3. Data Analysis with pandas
by Sebastian Raschka, Robert Layton, Martin Czygan, Phuong Vo.T.H, Fabrizio Romano,
Python: Real-World Data Science
- Data Science with Python
- Table of Contents
- Data Science with Python
- Meet Your Course Guide
- What's so cool about Data Science?
- Course Structure
- Course Journey
- The Course Roadmap and Timeline
- 1. Course Module 1: Python Fundamentals
- 1. Introduction and First Steps – Take a Deep Breath
- A proper introduction
- Enter the Python
- About Python
- What are the drawbacks?
- Who is using Python today?
- Setting up the environment
- What you need for this course
- How you can run a Python program
- How is Python code organized
- Python's execution model
- Guidelines on how to write good code
- The Python culture
- A note on the IDEs
- 2. Object-oriented Design
- 3. Objects in Python
- 4. When Objects Are Alike
- 5. Expecting the Unexpected
- 6. When to Use Object-oriented Programming
- 7. Python Data Structures
- 8. Python Object-oriented Shortcuts
- 9. Strings and Serialization
- 10. The Iterator Pattern
- 11. Python Design Patterns I
- 12. Python Design Patterns II
- 13. Testing Object-oriented Programs
- 14. Concurrency
- 1. Introduction and First Steps – Take a Deep Breath
- 2. Course Module 2: Data Analysis
- 1. Introducing Data Analysis and Libraries
- 2. NumPy Arrays and Vectorized Computation
- 3. Data Analysis with pandas
- 4. Data Visualization
- 5. Time Series
- 6. Interacting with Databases
- 7. Data Analysis Application Examples
- 3. Course Module 3: Data Mining
- 1. Getting Started with Data Mining
- 2. Classifying with scikit-learn Estimators
- 3. Predicting Sports Winners with Decision Trees
- 4. Recommending Movies Using Affinity Analysis
- 5. Extracting Features with Transformers
- 6. Social Media Insight Using Naive Bayes
- 7. Discovering Accounts to Follow Using Graph Mining
- 8. Beating CAPTCHAs with Neural Networks
- 9. Authorship Attribution
- 10. Clustering News Articles
- 11. Classifying Objects in Images Using Deep Learning
- 12. Working with Big Data
- 13. Next Steps…
- Chapter 1 – Getting Started with Data Mining
- Chapter 2 – Classifying with scikit-learn Estimators
- Chapter 3: Predicting Sports Winners with Decision Trees
- Chapter 4 – Recommending Movies Using Affinity Analysis
- Chapter 5 – Extracting Features with Transformers
- Chapter 6 – Social Media Insight Using Naive Bayes
- Chapter 7 – Discovering Accounts to Follow Using Graph Mining
- Chapter 8 – Beating CAPTCHAs with Neural Networks
- Chapter 9 – Authorship Attribution
- Chapter 10 – Clustering News Articles
- Chapter 11 – Classifying Objects in Images Using Deep Learning
- Chapter 12 – Working with Big Data
- More resources
- 4. Course Module 4: Machine Learning
- 1. Giving Computers the Ability to Learn from Data
- How to transform data into knowledge
- The three different types of machine learning
- An introduction to the basic terminology and notations
- A roadmap for building machine learning systems
- Using Python for machine learning
- 2. Training Machine Learning Algorithms for Classification
- 3. A Tour of Machine Learning Classifiers Using scikit-learn
- 4. Building Good Training Sets – Data Preprocessing
- 5. Compressing Data via Dimensionality Reduction
- 6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning
- 7. Combining Different Models for Ensemble Learning
- 8. Predicting Continuous Target Variables with Regression Analysis
- Introducing a simple linear regression model
- Exploring the Housing Dataset
- Implementing an ordinary least squares linear regression model
- Fitting a robust regression model using RANSAC
- Evaluating the performance of linear regression models
- Using regularized methods for regression
- Turning a linear regression model into a curve – polynomial regression
- A. Reflect and Test Yourself! Answers
- Module 2: Data Analysis
- Module 3: Data Mining
- Chapter 1: Getting Started with Data Mining
- Chapter 2: Classifying with scikit-learn Estimators
- Chapter 3: Predicting Sports Winners with Decision Trees
- Chapter 4: Recommending Movies Using Affinity Analysis
- Chapter 5: Extracting Features with Transformers
- Chapter 6: Social Media Insight Using Naive Bayes
- Chapter 7: Discovering Accounts to Follow Using Graph Mining
- Chapter 8: Beating CAPTCHAs with Neural Networks
- Chapter 9: Authorship Attribution
- Chapter 10: Clustering News Articles
- Chapter 11: Classifying Objects in Images Using Deep Learning
- Chapter 12: Working with Big Data
- Module 4: Machine Learning
- Chapter 1: Giving Computers the Ability to Learn from Data
- Chapter 2: Training Machine Learning
- Chapter 3: A Tour of Machine Learning Classifiers Using scikit-learn
- Chapter 4: Building Good Training Sets – Data Preprocessing
- Chapter 5: Compressing Data via Dimensionality Reduction
- Chapter 6: Learning Best Practices for Model Evaluation and Hyperparameter Tuning
- Chapter 7: Combining Different Models for Ensemble Learning
- Chapter 8: Predicting Continuous Target Variables with Regression Analysis
- B. Bibliography
- 1. Giving Computers the Ability to Learn from Data
- Index
In this chapter, we will explore another data analysis library called pandas. The goal of this chapter is to give you some basic knowledge and concrete examples for getting started with pandas.
pandas is a Python package that supports fast, flexible, and expressive data structures, as well as computing functions for data analysis. The following are some prominent features that pandas supports:
- Data structure with labeled axes. This makes the program clean and clear and avoids common errors from misaligned data.
- Flexible handling of missing data.
- Intelligent label-based slicing, fancy indexing, and subset creation of large datasets.
- Powerful arithmetic operations and statistical computations on a custom axis via axis label.
- Robust input and output support for loading or saving data from and to files, databases, or HDF5 format.
Related to pandas installation, we recommend an easy way, that is to install it as a part of Anaconda, a cross-platform distribution for data analysis and scientific computing. You can refer to the reference at http://docs.continuum.io/anaconda/ to download and install the library.
After installation, we can use it like other Python packages. Firstly, we have to import the following packages at the beginning of the program:
>>> import pandas as pd >>> import numpy as np
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