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Jingdong book

Python数据分析

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Jingdong book

Python数据分析

Name: Python数据分析
Brand: Jingdong book
SKU: 3118031251
Price: 34.65 USD
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实用的Python开源模块的大集合；
简单易懂、示例丰富的数据分析教程；
掌握数据可视化、机器学习等高端主题；
新手变身数据分析专家的上好读本；

Content Description

作为一种高级程序设计语言，Python凭借其简洁、易读及可扩展性日渐成为程序设计领域备受推崇的语言。同时，Python语言的数据分析功能也逐渐为大众所认可。
本书是一本介绍如何用Python进行数据分析的学习指南。全书共12章，从Python程序库入门、NumPy数组、matplotlib和pandas开始，陆续介绍了数据加工、数据处理和数据可视化等内容。同时，本书还介绍了信号处理、数据库、文本分析、机器学习、互操作性和性能优化等高级主题。在本书的结尾，还采用3个附录的形式为读者补充了一些重要概念、常用函数以及在线资源等重要内容。
本书示例丰富、简单易懂，非常适合对Python语言感兴趣或者想要使用Python语言进行数据分析的读者参考阅读。

Author Description

Ivan Idris，实验物理学硕士，学位论文侧重于应用计算机科学。毕业后，他曾经效力于多家公司，从事Java开发、数据仓库开发以及QA分析等方面的工作；目前，他的兴趣主要集中在商业智能、大数据和云计算等专业领域。
Ivan Idris以编写简洁可测试的程序代码以及撰写有趣的技术文章为乐，同时也是Packt出版社NumPy Beginner's Guide-Second Edition、NumPy Cookbook和Learning NumPy Array等书籍的作者。

Catalogue

第1章 Python程序库入门 1
1．1 本书用到的软件 2
1．1．1 软件的安装和设置 2
1．1．2 Windows平台 2
1．1．3 Linux平台 3
1．1．4 Mac OS X平台 4
1．2 从源代码安装NumPy、SciPy、matplotlib和IPython 6
1．3 用setuptools安装 7
1．4 NumPy数组 7
1．5 一个简单的应用 8
1．6 将IPython用作shell 11
1．7 学习手册页 13
1．8 IPython notebook 14
1．9 从何处寻求帮助和参考资料 14
1．10 小结 15
第2章 NumPy数组 16
2．1 NumPy数组对象 16
2．2 创建多维数组 18
2．3 选择NumPy数组元素 18
2．4 NumPy的数值类型 19
2．4．1 数据类型对象 21
2．4．2 字符码 21
2．4．3 Dtype构造函数 22
2．4．4 dtype属性 23
2．5 一维数组的切片与索引 23
2．6 处理数组形状 24
2．6．1 堆叠数组 27
2．6．2 拆分NumPy数组 30
2．6．3 NumPy数组的属性 33
2．6．4 数组的转换 39
2．7 创建数组的视图和拷贝 40
2．8 花式索引 41
2．9 基于位置列表的索引方法 43
2．10 用布尔型变量索引NumPy数组 44
2．11 NumPy数组的广播 46
2．12 小结 49
第3章统计学与线性代数 50
3．1 Numpy和Scipy模块 50
3．2 用NumPy进行简单的描述性统计计算 55
3．3 用NumPy进行线性代数运算 57
3．3．1 用NumPy求矩阵的逆 57
3．3．2 用NumPy解线性方程组 59
3．4 用NumPy计算特征值和特征向量 61
3．5 NumPy随机数 63
3．5．1 用二项式分布进行博弈 63
3．5．2 正态分布采样 66
3．5．3 用SciPy进行正态检验 67
3．6 创建掩码式NumPy数组 70
3．7 小结 75
第4章 pandas入门 76
4．1 pandas的安装与概览 77
4．2 pandas数据结构之DataFrame 78
4．3 pandas数据结构之Series 81
4．4 利用pandas查询数据 85
4．5 利用pandas的DataFrame进行统计计算 89
4．6 利用pandas的DataFrame实现数据聚合 91
4．7 DataFrame的串联与附加操作 95
4．8 连接DataFrames 96
4．9 处理缺失数据问题 99
4．10 处理日期数据 102
4．11 数据透视表 106
4．12 访问远程数据 107
4．13 小结 109
第5章数据的检索、加工与存储 110
5．1 利用NumPy和pandas对CSV文件进行写操作 110
5．2 NumPy．npy与pandas DataFrame 112
5．3 使用PyTables存储数据 115
5．4 Pandas DataFrame与HDF5仓库之间的读写操作 118
5．5 使用pandas读写Excel文件 120
5．6 使用REST Web服务和JSON 123
5．7 使用pandas读写JSON 124
5．8 解析RSS和Atom订阅 126
5．9 使用Beautiful Soup解析HTML 127
5．10 小结 134
第6章数据可视化 136
6．1 matplotlib的子库 137
6．2 matplotlib绘图入门 137
6．3 对数图 139
6．4 散点图 141
6．5 图例和注解 143
6．6 三维图 145
6．7 pandas绘图 148
6．8 时滞图 150
6．9 自相关图 151
6．10 Plot．ly 153
6．11 小结 155
第7章信号处理与时间序列 156
7．1 statsmodels子库 157
7．2 移动平均值 157
7．3 窗口函数 159
7．4 协整的定义 161
7．5 自相关 164
7．6 自回归模型 166
7．7 ARMA模型 170
7．8 生成周期信号 172
7．9 傅里叶分析 174
7．10 谱分析 177
7．11 滤波 177
7．12 小结 179
第8章应用数据库 180
8．1 基于sqlite3的轻量级访问 181
8．2 通过pandas访问数据库 183
8．3 SQLAlchemy 185
8．3．1 SQLAlchemy的安装和配置 186
8．3．2 通过SQLAlchemy填充数据库 188
8．3．3 通过SQLAlchemy查询数据库 189
8．4 Pony ORM 191
8．5 Dataset：懒人数据库 192
8．6 PyMongo与MongoDB 195
8．7 利用Redis存储数据 196
8．8 Apache Cassandra 197
8．9 小结 201
第9章分析文本数据和社交媒体 203
9．1 安装NLTK 203
9．2 滤除停用字、姓名和数字 206
9．3 词袋模型 208
9．4 词频分析 209
9．5 朴素贝叶斯分类 211
9．6 情感分析 214
9．7 创建词云 217
9．8 社交网络分析 222
9．9 小结 224
第10章预测性分析与机器学习 225
10．1 scikit-learn概貌 226
10．2 预处理 228
10．3 基于逻辑回归的分类 230
10．4 基于支持向量机的分类 232
10．5 基于ElasticNetCV的回归分析 235
10．6 支持向量回归 237
10．7 基于相似性传播算法的聚类分析 240
10．8 均值漂移算法 242
10．9 遗传算法 244
10．10 神经网络 249
10．11 决策树 251
10．12 小结 253
第11章 Python生态系统的外部环境和云计算 255
11．1 与MATLAB/Octave交换信息 256
11．2 Installing rpy2安装rpy2 257
11．3 连接R 257
11．4 为Java传递NumPy数组 260
11．5 集成SWIG和NumPy 261
11．6 集成Boost和Python 264
11．7 通过f2py使用Fortran代码 266
11．8 配置谷歌应用引擎 267
11．9 在PythonAnywhere上运行程序 269
11．10 使用Wakari 270
11．11 小结 271
第12章性能优化、性能分析与并发性 272
12．1 代码的性能分析 272
12．2 安装Cython 277
12．3 调用C代码 281
12．4 利用multiprocessing创建进程池 283
12．5 通过Joblib提高for循环的并发性 286
12．6 比较Bottleneck函数与NumPy函数 287
12．7 通过Jug实现MapReduce 289
12．8 安装MPI for Python 292
12．9 IPython Parallel 292
12．10 小结 296
附录A 重要概念 298
附录B 常用函数 303
附录C 在线资源 309

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