A crucial part of a data analysis involves creating and maintaining a data dictionary. A data dictionary is a table of metadata and notes on each column of data. One of the primary purposes of a data dictionary is to explain the meaning of the column names. The college dataset uses a lot of abbreviations that are likely to be unfamiliar to an analyst who is inspecting it for the first time.

A data dictionary for the college dataset is provided in the following college_data_dictionary.csv file:

>>> pd.read_csv('data/collge_data_dictionaray.csv')

As you can see, it is immensely helpful in deciphering the abbreviated column names. DataFrames are actually not the best place to store data dictionaries. A platform such as Excel or Google Sheets with easy ability to edit values and append columns is a better choice. Minimally, a column to keep track of notes on the data should be included in a data dictionary. A data dictionary is one of the first things that you can share as an analyst to collaborators.

It will often be the case that the dataset you are working with originated from a database whose administrators you will have to contact in order to get more information. Formal electronic databases generally have more formal representations of their data, called schemas. If possible, attempt to investigate your dataset with people who have expert knowledge on its design.