Pandas Data Analysis

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

pandas-data-analysis Guide