Data Explore

Data Exploration Skill

When exploring a dataset, follow this structured approach. Adapt based on whether the data is tabular (CSV/DataFrame), image-based, or text.

1. Schema and Structure

First, understand what you're working with:

• Load a sample (first 5 rows + last 5 rows)
• Column names, dtypes, and count of non-null values
• Dataset dimensions (rows x columns)
• Memory usage
• Identify the target variable if this is a supervised learning task

For tabular data:

df.info()
df.describe(include='all')
df.head()
df.dtypes.value_counts()

2. Missing Values

Map the missing data landscape:

• Count and percentage of missing values per column
• Pattern analysis: are values Missing Completely At Random (MCAR), Missing At Random (MAR), or Missing Not At Random (MNAR)?
• Identify columns with >50% missing (candidates for dropping)
• Check if missingness correlates with the target variable

missing = df.isnull().sum()
missing_pct = (missing / len(df) * 100).round(2)
missing_report = pd.DataFrame({'count': missing, 'pct': missing_pct})
missing_report[missing_report['count'] > 0].sort_values('pct', ascending=False)

3. Distribution Analysis

For each feature, characterize its distribution:

Numerical features:

• Min, max, mean, median, std
• Skewness and kurtosis
• Identify if log-transform would help (right-skewed data)

Categorical features:

• Cardinality (number of unique values)
• Value counts for top-10 categories
• Identify rare categories (<1% frequency)

4. Outlier Detection

Flag potential outliers:

• IQR method: values below Q1 - 1.5IQR or above Q3 + 1.5IQR
• Z-score method: values with |z| > 3
• Domain-specific checks (e.g., negative ages, future dates)

Q1 = df[col].quantile(0.25)
Q3 = df[col].quantile(0.75)
IQR = Q3 - Q1
outliers = df[(df[col] < Q1 - 1.5 * IQR) | (df[col] > Q3 + 1.5 * IQR)]

5. Correlations and Relationships

Identify feature relationships:

• Pearson correlation matrix for numerical features
• Flag highly correlated pairs (|r| > 0.8) as candidates for feature selection
• Check correlation with target variable
• For categorical features, use chi-squared test or Cramer's V

6. Data Quality Score

Summarize findings with a quality assessment:

• Completeness: % of non-null values across all cells
• Uniqueness: % of columns with no duplicates (where expected)
• Consistency: any type mismatches or encoding issues
• Validity: % of values within expected ranges

Present the final summary as a structured report.