Pandas

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
  • 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
  • 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
  • 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
• 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
• Visualization
  • Basic plotting: plot
  • Other plots
  • Plotting with missing data
  • Plotting Tools
  • Plot Formatting
  • Plotting directly with matplotlib
• 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
  • 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()
• 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
  • 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

Pandas Documentation

1. IO tools (text, CSV, HDF5, …)
2. Indexing and selecting data
3. MultiIndex / advanced indexing
4. Merge, join, and concatenate
5. Reshaping and pivot tables
6. Working with text data
7. Working with missing data
8. Categorical data
9. Nullable integer data type
10. Nullable Boolean Data Type
11. Visualization
12. Computational tools
13. Group By: split-apply-combine
14. Time series / date functionality
15. Time deltas
16. Styling
17. Options and settings
18. Enhancing performance
19. Scaling to large datasets
20. Sparse data structures
21. Frequently Asked Questions (FAQ)
22. Cookbook