Book Image

Hands-On Data Analysis with Pandas

By : Stefanie Molin
Book Image

Hands-On Data Analysis with Pandas

By: Stefanie Molin

Overview of this book

Data analysis has become a necessary skill in a variety of domains where knowing how to work with data and extract insights can generate significant value. Hands-On Data Analysis with Pandas will show you how to analyze your data, get started with machine learning, and work effectively with Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the powerful pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will be able to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding chapters, you will explore some applications of anomaly detection, regression, clustering, and classification using scikit-learn to make predictions based on past data. By the end of this book, you will be equipped with the skills you need to use pandas to ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets.

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Table of Contents (21 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Conventions used
Get in touch
Free Chapter
1
Section 1: Getting Started with Pandas
Section 1: Getting Started with Pandas
2
Introduction to Data Analysis
Introduction to Data Analysis
Chapter materials
Fundamentals of data analysis
Statistical foundations
Setting up a virtual environment
Summary
Exercises
Further reading
3
Working with Pandas DataFrames
Working with Pandas DataFrames
Chapter materials
Pandas data structures
Bringing data into a pandas DataFrame
Inspecting a DataFrame object
Grabbing subsets of the data
Adding and removing data
Summary
Exercises
Further reading
4
Section 2: Using Pandas for Data Analysis
Section 2: Using Pandas for Data Analysis
5
Data Wrangling with Pandas
Data Wrangling with Pandas
Chapter materials
What is data wrangling?
Collecting temperature data
Cleaning up the data
Restructuring the data
Handling duplicate, missing, or invalid data
Summary
Exercises
Further reading
6
Aggregating Pandas DataFrames
Aggregating Pandas DataFrames
Chapter materials
Database-style operations on DataFrames
DataFrame operations
Aggregations with pandas and numpy
Time series
Summary
Exercises
Further reading
7
Visualizing Data with Pandas and Matplotlib
Visualizing Data with Pandas and Matplotlib
Chapter materials
An introduction to matplotlib
Plotting with pandas
The pandas.plotting subpackage
Summary
Exercises
Further reading
8
Plotting with Seaborn and Customization Techniques
Plotting with Seaborn and Customization Techniques
Chapter materials
Utilizing seaborn for advanced plotting
Formatting
Customizing visualizations
Summary
Exercises
Further reading
9
Section 3: Applications - Real-World Analyses Using Pandas
Section 3: Applications - Real-World Analyses Using Pandas
10
Financial Analysis - Bitcoin and the Stock Market
Financial Analysis - Bitcoin and the Stock Market
Chapter materials
Building a Python package
Data extraction with pandas
Exploratory data analysis
Technical analysis of financial instruments
Modeling performance
Summary
Exercises
Further reading
11
Rule-Based Anomaly Detection
Rule-Based Anomaly Detection
Chapter materials
Simulating login attempts
Exploratory data analysis
Rule-based anomaly detection
Summary
Exercises
Further reading
12
Section 4: Introduction to Machine Learning with Scikit-Learn
Section 4: Introduction to Machine Learning with Scikit-Learn
13
Getting Started with Machine Learning in Python
Getting Started with Machine Learning in Python
Chapter materials
Learning the lingo
Exploratory data analysis
Preprocessing data
Clustering
Regression
Classification
Summary
Exercises
Further reading
14
Making Better Predictions - Optimizing Models
Making Better Predictions - Optimizing Models
Chapter materials
Hyperparameter tuning with grid search
Feature engineering
Ensemble methods
Inspecting classification prediction confidence
Addressing class imbalance
Regularization
Summary
Exercises
Further reading
15
Machine Learning Anomaly Detection
Machine Learning Anomaly Detection
Chapter materials
Exploring the data
Unsupervised methods
Supervised methods
Online learning
Summary
Exercises
Further reading
16
Section 5: Additional Resources
Section 5: Additional Resources
17
The Road Ahead
The Road Ahead
Data resources
Practicing working with data
Python practice
Summary
Exercises
Further reading
18
Solutions
Solutions
19
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Appendix
Appendix
Data analysis workflow
Choosing the appropriate visualization
Machine learning workflow

Exercises

Complete the following exercises to practice the skills covered in this chapter. Be sure to consult the Machine learning workflow section in the appendix as a refresher for the process of building models:

  1. Predict star temperature with elastic net linear regression as follows:
    1. Using the data/stars.csv file, build a pipeline to normalize the data with the MinMaxScaler and then run elastic net linear regression using all the numeric columns to predict the temperature of the star.
    2. Run grid search on the pipeline to find the best values for alpha, l1_ratio, and fit_intercept for the elastic net in the search space of your choice.
    3. Train the model on 75% of the initial data.
    4. Calculate the R2 of your model.
    5. Find the coefficients for each regressor and the intercept.
    6. Plot the residuals using the plot_residuals() function from the ml_utils.regression module.
  2. Perform multiclass...
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