One of the most valuable tools that I've used, when performing exploratory analysis, is building a data dictionary. It offers the following advantages:

- Identify areas of sparsity and areas of concentration in high-dimensional data sets
- Identify outliers and data glitches
- Get a good sense of what the data contains, and where to spend time (or not) in further data mining

**What is a data dictionary**

A data dictionary is a table with 3 or 4 columns. The first column represents a label: that is, the name of a variable, or a combination of multiple (up to 3) variables. The second column is the value attached to the label: the first and second columns actually constitute a name-value pair. The third column is a frequency count: it measures how many times the value (attached to the label in question) is found in the data set. You can add a 4-th column, that tells the dimension of the label (1 if it represents one variable, 2 if it represents a pair of two variables etc.)

Typically, you include all labels of dimension 1 and 2 with count > threshold (e.g. threshold = 5), but no or only very few values (the ones with high count) for labels of dimension 3. Labels of dimension 3 should be explored after having built the dictionary for dim 1 and 2, by drilling down on label/value of dim 2, that have a high count.

**Example of dictionary entry**

category~keyword travel~Tokyo 756 2

In this example, the entry corresponds to a label of dimension 2 (as indicated in column 4), and the simultaneous combination of the two values (travel, Tokyo) is found 756 times in the data set.

The first thing you want to do with a dictionary is to sort it using the following 3-dim index: column 4, then column 1, then column 3. Then look at the data and find patterns.**How do you build a dictionary**

Browse your data set sequentially. For each observation, store all label/value of dim 1 and dim 2 as hash table keys, and increment count by 1 for each of these label/value. In Perl, it can be performed with code such as $hash{"$label\t$value"}++.

If the hash table grows very large, stop, save the hash table on file then delete it in memory, and resume where you paused, with a new hash table. At the end, merge hash tables after ignoring hash entries where count is too small.

© 2020 TechTarget, Inc. Powered by

Badges | Report an Issue | Privacy Policy | Terms of Service

**Most Popular Content on DSC**

To not miss this type of content in the future, subscribe to our newsletter.

- Book: Applied Stochastic Processes
- Long-range Correlations in Time Series: Modeling, Testing, Case Study
- How to Automatically Determine the Number of Clusters in your Data
- New Machine Learning Cheat Sheet | Old one
- Confidence Intervals Without Pain - With Resampling
- Advanced Machine Learning with Basic Excel
- New Perspectives on Statistical Distributions and Deep Learning
- Fascinating New Results in the Theory of Randomness
- Fast Combinatorial Feature Selection

**Other popular resources**

- Comprehensive Repository of Data Science and ML Resources
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- 100 Data Science Interview Questions and Answers
- Cheat Sheets | Curated Articles | Search | Jobs | Courses
- Post a Blog | Forum Questions | Books | Salaries | News

**Archives:** 2008-2014 |
2015-2016 |
2017-2019 |
Book 1 |
Book 2 |
More

**Most popular articles**

- Free Book and Resources for DSC Members
- New Perspectives on Statistical Distributions and Deep Learning
- Time series, Growth Modeling and Data Science Wizardy
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- Comprehensive Repository of Data Science and ML Resources
- Advanced Machine Learning with Basic Excel
- Difference between ML, Data Science, AI, Deep Learning, and Statistics
- Selected Business Analytics, Data Science and ML articles
- How to Automatically Determine the Number of Clusters in your Data
- Fascinating New Results in the Theory of Randomness
- Hire a Data Scientist | Search DSC | Find a Job
- Post a Blog | Forum Questions

## You need to be a member of Data Science Central to add comments!

Join Data Science Central