---

name: explore-data description: > Explore and profile a dataset to understand its shape, types, distributions, quality, and suitability for analysis. TRIGGER when: user asks to "explore data", "profile data", "describe dataset", "data overview", "what's in this data", "data quality check", "EDA", "exploratory data analysis", or "understand this dataset". argument-hint: "<file_or_table>" user-invocable: true

Data Exploration and Profiling

You are an expert data analyst performing exploratory data analysis (EDA). When the user asks you to explore or profile a dataset, follow this structured process.

Step 1: First Look

Perform these checks immediately upon receiving data:

# Standard first-look commands
df.shape                    # (rows, columns)
df.dtypes                   # Column types
df.head(10)                 # First 10 rows
df.tail(5)                  # Last 5 rows
df.sample(5)                # Random sample
df.info(memory_usage="deep") # Memory and type summary
df.columns.tolist()         # All column names

Initial Report Template

DATASET: <name or filename>
SHAPE: <rows> rows x <columns> columns
MEMORY: <size in MB>
SOURCE: <file path, table name, or URL>
DATE RANGE: <if temporal data exists>

Step 2: Column Classification

Classify every column into one of these types:

Type	Detection Rule	Example
Numeric continuous	float, many unique values	revenue, temperature
Numeric discrete	int, few unique values	count, rating (1-5)
Categorical low-cardinality	string/int, < 20 unique values	status, country
Categorical high-cardinality	string, 20-1000 unique	city, product_name
Identifier	unique per row or nearly	id, email, UUID
Datetime	date/timestamp type or parseable string	created_at, date
Boolean	2 unique values	is_active, has_paid
Text / free-form	string, high uniqueness, variable length	description, comment
Constant	1 unique value	unused columns
JSON / nested	contains structured sub-data	metadata, properties

Step 3: Univariate Analysis

For Each Numeric Column

Statistic	Value
Count	N (non-null)
Missing	N (% of total)
Mean	X
Median	X
Std Dev	X
Min	X
Q1 (25%)	X
Q3 (75%)	X
Max	X
IQR	Q3 - Q1
Skewness	X (> 1 or < -1 = highly skewed)
Kurtosis	X (> 3 = heavy tails)
Zeros	N (% of total)
Negatives	N (% of total)
Distinct	N

For Each Categorical Column

Statistic	Value
Count	N (non-null)
Missing	N (%)
Unique	N
Top value	X (frequency, %)
Top 5 values	X1 (n%), X2 (n%), ...
Bottom 5 values	X1 (n%), X2 (n%), ...
Entropy	X (measure of uniformity)

For Each Datetime Column

Statistic	Value
Min date	X
Max date	X
Range	X days/months/years
Granularity	second / minute / hour / day / month
Gaps	List any gaps in expected frequency
Timezone	X or None

Step 4: Missing Data Analysis

Missing Data Report

| Column | Missing Count | Missing % | Pattern |
|--------|--------------|-----------|---------|
| col_a  | 0            | 0.0%      | Complete |
| col_b  | 150          | 15.0%     | MCAR    |
| col_c  | 300          | 30.0%     | MAR     |

Missingness Patterns

• MCAR (Missing Completely at Random): No pattern, random absence
• MAR (Missing at Random): Correlates with other observed variables
• MNAR (Missing Not at Random): Related to the missing value itself

Actions

• < 5% missing: Safe to drop or simple impute (mean/median/mode)
• 5-30% missing: Investigate pattern; use appropriate imputation

• 30% missing: Consider dropping column; document if kept

• Patterned missingness: Flag for domain expert review

Step 5: Data Quality Assessment

Check	Method	Flag If
Duplicates	df.duplicated().sum()	Any exact duplicates
Near-duplicates	Fuzzy matching on key columns	High similarity score
Constant columns	df.nunique() == 1	Always true
High-null columns	null% > 50%	Review for removal
Type mismatches	Numeric stored as string	Any occurrence
Outliers	IQR method: < Q1-1.5IQR or > Q3+1.5IQR	Count per column
Invalid values	Domain checks (negative age, future dates)	Any occurrence
Inconsistent categories	Case/spelling variations ("US", "us", "USA")	Any occurrence
PII detection	Pattern match (email, phone, SSN)	Flag for privacy review

Step 6: Bivariate Quick Scan

If the dataset has a clear target variable, compute:

• Numeric vs numeric: Correlation matrix (Pearson + Spearman)
• Numeric vs categorical: Group-by means and box plots
• Categorical vs categorical: Contingency table, Cramer's V
• Any vs target: Mutual information score

Correlation Flags

• |r| > 0.8: Strong correlation (potential multicollinearity)
• |r| > 0.95: Near-perfect correlation (likely redundant columns)
• Report top 10 strongest correlations

Step 7: Output Report

Structure the exploration report as:

1. Dataset Card (name, shape, source, date range, memory)
2. Column Inventory (table of all columns with type, nulls, uniques)
3. Key Statistics (summary stats for numeric and categorical)
4. Missing Data Report (table with counts, percentages, patterns)
5. Data Quality Flags (duplicates, outliers, inconsistencies)
6. Distribution Notes (skewed columns, bimodal, uniform)
7. Preliminary Correlations (top relationships found)
8. Recommendations (what to clean, transform, or investigate next)

Quality Checklist

• Every column has been classified and profiled
• Missing data is quantified with percentages
• Duplicates have been checked
• Data types are correct (no numbers stored as strings)
• Outliers are identified with counts
• PII columns are flagged
• At least one visualization per data type (histogram, bar chart, etc.)
• Recommendations for next steps are provided

Edge Cases

• Very wide data (100+ columns): Group by prefix, profile in batches, focus on non-null columns
• Very long data (10M+ rows): Sample for profiling (1% or 100K rows), note sampling
• All-null columns: Flag and recommend removal
• Mixed-type columns: Attempt coercion, flag failures
• Encoded data: Detect base64, URL-encoded, or hashed columns
• Nested JSON columns: Flatten one level and profile sub-fields