在Python中简洁使用CSV、JSON、XML!

在本文中，我们将与大家分享在Python中使用 CSV、JSON 和 XML 三种主流数据格式的最简单方法！

CSV数据

CSV文件是存储数据的最常见方式。你会发现，Kaggle竞赛中的大多数数据都是以这种方式存储的。我们可以使用内置的Python CSV库读写CSV。通常，我们会将数据读入一个列表。

观察下面的代码。当我们运行 csv.reader() 时，所有CSV数据都可以访问。函数的作用是：从CSV中读取一行；每次调用，它都会移动到下一行。我们还可以使用for循环遍历CSV的每一行，就像csvreader中的for行一样。确保每一行的列数相同，否则，在处理列表时可能会遇到一些错误。

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在Python中写入CSV同样容易。在单个列表中设置字段名，在列表中设置数据。我们将创建一个 writer() 对象，将数据写入文件，这与我们读取数据的方式非常相似。

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在使用Pandas时如何使用：

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我们甚至可以使用Pandas将CSV快速转换为单行的字典列表。将数据格式化为字典列表之后，我们将使用 dicttoxml 库将其转换为XML格式。我们还将它保存为JSON文件！

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JSON数据

JSON提供了一种干净且易于阅读的格式，因为它保持了字典式结构。就像CSV一样，Python有一个内置的JSON模块，使读写变得超级容易！当我们读取CSV时，它将成为一个字典。然后我们将字典写入文件。

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正如我们之前看到的，一旦我们有了数据，就可以通过Pandas轻松地转换为CSV，或者使用内置的Python CSV模块。在转换成XML时，dicttoxml库始终是我们的朋友。

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XML数据

XML有点不同于CSV和JSON。通常，CSV和JSON由于其简单性而被广泛使用。它们读、写和解释起来既简单又快捷。不需要额外的工作，而且解析JSON或CSV是非常轻量级的。

另一方面，XML往往要重一些。你正在发送更多的数据，这意味着你需要更多的带宽、更多的存储空间和更多的运行时间。但是与JSON和CSV相比，XML确实具有一些额外的特征：你可以使用命名空间来构建和共享标准结构、更好的表示继承，以及使用XML模式、DTD等表示数据的行业标准化方法。

要读取XML数据，我们将使用Python内置的带有子模块ElementTree的XML模块。我们可以使用xmltodictlibrary将ElementTree对象转换为字典。一旦有了字典，就可以像上面看到的那样转换为CSV、JSON或Pandas Dataframe！

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