Pandas Features.

The pandas documentation is quite hairy and long-winded. I am listing down the main topics from the pandas documentation.

Bold indicates I have tried out this feature e2e myself. I intend to to keep track of progress, as I try out the different features. Hence this list for bookkeeping.

1. 10 Minutes to Pandas:
   • Object Creation
   • Viewing Data
   • Selection
   • Missing Data
   • Operations
   • Merge
   • Grouping
   • Reshaping
   • Time Series
   • Categoricals
   • Plotting
   • Getting Data In/Out
   • Gotchas

2. Essential Basic Functionality.
   • Head and Tail
   • Attributes and the raw ndarray(s)
   • Accelerated operations
   • Flexible binary operations
   • Descriptive statistics
   • Function application
   • Reindexing and altering labels
   • Iteration
   • .dt accessor
   • Vectorized string methods
   • Sorting
   • Copying
   • dtypes
   • Selecting columns based on dtype

3. Indexing and Selecting data
   • Different Choices for Indexing
   • Basics
   • Attribute Access
   • Slicing ranges
   • Selection By Label
   • Selection By Position
   • Selecting Random Samples
   • Setting With Enlargement
   • Fast scalar value getting and setting
   • Boolean indexing
   • Indexing with isin
   • The where() Method and Masking
   • The query() Method (Experimental)
   • Duplicate Data
   • Dictionary-like get() method
   • The select() Method
   • The lookup() Method
   • Index objects
   • Set / Reset Index
   • Returning a view versus a copy

4. MultiIndex
   • Hierarchical indexing (MultiIndex)
   • Advanced indexing with hierarchical index
   • The need for sortedness withMultiIndex
   • Take Methods
   • CategoricalIndex
   • Float64Index

5. Working With Missing Data.
   • Missing data basics
   • Datetimes
   • Inserting missing data
   • Calculations with missing data
   • Cleaning / filling missing data
   • Missing data casting rules and indexing

6. Group By
   • Splitting an object into groups
   • Iterating through groups
   • Selecting a group
   • Aggregation
   • Transformation
   • Filtration
   • Dispatching to instance methods
   • Flexible apply
   • Other useful features
   • Automatic exclusion of “nuisance” columns
   • NA and NaT group handling
   • Grouping with ordered factors
   • Grouping with a Grouper specification
   • Taking the first rows of each group
   • Taking the nth row of each group
   • Enumerate group items
   • Plotting
   • Examples
   • Regrouping by factor
   • Returning a Series to propagate names

7. Merge, join, concatenate
   • Concatenating objects
   • Database-style DataFrame joining/merging

8. Reshaping And Pivot Tables
   • Reshaping by pivoting DataFrame objects
   • Reshaping by stacking and unstacking
   • Multiple Levels
   • Missing Data
   • With a MultiIndex
   • Reshaping by Melt
   • Combining with stats and GroupBy
   • Pivot tables and cross-tabulations
   • Cross tabulations
   • Adding margins (partial aggregates)
   • Tiling
   • Computing indicator / dummy variables
   • Factorizing values

9. Time Series / Date Functionality.
   • Overview
   • Time Stamps vs. Time Spans
   • Converting to Timestamps
   • Generating Ranges of Timestamps
   • DatetimeIndex
   • DateOffset objects
   • Time series-related instance methods
   • Resampling
   • Time Span Representation
   • Converting between Representations
   • Representing out-of-bounds spans
   • Time Zone Handling

10. Visualization.
    • Basic Plotting: plot
    • Other Plots
    • Plotting with Missing Data
    • Plotting Tools
    • Plot Formatting
    • Plotting directly with matplotlib
    • Trellis plotting interface