本书聚焦数据化运营实战，旨在帮助运营人员、产品经理及数据分析师掌握利用Python 解决实际业务问题的能力。全书摒弃枯燥的理论说教，强调技术与业务的深度融合，通过系统化的知识架构，带领读者从零开始构建数据驱动决策的核心技能体系。本书内容层层递进，涵盖从Python 编程基础、自动化办公到高级数据决策算法的完整路径。核心部分精选电信、银行、电商三大领域的真实案例，深入剖析客户流失预警、精准营销、信用评分及“人货场”运营策略，手把手教读者如何将Python 技术转化为可落地的商业决策方案。无论是编程零基础的运营新手，还是寻求技术进阶的数据分析师，都能从本书中获益。通过阅读本书，读者不仅能掌握numpy、pandas 等工具的使用，还能培养敏锐的数据洞察力，助力企业在激烈的市场竞争中实现精细化运营与业务增长。

王鑫，博士，中国传媒大学副教授，主要研究方向包括数据分析、信息可视化、人工智能、区块链等多个方面，近20年以上的数据分析行业从业经历，10年以上的企业内训经验。曾在中讯邮电咨询设计院有限公司（中国联通网络技术研究院）担任网络工程师，中国商业联合会数据分析委员会担任技术顾问。先后参入课题《中国联通移动网电路域设备重大故障监控与核查机制研究》、《联通集团总部本部MGW容灾及相关设备IP化改造新技术、新业务实验项目新建工程》、《有线电视用户大数据采集、分析、挖掘和决策支持系统》、《基于大数据面向新媒体的节目综合评价系统架构和方法研究》、《电影大数据分析决策系统与商业智能》等多个项目，累计发表国际学术论文10余篇，国家专利授权10余项，2015年荣获中国广播电影电视社会组织联合会颁发的"2015年度广播影视科技创新奖”， 2017年被中国商业联合会数据专业分析委员会评为"中国大数据人才培养体系标准”专家组成员，同年被全国高校大数据教育联盟授予"中国大数据创新百人”荣誉称号。目前与华为技术有限公司、中国商业联合会数据分析专业委员会、中软国际科技服务有限公司、广州嘉为科技有限公司、北京华信乾坤科技有限公司、北京极客邦科技有限公司、北京开课吧科技有限公司、广州初越网络技术有限公司等多家企培公司具有合作关系。

目 录
第一部分 Python 数据化运营基础
第1 章 Python 编程基础 ··················································································.003
1.1 Python 运行环境 ···············································································.003
1.1.1 Anaconda 的安装 ···································································.003
1.1.2 Jupyter Notebook 的使用 ··························································.005
1.2 Python 数据类型 ···············································································.006
1.2.1 变量和赋值 ··········································································.006
1.2.2 数值类型 ·············································································.007
1.2.3 字符串数据类型 ····································································.007
1.2.4 列表数据类型 ·······································································.008
1.2.5 元组数据类型 ·······································································.010
1.2.6 字典数据类型 ·······································································.011
1.2.7 集合数据类型 ·······································································.012
1.3 Python 流程控制和函数 ······································································.013
1.3.1 流程控制 ·············································································.013
1.3.2 函数 ···················································································.016
1.3.3 参数传递 ·············································································.017
1.3.4 函数进阶 ·············································································.018
1.4 Python 常用的数据分析模块 ································································.020
1.4.1 numpy ·················································································.020
1.4.2 pandas ·················································································.021
1.4.3 matplotlib ·············································································.022
1.4.4 seaborn ················································································.023
1.4.5 pyecharts··············································································.024
1.4.6 scikit-learn ···········································································.025
第2 章 Python 自动化办公 ···············································································.027
2.1 Python 文件管理与操作 ······································································.027
2.1.1 文件与目录的基本操作 ···························································.027
2.1.2 文件批量操作 ·······································································.030
2.1.3 文件压缩和解压缩 ·································································.031
2.1.4 文件权限和元数据 ·································································.032
2.2 Excel 自动化处理 ··············································································.032
2.2.1 Excel 文件的基本操作 ····························································.032
2.2.2 Excel 数据的分析与处理 ·························································.034
2.2.3 Excel 文件的格式化操作 ·························································.035
2.2.4 自动化生成Excel 图表 ····························································.035
2.2.5 批量处理Excel 文件 ·······························································.036
2.3 PDF 文件和Word 文件自动化处理 ························································.037
2.3.1 PDF 文件的自动化处理 ···························································.037
2.3.2 Word 文件的自动化处理 ··························································.040
第二部分 Python 数据化决策思维
第3 章 Python 数据化决策流程 ·········································································.045
3.1 Python 数据获取 ···············································································.045
3.2 Python 数据探索 ···············································································.048
3.2.1 数据描述方法 ·······································································.049
3.2.2 数理统计方法 ·······································································.049
3.3 Python 数据预处理 ············································································.051
3.3.1 数据清洗 ·············································································.051
3.3.2 数据集成 ·············································································.053
3.3.3 数据变换 ·············································································.054
3.3.4 数据归约 ·············································································.056
3.4 Python 数据可视化 ············································································.057
3.4.1 常规图表 ·············································································.057
3.4.2 饼图 ···················································································.058
3.4.3 环形图 ················································································.059
3.4.4 气泡图 ················································································.061
3.4.5 雷达图 ················································································.062
第4 章 Python 数据化决策算法 ·········································································.064
4.1 Python 预测场景数据化决策 ································································.064
4.1.1 决策树算法在预测场景下的应用 ···············································.064
4.1.2 逻辑回归算法在预测场景下的应用 ············································.069
4.1.3 朴素贝叶斯算法在预测场景下的应用 ·········································.073
4.1.4 KNN 算法在预测场景下的应用 ·················································.075
4.1.5 支持向量机算法在预测场景下的应用 ·········································.080
4.1.6 集成学习算法在预测场景下的应用 ············································.084
4.2 Python 营销场景数据化决策 ································································.086
4.2.1 主成分分析算法在营销场景下的应用 ·········································.086
4.2.2 聚类分析算法在营销场景下的应用 ············································.089
4.2.3 关联规则挖掘算法在营销场景下的应用 ······································.094
第三部分 Python 数据化决策实战
第5 章 电信领域数据化决策应用场景 ·································································.101
5.1 电信用户信用评分 ············································································.101
5.1.1 准备工作 ·············································································.102
5.1.2 划分训练集和测试集 ······························································.109
5.1.3 构建模型并进行评估 ······························································.109
5.1.4 运营策略 ·············································································.109
5.2 电信套餐精准推荐 ············································································.110
5.2.1 数据读取与预处理 ·································································.111
5.2.2 数据探索与可视化分析 ···························································.112
5.2.3 构建模型与评估效果 ······························································.120
5.2.4 套餐推荐策略 ·······································································.121
5.3 电信用户流失分析 ············································································.122
5.3.1 数据清洗与类型转换 ······························································.123
5.3.2 分析数据 ·············································································.124
5.3.3 分析不同特征对于用户流失的影响 ············································.132
5.3.4 模型的训练与评估 ·································································.133
5.3.5 结论 ···················································································.135
第6 章 银行数据化运营应用场景 ·······································································.136
6.1 银行客户画像分析与价值定位 ·····························································.137
6.1.1 加工与衍生 ··········································································.137
6.1.2 可视化探索 ··········································································.138
6.1.3 刻画客户画像 ·······································································.140
6.1.4 评估RFMR 模型分群结果在实际营销活动中的效果 ······················.142
6.1.5 构建基于传统RFM 标签与响应结果的预测模型 ···························.144
6.2 银行产品数据分析 ············································································.146
6.2.1 数据可视化探索（特征变量分析） ············································.147
6.2.2 数据可视化探索（目标变量分析） ············································.152
6.2.3 数据预处理（类别变量One-Hot 编码与合并） ·····························.153
6.2.4 模型构建与评估 ····································································.153
6.2.5 解释行为动因，提出营销优化建议 ············································.155
6.3 银行安全数据分析与财务甄别 ·····························································.157
6.3.1 基于上市公司的财务指标，构造衍生特征 ···································.158
6.3.2 数据可视化探索 ····································································.159
6.3.3 构建财务造假识别模型 ···························································.162
6.3.4 识别关键问题 ·······································································.166
6.3.5 行业财务造假分析 ·································································.171
第7 章 电商领域数据化决策应用场景 ·································································.175
7.1 基于“人”的数据分析与运营策略研究 ·················································.176
7.1.1 时间维度分析 ·······································································.177
7.1.2 产品维度分析 ·······································································.181
7.1.3 促销效果分析 ·······································································.185
7.1.4 客户价值分析 ·······································································.187
7.1.5 客户分层分析 ·······································································.192
7.2 基于“货”的数据分析及运营策略研究 ·················································.203
7.2.1 可视化探索 ··········································································.206
7.2.2 销量预测模型构建与评估 ························································.222
7.2.3 供应商评分体系设计 ······························································.227
7.2.4 采购优化建议生成 ·································································.231
7.2.5 断货预警机制构建 ·································································.232
7.2.6 联动优化策略与落地建议 ························································.235
7.3 基于“场”的数据分析及运营策略研究 ·················································.238
7.3.1 CTR 分析 ············································································.238
7.3.2 CVR 分析 ············································································.247
参考文献 ········································································································.255