فهرست مطالب

• Pandas in Action
• Dedication
• Copyright
• Front Matter
• Part 1: Core pandas
• Chapter 1: Introducing pandas
• 1.1 Data in the 21st century
• 1.2 Introducing pandas
• 1.3 A tour of pandas
• Summary
• Chapter 2: The Series object
• 2.1 Overview of a Series
• 2.2 Creating a Series from Python objects
• 2.3 Series attributes
• 2.4 Retrieving the first and last rows
• 2.5 Mathematical operations
• 2.6 Passing the Series to Python’s built-in functions
• 2.7 Coding challenge
• Summary
• Chapter 3: Series methods
• 3.1 Importing a data set with the read_csv function
• 3.2 Sorting a Series
• 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
• Summary
• Chapter 4: The DataFrame object
• 4.1 Overview of a DataFrame
• 4.2 Similarities between Series and DataFrames
• 4.3 Sorting a DataFrame
• 4.4 Sorting by index
• 4.5 Setting a new index
• 4.6 Selecting columns and rows from a DataFrame
• 4.7 Selecting rows from a DataFrame
• 4.8 Extracting values from Series
• 4.9 Renaming columns or rows
• 4.10 Resetting an index
• 4.11 Coding challenge
• Summary
• Chapter 5: Filtering a DataFrame
• 5.1 Optimizing a data set for memory use
• 5.2 Filtering by a single condition
• 5.3 Filtering by multiple conditions
• 5.4 Filtering by condition
• 5.5 Dealing with duplicates
• 5.6 Coding challenge
• Summary
• Part 2: Applied pandas
• Chapter 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
• 6.7 A note on regular expressions
• Summary
• Chapter 7: MultiIndex DataFrames
• 7.1 The MultiIndex object
• 7.2 MultiIndex DataFrames
• 7.3 Sorting a MultiIndex
• 7.4 Selecting with a MultiIndex
• 7.5 Cross-sections
• 7.6 Manipulating the Index
• 7.7 Coding challenge
• Summary
• Chapter 8: Reshaping and pivoting
• 8.1 Wide vs. narrow data
• 8.2 Creating a pivot table from a DataFrame
• 8.3 Stacking and unstacking index levels
• 8.4 Melting a data set
• 8.5 Exploding a list of values
• 8.6 Coding challenge
• Summary
• Chapter 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
• Summary
• Chapter 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
• Summary
• Chapter 11: Working with dates and times
• 11.1 Introducing the Timestamp object
• 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
• Summary
• Chapter 12: Imports and exports
• 12.1 Reading from and writing to JSON files
• 12.2 Reading from and writing to CSV files
• 12.3 Reading from and writing to Excel workbooks
• 12.4 Coding challenge
• Summary
• Chapter 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
• Chapter 14: Visualization
• 14.1 Installing matplotlib
• 14.2 Line charts
• 14.3 Bar graphs
• 14.4 Pie charts
• Summary
• Appendix A: Installation and setup
• Appendix B: Python crash course
• Appendix C: NumPy crash course
• Appendix D: Generating fake data with Faker
• Appendix E: Regular expressions
• Index