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Pandas 1.x Cookbook

You're reading from   Pandas 1.x Cookbook Practical recipes for scientific computing, time series analysis, and exploratory data analysis using Python

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Product type Paperback
Published in Feb 2020
Publisher Packt
ISBN-13 9781839213106
Length 626 pages
Edition 2nd Edition
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Authors (2):
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Theodore Petrou Theodore Petrou
Author Profile Icon Theodore Petrou
Theodore Petrou
Matthew Harrison Matthew Harrison
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Matthew Harrison
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Toc

Table of Contents (17) Chapters Close

Preface 1. Pandas Foundations 2. Essential DataFrame Operations FREE CHAPTER 3. Creating and Persisting DataFrames 4. Beginning Data Analysis 5. Exploratory Data Analysis 6. Selecting Subsets of Data 7. Filtering Rows 8. Index Alignment 9. Grouping for Aggregation, Filtration, and Transformation 10. Restructuring Data into a Tidy Form 11. Combining Pandas Objects 12. Time Series Analysis 13. Visualization with Matplotlib, Pandas, and Seaborn 14. Debugging and Testing Pandas 15. Other Books You May Enjoy
16. Index

Data dictionaries

A crucial part of data analysis involves creating and maintaining a data dictionary. A data dictionary is a table of metadata and notes on each column of data. One of the primary purposes of a data dictionary is to explain the meaning of the column names. The college dataset uses a lot of abbreviations that are likely to be unfamiliar to an analyst who is inspecting it for the first time.

A data dictionary for the college dataset is provided in the following college_data_dictionary.csv file:

>>> pd.read_csv("data/college_data_dictionary.csv")
    column_name  description
0        INSTNM  Institut...
1          CITY  City Loc...
2        STABBR  State Ab...
3          HBCU  Historic...
4       MENONLY  0/1 Men ...
..          ...          ...
22      PCTPELL  Percent ...
23     PCTFLOAN  Percent ...
24      UG25ABV  Percent ...
25  MD_EARN_...  Median E...
26  GRAD_DEB...  Median d...

As you can see, it is immensely helpful in deciphering...

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