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ApacheCN Pandas 译文集

2020-07-18 23:53 作者:绝不原创的飞龙  | 我要投稿
  • Pandas 秘籍(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/pandas-cookbook/README.md)

    • 零、前言(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/pandas-cookbook/ch00.md)

    • 一、Pandas 基础(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/pandas-cookbook/ch01.md)

    • 二、数据帧基本操作(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/pandas-cookbook/ch02.md)

    • 三、开始数据分析(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/pandas-cookbook/ch03.md)

    • 四、选择数据子集(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/pandas-cookbook/ch04.md)

    • 五、布尔索引(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/pandas-cookbook/ch05.md)

    • 六、索引对齐(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/pandas-cookbook/ch06.md)

    • 七、分组以进行汇总,过滤和转换(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/pandas-cookbook/ch07.md)

    • 八、将数据重组为整齐的表格(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/pandas-cookbook/ch08.md)

    • 九、组合 Pandas 对象(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/pandas-cookbook/ch09.md)

    • 十、时间序列分析(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/pandas-cookbook/ch10.md)

    • 十一、Pandas,Matplotlib 和 Seaborn 的可视化(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/pandas-cookbook/ch11.md)

  • Pandas 学习手册中文第二版(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/learning-pandas-2e/README.md)

    • 零、前言(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/learning-pandas-2e/0.md)

    • 一、Pandas 与数据分析(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/learning-pandas-2e/1.md)

    • 二、启动和运行 Pandas(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/learning-pandas-2e/2.md)

    • 三、用序列表示单变量数据(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/learning-pandas-2e/3.md)

    • 四、用数据帧表示表格和多元数据(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/learning-pandas-2e/4.md)

    • 五、数据帧的结构操作(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/learning-pandas-2e/5.md)

    • 六、索引数据(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/learning-pandas-2e/6.md)

    • 七、类别数据(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/learning-pandas-2e/7.md)

    • 八、数值统计方法(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/learning-pandas-2e/8.md)

    • 九、存取数据(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/learning-pandas-2e/9.md)

    • 十、整理数据(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/learning-pandas-2e/10.md)

    • 十一、合并,连接和重塑数据(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/learning-pandas-2e/11.md)

    • 十二、数据聚合(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/learning-pandas-2e/12.md)

    • 十三、时间序列建模(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/learning-pandas-2e/13.md)

    • 十四、可视化(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/learning-pandas-2e/14.md)

    • 十五、历史股价分析(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/learning-pandas-2e/15.md)

  • 精通 Pandas(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/master-pandas/README.md)

    • 零、前言(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/master-pandas/0.md)

    • 一、Pandas 和数据分析简介(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/master-pandas/1.md)

    • 二、Pandas 安装和支持软件(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/master-pandas/2.md)

    • 三、Pandas 数据结构(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/master-pandas/3.md)

    • 四、Pandas 的操作,第一部分 – 索引和选择(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/master-pandas/4.md)

    • 五、Pandas 的操作,第二部分 – 数据的分组,合并和重塑(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/master-pandas/5.md)

    • 六、处理缺失数据,时间序列和 Matplotlib 绘图(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/master-pandas/6.md)

    • 七、统计之旅 – 经典方法(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/master-pandas/7.md)

    • 八、贝叶斯统计简介(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/master-pandas/8.md)

    • 九、Pandas 库体系结构(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/master-pandas/9.md)

    • 十、R 与 Pandas 的比较(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/master-pandas/10.md)

    • 十一、机器学习简介(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/master-pandas/11.md)

  • NumPy 和 Pandas 数据分析实用指南(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/handson-data-analysis-numpy-pandas/README.md)

    • 零、前言(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/handson-data-analysis-numpy-pandas/0.md)

    • 一、配置 Python 数据分析环境(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/handson-data-analysis-numpy-pandas/1.md)

    • 二、探索 NumPy(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/handson-data-analysis-numpy-pandas/2.md)

    • 三、NumPy 数组上的运算(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/handson-data-analysis-numpy-pandas/3.md)

    • 四、Pandas 很有趣! 什么是 Pandas?(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/handson-data-analysis-numpy-pandas/4.md)

    • 五、Pandas 的算术,函数应用以及映射(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/handson-data-analysis-numpy-pandas/5.md)

    • 六、排序,索引和绘图(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/handson-data-analysis-numpy-pandas/6.md)

  • 精通 Pandas 探索性分析(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/master-exp-analysis-pandas/README.md)

    • 零、前言(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/master-exp-analysis-pandas/0.md)

    • 一、处理不同种类的数据集(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/master-exp-analysis-pandas/1.md)

    • 二、数据选择(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/master-exp-analysis-pandas/2.md)

    • 三、处理,转换和重塑数据(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/master-exp-analysis-pandas/3.md)

    • 四、像专业人士一样可视化数据(https://github.com/apachecn/apachecn-ds-zh/blob/master/docs/master-exp-analysis-pandas/4.md)


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