Practical Data Analysis Using Jupyter Notebook

封面

版权信息

About Packt

Why subscribe?

Foreword

About the author

Preface

Section 1: Data Analysis Essentials

Fundamentals of Data Analysis

The evolution of data analysis and why it is important

What makes a good data analyst?

Understanding data types and their significance

Data classifications and data attributes explained

Understanding data literacy

Summary

Further reading

Overview of Python and Installing Jupyter Notebook

Technical requirements

Installing Python and using Jupyter Notebook

Storing and retrieving data files

Hello World! – running your first Python code

Exploring Python packages

Summary

Future reading

Getting Started with NumPy

Technical requirements

Understanding a Python NumPy array and its importance

Making your first NumPy array

Practical use cases of NumPy and arrays

Summary

Further reading

Creating Your First pandas DataFrame

Technical requirements

Techniques for manipulating tabular data

Understanding pandas and DataFrames

Handling essential data formats

Data dictionaries and data types

Creating our first DataFrame

Summary

Further reading

Gathering and Loading Data in Python

Technical requirements

Introduction to SQL and relational databases

From SQL to pandas DataFrames

Data about your data explained

The importance of data lineage

Summary

Further reading

Section 2: Solutions for Data Discovery

Visualizing and Working with Time Series Data

Technical requirements

Data modeling for results

Anatomy of a chart and data viz best practices

Comparative analysis

The shape of the curve

Summary

Further reading

Exploring Cleaning Refining and Blending Datasets

Technical requirements

Retrieving viewing and storing tabular data

Learning how to restrict sort and sift through data

Cleaning refining and purifying data using Python

Combining and binning data

Summary

Further reading

Understanding Joins Relationships and Aggregates

Technical requirements

Foundations of join relationships

Join types in action

Explaining data aggregation

Summary statistics and outliers

Summary

Further reading

Plotting Visualization and Storytelling

Technical requirements

Explaining distribution analysis

Understanding outliers and trends

Geoanalytical techniques and tips

Finding patterns in data

Summary

Further reading

Section 3: Working with Unstructured Big Data

Exploring Text Data and Unstructured Data

Technical requirements

Preparing to work with unstructured data

Tokenization explained

Counting words and exploring results

Normalizing text techniques

Excluding words from analysis

Summary

Further reading

Practical Sentiment Analysis

Technical requirements

Why sentiment analysis is important

Elements of an NLP model

Sentiment analysis packages

Sentiment analysis in action

Summary

Further reading

Bringing It All Together