data-analysis

name	data-analysis
description	Perform data analysis and generate visualizations using Python (Pandas, Matplotlib, Seaborn). Supports CSV, JSON, and Excel formats.
license	MIT

Data Analysis Skill

This skill enables deep data exploration, cleaning, aggregation, and visualization using Python's standard data science stack.

Core Capabilities

1. Read Data: Load data from CSV (.csv), Excel (.xlsx), JSON (.json), or Parquet files.
2. Clean & Transform: Filter rows, handle missing values, reshape data (melt/pivot).
3. Aggregate: Group by columns, calculate sums/means/counts or custom metrics.
4. Visualize: Generate static plots (bar, line, scatter, histogram, boxplot, heatmap) using Matplotlib and Seaborn.

Dependencies

• pandas (pip install pandas openpyxl)
• matplotlib (pip install matplotlib)
• seaborn (pip install seaborn)

Workflow Examples

1. Basic Statistical Summary

import pandas as pd

# Load data
df = pd.read_csv('data.csv')

# Inspect basics
print(df.head())
print(df.info())
print(df.describe())

# Check for missing values
print(df.isnull().sum())

2. Visualization: Correlation Heatmap

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

df = pd.read_csv('dataset.csv')
corr = df.corr()

plt.figure(figsize=(10, 8))
sns.heatmap(corr, annot=True, cmap='coolwarm')
plt.title('Correlation Matrix')
plt.savefig('correlation_heatmap.png')

3. Aggregation and Bar Chart

import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_excel('sales_data.xlsx')

# Group by Category and Sum Sales
category_sales = df.groupby('Category')['Sales'].sum().sort_values(ascending=False)

plt.figure(figsize=(12, 6))
category_sales.plot(kind='bar', color='skyblue')
plt.title('Total Sales by Category')
plt.xlabel('Category')
plt.ylabel('Sales ($)')
plt.tight_layout()
plt.savefig('sales_by_category.png')

Best Practices

• Avoid large loops: Use vectorized Pandas operations.
• Handle Errors: Wrap file reading in try-except blocks.
• Visual Clarity: Always label axes and titles in plots.
• File Formats: Specify engine='openpyxl' for .xlsx files if needed.