November 2022
Intermediate to advanced
392 pages
10h 8m
Chinese
Content preview from Python算法交易
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处理金融数据
｜
65
'LOW': '238.9741',
'OPEN': '242.8',
'COUNT': '293699.0',
'VOLUME': '32470017.0'}]
在这里实例化
csv.DictReader
迭代器对象，该对象将每个数据行读入一个
dict
对象中，其中包含表头信息。
现在，基于单个
dict
对象，更易于实现聚合了。但是，在查看相应的
Python
代码后，
仍然无法说出一种方便的方式来计算
Apple
收盘价的均值：
In [10]: sum([float(l['CLOSE']) for l in data]) / len(data)
Out[10]: 272.38619047619045
首先，通过具有所有收盘值的列表推导生成一个新的列表对象。第二，将所有
这些值相加。第三，用得出的总和除以收盘价的数量。
这是
pandas
在
Python
社区中如此受欢迎的主要原因之一。与纯
Python
相比，它让
数据的导入和金融时序数据集的处理更加方便（并且通常也更快）。
3.1.3
使用
pandas
从
CSV
文件读取
从现在开始，本节将使用
pandas
来处理
Apple
股票价格数据集。使用的主要函数是
read_csv()
，它允许通过不同的参数进行许多自定义操作（请参阅
r
ead_csv()
API
参考（
https://oreil.ly/ IAVfO
））。作为数据读取过程的结果，
read_csv()
产生一个
DataFrame
对象，该对象是使用
pandas
存储（表格）数据的主要手段。 ...
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