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Describe

As explained in Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI

The pandas describe method computes summary descriptive statistics for data columns.

Use pandas describe to inspect basic descriptive statistics for numeric columns. It ignores missing data and helps reveal unusual ranges, outliers, and skewed distributions before modeling or correction. section 2.4.2

Inspect the Auto MPG features

Run data.describe() after checking missing values and standard deviations. Compare each feature’s minimum and maximum with its mean; unusually distant values may indicate outliers, skew, collection errors, or mixed measurement methods. section 2.4.2

Use the output to:

  • Assess feature ranges and variation alongside standard deviations. section 2.4.2
  • Investigate suspicious values with charts and further data checks before removing or correcting them. section 2.4.2

How Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI covers it

How deep it goes
Explains numeric summaries and their interpretation, then defers corrective decisions to closer checks with statistics and charts.
Examples
Uses the Auto MPG dataset, numeric feature groups, and the data.describe() command to examine ranges and distributions.
What it assumes
Assumes a pandas DataFrame with numeric features and basic familiarity with missing values and standard deviation.
What you can do after
Diagnose potential outliers, skew, and collection errors that warrant further investigation before modeling.
How it connects
Connects descriptive statistics with missing values, standard deviation, outliers, skewed distributions, charts, and data correction.
Cover of Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI
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This explainer was generated by Manning's knowledge graph from the text of Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI. The section links point to the parts of the book it draws on.