User Guide

The User Guide covers all of pandas by topic area. Each of the subsections introduces a topic (such as “working with missing data”), and discusses how pandas approaches the problem, with many examples throughout.

Users brand-new to pandas should start with 10 minutes to pandas.

For a high level summary of the pandas fundamentals, see Intro to data structures and Essential basic functionality.

Further information on any specific method can be obtained in the API reference.

• 10 minutes to pandas
  • Object creation
  • Viewing data
  • Selection
  • Missing data
  • Operations
  • Merge
  • Grouping
  • Reshaping
  • Time series
  • Categoricals
  • Plotting
  • Getting data in/out
  • Gotchas
• Intro to data structures
  • Series
  • DataFrame
• Essential basic functionality
  • Head and tail
  • Attributes and underlying data
  • 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
• IO tools (text, CSV, HDF5, …)
  • CSV & text files
  • JSON
  • HTML
  • LaTeX
  • XML
  • Excel files
  • OpenDocument Spreadsheets
  • Binary Excel (.xlsb) files
  • Clipboard
  • Pickling
  • msgpack
  • HDF5 (PyTables)
  • Feather
  • Parquet
  • ORC
  • SQL queries
  • Google BigQuery
  • Stata format
  • SAS formats
  • SPSS formats
  • Other file formats
  • Performance considerations
• Indexing and selecting data
  • Different choices for indexing
  • Basics
  • Attribute access
  • Slicing ranges
  • Selection by label
  • Selection by position
  • Selection by callable
  • Combining positional and label-based indexing
  • Indexing with list with missing labels is deprecated
  • Selecting random samples
  • Setting with enlargement
  • Fast scalar value getting and setting
  • Boolean indexing
  • Indexing with isin
  • The where() Method and Masking
  • Setting with enlargement conditionally using numpy()
  • The query() Method
  • Duplicate data
  • Dictionary-like get() method
  • Looking up values by index/column labels
  • Index objects
  • Set / reset index
  • Returning a view versus a copy
• MultiIndex / advanced indexing
  • Hierarchical indexing (MultiIndex)
  • Advanced indexing with hierarchical index
  • Sorting a MultiIndex
  • Take methods
  • Index types
  • Miscellaneous indexing FAQ
• Merge, join, concatenate and compare
  • Concatenating objects
  • Database-style DataFrame or named Series joining/merging
  • Timeseries friendly merging
  • Comparing objects
• Reshaping and pivot tables
  • Reshaping by pivoting DataFrame objects
  • Reshaping by stacking and unstacking
  • Reshaping by melt
  • Combining with stats and GroupBy
  • Pivot tables
  • Cross tabulations
  • Tiling
  • Computing indicator / dummy variables
  • Factorizing values
  • Examples
  • Exploding a list-like column
• Working with text data
  • Text data types
  • String methods
  • Splitting and replacing strings
  • Concatenation
  • Indexing with .str
  • Extracting substrings
  • Testing for strings that match or contain a pattern
  • Creating indicator variables
  • Method summary
• Working with missing data
  • Values considered “missing”
  • Inserting missing data
  • Calculations with missing data
  • Sum/prod of empties/nans
  • NA values in GroupBy
  • Filling missing values: fillna
  • Filling with a PandasObject
  • Dropping axis labels with missing data: dropna
  • Interpolation
  • Replacing generic values
  • String/regular expression replacement
  • Numeric replacement
  • Experimental NA scalar to denote missing values
• Duplicate Labels
  • Consequences of Duplicate Labels
  • Duplicate Label Detection
  • Disallowing Duplicate Labels
• Categorical data
  • Object creation
  • CategoricalDtype
  • Description
  • Working with categories
  • Sorting and order
  • Comparisons
  • Operations
  • Data munging
  • Getting data in/out
  • Missing data
  • Differences to R’s factor
  • Gotchas
• Nullable integer data type
  • Construction
  • Operations
  • Scalar NA Value
• Nullable Boolean data type
  • Indexing with NA values
  • Kleene logical operations
• Chart Visualization
  • Basic plotting: plot
  • Other plots
  • Plotting with missing data
  • Plotting tools
  • Plot formatting
  • Plotting directly with matplotlib
  • Plotting backends
• Table Visualization
  • Styler Object and HTML
  • Formatting the Display
  • Methods to Add Styles
  • Table Styles
  • Setting Classes and Linking to External CSS
  • Styler Functions
  • Tooltips and Captions
  • Finer Control with Slicing
  • Optimization
  • Builtin Styles
  • Sharing styles
  • Limitations
  • Other Fun and Useful Stuff
  • Export to Excel
  • Export to LaTeX
  • More About CSS and HTML
  • Extensibility
• Computational tools
  • Statistical functions
• Group by: split-apply-combine
  • Splitting an object into groups
  • Iterating through groups
  • Selecting a group
  • Aggregation
  • Transformation
  • Filtration
  • Dispatching to instance methods
  • Flexible apply
  • Numba Accelerated Routines
  • Other useful features
  • Examples
• Windowing Operations
  • Overview
  • Rolling window
  • Weighted window
  • Expanding window
  • Exponentially Weighted window
• Time series / date functionality
  • Overview
  • Timestamps vs. time spans
  • Converting to timestamps
  • Generating ranges of timestamps
  • Timestamp limitations
  • Indexing
  • Time/date components
  • DateOffset objects
  • Time series-related instance methods
  • Resampling
  • Time span representation
  • Converting between representations
  • Representing out-of-bounds spans
  • Time zone handling
• Time deltas
  • Parsing
  • Operations
  • Reductions
  • Frequency conversion
  • Attributes
  • TimedeltaIndex
  • Resampling
• Options and settings
  • Overview
  • Available options
  • Getting and setting options
  • Setting startup options in Python/IPython environment
  • Frequently used options
  • Number formatting
  • Unicode formatting
  • Table schema display
• Enhancing performance
  • Cython (writing C extensions for pandas)
  • Numba (JIT compilation)
  • Expression evaluation via eval()
• Scaling to large datasets
  • Load less data
  • Use efficient datatypes
  • Use chunking
  • Use other libraries
• Sparse data structures
  • SparseArray
  • SparseDtype
  • Sparse accessor
  • Sparse calculation
  • Migrating
  • Interaction with scipy.sparse
• Frequently Asked Questions (FAQ)
  • DataFrame memory usage
  • Using if/truth statements with pandas
  • Mutating with User Defined Function (UDF) methods
  • NaN, Integer NA values and NA type promotions
  • Differences with NumPy
  • Thread-safety
  • Byte-ordering issues
• Cookbook
  • Idioms
  • Selection
  • Multiindexing
  • Missing data
  • Grouping
  • Timeseries
  • Merge
  • Plotting
  • Data in/out
  • Computation
  • Timedeltas
  • Creating example data

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