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.

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

• IO Tools (Text, CSV, HDF5, …)
  • CSV & Text files
  • JSON
  • HTML
  • Excel files
  • Clipboard
  • Pickling
  • msgpack
  • HDF5 (PyTables)
  • Feather
  • Parquet
  • SQL Queries
  • Google BigQuery
  • Stata Format
  • SAS 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
  • IX Indexer is Deprecated
  • 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
  • The query() Method
  • Duplicate Data
  • Dictionary-like get() method
  • The lookup() Method
  • 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, and concatenate
  • Concatenating objects
  • Database-style DataFrame or named Series joining/merging
  • Timeseries friendly merging
• 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
• Working with Text Data
  • 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”
  • 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
• 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
• Visualization
  • Basic Plotting: plot
  • Other Plots
  • Plotting with Missing Data
  • Plotting Tools
  • Plot Formatting
  • Plotting directly with matplotlib
  • Trellis plotting interface
• Computational tools
  • Statistical Functions
  • Window Functions
  • Aggregation
  • Expanding Windows
  • Exponentially Weighted Windows
• 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
  • Other useful features
  • Examples
• Time Series / Date functionality
  • Overview
  • Timestamps vs. Time Spans
  • Converting to Timestamps
  • Generating Ranges of Timestamps
  • Timestamp Limitations
  • Indexing
  • Iterating through groups
  • 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
• Styling
  • Building Styles
  • Finer Control: Slicing
  • Finer Control: Display Values
  • Builtin Styles
  • Sharing Styles
  • Other Options
  • Fun stuff
  • Export to Excel
  • Extensibility
• Options and Settings
  • Overview
  • Getting and Setting Options
  • Setting Startup Options in python/ipython Environment
  • Frequently Used Options
  • Available Options
  • Number Formatting
  • Unicode Formatting
  • Table Schema Display
• Enhancing Performance
  • Cython (Writing C extensions for pandas)
  • Using Numba
  • Expression Evaluation via eval()
• Sparse data structures
  • Sparse Accessor
  • SparseArray
  • SparseIndex objects
  • Sparse Dtypes
  • Sparse Calculation
  • Interaction with scipy.sparse
• Frequently Asked Questions (FAQ)
  • DataFrame memory usage
  • Using If/Truth Statements with pandas
  • 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
  • Aliasing Axis Names
  • Creating Example Data