data-analyst

-- Daily aggregation
SELECT 
    DATE(created_at) as date,
    COUNT(*) as daily_count,
    SUM(amount) as daily_total
FROM transactions
GROUP BY DATE(created_at)
ORDER BY date DESC;
 
-- Month-over-month comparison
SELECT 
    DATE_TRUNC('month', created_at) as month,
    COUNT(*) as count,
    LAG(COUNT(*)) OVER (ORDER BY DATE_TRUNC('month', created_at)) as prev_month,
    (COUNT(*) - LAG(COUNT(*)) OVER (ORDER BY DATE_TRUNC('month', created_at))) / 
        NULLIF(LAG(COUNT(*)) OVER (ORDER BY DATE_TRUNC('month', created_at)), 0) * 100 as growth_pct
FROM transactions
GROUP BY DATE_TRUNC('month', created_at)
ORDER BY month;

-- User cohort by signup month
SELECT 
    DATE_TRUNC('month', u.created_at) as cohort_month,
    DATE_TRUNC('month', o.created_at) as activity_month,
    COUNT(DISTINCT u.id) as users
FROM users u
LEFT JOIN orders o ON u.id = o.user_id
GROUP BY cohort_month, activity_month
ORDER BY cohort_month, activity_month;

-- Conversion funnel
WITH funnel AS (
    SELECT
        COUNT(DISTINCT CASE WHEN event = 'page_view' THEN user_id END) as views,
        COUNT(DISTINCT CASE WHEN event = 'signup' THEN user_id END) as signups,
        COUNT(DISTINCT CASE WHEN event = 'purchase' THEN user_id END) as purchases
    FROM events
    WHERE date >= CURRENT_DATE - INTERVAL '30 days'
)
SELECT 
    views,
    signups,
    ROUND(signups * 100.0 / NULLIF(views, 0), 2) as signup_rate,
    purchases,
    ROUND(purchases * 100.0 / NULLIF(signups, 0), 2) as purchase_rate
FROM funnel;

-- Find duplicates
SELECT email, COUNT(*)
FROM users
GROUP BY email
HAVING COUNT(*) > 1;
 
-- Find nulls
SELECT 
    COUNT(*) as total,
    SUM(CASE WHEN email IS NULL THEN 1 ELSE 0 END) as null_emails,
    SUM(CASE WHEN name IS NULL THEN 1 ELSE 0 END) as null_names
FROM users;
 
-- Standardize text
UPDATE products
SET category = LOWER(TRIM(category));
 
-- Remove outliers (IQR method)
WITH stats AS (
    SELECT 
        PERCENTILE_CONT(0.25) WITHIN GROUP (ORDER BY value) as q1,
        PERCENTILE_CONT(0.75) WITHIN GROUP (ORDER BY value) as q3
    FROM data
)
SELECT * FROM data, stats
WHERE value BETWEEN q1 - 1.5*(q3-q1) AND q3 + 1.5*(q3-q1);

# Data Quality Audit: [Dataset]
 
## Row-Level Checks
- [ ] Total row count: [X]
- [ ] Duplicate rows: [X]
- [ ] Rows with any null: [X]
 
## Column-Level Checks
| Column | Type | Nulls | Unique | Min | Max | Issues |
|--------|------|-------|--------|-----|-----|--------|
| [col] | [type] | [n] | [n] | [v] | [v] | [notes] |
 
## Data Lineage
- Source: [Where data came from]
- Last updated: [Date]
- Known issues: [List]
 
## Cleaning Actions Taken
1. [Action and reason]
2. [Action and reason]

import pandas as pd
 
# Load data
df = pd.read_csv('data.csv')  # or pd.read_excel('data.xlsx')
 
# Basic exploration
print(df.shape)  # (rows, columns)
print(df.info())  # Column types and nulls
print(df.describe())  # Numeric statistics
 
# Data cleaning
df = df.drop_duplicates()
df['date'] = pd.to_datetime(df['date'])
df['amount'] = df['amount'].fillna(0)
 
# Analysis
summary = df.groupby('category').agg({
    'amount': ['sum', 'mean', 'count'],
    'quantity': 'sum'
}).round(2)
 
# Export
summary.to_csv('analysis_output.csv')

# Filtering
filtered = df[df['status'] == 'active']
filtered = df[df['amount'] > 1000]
filtered = df[df['date'].between('2024-01-01', '2024-12-31')]
 
# Aggregation
by_category = df.groupby('category')['amount'].sum()
pivot = df.pivot_table(values='amount', index='month', columns='category', aggfunc='sum')
 
# Window functions
df['running_total'] = df['amount'].cumsum()
df['pct_change'] = df['amount'].pct_change()
df['rolling_avg'] = df['amount'].rolling(window=7).mean()
 
# Merging
merged = pd.merge(df1, df2, on='id', how='left')

import matplotlib.pyplot as plt
import seaborn as sns
 
# Set style
plt.style.use('seaborn-v0_8-whitegrid')
sns.set_palette("husl")
 
# Line chart (trends)
plt.figure(figsize=(10, 6))
plt.plot(df['date'], df['value'], marker='o')
plt.title('Trend Over Time')
plt.xlabel('Date')
plt.ylabel('Value')
plt.xticks(rotation=45)
plt.tight_layout()
plt.savefig('trend.png', dpi=150)
 
# Bar chart (comparisons)
plt.figure(figsize=(10, 6))
sns.barplot(data=df, x='category', y='amount')
plt.title('Amount by Category')
plt.xticks(rotation=45)
plt.tight_layout()
plt.savefig('comparison.png', dpi=150)
 
# Heatmap (correlations)
plt.figure(figsize=(10, 8))
sns.heatmap(df.corr(), annot=True, cmap='coolwarm', center=0)
plt.title('Correlation Matrix')
plt.tight_layout()
plt.savefig('correlation.png', dpi=150)

Revenue by Month (in $K)
========================
Jan: ████████████████ 160
Feb: ██████████████████ 180
Mar: ████████████████████████ 240
Apr: ██████████████████████ 220
May: ██████████████████████████ 260
Jun: ████████████████████████████ 280

# [Report Name]
**Period:** [Date range]
**Generated:** [Date]
**Author:** [Agent/Human]
 
## Executive Summary
[2-3 sentences with key findings]
 
## Key Metrics
 
| Metric | Current | Previous | Change |
|--------|---------|----------|--------|
| [Metric] | [Value] | [Value] | [+/-X%] |
 
## Detailed Analysis
 
### [Section 1]
[Analysis with supporting data]
 
### [Section 2]
[Analysis with supporting data]
 
## Visualizations
[Insert charts]
 
## Insights
1. **[Insight]**: [Supporting evidence]
2. **[Insight]**: [Supporting evidence]
 
## Recommendations
1. [Actionable recommendation]
2. [Actionable recommendation]
 
## Methodology
- Data source: [Source]
- Date range: [Range]
- Filters applied: [Filters]
- Known limitations: [Limitations]
 
## Appendix
[Supporting data tables]

#!/bin/bash
# generate-report.sh
 
# Pull latest data
python scripts/extract_data.py --output data/latest.csv
 
# Run analysis
python scripts/analyze.py --input data/latest.csv --output reports/
 
# Generate report
python scripts/format_report.py --template weekly --output reports/weekly-$(date +%Y-%m-%d).md
 
echo "Report generated: reports/weekly-$(date +%Y-%m-%d).md"

# Full descriptive statistics
stats = df['amount'].describe()
print(stats)
 
# Additional stats
print(f"Median: {df['amount'].median()}")
print(f"Mode: {df['amount'].mode()[0]}")
print(f"Skewness: {df['amount'].skew()}")
print(f"Kurtosis: {df['amount'].kurtosis()}")
 
# Correlation
correlation = df['sales'].corr(df['marketing_spend'])
print(f"Correlation: {correlation:.3f}")

# Analysis Request
 
## Question
[What are we trying to answer?]
 
## Context
[Why does this matter? What decision will it inform?]
 
## Data Available
- [Dataset 1]: [Description]
- [Dataset 2]: [Description]
 
## Expected Output
- [Deliverable 1]
- [Deliverable 2]
 
## Timeline
[When is this needed?]
 
## Notes
[Any constraints or considerations]

# Run query from file
./scripts/query.sh --file queries/daily-report.sql
 
# Run inline query
./scripts/query.sh "SELECT COUNT(*) FROM users"
 
# Save output to file
./scripts/query.sh --file queries/export.sql --output data/export.csv

# Basic analysis
python scripts/analyze.py --input data/sales.csv
 
# With specific analysis type
python scripts/analyze.py --input data/sales.csv --type cohort
 
# Generate report
python scripts/analyze.py --input data/sales.csv --report weekly