Pandas in action

Of all the introductory pandas books I've read--and I did read a few--this is the best, by a mile. Erico Lendzian, idibu.com Take the next steps in your data science career! This friendly and hands-on guide shows you how to start mastering Pandas with skills you already know from spreadsheet so...

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Bibliographic Details
Other Authors: Paskhaver, Boris, author (author)
Format: eBook
Language:Inglés
Published: Shelter Island, New York : Manning Publications Company [2021]
Edition:[First edition]
Subjects:
See on Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009635335006719
Table of Contents:
  • Intro
  • Pandas in Action
  • Dedication
  • Copyright
  • contents
  • front matter
  • preface
  • acknowledgments
  • about this book
  • Who should read this book
  • How this book is organized: A road map
  • About the code
  • liveBook discussion forum
  • Other online resources
  • about the author
  • about the cover illustration
  • Part 1. Core pandas
  • 1 Introducing pandas
  • 1.1 Data in the 21st century
  • 1.2 Introducing pandas
  • 1.2.1 Pandas vs. graphical spreadsheet applications
  • 1.2.2 Pandas vs. its competitors
  • 1.3 A tour of pandas
  • 1.3.1 Importing a data set
  • 1.3.2 Manipulating a DataFrame
  • 1.3.3 Counting values in a Series
  • 1.3.4 Filtering a column by one or more criteria
  • 1.3.5 Grouping data
  • Summary
  • 2 The Series object
  • 2.1 Overview of a Series
  • 2.1.1 Classes and instances
  • 2.1.2 Populating the Series with values
  • 2.1.3 Customizing the Series index
  • 2.1.4 Creating a Series with missing values
  • 2.2 Creating a Series from Python objects
  • 2.3 Series attributes
  • 2.4 Retrieving the first and last rows
  • 2.5 Mathematical operations
  • 2.5.1 Statistical operations
  • 2.5.2 Arithmetic operations
  • 2.5.3 Broadcasting
  • 2.6 Passing the Series to Python's built-in functions
  • 2.7 Coding challenge
  • 2.7.1 Problems
  • 2.7.2 Solutions
  • Summary
  • 3 Series methods
  • 3.1 Importing a data set with the read_csv function
  • 3.2 Sorting a Series
  • 3.2.1 Sorting by values with the sort_values method
  • 3.2.2 Sorting by index with the sort_index method
  • 3.2.3 Retrieving the smallest and largest values with the nsmallest and nlargest methods
  • 3.3 Overwriting a Series with the inplace parameter
  • 3.4 Counting values with the value_counts method
  • 3.5 Invoking a function on every Series value with the apply method
  • 3.6 Coding challenge
  • 3.6.1 Problems
  • 3.6.2 Solutions
  • Summary
  • 4 The DataFrame object.
  • 4.1 Overview of a DataFrame
  • 4.1.1 Creating a DataFrame from a dictionary
  • 4.1.2 Creating a DataFrame from a NumPy ndarray
  • 4.2 Similarities between Series and DataFrames
  • 4.2.1 Importing a DataFrame with the read_csv function
  • 4.2.2 Shared and exclusive attributes of Series and DataFrames
  • 4.2.3 Shared methods of Series and DataFrames
  • 4.3 Sorting a DataFrame
  • 4.3.1 Sorting by a single column
  • 4.3.2 Sorting by multiple columns
  • 4.4 Sorting by index
  • 4.4.1 Sorting by row index
  • 4.4.2 Sorting by column index
  • 4.5 Setting a new index
  • 4.6 Selecting columns and rows from a DataFrame
  • 4.6.1 Selecting a single column from a DataFrame
  • 4.6.2 Selecting multiple columns from a DataFrame
  • 4.7 Selecting rows from a DataFrame
  • 4.7.1 Extracting rows by index label
  • 4.7.2 Extracting rows by index position
  • 4.7.3 Extracting values from specific columns
  • 4.8 Extracting values from Series
  • 4.9 Renaming columns or rows
  • 4.10 Resetting an index
  • 4.11 Coding challenge
  • 4.11.1 Problems
  • 4.11.2 Solutions
  • Summary
  • 5 Filtering a DataFrame
  • 5.1 Optimizing a data set for memory use
  • 5.1.1 Converting data types with the astype method
  • 5.2 Filtering by a single condition
  • 5.3 Filtering by multiple conditions
  • 5.3.1 The AND condition
  • 5.3.2 The OR condition
  • 5.3.3 Inversion with ~
  • 5.3.4 Methods for Booleans
  • 5.4 Filtering by condition
  • 5.4.1 The isin method
  • 5.4.2 The between method
  • 5.4.3 The isnull and notnull methods
  • 5.4.4 Dealing with null values
  • 5.5 Dealing with duplicates
  • 5.5.1 The duplicated method
  • 5.5.2 The drop_duplicates method
  • 5.6 Coding challenge
  • 5.6.1 Problems
  • 5.6.2 Solutions
  • Summary
  • Part 2. Applied pandas
  • 6 Working with text data
  • 6.1 Letter casing and whitespace
  • 6.2 String slicing
  • 6.3 String slicing and character replacement.
  • 6.4 Boolean methods
  • 6.5 Splitting strings
  • 6.6 Coding challenge
  • 6.6.1 Problems
  • 6.6.2 Solutions
  • 6.7 A note on regular expressions
  • Summary
  • 7 MultiIndex DataFrames
  • 7.1 The MultiIndex object
  • 7.2 MultiIndex DataFrames
  • 7.3 Sorting a MultiIndex
  • 7.4 Selecting with a MultiIndex
  • 7.4.1 Extracting one or more columns
  • 7.4.2 Extracting one or more rows with loc
  • 7.4.3 Extracting one or more rows with iloc
  • 7.5 Cross-sections
  • 7.6 Manipulating the Index
  • 7.6.1 Resetting the index
  • 7.6.2 Setting the index
  • 7.7 Coding challenge
  • 7.7.1 Problems
  • 7.7.2 Solutions
  • Summary
  • 8 Reshaping and pivoting
  • 8.1 Wide vs. narrow data
  • 8.2 Creating a pivot table from a DataFrame
  • 8.2.1 The pivot_table method
  • 8.2.2 Additional options for pivot tables
  • 8.3 Stacking and unstacking index levels
  • 8.4 Melting a data set
  • 8.5 Exploding a list of values
  • 8.6 Coding challenge
  • 8.6.1 Problems
  • 8.6.2 Solutions
  • Summary
  • 9 The GroupBy object
  • 9.1 Creating a GroupBy object from scratch
  • 9.2 Creating a GroupBy object from a data set
  • 9.3 Attributes and methods of a GroupBy object
  • 9.4 Aggregate operations
  • 9.5 Applying a custom operation to all groups
  • 9.6 Grouping by multiple columns
  • 9.7 Coding challenge
  • 9.7.1 Problems
  • 9.7.2 Solutions
  • Summary
  • 10 Merging, joining, and concatenating
  • 10.1 Introducing the data sets
  • 10.2 Concatenating data sets
  • 10.3 Missing values in concatenated DataFrames
  • 10.4 Left joins
  • 10.5 Inner joins
  • 10.6 Outer joins
  • 10.7 Merging on index labels
  • 10.8 Coding challenge
  • 10.8.1 Problems
  • 10.8.2 Solutions
  • Summary
  • 11 Working with dates and times
  • 11.1 Introducing the Timestamp object
  • 11.1.1 How Python works with datetimes
  • 11.1.2 How pandas works with datetimes
  • 11.2 Storing multiple timestamps in a DatetimeIndex.
  • 11.3 Converting column or index values to datetimes
  • 11.4 Using the DatetimeProperties object
  • 11.5 Adding and subtracting durations of time
  • 11.6 Date offsets
  • 11.7 The Timedelta object
  • 11.8 Coding challenge
  • 11.8.1 Problems
  • 11.8.2 Solutions
  • Summary
  • 12 Imports and exports
  • 12.1 Reading from and writing to JSON files
  • 12.1.1 Loading a JSON file Into a DataFrame
  • 12.1.2 Exporting a DataFrame to a JSON file
  • 12.2 Reading from and writing to CSV files
  • 12.3 Reading from and writing to Excel workbooks
  • 12.3.1 Installing the xlrd and openpyxl libraries in an Anaconda environment
  • 12.3.2 Importing Excel workbooks
  • 12.3.3 Exporting Excel workbooks
  • 12.4 Coding challenge
  • 12.4.1 Problems
  • 12.4.2 Solutions
  • Summary
  • 13 Configuring pandas
  • 13.1 Getting and setting pandas options
  • 13.2 Precision
  • 13.3 Maximum column width
  • 13.4 Chop threshold
  • 13.5 Option context
  • Summary
  • 14 Visualization
  • 14.1 Installing matplotlib
  • 14.2 Line charts
  • 14.3 Bar graphs
  • 14.4 Pie charts
  • Summary
  • Appendix A. Installation and setup
  • A.1 The Anaconda distribution
  • A.2 The macOS setup process
  • A.2.1 Installing Anaconda in macOS
  • A.2.2 Launching Terminal
  • A.2.3 Common Terminal commands
  • A.3 The Windows setup process
  • A.3.1 Installing Anaconda in Windows
  • A.3.2 Launching Anaconda Prompt
  • A.3.3 Common Anaconda Prompt commands
  • A.4 Creating a new Anaconda environment
  • A.5 Anaconda Navigator
  • A.6 The basics of Jupyter Notebook
  • Appendix B. Python crash course
  • B.1 Simple data types
  • B.1.1 Numbers
  • B.1.2 Strings
  • B.1.3 Booleans
  • B.1.4 The None object
  • B.2 Operators
  • B.2.1 Mathematical operators
  • B.2.2 Equality and inequality operators
  • B.3 Variables
  • B.4 Functions
  • B.4.1 Arguments and return values
  • B.4.2 Custom functions
  • B.5 Modules
  • B.6 Classes and objects.
  • B.7 Attributes and methods
  • B.8 String methods
  • B.9 Lists
  • B.9.1 List iteration
  • B.9.2 List comprehension
  • B.9.3 Converting a string to a list and vice versa
  • B.10 Tuples
  • B.11 Dictionaries
  • B.11.1 Dictionary Iteration
  • B.12 Sets
  • Appendix C. NumPy crash course
  • C.1 Dimensions
  • C.2 The ndarray object
  • C.2.1 Generating a numeric range with the arange method
  • C.2.2 Attributes on a ndarray object
  • C.2.3 The reshape method
  • C.2.4 The randint function
  • C.2.5 The randn function
  • C.3 The nan object
  • Appendix D. Generating fake data with Faker
  • D.1 Installing Faker
  • D.2 Getting started with Faker
  • D.3 Populating a DataFrame with fake values
  • Appendix E. Regular expressions
  • E.1 Introduction to Python's re module
  • E.2 Metacharacters
  • E.3 Advanced search patterns
  • E.4 Regular expressions and pandas
  • index.