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Practical Data Analysis Using Jupyter Notebook

You're reading from Practical Data Analysis Using Jupyter Notebook Learn how to speak the language of data by extracting useful and actionable insights using Python

Product type Paperback

Published in Jun 2020

Publisher Packt

ISBN-13 9781838826031

Length 322 pages

Edition 1st Edition

Languages

Python

Tools

Jupyter

Concepts

Data Analysis

Author (1):

Marc Wintjen

View More author details

Table of Contents (18) Chapters

Preface

1. Section 1: Data Analysis Essentials

2. Fundamentals of Data Analysis FREE CHAPTER

3. Overview of Python and Installing Jupyter Notebook

4. Getting Started with NumPy

5. Creating Your First pandas DataFrame

6. Gathering and Loading Data in Python

7. Section 2: Solutions for Data Discovery

8. Visualizing and Working with Time Series Data

9. Exploring, Cleaning, Refining, and Blending Datasets

10. Understanding Joins, Relationships, and Aggregates

11. Plotting, Visualization, and Storytelling

12. Section 3: Working with Unstructured Big Data

13. Exploring Text Data and Unstructured Data

14. Practical Sentiment Analysis

15. Bringing It All Together

16. Works Cited

17. Other Books You May Enjoy

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Further reading

You can refer to the following links for more information on the topics of this chapter:

A nice walk-through of filtering and grouping using DataFrames:https://github.com/bhavaniravi/pandas_tutorial/blob/master/Pandas_Basics_To_Beyond.ipynb
Comparison of SQL features and their equivalent pandas functions: https://pandas.pydata.org/pandas-docs/stable/getting_started/comparison/comparison_with_sql.html
Additional information on exporting data to Excel: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_excel.html#pandas.DataFrame.to_excel
Examples of SQL date and time functions: https://www.postgresql.org/docs/8.1/functions-datetime.html

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Authors (1)

Marc Wintjen

Marc Wintjen

Marc Wintjen is a Risk Analytics Architect at Bloomberg LP with over 20 years of professional experience. An evangelist for data literacy, hes known as the Data Mensch by helping others make data driven decisions. His passion for all things data has evolved from SQL and Data Warehousing to Big Data Analytics and Data Visualizations.

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