Howmany-Zeta Data Analysis Skill
This skill provides guidance and tools for data analysis workflows using Python's data science ecosystem.
When to Use This Skill
Use this skill when you need to:
- Load and explore datasets from various file formats
- Clean and preprocess data for analysis
- Perform statistical analysis and compute metrics
- Create visualizations to understand data patterns
- Transform and aggregate data for reporting
Supported Data Formats
This skill supports the following data formats:
| Format | Extension | Library |
|---|---|---|
| CSV | .csv |
pandas |
| JSON | .json |
pandas |
| Parquet | .parquet |
pandas + pyarrow |
| Excel | .xlsx, .xls |
pandas + openpyxl |
| SQL | Database connection | pandas + sqlalchemy |
Analysis Workflow Overview
A typical data analysis workflow follows these steps:
- Data Loading: Read data from files or databases into pandas DataFrames
- Data Inspection: Explore structure, types, and basic statistics
- Data Cleaning: Handle missing values, duplicates, and outliers
- Data Transformation: Reshape, aggregate, and derive new features
- Statistical Analysis: Compute descriptive and inferential statistics
- Visualization: Create charts and plots to communicate insights
- Export Results: Save processed data and analysis outputs
Quick Start
import pandas as pd
import matplotlib.pyplot as plt
# Load data
df = pd.read_csv('data.csv')
# Explore
print(df.info())
print(df.describe())
# Visualize
df.plot(kind='hist')
plt.show()
Available Scripts
- validate-data: Validates data file format and structure before analysis