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Documentation Index

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Exploratory data analysis (EDA) is usually the first step in any data workflow. Sourcetable automates the tedious parts — profiling, distribution analysis, and correlation — so you can jump straight to insights.

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Automated data profiling

Ask the AI to profile your dataset and it generates a comprehensive summary: 
"Profile this dataset and give me a summary of all columns"
The AI examines every column and reports:
  • Data types — numeric, categorical, datetime, text, boolean
  • Missing values — count and percentage per column
  • Unique values — cardinality for each column
  • Basic statistics — mean, median, mode, std dev, min, max, quartiles
  • Distribution shape — skewness and kurtosis for numeric columns

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Distribution analysis

"Show the distribution of revenue across all customers"
The AI generates histograms, box plots, or violin plots depending on your data. It identifies:
  • Normal vs. skewed distributions
  • Outliers beyond 1.5x IQR
  • Bimodal or multimodal patterns
  • Log-normal distributions common in financial data

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Correlation analysis

"Run a correlation analysis on all numeric columns and show a heatmap"
Sourcetable calculates Pearson, Spearman, or Kendall correlations and renders an interactive heatmap. It highlights:
  • Strong positive correlations (> 0.7)
  • Strong negative correlations (< -0.7)
  • Multicollinearity between features
  • Unexpected relationships

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Automated insights

"What are the most interesting patterns in this data?"
The AI scans your dataset and surfaces:
  • Columns with high missing value rates
  • Highly correlated feature pairs
  • Categorical columns with imbalanced classes
  • Temporal trends and seasonality
  • Potential data quality issues (duplicates, inconsistent formats)

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Example prompts

GoalPrompt
Full profile”Profile this dataset — show data types, missing values, and statistics for every column”
Compare groups”Compare the distribution of salary between departments”
Find outliers”Identify outliers in the revenue column using IQR and Z-score methods”
Time patterns”Show how monthly sales have trended over the past 2 years”
Category breakdown”Break down customer count by region and show percentages”