Data Analysis Standards

When to Use

• Exploring a new dataset (shape, distributions, quality)
• Cleaning and transforming data after pipeline ingestion
• Computing aggregations, groupings, and statistical summaries
• Writing Jupyter or Marimo notebooks for analysis
• Querying Parquet or CSV files with DuckDB SQL
• Choosing between pandas, Polars, and DuckDB for a task
• Building reproducible analysis workflows
• Auditing existing notebooks or analysis scripts for correctness, performance, or readability

Tool Selection

Decision Guide

• Default to Polars for any repeatable analysis or transform — faster, stricter types, lower memory.
• Use pandas when a downstream library requires it (sklearn, certain plotting libraries) or for quick one-off exploration.
• Use DuckDB SQL when the question is naturally expressed in SQL (aggregations, joins, window functions) and data lives in Parquet files.
• Combine freely: Polars and DuckDB share Apache Arrow as the memory format — zero-copy conversion between them.

Exploratory Data Analysis (EDA) Workflow

Follow this sequence when encountering a new dataset:

1. Shape and Schema

import polars as pl

df = pl.read_parquet("data/raw/users.parquet")

print(f"Rows: {df.height:,}, Columns: {df.width}")
print(df.schema)
print(df.head(5))

2. Null and Completeness Audit

null_report = df.select(
    pl.all().null_count().name.suffix("_nulls"),
).unpivot().sort("value", descending=True)
print(null_report)

# Percentage complete
completeness = df.select(
    (1 - pl.all().null_count() / df.height).name.suffix("_complete"),
).unpivot().sort("value")
print(completeness)

3. Descriptive Statistics

# Numeric columns
print(df.select(pl.col(pl.NUMERIC_DTYPES)).describe())

# Categorical value counts
for col in df.select(pl.col(pl.String)).columns:
    print(f"\n{col}:")
    print(df.group_by(col).len().sort("len", descending=True).head(10))

4. Distributions and Outliers

# Quick histogram via DuckDB for large data
import duckdb

duckdb.sql("""
    SELECT histogram(amount, 20) AS amount_hist
    FROM 'data/raw/orders.parquet'
""").show()

# Outlier detection with IQR
q1, q3 = df.select(
    pl.col("amount").quantile(0.25).alias("q1"),
    pl.col("amount").quantile(0.75).alias("q3"),
).row(0)
iqr = q3 - q1
outliers = df.filter(
    (pl.col("amount") < q1 - 1.5 * iqr) | (pl.col("amount") > q3 + 1.5 * iqr)
)
print(f"Outliers: {outliers.height} rows ({outliers.height / df.height:.1%})")

5. Temporal Patterns

daily = (
    df.with_columns(pl.col("created_at").cast(pl.Date).alias("date"))
    .group_by("date")
    .agg(pl.len().alias("count"))
    .sort("date")
)
print(daily)

Data Cleaning Patterns

Standard Cleaning Pipeline

def clean_users(df: pl.DataFrame) -> pl.DataFrame:
    """Clean raw user data."""
    return (
        df
        # Drop exact duplicates
        .unique()
        # Normalize text columns
        .with_columns(
            pl.col("email").str.to_lowercase().str.strip_chars(),
            pl.col("name").str.strip_chars(),
        )
        # Filter invalid rows
        .filter(pl.col("email").str.contains(r"^[^@]+@[^@]+\.[^@]+$"))
        # Cast types
        .with_columns(
            pl.col("created_at").str.to_datetime("%Y-%m-%dT%H:%M:%SZ"),
        )
        # Drop columns not needed downstream
        .drop("raw_html", "debug_info")
    )

Handling Missing Data

# Fill with default
df = df.with_columns(pl.col("country").fill_null("Unknown"))

# Forward fill time-series
df = df.sort("timestamp").with_columns(pl.col("temperature").forward_fill())

# Flag missing
df = df.with_columns(pl.col("amount").is_null().alias("amount_missing"))

Aggregation Patterns

Group-By Aggregations

summary = (
    df.group_by("category")
    .agg(
        pl.len().alias("count"),
        pl.col("amount").sum().alias("total"),
        pl.col("amount").mean().alias("avg"),
        pl.col("amount").median().alias("median"),
        pl.col("amount").std().alias("std"),
        pl.col("amount").quantile(0.95).alias("p95"),
    )
    .sort("total", descending=True)
)

Window Functions

df = df.with_columns(
    # Running total per user
    pl.col("amount")
    .cum_sum()
    .over("user_id")
    .alias("running_total"),
    # Rank within category
    pl.col("amount")
    .rank(method="dense", descending=True)
    .over("category")
    .alias("rank_in_category"),
)

Time-Series Resampling

monthly = (
    df.sort("date")
    .group_by_dynamic("date", every="1mo")
    .agg(
        pl.col("amount").sum().alias("monthly_total"),
        pl.col("user_id").n_unique().alias("unique_users"),
    )
)

DuckDB SQL for Analysis

Query Parquet files directly without loading into Python memory:

import duckdb

# Aggregate across partitioned Parquet files
result = duckdb.sql("""
    SELECT
        date_trunc('month', created_at) AS month,
        country,
        count(*) AS orders,
        sum(amount) AS revenue,
        avg(amount) AS avg_order_value
    FROM 'data/raw/orders/**/*.parquet'
    GROUP BY 1, 2
    ORDER BY 1, 4 DESC
""")

# Convert to Polars for further manipulation
df = result.pl()

DuckDB + Polars Integration

import duckdb
import polars as pl

# Register Polars DataFrame as a DuckDB table
df = pl.read_parquet("data/cleaned/users.parquet")
duckdb.sql("SELECT * FROM df WHERE country = 'US' LIMIT 10").show()

DuckDB automatically detects Polars and pandas DataFrames by variable name — no explicit registration needed.

Notebook Best Practices

Structure

Every analysis notebook should follow this structure:

1. Title and objective — Markdown cell stating what question the notebook answers.
2. Imports and config — Single cell with all imports; set display options.
3. Data loading — Load from Parquet (pipeline output), not raw APIs.
4. Exploration — EDA steps (shape, nulls, distributions).
5. Analysis — The actual computation answering the objective.
6. Visualization — Charts supporting the conclusions (see data-visualization skill).
7. Conclusions — Markdown cell summarizing findings.

Config Cell Template

import polars as pl
import duckdb
from pathlib import Path

# Display settings
pl.Config.set_tbl_rows(20)
pl.Config.set_fmt_str_lengths(80)

DATA_DIR = Path("data")

Rules

• Never call APIs from notebooks — load from pipeline outputs (Parquet files). Pipelines are the ingestion layer; notebooks are the analysis layer.
• Use relative paths from the project root, not absolute paths.
• Each notebook answers one question — don't create monolithic analysis notebooks.
• Clear outputs before committing — notebooks with output diffs are unreadable in git.
• Name notebooks descriptively: 01_user_churn_analysis.ipynb, not Untitled3.ipynb.
• Pin the Python kernel to the project's uv environment.

Performance Tips

Polars Lazy Evaluation

For large datasets, use lazy evaluation to let Polars optimize the query plan:

result = (
    pl.scan_parquet("data/raw/events/*.parquet")  # lazy — no data loaded yet
    .filter(pl.col("event_type") == "purchase")
    .group_by("user_id")
    .agg(pl.col("amount").sum().alias("total_spent"))
    .sort("total_spent", descending=True)
    .head(100)
    .collect()  # executes the optimized plan
)

Memory Management

• Use scan_parquet (lazy) instead of read_parquet (eager) for datasets larger than available RAM.
• Select only needed columns: pl.scan_parquet(...).select(["id", "amount"]) avoids loading unused columns.
• Filter early: Push filters before aggregations to reduce data volume.
• Stream large results: Use collect(streaming=True) for datasets that don't fit in memory even after filtering.

Cross-References

• Data pipelines: See the data-pipelines skill for dlt-based ingestion that produces the Parquet files analyzed here.
• Data visualization: See the data-visualization skill for plotting analysis results in notebooks.
• Charts (web): See the charts skill for React/Recharts visualization when building web dashboards from analysis outputs.
• Python conventions: See the python skill for type hints, project setup, and Ruff configuration.
• Database queries: See the database skill when analysis requires querying PostgreSQL or DynamoDB directly.