pandas-data-analysis

name	Pandas Data Analysis
description	Master data manipulation, analysis, and visualization with Pandas, NumPy, and Matplotlib
version	2.1.0
sasmp_version	1.3.0
bonded_agent	03-data-science
bond_type	PRIMARY_BOND
retry_strategy	exponential_backoff
observability	{"logging":true,"metrics":"data_processing_time"}

Pandas Data Analysis

Overview

Master data analysis with Pandas, the powerful Python library for data manipulation and analysis. Learn to clean, transform, analyze, and visualize data effectively.

Learning Objectives

• Load and manipulate data from various sources (CSV, Excel, SQL, APIs)
• Clean and transform messy datasets
• Perform exploratory data analysis (EDA)
• Aggregate and group data for insights
• Create compelling visualizations
• Optimize performance for large datasets

Core Topics

1. Pandas DataFrames & Series

• Creating DataFrames from various sources
• Indexing and selecting data (loc, iloc, at, iat)
• Filtering and boolean indexing
• Adding/removing columns and rows
• Data types and conversions

Code Example:

import pandas as pd
import numpy as np

# Create DataFrame
data = {
    'name': ['Alice', 'Bob', 'Charlie', 'David'],
    'age': [25, 30, 35, 28],
    'salary': [50000, 60000, 75000, 55000],
    'department': ['IT', 'HR', 'IT', 'Sales']
}
df = pd.DataFrame(data)

# Indexing and filtering
it_employees = df[df['department'] == 'IT']
high_earners = df.loc[df['salary'] > 55000, ['name', 'salary']]

# Adding calculated columns
df['annual_bonus'] = df['salary'] * 0.10
df['age_group'] = pd.cut(df['age'], bins=[0, 30, 40, 100], labels=['Young', 'Mid', 'Senior'])

print(df)

2. Data Cleaning & Transformation

• Handling missing data (dropna, fillna, interpolate)
• Removing duplicates
• String operations and text cleaning
• Date/time parsing and manipulation
• Type conversions and casting
• Applying custom functions (apply, map, applymap)

Code Example:

import pandas as pd

# Load data with missing values
df = pd.read_csv('sales_data.csv')

# Handle missing values
df['price'].fillna(df['price'].median(), inplace=True)
df['category'].fillna('Unknown', inplace=True)
df.dropna(subset=['customer_id'], inplace=True)

# Clean text data
df['product_name'] = df['product_name'].str.strip().str.lower()
df['product_name'] = df['product_name'].str.replace('[^a-zA-Z0-9 ]', '', regex=True)

# Convert dates
df['order_date'] = pd.to_datetime(df['order_date'])
df['year'] = df['order_date'].dt.year
df['month'] = df['order_date'].dt.month

# Remove duplicates
df.drop_duplicates(subset=['order_id'], keep='first', inplace=True)

# Apply custom function
def categorize_price(price):
    if price < 50:
        return 'Low'
    elif price < 100:
        return 'Medium'
    else:
        return 'High'

df['price_category'] = df['price'].apply(categorize_price)

3. Aggregation & Grouping

• GroupBy operations
• Aggregation functions (sum, mean, count, etc.)
• Pivot tables and cross-tabulation
• Multi-level indexing
• Window functions (rolling, expanding)

Code Example:

import pandas as pd

# Sample sales data
df = pd.read_csv('sales.csv')

# GroupBy aggregation
dept_stats = df.groupby('department').agg({
    'salary': ['mean', 'min', 'max'],
    'employee_id': 'count'
})

# Multiple groupby
sales_by_region_product = df.groupby(['region', 'product_category'])['sales'].sum()

# Pivot table
pivot = df.pivot_table(
    values='sales',
    index='product_category',
    columns='quarter',
    aggfunc='sum',
    fill_value=0
)

# Rolling window (moving average)
df['sales_ma_7d'] = df.groupby('product_id')['sales'].transform(
    lambda x: x.rolling(window=7, min_periods=1).mean()
)

# Cumulative sum
df['cumulative_sales'] = df.groupby('product_id')['sales'].cumsum()

4. Data Visualization

• Matplotlib basics
• Seaborn for statistical plots
• Pandas built-in plotting
• Customizing plots
• Creating dashboards

Code Example:

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

# Set style
sns.set_style('whitegrid')

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

# 1. Line plot - Sales trend over time
df.groupby('month')['sales'].sum().plot(kind='line', figsize=(10, 6))
plt.title('Monthly Sales Trend')
plt.xlabel('Month')
plt.ylabel('Total Sales ($)')
plt.show()

# 2. Bar plot - Sales by category
category_sales = df.groupby('category')['sales'].sum().sort_values(ascending=False)
category_sales.plot(kind='bar', figsize=(10, 6))
plt.title('Sales by Category')
plt.xlabel('Category')
plt.ylabel('Total Sales ($)')
plt.xticks(rotation=45)
plt.show()

# 3. Histogram - Price distribution
df['price'].hist(bins=30, figsize=(10, 6))
plt.title('Price Distribution')
plt.xlabel('Price ($)')
plt.ylabel('Frequency')
plt.show()

# 4. Box plot - Salary by department
df.boxplot(column='salary', by='department', figsize=(10, 6))
plt.title('Salary Distribution by Department')
plt.suptitle('')
plt.show()

# 5. Heatmap - Correlation matrix
corr = df[['age', 'salary', 'years_experience']].corr()
sns.heatmap(corr, annot=True, cmap='coolwarm', center=0)
plt.title('Correlation Matrix')
plt.show()

Hands-On Practice

Project 1: Customer Analytics

Analyze customer purchase behavior and segmentation.

Requirements:

• Load customer transaction data
• Clean and prepare dataset
• Calculate RFM (Recency, Frequency, Monetary) metrics
• Customer segmentation
• Visualize insights
• Generate executive summary

Key Skills: Data cleaning, aggregation, visualization

Project 2: Time Series Analysis

Analyze sales trends and forecast future performance.

Requirements:

• Load time series data
• Handle missing dates
• Calculate moving averages
• Identify trends and seasonality
• Detect anomalies
• Create interactive visualizations

Key Skills: Time series operations, rolling windows, plotting

Project 3: Data Quality Report

Build automated data quality assessment tool.

Requirements:

• Check for missing values
• Identify duplicates
• Detect outliers
• Validate data types
• Generate quality metrics
• Export HTML report

Key Skills: Data validation, statistical analysis, reporting

Assessment Criteria

• Load and clean real-world datasets efficiently
• Perform complex data transformations
• Use GroupBy for aggregations
• Create insightful visualizations
• Handle missing and inconsistent data
• Optimize performance for large datasets
• Document analysis with clear explanations

Resources

Official Documentation

• Pandas Docs - Official documentation
• NumPy Docs - NumPy documentation
• Matplotlib Docs - Plotting library

Learning Platforms

• Kaggle - Free Pandas course
• DataCamp - Interactive courses
• Python for Data Analysis - Wes McKinney's book

Tools

• Jupyter Notebook - Interactive development
• Google Colab - Cloud notebooks
• Anaconda - Data science distribution

Next Steps

After mastering Pandas, explore:

• Scikit-learn - Machine learning
• SQL - Database querying
• Apache Spark - Big data processing
• Tableau/Power BI - Business intelligence tools