Attribute access on invalidly named columns

Question

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

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

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.

Answer 1

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.