A Data Profiler Guide to Discover Unknown Datasets

Data profiling is the art of examining and summarizing a dataset’s characteristics. It helps us to understand the data structure, content, and potential pitfalls before diving into analysis.

Imagine trying to navigate a new city without a map. Data profiling acts as your map for the data landscape. By performing basic checks and techniques, you gain a fundamental understanding of the data structure, content, and potential pitfalls. This knowledge is crucial before diving headfirst into analysis, saving you from potential errors and wasted time down the road.

There are many tools that help you to navigate easier through complex datasets, for instance:
👉 Pandas Profiling
👉 DataProfiler
👉 Lux
👉 Ydata-profiling

I am currently using Data Profile from Databricks in my daily routine. Let’s delve into the key areas I focus on when first encountering an unknown dataset ⤵️.

Zero Values

Missing data can significantly impact analysis. For instance, in a performance dataset, a high percentage (like the 19% of zeros I found in the “spent” column) raises red flags. We need to investigate whether this data is accurate or requires imputation (filling in missing values strategically).

Category Balance

A skewed distribution of categories can skew our analysis. A “channel” parameter where LinkedIn dominates over Facebook, as shown in a bar chart. This imbalance might require techniques like oversampling (increasing the representation of the underrepresented category) or undersampling (decreasing the dominant category) to achieve fairer representation in our analysis.

Missing Values

Sometimes, entire variables hold little value due to excessive missing data. In my example, a variable with over 96% missing values might be best excluded from further analysis. A heatmap can effectively visualize the distribution of missing values across the entire dataset, helping you to identify problematic variables.

Data Distribution

Understanding the spread of your data is crucial. Analyzing statistics like minimum, median, average, maximum, and standard deviation (like what I did for the “clicks” variable) paints a picture of the data’s overall shape. You can create histograms or boxplots to visualize the distribution for each variable, allowing you to identify potential outliers or skewed data.

Data Types

Mixing data types can lead to errors during calculations. For instance, having “daily budget” as a decimal and “spent” as a double can cause issues when calculating pacing. Ensuring consistent data types is essential. Use a table to clearly show the data type assigned to each column in your dataset.

Outliers

Extreme outliers can skew results. For example, an “impressions” variable with an average of 155 but a maximum of 26,000 suggests outliers, as shown in a variable overview. These outliers might require investigation or removal depending on the context of your analysis.

By investing a little time upfront to understand your data, you’ll save yourself significant time and frustration later.

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