Inside a 32-Column Dataset: What the Data Dictionary Reveals

By shrijeetverma13 · August 5, 2026

This analysis breaks down the structure, naming patterns, and documentation quality of a dataset with 32 columns spanning integers, floats, and strings.…

The most prevalent data type in the dataset is 'int' (integer), accounting for 13 out of 32 columns, which is 40.6% of all fields. This shows the dataset's structure is primarily built around numeric, whole-number data.

The 'string' type has the longest average description length at 62.29 characters, suggesting it tends to involve more complex or detailed descriptions compared to other types.

The columns with the shortest descriptions—potential signs of documentation gaps—are 'weight kg' (11 characters: "Weight (kg)"), 'adoption id' (18 characters), 'house trained' (18 characters), 'age years' (19 characters), and 'neutered' (20 characters). These brief descriptions may need more detail to be fully clear to users.

Out of the 32 columns in the data dictionary, integers are the most common type with 13 columns (40.6%), followed by floats with 12 columns (37.5%), and strings with 7 columns (21.9%).

Looking at the 32 column names in this dataset, the most frequently recurring keywords are 'adoption', 'level', and 'has', each appearing 3 times, followed by 'size', 'type', 'days', 'returned', and 'score' with 2 occurrences each. The keyword 'adoption' stands out as the most common concept, suggesting that customer or product adoption is a central business theme in this dataset. A horizontal bar chart visualizes the top 15 recurring keywords, making it easy to see which concepts show up most often in the column naming.

The analysis set out to compare column names against their data types using common keyword patterns (like 'id', 'date', 'price', 'count'), but no columns in this dataset matched any of the predefined keywords, so a direct alignment score couldn't be calculated. Visual charts were still generated to show the breakdown of aligned, misaligned, and unmatched columns, along with a detailed table of column names and types.

A chart and table were generated showing the percentage of columns within each type category that include descriptions with slash-separated categorical value options (like 'small / medium / large'). The visualization breaks down both the count and percentage per type, allowing you to see which column types most commonly document their categorical options this way.

An attempt was made to extract and count the most common keywords from the description column. A results table was generated as part of this analysis, though the keyword extraction step encountered a technical issue that prevented the frequency counts from being fully populated this time.

The analysis found 1 column whose name suggests a boolean or flag field (e.g., starting with 'is ', 'has ', or containing 'flag') but whose declared type is not boolean. This mismatch could indicate a data type inconsistency worth reviewing. A chart and data table have been generated showing the mismatch broken down by declared type.

The data dictionary contains 32 columns spanning 3 unique data types. Descriptions average 46.3 characters in length. The majority of columns (78.1%) are numeric, while the remaining 21.9% are categorical. A bar chart shows the distribution of data types, a pie chart compares numeric versus categorical columns, and a summary table consolidates all these key statistics.

The sentiment analysis of the description text shows that most entries (84.4%) are neutral in tone, with a small portion being positive (9.4%) and negative (6.2%). The overall average polarity score of 0.021 indicates the text leans very slightly positive but is mostly balanced and factual in nature. A bar chart was created to visualize this distribution across the three sentiment categories.

A word cloud was generated from the 'description' column, visually highlighting the most frequently occurring words based on their size—larger words appear more often in the dataset.