save_as_workbook(worksheets=[("Sheet1", kano)], workbook_name="result.xlsx")
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print("已完成")
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240
regions/main.py
240
regions/main.py
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@ -1,5 +1,4 @@
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# -*- coding: utf-8 -*-
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"""
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脚本说明：
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根据行政区划数据就待转化数据集进行转化
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@ -7,186 +6,131 @@
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行政区划数据集来源于 https://lbsyun.baidu.com/faq/api?title=webapi/download
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"""
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import re
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import json
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import numpy
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import pandas
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import time
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import os
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import sys
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sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
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import numpy
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import pandas
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sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
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from utils.pandas_extension import save_as_workbook
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print('1、读取Excel并创建数据集...', end = '')
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print("1、读取Excel并创建数据集...", end="")
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try:
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#待转化数据集
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dataset = pandas.read_excel(io = 'dataset.xlsx', sheet_name = 'Sheet1')
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#行政区划数据集
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dataset_regions = pandas.read_excel(io = 'dataset.xlsx', sheet_name = 'Sheet2')
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# 待转化数据集
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dataset = pandas.read_excel(io="dataset.xlsx", sheet_name="Sheet1")
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# 行政区划数据集
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dataset_regions = pandas.read_excel(io="dataset.xlsx", sheet_name="Sheet2")
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except:
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print('读取Excel或创建数据集发生异常，脚本终止')
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print("读取Excel或创建数据集发生异常，脚本终止")
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print()
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exit()
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print('已完成')
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print("已完成")
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print()
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print('2、转化数据')
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print("2、转化数据")
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print()
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print("2.1 基于行政区划数据集生成省级、地级和县级行政区字典", end="")
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# 县级名称和县级编码
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regions = dataset_regions[
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["省级名称", "省级编码", "地级名称", "地级编码", "县级名称", "县级编码"]
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].drop_duplicates()
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print("已完成")
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print()
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print('2.1 基于行政区划数据集生成省级、地级和县级行政区字典', end = '')
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#县级名称和县级编码
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regions = dataset_regions[['省级名称', '省级编码', '地级名称', '地级编码', '县级名称', '县级编码']].drop_duplicates()
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print('已完成')
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print()
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print('2.2 遍历并转化数据...', end = '')
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dataset.replace(to_replace = {numpy.nan: pandas.NA, None: pandas.NA, '': pandas.NA}, inplace = True)
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print("2.2 遍历并转化数据...", end="")
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dataset.replace(
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to_replace={numpy.nan: pandas.NA, None: pandas.NA, "": pandas.NA}, inplace=True
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)
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for index, row in dataset.iterrows():
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province_name = row['省名称']
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province_name = row["省名称"]
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if province_name is not pandas.NA:
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try:
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#根据省名称匹配省级行政区字典并填充省区划编码
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row['省区划编码'] = str(dataset_regions.loc[dataset_regions['省级名称'] == province_name, '省级编码'].iat[0])
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# 根据省名称匹配省级行政区字典并填充省区划编码
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row["省区划编码"] = str(
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dataset_regions.loc[
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dataset_regions["省级名称"] == province_name, "省级编码"
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].iat[0]
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)
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except:
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row['省区划编码'] = '未查询到省区划编码'
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row['与区划编码对比结果'] = '待确定'
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#省编码数据类型转为字符
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row['省编码'] = str(int(row['省编码']))
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row["省区划编码"] = "未查询到省区划编码"
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row["与区划编码对比结果"] = "待确定"
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# 省编码数据类型转为字符
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row["省编码"] = str(int(row["省编码"]))
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else:
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row['省区划编码'] = '省名称为空'
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row['省编码'] = '省名称为空'
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row['省名称'] = '省名称为空'
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row['与区划编码对比结果'] = '待确定'
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city_name = row['市名称']
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if row['区划类型'] != '省':
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row["省区划编码"] = "省名称为空"
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row["省编码"] = "省名称为空"
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row["省名称"] = "省名称为空"
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row["与区划编码对比结果"] = "待确定"
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city_name = row["市名称"]
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if row["区划类型"] != "省":
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if city_name is not pandas.NA:
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try:
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#根据市名称匹配地级行政区字典并填充市区划编码
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row['市区划编码'] = str(dataset_regions.loc[dataset_regions['地级名称'] == city_name, '地级编码'].iat[0])
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# 根据市名称匹配地级行政区字典并填充市区划编码
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row["市区划编码"] = str(
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dataset_regions.loc[
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dataset_regions["地级名称"] == city_name, "地级编码"
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].iat[0]
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)
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except:
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row['市区划编码'] = '未查询到市区划编码'
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row['与区划编码对比结果'] = '待确定'
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#市编码数据类型转为字符
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row['市编码'] = str(int(row['市编码']))
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row["市区划编码"] = "未查询到市区划编码"
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row["与区划编码对比结果"] = "待确定"
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# 市编码数据类型转为字符
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row["市编码"] = str(int(row["市编码"]))
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else:
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row['市区划编码'] = '市名称为空'
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row['市编码'] = '市名称为空'
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row['市名称'] = '市名称为空'
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row['与区划编码对比结果'] = '待确定'
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row["市区划编码"] = "市名称为空"
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row["市编码"] = "市名称为空"
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row["市名称"] = "市名称为空"
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row["与区划编码对比结果"] = "待确定"
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else:
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row['市区划编码'] = ''
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row['市编码'] = ''
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row['市名称'] = ''
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region_name = row['区县名称']
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if row['区划类型'] == '区县':
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row["市区划编码"] = ""
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row["市编码"] = ""
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row["市名称"] = ""
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region_name = row["区县名称"]
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if row["区划类型"] == "区县":
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if region_name is not pandas.NA:
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try:
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#根据区县名称匹配县级行政区字典并填充区县区划编码
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row['区县区划编码'] = str(regions.loc[regions['县级名称'] == region_name, '县级编码'].iat[0])
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if row['省名称'] == '省名称为空' or row['市名称'] == '市名称为空':
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#若省名称或市名称为空则补充说明
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row['与区划编码对比说明'] = '该区县所属{}/{}'.format(str(regions.loc[regions['县级名称'] == region_name, '省级名称'].iat[0]), str(regions.loc[regions['县级名称'] == region_name, '地级名称'].iat[0]))
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# 根据区县名称匹配县级行政区字典并填充区县区划编码
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row["区县区划编码"] = str(
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regions.loc[regions["县级名称"] == region_name, "县级编码"].iat[0]
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)
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if row["省名称"] == "省名称为空" or row["市名称"] == "市名称为空":
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# 若省名称或市名称为空则补充说明
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row["与区划编码对比说明"] = "该区县所属{}/{}".format(
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str(
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regions.loc[
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regions["县级名称"] == region_name, "省级名称"
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].iat[0]
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),
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str(
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regions.loc[
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regions["县级名称"] == region_name, "地级名称"
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].iat[0]
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),
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)
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except:
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row['区县区划编码'] = '未查询到区县区划编码'
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row['与区划编码对比结果'] = '待确定'
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#县编码数据类型转为字符
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row['区县编码'] = str(int(row['区县编码']))
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row["区县区划编码"] = "未查询到区县区划编码"
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row["与区划编码对比结果"] = "待确定"
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# 县编码数据类型转为字符
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row["区县编码"] = str(int(row["区县编码"]))
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else:
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row['区县区划编码'] = '区县名称为空'
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row['区县编码'] = '区县名称为空'
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row['区县名称'] = '区县名称为空'
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row['与区划编码对比结果'] = '待确定'
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row["区县区划编码"] = "区县名称为空"
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row["区县编码"] = "区县名称为空"
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row["区县名称"] = "区县名称为空"
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row["与区划编码对比结果"] = "待确定"
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else:
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row['区县区划编码'] = ''
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row['区县编码'] = ''
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row['区县名称'] = ''
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row["区县区划编码"] = ""
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row["区县编码"] = ""
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row["区县名称"] = ""
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dataset.iloc[index] = row
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dataset.fillna(value="", inplace=True)
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dataset.fillna(value = '', inplace = True)
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print('已完成')
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print("已完成")
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print()
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print('正在保存为EXCEL...', end = '')
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print("正在保存为EXCEL...", end="")
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SaveAsExcel(worksheets = [('Sheet1', dataset)], save_path = 'results.xlsx')
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save_as_workbook(workbook_name="results.xlsx", worksheets=[("Sheet1", dataset)])
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print('已完成')
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print("已完成")
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print()
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'''
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修改记录
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'''
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11
rfm/main.py
11
rfm/main.py
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@ -1,22 +1,25 @@
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# -*- coding: utf-8 -*-
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"""
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基于RFM模型生成数据分析报告
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"""
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# 导入模块
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import statistics
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from datetime import datetime
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from decimal import Decimal, ROUND_HALF_UP
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from pathlib import Path
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import statistics
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import sys
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import pandas
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from jinja2 import Environment, FileSystemLoader
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import pandas
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from utils.mysql import MySQLClient
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from utils.pandas_extension import DrawAsHTML
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sys.path.append(Path(__file__).parent.parent.as_posix())
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# 函数说明：根据RFM编码映射为客户分类
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def map_classification(r_encoded, f_encoded, m_encoded):
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@ -10,25 +10,16 @@
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from pathlib import Path
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import pandas
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from prettytable import PrettyTable
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from openpyxl import Workbook
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from openpyxl.styles import Font, Alignment, Border, Side, PatternFill
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from openpyxl.utils import get_column_letter
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from pyecharts import __file__ as default_path, options
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from pyecharts.components import Table
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from pyecharts.charts import Scatter, Pie, Bar, Line, HeatMap
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from pyecharts.globals import ThemeType, CurrentConfig
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from jinja2 import Environment, FileSystemLoader
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from openpyxl import Workbook
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from openpyxl.styles import Alignment, Border, Font, PatternFill, Side
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from openpyxl.utils import get_column_letter
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import pandas
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from prettytable import PrettyTable
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from pyecharts import __file__ as default_path, options
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from pyecharts.charts import Bar, Line, Pie, Scatter
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from pyecharts.components import Table
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from pyecharts.globals import CurrentConfig, ThemeType
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def open_csv(file_name):
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@ -101,61 +92,46 @@ def save_as_workbook(worksheets, workbook_name):
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# 创建工作簿
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workbook = Workbook()
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# 删除默认创建的工作表
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for worksheet_name in workbook.sheetnames:
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workbook.remove(workbook[worksheet_name])
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for worksheet_name, worksheet_dataframe in worksheets:
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# 若工作表数据体为空则跳过
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if not worksheet_dataframe.empty:
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# 创建工作表
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worksheet = workbook.create_sheet(title=worksheet_name)
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# 若为多层行索引则重置行索引（单层行索引则跳过）
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if isinstance(worksheet_dataframe.index, pandas.MultiIndex):
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worksheet_dataframe.reset_index(inplace=True)
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# 遍历列索引层
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for level in range(worksheet_dataframe.columns.nlevels):
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# 添加列名行
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worksheet.append(
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worksheet_dataframe.columns.get_level_values(level).tolist()
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) # 工作表一般包括标题行、列名行和数据行，在这里仅考虑列名行和数据行
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# 初始化合并单元格开始列号
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merge_start_column = 0
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# 初始化上一个单元格值
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previous_cell_value = worksheet.cell(
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row=level + 1, column=merge_start_column + 1
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).value
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for column in range(
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len(worksheet_dataframe.columns.get_level_values(level))
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):
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# 单元格值
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value = worksheet.cell(
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row=level + 1, column=column + 1
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).value # 工作表数据体为PANDAS.DATAFRAME，行号和列号从0开始，工作表为OPENPYXL，行号和列号从1开始
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if value != previous_cell_value:
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# 合并单元格结束列号
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merge_end_column = column - 1
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|
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# 判断合并单元格结束列号和合并单元格开始列号的差值是否大于0
|
||||
if (
|
||||
previous_cell_value
|
||||
and merge_end_column - merge_start_column > 0
|
||||
):
|
||||
|
||||
# 合并单元格（同行合并）
|
||||
worksheet.merge_cells(
|
||||
start_row=level + 1,
|
||||
|
|
@ -163,26 +139,20 @@ def save_as_workbook(worksheets, workbook_name):
|
|||
start_column=merge_start_column + 1,
|
||||
end_column=merge_end_column + 1,
|
||||
)
|
||||
|
||||
# 重新赋值合并单元格开始列号
|
||||
merge_start_column = column
|
||||
|
||||
# 重新赋值上一单元格值
|
||||
previous_cell_value = value
|
||||
|
||||
# 若单元格值和上一个单元格值相同，若列号为最大值则合并单元格
|
||||
else:
|
||||
|
||||
# 判断列号是否为最大值
|
||||
if (
|
||||
column
|
||||
== len(worksheet_dataframe.columns.get_level_values(level))
|
||||
- 1
|
||||
):
|
||||
|
||||
# 重新赋值合并单元格结束列号
|
||||
merge_end_column = column
|
||||
|
||||
# 合并单元格（同行合并）
|
||||
worksheet.merge_cells(
|
||||
start_row=level + 1,
|
||||
|
|
@ -193,7 +163,6 @@ def save_as_workbook(worksheets, workbook_name):
|
|||
|
||||
# 若单元格值为空则同列合并
|
||||
if not value:
|
||||
|
||||
# 合并单元格（同列合并）
|
||||
worksheet.merge_cells(
|
||||
start_row=level,
|
||||
|
|
@ -204,27 +173,21 @@ def save_as_workbook(worksheets, workbook_name):
|
|||
|
||||
# 工作表列宽：24磅
|
||||
for column in range(worksheet_dataframe.shape[1]):
|
||||
|
||||
worksheet.column_dimensions[get_column_letter(column + 1)].width = 24
|
||||
|
||||
# 列名行行高：24磅
|
||||
for level in range(worksheet_dataframe.columns.nlevels):
|
||||
|
||||
worksheet.row_dimensions[level + 1].height = 24
|
||||
|
||||
# 列名行单元格样式
|
||||
for column in range(
|
||||
len(worksheet_dataframe.columns.get_level_values(level))
|
||||
):
|
||||
|
||||
cell = worksheet.cell(level + 1, column + 1)
|
||||
|
||||
# 字体
|
||||
cell.font = Font(bold=True, size=12, name="Arial", color="00FFFFFF")
|
||||
|
||||
# 对齐方式
|
||||
cell.alignment = Alignment(horizontal="center", vertical="center")
|
||||
|
||||
# 边框
|
||||
cell.border = Border(
|
||||
left=Side(style="thin", color="00333333"),
|
||||
|
|
@ -232,32 +195,24 @@ def save_as_workbook(worksheets, workbook_name):
|
|||
top=Side(style="thin", color="00333333"),
|
||||
bottom=Side(style="thin", color="00333333"),
|
||||
)
|
||||
|
||||
# 填充
|
||||
cell.fill = PatternFill(fill_type="solid", start_color="003366FF")
|
||||
|
||||
for row, row_data in worksheet_dataframe.iterrows():
|
||||
|
||||
# 数据行行高：20磅
|
||||
worksheet.row_dimensions[
|
||||
row + worksheet_dataframe.columns.nlevels + 1
|
||||
].height = 20
|
||||
|
||||
for column in range(worksheet_dataframe.shape[1]):
|
||||
|
||||
cell = worksheet.cell(
|
||||
row + worksheet_dataframe.columns.nlevels + 1, column + 1
|
||||
)
|
||||
|
||||
# 单元格值
|
||||
cell.value = row_data.iloc[column]
|
||||
|
||||
# 字体
|
||||
cell.font = Font(size=12, name="Arial", color="00333333")
|
||||
|
||||
# 对齐方式
|
||||
cell.alignment = Alignment(horizontal="center", vertical="center")
|
||||
|
||||
cell.border = Border(
|
||||
left=Side(style="thin", color="00333333"),
|
||||
right=Side(style="thin", color="00333333"),
|
||||
|
|
|
|||
|
|
@ -0,0 +1,60 @@
|
|||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
神经网络
|
||||
"""
|
||||
|
||||
# 导入模块
|
||||
from typing import List
|
||||
import numpy
|
||||
|
||||
|
||||
class NeuralNetwork:
|
||||
"""
|
||||
神经网络
|
||||
"""
|
||||
|
||||
# 激活函数和其导数函数
|
||||
FUNCTIONS = {
|
||||
"relu": {
|
||||
"activate": lambda x: numpy.maximum(0, x),
|
||||
"derivative": lambda x: numpy.where(x > 0, 1, 0),
|
||||
},
|
||||
"linear": {
|
||||
"activate": lambda x: x,
|
||||
"derivative": lambda x: numpy.ones_like(x),
|
||||
}, # 适合回归任务的输出层
|
||||
"softmax": {
|
||||
"activate": lambda x: numpy.exp(x) / numpy.sum(numpy.exp(x), axis=1),
|
||||
"derivative": lambda x: x * (1 - x),
|
||||
}, # 适合分类任务的输出层
|
||||
}
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
hidden_layer_neurons: List[int] = [10],
|
||||
hidden_layer_function: str = "relu",
|
||||
output_layer_function: str = "softmax",
|
||||
):
|
||||
"""
|
||||
初始化
|
||||
:param hidden_layer_neurons: 隐含层神经元数量
|
||||
:param hidden_layer_function: 隐含层函数
|
||||
:param output_layer_function: 输出层函数
|
||||
"""
|
||||
# 检查函数是否存在
|
||||
if not (
|
||||
hidden_layer_function in self.FUNCTIONS
|
||||
and output_layer_function in self.FUNCTIONS
|
||||
):
|
||||
raise RuntimeError("所输入的隐含层或输出层函数未定义")
|
||||
|
||||
# 初始化隐含层的激活函数和导数函数
|
||||
self.hidden_layer_activate, self.hidden_layer_derivative = (
|
||||
self.FUNCTIONS[hidden_layer_function]["activate"],
|
||||
self.FUNCTIONS[hidden_layer_function]["derivative"],
|
||||
)
|
||||
# 初始化输出层的激活函数和导数函数
|
||||
self.output_layer_activate, self.output_layer_derivative = (
|
||||
self.FUNCTIONS[output_layer_function]["activate"],
|
||||
self.FUNCTIONS[output_layer_function]["derivative"],
|
||||
)
|
||||
Loading…
Reference in New Issue