By name: data-analyst description: SQL queries, spreadsheet analysis, charts, and statistical methods. Use when the user has data to analyze, needs visualizations, or wants insights from CSV/JSON/Excel files. version: "1.0.0" license: MIT compatibility: Python 3.8+ with pandas, matplotlib, seaborn, scipy metadata: author: hermeshub hermes: tags: [data-analysis, sql, charts, statistics, visualization] category: data requires_tools: [terminal]
Data Analyst
End-to-end data analysis with visualization and reporting.
When to Use
- User provides a dataset (CSV, JSON, Excel, SQLite)
- User asks for data exploration, trends, or patterns
- User needs charts, graphs, or visualizations
- User wants statistical analysis or hypothesis testing
- User asks for a summary report from data
Procedure
- Load and inspect the data (shape, dtypes, nulls, head)
- Clean: handle missing values, fix types, remove duplicates
- Explore: distributions, correlations, outliers
- Analyze: answer the specific question or find patterns
- Visualize: create appropriate charts
- Report: structured findings with actionable insights
Analysis Toolkit
Quick Stats
import pandas as pd
df = pd.read_csv("data.csv")
print(df.describe())
print(df.info())
print(df.isnull().sum())
Visualization
import matplotlib.pyplot as plt
import seaborn as sns
# Distribution
sns.histplot(df['column'], kde=True)
# Correlation
sns.heatmap(df.corr(), annot=True)
# Time series
df.plot(x='date', y='value', figsize=(12,6))
plt.savefig('chart.png', dpi=150, bbox_inches='tight')
Statistical Tests
from scipy import stats
# T-test
t_stat, p_val = stats.ttest_ind(group_a, group_b)
# Correlation
r, p = stats.pearsonr(x, y)
Output Format
- Always start with a data summary (rows, columns, types)
- Show key statistics before diving into analysis
- Every chart must have title, axis labels, and legend
- End with actionable recommendations
Pitfalls
- Always check for null values before calculations
- Verify data types (strings disguised as numbers)
- Watch for survivorship bias in time series
- State sample sizes and confidence intervals
- Don't confuse correlation with causation
Verification
- Row counts match expected after cleaning
- Charts render correctly and save to disk
- Statistical results include p-values and effect sizes