data-analysis

name	data-analysis
description	Data processing, analysis, and visualization with Python/JavaScript. Use for data exploration, pandas operations, chart generation, and insights extraction.

📊 Data Analysis Skill

Python Data Processing

Pandas Basics

import pandas as pd
import numpy as np

# Read data
df = pd.read_csv('data.csv')
df = pd.read_json('data.json')
df = pd.read_excel('data.xlsx')

# Basic exploration
df.head()        # First 5 rows
df.info()        # Column types
df.describe()    # Statistics
df.shape         # (rows, cols)

Data Cleaning

# Handle missing values
df.dropna()                    # Drop rows with NaN
df.fillna(0)                   # Fill NaN with value
df['col'].fillna(df['col'].mean())  # Fill with mean

# Remove duplicates
df.drop_duplicates()

# Type conversion
df['date'] = pd.to_datetime(df['date'])
df['price'] = df['price'].astype(float)

Aggregations

# Group by
df.groupby('category')['sales'].sum()
df.groupby(['year', 'month']).agg({
    'sales': 'sum',
    'quantity': 'mean',
    'price': ['min', 'max']
})

# Pivot tables
pd.pivot_table(df, values='sales', index='category', columns='year')

---

Visualization

Matplotlib/Seaborn

import matplotlib.pyplot as plt
import seaborn as sns

# Basic line chart
plt.figure(figsize=(10, 6))
plt.plot(df['date'], df['sales'])
plt.title('Sales Over Time')
plt.xlabel('Date')
plt.ylabel('Sales')
plt.savefig('chart.png')

# Seaborn heatmap
sns.heatmap(df.corr(), annot=True, cmap='coolwarm')

Chart.js (JavaScript)

new Chart(ctx, {
  type: 'bar',
  data: {
    labels: ['Jan', 'Feb', 'Mar'],
    datasets: [{
      label: 'Sales',
      data: [12, 19, 3],
      backgroundColor: 'rgba(99, 102, 241, 0.5)'
    }]
  }
});

---

Common Analysis Patterns

Task	Code
Top N items	df.nlargest(10, 'sales')
Date filtering	df[df['date'] >= '2024-01-01']
Rolling average	df['sales'].rolling(7).mean()
Year-over-year	df.groupby(df['date'].dt.year)
Percentiles	df['sales'].quantile([0.25, 0.5, 0.75])

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Analysis Checklist

• Load and inspect data
• Handle missing values
• Clean and transform
• Exploratory analysis
• Create visualizations
• Extract insights
• Document findings