Pandas Data Analysis

Work with Real Data Like a Pro#

Welcome to Pandas Data Analysis—the module that transforms you from a NumPy user into a professional data analyst! Pandas builds on NumPy to provide powerful, labeled data structures that make working with real-world datasets intuitive and efficient.

If NumPy is the engine, pandas is the complete vehicle. You will learn to clean messy data, filter rows based on complex conditions, group and aggregate information, and combine multiple datasets—the core skills every data analyst uses daily.

Why Learn Pandas After NumPy?#

You mastered NumPy arrays and vectorized operations. Now you are ready for pandas, which enhances NumPy with labels, mixed data types, and high-level methods designed specifically for data analysis.

What NumPy Cannot Do (But Pandas Can):

• Column Names: NumPy uses numeric indices (arr[:, 5]). Pandas uses meaningful names (df['revenue']).
• Mixed Data Types: NumPy arrays hold one type. Pandas DataFrames mix numbers, text, dates, and categories in one structure.
• Missing Data Handling: Pandas has sophisticated tools for dealing with missing values.
• Group Operations: Pandas makes it easy to calculate statistics by category (average sales per region).
• Data Alignment: Pandas automatically aligns data by labels when combining datasets.

Everything You Learned Transfers:

What You Will Learn#

This module covers everything you need to analyze real-world datasets through 21 comprehensive lessons:

Foundation Skills (Lessons 1-6)

• Understanding DataFrames and Series (labeled 2D and 1D data)
• Loading and exploring datasets with .head(), .info(), .describe()
• Selecting data by labels (.loc[]) and positions (.iloc[])
• Working with Series operations and value counts
• Working with dates and times for time-series analysis

Filtering and Transformation (Lessons 7-10)

• Boolean filtering with multiple conditions
• Sorting and ranking data
• Adding and modifying columns with calculations
• Applying custom functions to transform data

Data Cleaning (Lessons 11-13)

• Handling missing values (detect, drop, fill)
• Converting data types and cleaning formats
• Removing duplicates and handling outliers
• Preparing messy data for analysis

String Operations (Lesson 14)

• Vectorized string methods for text data
• Extracting patterns and cleaning text
• Filtering based on string criteria

Aggregation and Grouping (Lessons 15-16)

• GroupBy operations for category-based analysis
• Aggregating with multiple functions
• Creating pivot tables for data summaries

Combining Datasets (Lessons 17-18)

• Concatenating DataFrames vertically and horizontally
• Merging datasets with database-style joins
• Understanding inner, outer, left, and right joins

Advanced Topics (Lessons 19-20)

• Working with hierarchical indexes (MultiIndex)
• Window functions and rolling calculations

Real-World Application (Lesson 21)

• Complete data analysis project
• Applying all skills to real datasets
• End-to-end workflow from loading to insights

Perfect For#

This module is designed for you if you:

• Completed the NumPy Fundamentals module
• Understand arrays, vectorization, and Boolean indexing
• Want to work with realistic, messy datasets
• Need to clean and prepare data for analysis
• Are preparing for data visualization or machine learning
• Want the skills professional data analysts use every day

Prerequisites: Completion of NumPy Fundamentals module or equivalent knowledge of NumPy arrays, indexing, and operations.

From Arrays to DataFrames#

NumPy arrays are powerful but limited. Real data has column names, mixed types, and messy values. Pandas solves these problems.

NumPy Approach (position-based, single type):

# NumPy: work with positions
import numpy as np
data = np.array([[101, 25000, 5],
                 [102, 30000, 3],
                 [103, 28000, 4]])

# What is column 1? You must remember!
salaries = data[:, 1]

Pandas Approach (label-based, mixed types):

# Pandas: work with names
import pandas as pd
df = pd.DataFrame({
    'employee_id': [101, 102, 103],
    'salary': [25000, 30000, 28000],
    'years': [5, 3, 4]
})

# Self-documenting and clear
salaries = df['salary']

Labels make your code readable and your analysis transparent.

Real-World Applications#

Throughout this module, you will work with realistic datasets:

• Fortune 500 Companies: Analyze revenue, profits, and employees
• World Happiness Reports: Combine multi-year data and analyze trends
• Laptop Prices: Clean messy data with encoding issues and formatting problems
• Time Series Data: Work with dates, times, and trends over time

These are the same types of datasets you will encounter in professional data analysis roles.

Learning Outcomes#

By completing Pandas Data Analysis, you will confidently:

• Load CSV and Excel files into pandas DataFrames
• Explore datasets to understand structure and content
• Select specific rows and columns using labels and positions
• Filter data based on single and multiple conditions
• Sort data and identify top/bottom values
• Create new columns from calculations
• Apply custom functions to transform data
• Handle missing values appropriately
• Clean and standardize messy data
• Remove duplicates and handle outliers
• Work with text data using vectorized string methods
• Group data by categories and calculate aggregate statistics
• Create pivot tables for data summaries
• Concatenate multiple datasets
• Merge datasets using database-style joins
• Work with hierarchical indexes and time-based windows
• Complete end-to-end data analysis projects

These are the core competencies of professional data analysts.

Your Path Forward#

After completing Pandas Data Analysis, you will be ready for:

• Data Visualization: Create compelling charts and graphs with Matplotlib and Seaborn
• Statistical Analysis: Perform hypothesis testing and statistical modeling
• Machine Learning: Use scikit-learn for predictive modeling
• Advanced Analytics: Time series forecasting, AB testing, and more
• Data Engineering: Build data pipelines and ETL processes

Pandas is the foundation. Everything else builds on it.

Get Started Now#

Ready to analyze real data with pandas? Begin with Lesson 1 or explore the complete module overview to see everything you will learn.

Begin Your Journey to Professional Data Analysis#

Every data scientist, data analyst, and machine learning engineer uses pandas. It is the most important Python library for working with tabular data. The skills you learn here will serve you throughout your entire career.

Start learning pandas today. Master the tools professionals use!