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Hands-On Data Analysis with Pandas

You're reading from   Hands-On Data Analysis with Pandas A Python data science handbook for data collection, wrangling, analysis, and visualization

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Product type Paperback
Published in Apr 2021
Publisher Packt
ISBN-13 9781800563452
Length 788 pages
Edition 2nd Edition
Languages
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Author (1):
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Stefanie Molin Stefanie Molin
Author Profile Icon Stefanie Molin
Stefanie Molin
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Toc

Table of Contents (21) Chapters Close

Preface 1. Section 1: Getting Started with Pandas
2. Chapter 1: Introduction to Data Analysis FREE CHAPTER 3. Chapter 2: Working with Pandas DataFrames 4. Section 2: Using Pandas for Data Analysis
5. Chapter 3: Data Wrangling with Pandas 6. Chapter 4: Aggregating Pandas DataFrames 7. Chapter 5: Visualizing Data with Pandas and Matplotlib 8. Chapter 6: Plotting with Seaborn and Customization Techniques 9. Section 3: Applications – Real-World Analyses Using Pandas
10. Chapter 7: Financial Analysis – Bitcoin and the Stock Market 11. Chapter 8: Rule-Based Anomaly Detection 12. Section 4: Introduction to Machine Learning with Scikit-Learn
13. Chapter 9: Getting Started with Machine Learning in Python 14. Chapter 10: Making Better Predictions – Optimizing Models 15. Chapter 11: Machine Learning Anomaly Detection 16. Section 5: Additional Resources
17. Chapter 12: The Road Ahead 18. Solutions
19. Other Books You May Enjoy Appendix

Formatting plots with matplotlib

A big part of making our visualizations presentable is choosing the right plot type and having them well labeled so they are easy to interpret. By carefully tuning the final appearance of our visualizations, we make them easier to read and understand.

Let's now move to the 2-formatting_plots.ipynb notebook, run the setup code to import the packages we need, and read in the Facebook stock data and COVID-19 daily new cases data:

>>> %matplotlib inline
>>> import matplotlib.pyplot as plt
>>> import numpy as np
>>> import pandas as pd 
>>> fb = pd.read_csv(
...     'data/fb_stock_prices_2018.csv', 
...     index_col='date', 
...     parse_dates=True
... ) 
>>> covid = pd.read_csv('data/covid19_cases.csv').assign(
...     date=lambda x: \
...   &...
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