November 2022
Intermediate to advanced
392 pages
10h 8m
Chinese
Content preview from Python算法交易
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Python
、
NumPy
、
matplotlib
和
pandas
｜
359
附图 5：线性、三次和九次回归
pandas
pandas
是一个包，你可以使用它高效地管理和操作时间序列数据和其他表格数据结
构。它甚至允许在内存中相当大的数据集上执行复杂的数据分析任务。虽然重点在
于内存中操作，但内存外（磁盘上）操作也有多种选择。虽然
pandas
提供了许多不
同的数据结构，内嵌强大的类，但最常用的结构是
DataFrame
类，它类似于关系（
SQL
）
数据库的典型表，用于管理例如金融时序数据。这是我们在本节中要关注的内容。
DataFrame
类
在最基本的形式中，
DataFrame
对象的特征在于索引、列名和表格数据。为了更具
体地说明这一点，请参考以下示例数据集。
In [148]: import pandas as pd
In [149]: np.random.seed(1000)
In [150]: raw = np.random.standard_normal((10, 3)).cumsum(axis=0)
360
｜
附录
In [151]: index = pd.date_range('2022-1-1', periods=len(raw), freq='M')
In [152]: columns ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.