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Hands-On Data Analysis with Pandas

Hands-On Data Analysis with Pandas: Efficiently perform data collection, wrangling, analysis, and visualization using Python

Stefanie Molin

R$50 per month

4.7 (11 Ratings)

Paperback Jul 2019 740 pages 1st Edition

R$80 ~~R$218.99~~

Renews at R$50p/m

Stefanie Molin

R$50 per month

4.7 (11 Ratings)

Paperback Jul 2019 740 pages 1st Edition

R$80 ~~R$218.99~~

Renews at R$50p/m

R$80 ~~R$218.99~~

Renews at R$50p/m

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View table of contents

Preview Book

Key benefits

Perform efficient data analysis and manipulation tasks using pandas
Apply pandas to different real-world domains with the help of step-by-step demonstrations
Get accustomed to using pandas as an effective data exploration tool.

Description

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.

Who is this book for?

This book is for data analysts, data science beginners, and Python developers who want to explore each stage of data analysis and scientific computing using a wide range of datasets. You will also find this book useful if you are a data scientist looking to implement pandas in machine learning. Working knowledge of Python programming language will be beneficial.

What you will learn

Understand how data analysts and scientists gather and analyze data
Perform data analysis and data wrangling using Python
Combine, group, and aggregate data from multiple sources
Create data visualizations with pandas, matplotlib, and seaborn
Apply machine learning (ML) algorithms to identify patterns and make predictions
Use Python data science libraries to analyze real-world datasets
Use pandas to solve common data representation and analysis problems
Build Python scripts, modules, and packages for reusable analysis code

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50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.

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Product Details

Publication date : Jul 26, 2019

Length: 740 pages

Edition : 1st

Language : English

ISBN-13 : 9781789615326

Category :

Data

Languages :

Concepts :

Tools :

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Table of Contents

19 Chapters

Section 1: Getting Started with Pandas

Section 1: Getting Started with Pandas

Introduction to Data Analysis

Introduction to Data Analysis

Chapter materials

Fundamentals of data analysis

Statistical foundations

Setting up a virtual environment

Further reading

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

Further reading

Section 2: Using Pandas for Data Analysis

Section 2: Using Pandas for Data Analysis

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

Further reading

Aggregating Pandas DataFrames

Aggregating Pandas DataFrames

Chapter materials

Database-style operations on DataFrames

DataFrame operations

Aggregations with pandas and numpy

Further reading

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

Further reading

Plotting with Seaborn and Customization Techniques

Plotting with Seaborn and Customization Techniques

Chapter materials

Utilizing seaborn for advanced plotting

Customizing visualizations

Further reading

Section 3: Applications - Real-World Analyses Using Pandas

Section 3: Applications - Real-World Analyses Using Pandas

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

Further reading

Rule-Based Anomaly Detection

Rule-Based Anomaly Detection

Chapter materials

Simulating login attempts

Exploratory data analysis

Rule-based anomaly detection

Further reading

Section 4: Introduction to Machine Learning with Scikit-Learn

Section 4: Introduction to Machine Learning with Scikit-Learn

Getting Started with Machine Learning in Python

Getting Started with Machine Learning in Python

Chapter materials

Learning the lingo

Exploratory data analysis

Preprocessing data

Further reading

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

Further reading

Machine Learning Anomaly Detection

Machine Learning Anomaly Detection

Chapter materials

Exploring the data

Unsupervised methods

Supervised methods

Online learning

Further reading

Section 5: Additional Resources

Section 5: Additional Resources

The Road Ahead

Practicing working with data

Python practice

Further reading

Solutions

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About the author

Stefanie Molin

Stefanie Molin

Stefanie Molin is a data scientist and software engineer at Bloomberg LP in NYC, tackling tough problems in information security, particularly revolving around anomaly detection, building tools for gathering data, and knowledge sharing. She has extensive experience in data science, designing anomaly detection solutions, and utilizing machine learning in both R and Python in the AdTech and FinTech industries. She holds a B.S. in operations research from Columbia University's Fu Foundation School of Engineering and Applied Science, with minors in economics, and entrepreneurship and innovation. In her free time, she enjoys traveling the world, inventing new recipes, and learning new languages spoken among both people and computers.

See other products by Stefanie Molin

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