无效命名列上的属性访问 [英] Attribute access on invalidly named columns

问题描述

我使用Pandas 0.11.0，试图从具有以下结构的CSV文件中读取数据:

Using Pandas 0.11.0, I am trying to read in data from a CSV file with the following structure:

Date/Time   Data1    Data2
  5/10/13      23     17.0
  5/10/14      20     17.1
  5/10/15      27     17.3

为了基于现有数据创建新列，我将使用

In order to create a new column based on existing data, I would use attribute access of the fashion:

df["Result"] = 2.0 * df.Data2

但是，由于日期/时间"不是有效的属性名称，因此建议根据数据/时间"列中的数据创建新列的建议方法是什么?我希望在使用read_csv方法时不必手动指定所有列名.

However, because "Date/Time" is not a valid attribute name, what is the recommended way to create a new column based on the data in the "Data/Time" column? I would prefer not to have to manually specify all column names when using the read_csv method.

推荐答案

使用df['Date/Time'].选择列df.column_name的属性访问样式只是df['column_name']的便捷快捷方式.如果您的列名不是有效的Python标识符，如日期/时间"中一样.您可以更改名称，也可以使用长格式.

Use df['Date/Time']. The attribute access style of selecting a column, df.column_name, is merely a convenient shortcut for df['column_name']. It is simply not possible to use this convenience when your column names are not valid Python identifiers, as in 'Date/Time'. You can change the name, or you can use the long form.

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