Pandas for Everyone Python Data Analysis

The Hands-On, Example-Rich Introduction to Pandas Data Analysis in Python

 

Today, analysts must manage data characterized by extraordinary variety, velocity, and volume. Using the open source Pandas library, you can use Python to rapidly automate and perform virtually any data analysis task, no matter how large or complex. Pandas can help you ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets.

 

Pandas for Everyone brings together practical knowledge and insight for solving real problems with Pandas, even if you’re new to Python data analysis. Daniel Y. Chen introduces key concepts through simple but practical examples, incrementally building on them to solve more difficult, real-world problems.

 

Chen gives you a jumpstart on using Pandas with a realistic dataset and covers combining datasets, handling missing data, and structuring datasets for easier analysis and visualization. He demonstrates powerful data cleaning techniques, from basic string manipulation to applying functions simultaneously across dataframes.

 

Once your data is ready, Chen guides you through fitting models for prediction, clustering, inference, and exploration. He provides tips on performance and scalability, and introduces you to the wider Python data analysis ecosystem. 

• Work with DataFrames and Series, and import or export data
• Create plots with matplotlib, seaborn, and pandas
• Combine datasets and handle missing data
• Reshape, tidy, and clean datasets so they’re easier to work with
• Convert data types and manipulate text strings
• Apply functions to scale data manipulations
• Aggregate, transform, and filter large datasets with groupby
• Leverage Pandas’ advanced date and time capabilities
• Fit linear models using statsmodels and scikit-learn libraries
• Use generalized linear modeling to fit models with different response variables
• Compare multiple models to select the “best”
• Regularize to overcome overfitting and improve performance
• Use clustering in unsupervised machine learning

Daniel Y. Chen is a graduate student in the interdisciplinary Ph.D. program in Genetics, Bioinformatics & Computational Biology (GBCB) at Virginia Polytechnic Institute and State University (Virginia Tech). He is involved with Software Carpentry as an instructor and Mentoring Committee Member, and currently serves as the Assessment Committee Chair. He completed his master’s degree in public health at Columbia University Mailman School of Public Health in Epidemiology with a certificate in Advanced Epidemiology, and is currently extending his master’s thesis work in the Social and Decision Analytics Laboratory under the Virginia Bioinformatics Institute on attitude diffusion in social networks.

Part I. Introduction
0. Setting Up
1. Introduction to Panda's Dataframes
2. Dataframe Components
3. Performing Statistics and Calculations on Sliced and Grouped Dataframes
4. Plotting in Matplotlib

Part II. Data Munging
5. Basic Data Cleaning
6. Reshaping Dataframes
7. Missing Values
8. Working with Dates
9. Working with Multiple Dataframes
10. Working with Databases

Part III. Modeling
11. Basic Statistics
12. Linear Models and Regression
13. Survival Analysis
14. Model Selection and Diagnostics
15. Time Series

Part IV. Machine Learning
16. Supervised Learning
17. Unsupervised Learning

Part V. Reproducible Documents (Literate Programming)
18. Jupyter Notebook
19. Pweave

Appendices