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
- Intro to data structures
- Essential basic functionality
- IO tools (text, CSV, HDF5, …)
-
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
- Merge, join, concatenate and compare
- Reshaping and pivot tables
- Working with text data
-
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
NAscalar to denote missing values
- Duplicate Labels
- Categorical data
- Nullable integer data type
- Nullable Boolean data type
- Chart Visualization
-
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
- Group by: split-apply-combine
- Windowing Operations
-
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
- Options and settings
- Enhancing performance
- Scaling to large datasets
- Sparse data structures
- Frequently Asked Questions (FAQ)
- Cookbook
© 2008–2021, AQR Capital Management, LLC, Lambda Foundry, Inc. and PyData Development Team
Licensed under the 3-clause BSD License.
https://pandas.pydata.org/pandas-docs/version/1.3.4/user_guide/index.html