Pandas Documentation
Attributes
Classs
Exceptions
Functions
Guides
- 10 minutes to pandas
- API reference
- Categorical data
- Chart visualization
- Community tutorials
- Comparison with R / R libraries
- Comparison with SAS
- Comparison with SQL
- Comparison with Stata
- Comparison with other tools
- Comparison with spreadsheets
- Contributing to pandas
- Contributing to the code base
- Contributing to the documentation
- Contributor community
- Cookbook
- Copy on write
- Copy-on-Write (CoW)
- Creating a development environment
- DataFrame
- Date offsets
- Debugging C extensions
- Developer
- Development
- Duplicate Labels
- Enhancing performance
- Essential basic functionality
- Extending pandas
- Extensions
- Frequently Asked Questions (FAQ)
- General functions
- Getting started
- Getting started tutorials
- Group by: split-apply-combine
- GroupBy
- How do I create plots in pandas?
- How do I read and write tabular data?
- How do I select a subset of a DataFrame?
- How to calculate summary statistics
- How to combine data from multiple tables
- How to create new columns derived from existing columns
- How to handle time series data with ease
- How to manipulate textual data
- How to reshape the layout of tables
- IO tools (text, CSV, HDF5, …)
- Index objects
- Indexing and selecting data
- Input/output
- Installation
- Internals
- Intro to data structures
- Merge, join, concatenate and compare
- Missing values
- MultiIndex / advanced indexing
- Nullable Boolean data type
- Nullable integer data type
- Options and settings
- Options and settings
- Package overview
- Plotting
- Policies
- PyArrow Functionality
- Release notes
- Resampling
- Reshaping and pivot tables
- Scaling to large datasets
- Series
- Sparse data structures
- Style
- Table Visualization
- Testing
- Time deltas
- Time series / date functionality
- User Guide
- Using Gitpod for pandas development
- Version 0.10.0 (December 17, 2012)
- Version 0.10.1 (January 22, 2013)
- Version 0.11.0 (April 22, 2013)
- Version 0.12.0 (July 24, 2013)
- Version 0.13.0 (January 3, 2014)
- Version 0.13.1 (February 3, 2014)
- Version 0.14.0 (May 31 , 2014)
- Version 0.14.1 (July 11, 2014)
- Version 0.15.0 (October 18, 2014)
- Version 0.15.1 (November 9, 2014)
- Version 0.15.2 (December 12, 2014)
- Version 0.16.0 (March 22, 2015)
- Version 0.16.1 (May 11, 2015)
- Version 0.16.2 (June 12, 2015)
- Version 0.17.0 (October 9, 2015)
- Version 0.17.1 (November 21, 2015)
- Version 0.18.0 (March 13, 2016)
- Version 0.18.1 (May 3, 2016)
- Version 0.19.0 (October 2, 2016)
- Version 0.19.1 (November 3, 2016)
- Version 0.19.2 (December 24, 2016)
- Version 0.20.1 (May 5, 2017)
- Version 0.20.2 (June 4, 2017)
- Version 0.20.3 (July 7, 2017)
- Version 0.21.0 (October 27, 2017)
- Version 0.21.1 (December 12, 2017)
- Version 0.22.0 (December 29, 2017)
- Version 0.5.0 (October 24, 2011)
- Version 0.6.0 (November 25, 2011)
- Version 0.6.1 (December 13, 2011)
- Version 0.7.0 (February 9, 2012)
- Version 0.7.1 (February 29, 2012)
- Version 0.7.2 (March 16, 2012)
- Version 0.7.3 (April 12, 2012)
- Version 0.8.0 (June 29, 2012)
- Version 0.8.1 (July 22, 2012)
- Version 0.9.0 (October 7, 2012)
- Version 0.9.1 (November 14, 2012)
- Versions 0.4.1 through 0.4.3 (September 25 - October 9, 2011)
- What kind of data does pandas handle?
- What’s new in 0.23.0 (May 15, 2018)
- What’s new in 0.23.1 (June 12, 2018)
- What’s new in 0.23.2 (July 5, 2018)
- What’s new in 0.23.3 (July 7, 2018)
- What’s new in 0.23.4 (August 3, 2018)
- What’s new in 0.24.0 (January 25, 2019)
- What’s new in 0.24.1 (February 3, 2019)
- What’s new in 0.24.2 (March 12, 2019)
- What’s new in 0.25.0 (July 18, 2019)
- What’s new in 0.25.1 (August 21, 2019)
- What’s new in 0.25.2 (October 15, 2019)
- What’s new in 0.25.3 (October 31, 2019)
- What’s new in 1.0.0 (January 29, 2020)
- What’s new in 1.0.1 (February 5, 2020)
- What’s new in 1.0.2 (March 12, 2020)
- What’s new in 1.0.3 (March 17, 2020)
- What’s new in 1.0.4 (May 28, 2020)
- What’s new in 1.0.5 (June 17, 2020)
- What’s new in 1.1.0 (July 28, 2020)
- What’s new in 1.1.1 (August 20, 2020)
- What’s new in 1.1.2 (September 8, 2020)
- What’s new in 1.1.3 (October 5, 2020)
- What’s new in 1.1.4 (October 30, 2020)
- What’s new in 1.1.5 (December 07, 2020)
- What’s new in 1.2.0 (December 26, 2020)
- What’s new in 1.2.1 (January 20, 2021)
- What’s new in 1.2.2 (February 09, 2021)
- What’s new in 1.2.3 (March 02, 2021)
- What’s new in 1.2.4 (April 12, 2021)
- What’s new in 1.2.5 (June 22, 2021)
- What’s new in 1.3.0 (July 2, 2021)
- What’s new in 1.3.1 (July 25, 2021)
- What’s new in 1.3.2 (August 15, 2021)
- What’s new in 1.3.3 (September 12, 2021)
- What’s new in 1.3.4 (October 17, 2021)
- What’s new in 1.3.5 (December 12, 2021)
- What’s new in 1.4.0 (January 22, 2022)
- What’s new in 1.4.1 (February 12, 2022)
- What’s new in 1.4.2 (April 2, 2022)
- What’s new in 1.4.3 (June 23, 2022)
- What’s new in 1.4.4 (August 31, 2022)
- What’s new in 1.5.0 (September 19, 2022)
- What’s new in 1.5.1 (October 19, 2022)
- What’s new in 1.5.2 (November 21, 2022)
- What’s new in 1.5.3 (January 18, 2023)
- What’s new in 2.0.0 (April 3, 2023)
- What’s new in 2.0.1 (April 24, 2023)
- What’s new in 2.0.2 (May 29, 2023)
- What’s new in 2.0.3 (June 28, 2023)
- What’s new in 2.1.0 (Aug 30, 2023)
- What’s new in 2.1.1 (September 20, 2023)
- What’s new in 2.1.2 (October 26, 2023)
- What’s new in 2.1.3 (November 10, 2023)
- What’s new in 2.1.4 (December 8, 2023)
- What’s new in 2.2.0 (January 19, 2024)
- What’s new in 2.2.1 (February 22, 2024)
- What’s new in 2.2.2 (April 10, 2024)
- What’s new in 2.2.3 (September 20, 2024)
- Window
- Windowing operations
- Working with missing data
- Working with text data
- pandas arrays, scalars, and data types
- pandas docstring guide
- pandas maintenance
- pandas.errors.OptionError
- pandas.errors.OutOfBoundsDatetime
- pandas.errors.OutOfBoundsTimedelta
- pandas.option_context
- pandas.tseries.offsets.BQuarterBegin
- pandas.tseries.offsets.BQuarterEnd
- pandas.tseries.offsets.BYearBegin
- pandas.tseries.offsets.BYearEnd
- pandas.tseries.offsets.BusinessDay
- pandas.tseries.offsets.BusinessHour
- pandas.tseries.offsets.BusinessMonthBegin
- pandas.tseries.offsets.BusinessMonthEnd
- pandas.tseries.offsets.CustomBusinessDay
- pandas.tseries.offsets.CustomBusinessHour
- pandas.tseries.offsets.CustomBusinessMonthBegin
- pandas.tseries.offsets.CustomBusinessMonthEnd
- pandas.tseries.offsets.DateOffset
- pandas.tseries.offsets.Day
- pandas.tseries.offsets.Easter
- pandas.tseries.offsets.FY5253
- pandas.tseries.offsets.FY5253Quarter
- pandas.tseries.offsets.Hour
- pandas.tseries.offsets.LastWeekOfMonth
- pandas.tseries.offsets.Micro
- pandas.tseries.offsets.Milli
- pandas.tseries.offsets.Minute
- pandas.tseries.offsets.MonthBegin
- pandas.tseries.offsets.MonthEnd
- pandas.tseries.offsets.Nano
- pandas.tseries.offsets.QuarterBegin
- pandas.tseries.offsets.QuarterEnd
- pandas.tseries.offsets.Second
- pandas.tseries.offsets.SemiMonthBegin
- pandas.tseries.offsets.SemiMonthEnd
- pandas.tseries.offsets.Tick
- pandas.tseries.offsets.Week
- pandas.tseries.offsets.WeekOfMonth
- pandas.tseries.offsets.YearBegin
- pandas.tseries.offsets.YearEnd
Methods
Modules
Sections
- .dt accessor
- 1. Remove UUID and cell_ids
- 2. Use table styles
- 3. Set classes instead of using Styler functions
- 4. Don't use tooltips
- 5. If every byte counts use string replacement
- ADBC Driver support in to_sql and read_sql
- API changes
- API changes
- API changes
- API changes
- API changes
- API changes
- API changes
- API changes
- API changes
- API changes
- API changes
- API changes
- API changes
- API changes
- API changes
- API changes
- API changes
- API changes
- API changes to integer indexing
- API tweaks regarding label-based slicing
- About docstrings and standards
- About the pandas documentation
- Accelerated operations
- Access data in the cloud
- Accessing datetime fields of Index now return Index
- Accessing the values in a Series or Index
- Accessors
- Acknowledgments
- Acting on Data
- Acting on the Index and Column Headers
- Added check_freq argument to testing.assert_frame_equal and testing.assert_series_equal
- Added support for new Python version
- Adding a row
- Adding an ad hoc index
- Adding margins
- Addition of str.extractall
- Addition/subtraction of NaN from DataFrame
- Additional methods for dt accessor
- Advanced SQLAlchemy queries
- Advanced indexing with hierarchical index
- Advanced queries
- Advanced reindexing and alignment
- Aggregating statistics
- Aggregating statistics grouped by category
- Aggregating with a dict
- Aggregating with multiple functions
- Aggregation
- Aggregation
- Aggregation
- Aggregation
- Aggregation API
- Aggregation with User-Defined Functions
- Aligning objects with each other with align
- All dtypes can now be converted to StringDtype
- Allow NA in groupby key
- Alternate constructors
- Ambiguous times when localizing
- Anchored offset semantics
- Anchored offsets
- Andrews curves
- Appending new categories
- Appending rows to a DataFrame
- Apply
- Applying different functions to DataFrame columns
- Applying elementwise functions
- Applying multiple functions at once
- Area plot
- Argument dtype_backend, to return pyarrow-backed or numpy-backed nullable dtypes
- Arithmetic
- Arithmetic operations
- Arithmetic operators
- Arithmetic with 3rd party types
- Assembling datetime from multiple DataFrame columns
- Assembling datetimes
- Assertion functions
- Assigning new columns in method chains
- Assignment to multiple columns of a DataFrame when some columns do not exist
- Attribute access
- Attributes
- Attributes
- Attributes and underlying data
- Attributes and underlying data
- Autocorrelation plot
- Automatic date tick adjustment
- Automatically "sniffing" the delimiter
- Automatically “sniffing” the delimiter
- Available options
- Avoid NumPy object dtype for strings by default
- Avoid using names from MultiIndex.levels
- BQuarterBegin
- BQuarterEnd
- BYearBegin
- BYearEnd
- Background Gradient and Text Gradient
- Backporting
- Backward resample
- Backwards compatibility
- Backwards incompatible API changes
- Backwards incompatible API changes
- Backwards incompatible API changes
- Backwards incompatible API changes
- Backwards incompatible API changes
- Backwards incompatible API changes
- Backwards incompatible API changes
- Backwards incompatible API changes
- Backwards incompatible API changes
- Backwards incompatible API changes
- Backwards incompatible API changes
- Backwards incompatible API changes
- Backwards incompatible API changes
- Backwards incompatible API changes
- Backwards incompatible API changes
- Backwards incompatible API changes
- Backwards incompatible API changes
- Bar charts
- Bar plots
- Base R
- Basic
- Basic data structures in pandas
- Basic indexing on axis with MultiIndex
- Basic plotting: plot
- Basics
- Basics
- Becoming a pandas maintainer
- Behavior differences
- Behavior of concat with empty or all-NA DataFrame columns
- Behavior of groupby with categorical groupers (GH 48645)
- Benchmark machine
- Better pretty-printing of DataFrames in a terminal
- Better repr for MultiIndex
- Better support for compressed URLs in read_csv
- Binary Excel (.xlsb) files
- Binary files
- Binary operator functions
- Binary operator functions
- Binary ufuncs on Series now align
- Binary window functions
- Binning data with cut and qcut
- Bitwise boolean
- Boolean data type with missing values support
- Boolean indexing
- Boolean indexing
- Boolean operators
- Boolean reductions
- Boolean values
- Bootstrap plot
- Box plots
- Breaking changes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug fixes
- Bug in QuarterBegin with n=0
- Bug report function
- Bug reports and enhancement requests
- Build
- Build changes
- Build changes
- Build changes
- Build changes
- Build changes
- Building criteria
- Building main branch documentation
- Building the documentation
- Built-in aggregation methods
- Built-in filtrations
- Built-in transformation methods
- Builtin Styles
- Builtin styles
- Business hour
- BusinessDay
- BusinessHour
- BusinessMonthBegin
- BusinessMonthEnd
- By default Categorical.min() now returns the minimum instead of np.nan
- By group processing
- By group processing
- By index
- By indexes and values
- By values
- Byte-ordering issues
- CSS Classes and Ids
- CSS Hierarchies
- CSV
- CSV
- CSV
- CSV & text files
- Calamine (Excel and ODS files)
- Calamine engine for read_excel()
- Calculations with missing data
- Calendar
- Calling NumPy ufuncs on non-aligned DataFrames
- Can I come back to a previous workspace?
- Can I install additional VSCode extensions?
- Categorical
- Categorical
- Categorical
- Categorical
- Categorical
- Categorical
- Categorical
- Categorical
- Categorical
- Categorical
- Categorical
- Categorical
- Categorical
- Categorical
- Categorical
- Categorical
- Categorical
- Categorical accessor
- Categorical changes
- Categorical components
- Categorical concatenation
- Categorical data
- Categorical dtypes are preserved during GroupBy
- Categorical index
- Categorical is not a numpy array
- Categorical.argsort now places missing values at the end
- Categorical.rename_categories accepts a dict-like
- Categorical.unique now always maintains same dtype as original
- CategoricalDtype
- CategoricalDtype for specifying categoricals
- CategoricalIndex
- CategoricalIndex
- CategoricalIndex
- Categoricals
- Categoricals
- Categoricals in Series/DataFrame
- Caveats
- Caveats
- Cell converters
- Centered datetime-like rolling windows
- Centering windows
- Chained Assignment
- Chained assignment
- Change in default floating precision for read_csv and read_table
- Changes in read_csv exceptions
- Changes in timedelta
- Changes to Categorical.unique
- Changes to Excel with MultiIndex
- Changes to Index comparisons
- Changes to Series [] operator
- Changes to bool passed as header in parsers
- Changes to boolean comparisons vs. None
- Changes to display.precision option
- Changes to dtype assignment behaviors
- Changes to eval
- Changes to make output of DataFrame.apply consistent
- Changes to msgpack
- Changes to rename
- Changes to sorting API
- Changes to str.cat
- Changes to str.extract
- Changes to to_datetime and to_timedelta
- Changing case
- Changing case
- Changing case
- Changing the sort parameter for Index set operations
- Cleaning up old issues
- Cleaning up old pull requests
- Clipboard
- Clipboard
- Clipboard
- Closing issues
- Code standards
- Colormaps
- Column and index locations and names
- Column metadata
- Column order is preserved when passing a list of dicts to DataFrame
- Column selection, addition, deletion
- Combining / comparing / joining / merging
- Combining / comparing / joining / merging
- Combining / joining / set operations
- Combining aliases
- Combining overlapping data sets
- Combining positional and label-based indexing
- Coming from...
- Coming from…
- Comments
- Comments and empty lines
- Community
- Community meeting
- Community slack
- Comparing array-like objects
- Comparing if objects are equivalent
- Comparing two DataFrame or two Series and summarizing the differences
- Comparison operators
- Comparisons
- Compatibility
- Compatibility with Apache Arrow
- Compatibility with MultiIndex
- Components
- Compressed pickle files
- Compression
- Compression
- Computation
- Computation
- Computations / descriptive stats
- Computations / descriptive stats
- Computations / descriptive stats
- Computing rolling pairwise covariances and correlations
- Concat
- Concat of different float dtypes will not automatically upcast
- Concatenating DataFrame Outputs
- Concatenating Series and DataFrame together
- Concatenating a Series and an indexed object into a Series, with alignment
- Concatenating a Series and many objects into a Series
- Concatenating a Series and something array-like into a Series
- Concatenating a Series and something list-like into a Series
- Concatenating a single Series into a string
- Concatenating objects
- Concatenating sparse values
- Concatenation
- Concatenation changes
- Concatenation will no longer sort
- Conditional HTML formatting
- Configuration
- Consequences of Duplicate Labels
- Consistency across groupby reductions
- Consistency of DataFrame Reductions
- Consistency of range functions
- Consistent casting with setting into Boolean Series
- Consistent parsing
- Console display
- Consortium Standard
- Constant series
- Constructing a DataFrame from values
- Constructing a DataFrame from values
- Constructing a DataFrame from values
- Construction
- Construction with datetime64 or timedelta64 dtype with unsupported resolution
- Constructor
- Constructor
- Constructors
- Contributors
- Contributors
- Contributors
- Contributors
- Contributors
- Contributors
- Contributors
- Contributors
- Contributors
- Contributors
- Contributors
- Contributors
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- Control grouped column(s) placement with group_keys
- Control of index with group_keys in DataFrame.resample()
- Controlling behavior
- Controlling the labels
- Controlling the legend
- Conventions for the examples
- Conversion
- Conversion
- Conversion
- Conversion
- Conversion
- Conversion
- Conversion
- Conversion
- Conversion
- Conversion
- Conversion
- Conversion
- Conversion
- Conversion
- Conversion
- Conversion
- Conversion
- Conversion
- Conversion
- Conversion
- Conversion
- Conversions
- Converting between representations
- Converting timezone-aware Series and Index to NumPy arrays
- Converting to Python datetimes
- Converting to markdown
- Converting to timestamps
- Copies vs. in place operations
- Copies vs. in place operations
- Copies vs. in place operations
- Copies vs. in place operations
- Copy on Write
- Copy-on-Write
- Copy-on-Write improvements
- Copy-on-Write improvements
- Copy-on-Write optimizations
- Copying
- Correlation
- Count number of records by category
- Create a fork of pandas
- Create a pandas Series based on one or more conditions
- Creating a MultiIndex (hierarchical index) object
- Creating a feature branch
- Creating example data
- Creating indicator variables
- Cross-section
- Custom Function Examples
- Custom HTTP(s) headers when reading csv or json files
- Custom business days
- Custom business hour
- Custom business hour
- Custom describe
- Custom formatters for timeseries plots
- Custom frequency ranges
- Custom window rolling
- CustomBusinessDay
- CustomBusinessHour
- CustomBusinessMonthBegin
- CustomBusinessMonthEnd
- Cython (writing C extensions for pandas)
- DELETE
- Data Cell CSS Classes
- Data Structure Integration
- Data alignment and arithmetic
- Data alignment and using reindex
- Data conversion
- Data in/out
- Data input / output
- Data input / output
- Data input / output
- Data interop
- Data manipulations
- Data munging
- Data operations
- Data operations
- Data operations
- Data structures
- Data structures
- Data structures
- Data structures
- Data type introspection
- DataFrame
- DataFrame
- DataFrame
- DataFrame
- DataFrame GroupBy ffill/bfill no longer return group labels
- DataFrame arithmetic operations broadcasting changes
- DataFrame assign
- DataFrame column attribute access and IPython completion
- DataFrame column selection in GroupBy
- DataFrame comparison operations broadcasting changes
- DataFrame constructor honors copy=False with dict
- DataFrame creation
- DataFrame describe on an empty Categorical / object column will return top and freq
- DataFrame interchange protocol implementation
- DataFrame interoperability with NumPy functions
- DataFrame memory usage
- DataFrame reductions preserve extension dtypes
- DataFrame repr changes
- DataFrame to LaTeX has a new render engine
- DataFrame.astype performs column-wise conversion to Categorical
- DataFrame.combine_first()
- DataFrame.from_dict and DataFrame.to_dict have new 'tight' option
- DataFrame.interpolate has gained the limit_area kwarg
- DataFrame.join()
- DataFrame.merge() preserves right frame's row order
- DataFrame.rename now only accepts one positional argument
- DataFrame.sort_index changes
- DataFrameGroupBy computations / descriptive stats
- DataFrameGroupBy.cumsum() and DataFrameGroupBy.cumprod() overflow instead of lossy casting to float
- DataFrameGroupBy.nth() and SeriesGroupBy.nth() now behave as filtrations
- DataFrameGroupBy.rolling and SeriesGroupBy.rolling no longer return grouped-by column in values
- DataFrameGroupBy.rolling and SeriesGroupBy.rolling with MultiIndex no longer drop levels in the result
- DataFrameGroupBy.value_counts with non-grouping categorical columns and observed=True
- DataFrameGroupby.agg() lost results with as_index=False when relabeling columns
- DataTypes
- Dataframes
- Date Handling
- Date functionality
- Date functionality
- Date functionality
- Date handling
- Date parsing functions
- DateOffset
- DateOffset objects
- Datetime as index
- Datetime data types
- Datetime formats
- Datetime handling
- Datetime methods
- Datetime properties
- Datetime with TZ
- Datetime-like
- DatetimeIndex
- Datetimelike
- Datetimelike
- Datetimelike
- Datetimelike
- Datetimelike
- Datetimelike
- Datetimelike
- Datetimelike
- Datetimelike
- Datetimelike
- Datetimelike
- Datetimelike
- Datetimelike API changes
- Datetimelike API changes
- Datetimelike properties
- Datetimelike rounding
- Datetimes
- Datetimes are now parsed with a consistent format
- Datetimes conversion
- Day
- Dealing with Unicode data
- Debugging
- Debugging locally
- Declaring C types
- Dedicated string data type
- Dedicated string data type (backed by Arrow) by default
- Default dtype of empty pandas.Series
- Default value for the ordered parameter of CategoricalDtype
- Define original properties
- Defined levels
- Defining custom windows for rolling operations
- Delete from a table
- Density plot
- Dependencies
- Dependencies have increased minimum versions
- Dependencies have increased minimum versions
- Deprecate .ix
- Deprecate .plotting
- Deprecate Panel
- Deprecate Panel
- Deprecate aliases M, Q, Y, etc. in favour of ME, QE, YE, etc. for offsets
- Deprecate groupby.agg() with a dictionary when renaming
- Deprecated DataFrame.append and Series.append
- Deprecated Int64Index, UInt64Index & Float64Index
- Deprecated automatic downcasting
- Deprecated dropping nuisance columns in DataFrame reductions and DataFrameGroupBy operations
- Deprecated parsing datetimes with mixed time zones
- Deprecated silent upcasting in setitem-like Series operations
- Deprecations
- Deprecations
- Deprecations
- Deprecations
- Deprecations
- Deprecations
- Deprecations
- Deprecations
- Deprecations
- Deprecations
- Deprecations
- Deprecations
- Deprecations
- Deprecations
- Deprecations
- Deprecations
- Deprecations
- Deprecations
- Deprecations
- Deprecations
- Deprecations
- Deprecations
- Deprecations
- Deprecations
- Deprecations
- Deprecations
- Deprecations
- Deprecations
- Description
- Description
- Descriptive statistics
- Development Changes
- Development changes
- Development team
- Development workflow with Gitpod
- Dialect
- Dictionary-like get() method
- Differences to R's factor
- Differences with NumPy
- Different choices for indexing
- Disabling compiler directives
- Disallow astype conversion to non-supported datetime64/timedelta64 dtypes
- Disallowing Duplicate Labels
- Discretization and quantiling
- Disk vs memory
- Disk vs memory
- Display alignment with Unicode East Asian width
- Display changes
- Do something with a DataFrame or Series
- Documentation changes
- Documentation changes
- Documentation improvements
- Documenting your code
- Downcast values to smallest possible dtype in to_numeric
- Downsampling
- Drop Duplicates
- Drop a column
- Drop a column
- Drop a column
- Drop rows with missing values
- Drop rows with missing values
- Dropping labels from an axis
- Dropping missing data
- Dtype conversion
- Dtype conversions
- Dtype gotchas
- Dtype specifications
- Dtypes
- Duplicate Label Detection
- Duplicate Label Propagation
- Duplicate data
- Duplicate names parsing
- Each column in a DataFrame is a Series
- Easter
- Editor support
- Empty DataFrames/Series will now default to have a RangeIndex
- Endpoints are inclusive
- Engine connection examples
- Enhancements
- Enhancements
- Enhancements
- Enhancements
- Enhancements
- Enhancements
- Enhancements
- Enhancements
- Enhancements
- Enhancements
- Enhancements
- Enhancements
- Enhancements
- Enhancements
- Enhancements
- Enhancements
- Enhancements
- Enhancements
- Enhancements
- Enhancements
- Enhancements
- Enhancements
- Enhancements
- Enhancements
- Enhancements
- Enumerate group items
- Enumerate groups
- Epoch timestamps
- Equality semantics
- Error handling
- Error handling
- Evaluation order matters
- Exact indexing
- Example
- Examples
- Excel
- Excel
- Excel
- Excel charts with pandas, vincent and xlsxwriter
- Excel files
- Excel files
- Excel files
- Excel output for styled DataFrames
- Excel writer engines
- ExcelFile class
- ExcelWriter attributes
- Exceptions and warnings
- Exclusion of non-numeric columns
- Exercises for new users
- Expanding data
- Expanding window
- Expanding window functions
- Experimental
- Experimental
- Experimental
- Experimental
- Experimental NA scalar to denote missing values
- Experimental features
- Experimental new features
- Experimental nullable data types for float data
- Exponentially weighted window
- Exponentially-weighted window functions
- Export to Excel
- Export to LaTeX
- Exporting data
- Exporting data
- Exporting data
- Expression evaluation limitations with numexpr
- Expression evaluation via eval()
- Extended verbose info output for DataFrame
- Extending pandas with custom types (experimental)
- Extensibility
- Extension type changes
- Extension types
- ExtensionArray
- ExtensionArray
- ExtensionArray
- ExtensionArray
- ExtensionArray
- ExtensionArray
- ExtensionArray
- ExtensionArray
- ExtensionArray
- ExtensionArray
- ExtensionArray operator support
- ExtensionDtype
- Extract all matches in each subject (extractall)
- Extract first match in each subject (extract)
- Extracting nth word
- Extracting nth word
- Extracting nth word
- Extracting substring by position
- Extracting substring by position
- Extracting substring by position
- Extracting substrings
- Extraction of matching patterns from strings
- FAQ's and troubleshooting
- FULL JOIN
- FY5253
- FY5253Quarter
- Failed Integer Lookups on MultiIndex Raise KeyError
- Failed label-based lookups always raise KeyError
- Fallback behavior
- Fast scalar value getting and setting
- Feather
- Feather
- File parsing new features
- Files with an "implicit" index column
- Files with fixed width columns
- Fill Handle
- Filling by value
- Filling forward / backward
- Filling missing data
- Filling while reindexing
- Filtering
- Filtering
- Filtering
- Filtering columns (usecols)
- Filtration
- Find and Replace
- Finding an issue to contribute to
- Finding length of string
- Finding length of string
- Finding length of string
- Finding position of substring
- Finding position of substring
- Finding position of substring
- Fine-grained NumPy errstate
- Finer Control with Slicing
- Fixed format
- Fixed regressions
- Fixed regressions
- Fixed regressions
- Fixed regressions
- Fixed regressions
- Fixed regressions
- Fixed regressions
- Fixed regressions
- Fixed regressions
- Fixed regressions
- Fixed regressions
- Fixed regressions
- Fixed regressions
- Fixed regressions
- Fixed regressions
- Fixed regressions
- Fixed regressions
- Fixed regressions
- Fixed regressions
- Fixed regressions
- Fixed regressions
- Fixed regressions
- Fixed regressions
- Fixed regressions
- Fixed regressions
- Fixed regressions
- Fixed regressions
- Fixed regressions
- Fixed regressions
- Fixed regressions
- Fixed regressions
- Fixed regressions
- Fixed regressions
- Fixed regressions
- Fixed regressions
- Fixed regressions
- Fixed regressions
- Fixed regressions
- Fixed regressions
- Fixed regressions
- Fixed regressions
- Flags
- Flags
- Flat file
- Flexible apply
- Flexible binary operations
- Flexible comparison methods
- Flexible comparisons
- Float64Index API change
- Fold
- Fold argument support in Timestamp constructor
- Forking the pandas repository
- Formatting Values
- Formatting of integers in FloatIndex
- Formatting the Display
- Forward fill from previous rows
- Forward fill from previous rows
- Frequencies
- Frequency conversion
- Frequency conversion
- Frequency conversion and resampling with PeriodIndex
- Frequently used options
- From a Series
- From a dict of tuples
- From a list of dataclasses
- From a list of dicts
- From a list of namedtuples
- From dict of Series or dicts
- From dict of ndarrays / lists
- From structured or record array
- From timestamps to epoch
- Function .to_datetime() changes
- Function application
- Function application
- Function application
- Function application helper
- Function application, GroupBy & window
- Function application, GroupBy & window
- Function get_dummies now supports dtype argument
- Function merge_asof for asof-style time-series joining
- Function read_csv will progressively enumerate chunks
- Function read_html enhancements
- GROUP BY
- General DataFrame combine
- General parsing configuration
- General plot style arguments
- General rules
- General terminology translation
- General terminology translation
- General terminology translation
- Generating ranges of intervals
- Generating ranges of time deltas
- Generating ranges of timestamps
- Getitem ([])
- Getting
- Getting and setting options
- Getting data in/out
- Getting started with Git
- Getting support
- GitHub issue tracker
- Gitpod
- Gitpod GitHub integration
- Google BigQuery
- Google BigQuery
- Google BigQuery enhancements
- Google BigQuery enhancements
- Gotchas
- Gotchas
- GroupBy
- GroupBy
- GroupBy API changes
- GroupBy aggregation with multiple lambdas
- GroupBy aggregation with relabeling
- GroupBy describe formatting
- GroupBy dropna
- GroupBy enhancements
- GroupBy object attributes
- GroupBy objects now have a pipe method
- GroupBy on categoricals
- GroupBy sorting
- GroupBy supports EWM operations directly
- GroupBy with MultiIndex
- GroupBy.apply on DataFrame evaluates first group only once
- GroupBy/resample/rolling
- GroupBy/resample/rolling
- GroupBy/resample/rolling
- GroupBy/resample/rolling
- GroupBy/resample/rolling
- GroupBy/resample/rolling
- GroupBy/resample/rolling
- GroupBy/resample/rolling
- GroupBy/resample/rolling
- GroupBy/resample/rolling
- GroupBy/resample/rolling
- GroupBy/resample/rolling
- Groupby by indexer to 'resample' data
- Groupby methods agg and transform no longer changes return dtype for callables
- Groupby positional indexing
- Groupby/resample/rolling
- Groupby/resample/rolling
- Groupby/resample/rolling
- Groupby/resample/rolling
- Groupby/resample/rolling
- Groupby/resample/rolling
- Groupby/resample/rolling
- Grouper and resample now supports the arguments origin and offset
- Grouping
- Grouping
- Grouping DataFrame with Index levels and columns
- Grouping and summarizing
- Grouping by a Categorical
- Grouping with a grouper specification
- Grouping with ordered factors
- Guides
- HDF5 (PyTables)
- HDFStore
- HDFStore
- HDFStore API changes
- HDFStore dropna behavior
- HDFStore where string comparison
- HDFStore: PyTables (HDF5)
- HTML
- HTML
- HTML
- HTML
- HTML Escaping
- HTML Table Parsing Gotchas
- Handling "bad" lines
- Handling ImportErrors
- Handling column names
- Handling indexes
- Handling of (un)observed Categorical values
- Handling “bad” lines
- Hashing
- Head and tail
- Hexagonal bin plot
- Hiding Data
- Hierarchical indexing (MultiIndex)
- Hierarchical keys
- Highlight Between
- Highlight Min or Max
- Highlight Null
- Highlight Quantile
- Histograms
- Holidays / holiday calendars
- Hour
- How do I filter specific rows from a DataFrame?
- How do I select specific columns from a DataFrame?
- How do I select specific rows and columns from a DataFrame?
- How long does my workspace stay active if I'm not using it?
- How long is my Gitpod workspace kept for?
- How to build the pandas documentation
- How to enable CoW
- How to read these guides
- I authenticated through GitHub but I still cannot commit to the repository through Gitpod.
- I registered on Gitpod but I still cannot see a Gitpod button in my repositories.
- I/O
- I/O
- I/O
- I/O
- I/O
- I/O
- I/O
- I/O Reading
- INNER JOIN
- IO
- IO
- IO
- IO
- IO
- IO
- IO
- IO
- IO
- IO
- IO
- IO
- IO and LZMA
- IO enhancements
- IO tools (text, CSV, HDF5, ...)
- Idioms
- If/then logic
- If/then logic
- If/then logic
- Ignoring dtypes in concat with empty or all-NA columns
- Ignoring indexes on the concatenation axis
- Ignoring line comments and empty lines
- Importing and exporting data
- Importing from other DataFrame libraries
- Improved error handling during item assignment in pd.eval
- Improved warning messages
- Improved warnings when attempting to create columns
- Improvements in the SQL IO module
- Improvements to the Parquet IO functionality
- Incompatible Index type unions
- Inconsistent date string parsing
- Increased minimum version for Python
- Increased minimum version for Python
- Increased minimum version for Python
- Increased minimum version for Python
- Increased minimum versions for dependencies
- Increased minimum versions for dependencies
- Increased minimum versions for dependencies
- Increased minimum versions for dependencies
- Increased minimum versions for dependencies
- Increased minimum versions for dependencies
- Increased minimum versions for dependencies
- Increased minimum versions for dependencies
- Increased minimum versions for dependencies
- Increased minimum versions for dependencies
- Increased minimum versions for dependencies
- Index
- Index
- Index
- Index
- Index
- Index + / - no longer used for set operations
- Index can hold arbitrary ExtensionArrays
- Index can now hold numpy numeric dtypes
- Index columns and trailing delimiters
- Index division by zero fills correctly
- Index metadata descriptors
- Index objects
- Index of min/max values
- Index representation
- Index types
- Index.difference and .symmetric_difference changes
- Index.intersection and inner join now preserve the order of the left Index
- Index.unique consistently returns Index
- Index/column name preservation when aggregating
- Indexer dtype changes
- Indexes
- Indexing
- Indexing
- Indexing
- Indexing
- Indexing
- Indexing
- Indexing
- Indexing
- Indexing
- Indexing
- Indexing
- Indexing
- Indexing
- Indexing
- Indexing
- Indexing
- Indexing
- Indexing
- Indexing
- Indexing
- Indexing
- Indexing
- Indexing / selection
- Indexing API changes
- Indexing an IntervalIndex with Interval objects
- Indexing changes
- Indexing potentially changes underlying Series dtype
- Indexing with .str
- Indexing with NA values
- Indexing with []
- Indexing with a Boolean Index
- Indexing with a list with missing labels is deprecated
- Indexing with an IntervalIndex
- Indexing with date strings with UTC offsets
- Indexing with isin
- Indexing with nullable boolean arrays
- Indexing, iteration
- Indexing, iteration
- Indexing, iteration
- Indexing, iteration
- Inferring datetime format
- Infinity
- Inplace operation when setting values with loc and iloc
- Inserting missing data
- Insertion method
- Installation
- Installing from PyPI
- Installing from source
- Installing optional dependencies with pip extras
- Installing pandas
- Installing the development version of pandas
- Installing with Anaconda
- Installing with Miniconda
- Instantiation from dicts preserves dict insertion order for Python 3.6+
- Institutional partners
- Integer addition/subtraction with datetimes and timedeltas is deprecated
- Integer indexing
- Integration with Apache Parquet file format
- Interaction with scipy.sparse
- Interaction with scipy.sparse
- Internal refactoring
- Internal refactoring
- International date formats
- Interpolation
- Interpolation limits
- Interval
- Interval
- Interval
- Interval
- Interval
- Interval
- Interval
- Interval
- Interval
- Interval
- Interval
- Interval
- IntervalIndex
- IntervalIndex
- IntervalIndex
- IntervalIndex components
- Intervals
- Intro to pandas
- Invalid data
- Investigating regressions
- Issue triage
- Iterable introspection
- Iterating through files chunk by chunk
- Iterating through groups
- Iterating through groups
- Iteration
- Iteration
- Iteration of Series/Index will now return Python scalars
- Iterator
- JOIN
- JSON
- JSON
- JSON normalize with max_level param support
- JSON read/write round-trippable with orient='table'
- Join
- Join tables using a common identifier
- Joining a single Index to a MultiIndex
- Joining logic of the resulting axis
- Joining multiple DataFrame
- Joining with two MultiIndex
- Joining with two multi-indexes
- Joyful pandas
- Keep certain columns
- Keep certain columns
- Keep certain columns
- KeyErrors raised by loc specify missing labels
- Keyword argument dtype for data IO
- Kleene logical operations
- Known issues
- LEFT OUTER JOIN
- LIMIT
- LaTeX
- Label-based integer slicing on a Series with an Int64Index or RangeIndex
- Lag plot
- LastWeekOfMonth
- Latex
- Latex representation
- Learn pandas by Hernan Rojas
- Levels
- License
- Limitations
- Limiting output
- Limiting output
- Limiting output
- Limits on filling while reindexing
- Line delimited json
- List accessor
- Load less data
- Local variables
- Logical operations
- Logical operators
- Logical reductions over entire DataFrame
- Long to wide table format
- Looking up values by index/column labels
- Magnify
- Making a pull request
- Making code changes
- Map on Index types now return other Index types
- Mask
- Matching / broadcasting behavior
- Memory usage
- Memory usage
- Memory usage for Index is more accurate
- Merge
- Merge
- Merge key uniqueness
- Merge result indicator
- Merge types
- Merging
- Merging
- Merging
- Merging / concatenation
- Merging changes
- Merging on a combination of columns and index levels
- Merging on a combination of columns and index levels
- Merging pull requests
- Metadata
- Metadata
- Metadata
- Metadata
- Metadata
- Method .assign() accepts dependent arguments
- Method .describe() changes
- Method .groupby(..) syntax with window and resample operations
- Method .groupby(..).nth() changes
- Method .rank() handles inf values when NaN are present
- Method .rolling() is now time-series aware
- Method .to_datetime() has gained an origin parameter
- Method agg API for DataFrame/Series
- Method chaining improvements
- Method convert_dtypes to ease use of supported extension dtypes
- Method drop now also accepts index/columns keywords
- Method get_dummies now returns integer dtypes
- Method infer_objects type conversion
- Method read_csv has improved support for duplicate column names
- Method read_csv supports parsing Categorical directly
- Method summary
- Method to_datetime error changes
- Method to_xarray
- Methods
- Methods
- Methods
- Methods .loc[], .iloc[], .ix[]
- Methods .where() and .mask()
- Methods rename, reindex now also accept axis keyword
- Methods to Add Styles
- Micro
- Migrating to Copy-on-Write
- Milli
- Minute
- Miscellaneous indexing FAQ
- Missing
- Missing
- Missing
- Missing
- Missing
- Missing
- Missing
- Missing
- Missing
- Missing
- Missing
- Missing
- Missing data
- Missing data
- Missing data
- Missing data
- Missing data
- Missing data
- Missing data / operations with fill values
- Missing data handling
- Missing data handling
- Missing value representation for NumPy types
- Missing values
- Missing values
- Modern pandas
- Modifying and computations
- Modules privacy has changed
- MonthBegin
- MonthEnd
- More About CSS and HTML
- Multi column sorting
- Multi-column factorization
- Multi-indexing using slicers
- Multi-threaded CSV reading with a new CSV Engine based on pyarrow
- MultiIndex
- MultiIndex
- MultiIndex
- MultiIndex
- MultiIndex
- MultiIndex
- MultiIndex
- MultiIndex
- MultiIndex
- MultiIndex
- MultiIndex
- MultiIndex
- MultiIndex
- MultiIndex
- MultiIndex components
- MultiIndex constructed from levels and codes
- MultiIndex constructor with a single level
- MultiIndex constructors
- MultiIndex constructors, groupby and set_index preserve categorical dtypes
- MultiIndex properties
- MultiIndex query() Syntax
- MultiIndex selecting
- MultiIndex.get_indexer interprets method argument correctly
- Multiindexing
- Multiple levels
- Multiple table queries
- Mutability and copying of data
- Mutating with User Defined Function (UDF) methods
- My terminal is blank - there is no cursor and it's completely unresponsive
- N dimensional panels (experimental)
- NA and missing data handling
- NA boolean comparison API change
- NA group handling
- NA in a boolean context
- NA naming changes
- NA semantics
- NA type promotions for NumPy types
- NA values
- NaT and Timedelta operations
- Name attribute
- Named aggregation
- Naming and using columns
- Nano
- Native PyArrow-backed ExtensionArray
- Never operate inplace when setting frame[keys] = values
- New DataFrame.map() method and support for ExtensionArrays
- New Index methods
- New Styler.pipe() method
- New columns
- New contributor meeting
- New deprecation policy
- New features
- New features
- New features
- New features
- New features
- New features
- New features
- New features
- New features
- New features
- New features
- New features
- New features
- New features
- New features
- New features
- New features
- New features
- New features
- New features
- New features
- New features
- New features
- New features
- New features
- New functions or methods
- New implementation of DataFrame.stack()
- New keywords
- New observed keyword for excluding unobserved categories in GroupBy
- New plotting methods
- New repr for IntervalArray
- No automatic Matplotlib converters
- Non-monotonic PeriodIndex partial string slicing
- Non-monotonic indexes require exact matches
- Nonexistent times when localizing
- Normalization
- Normalization
- Notable bug fixes
- Notable bug fixes
- Notable bug fixes
- Notable bug fixes
- Notable bug fixes
- Notable bug fixes
- Notable bug fixes
- Notes & caveats
- Null-values are no longer coerced to NaN-value in value_counts and mode
- Nullable Boolean
- Nullable float
- Nullable integer
- NumPy datetime64 dtype and 1.6 dependency
- NumPy function compatibility
- NumPy ufuncs
- NumPy universal functions
- Numba (JIT compilation)
- Numba Accelerated Routines
- Numba engine
- Number formatting
- Numeric
- Numeric
- Numeric
- Numeric
- Numeric
- Numeric
- Numeric
- Numeric
- Numeric
- Numeric
- Numeric
- Numeric
- Numeric
- Numeric
- Numeric
- Numeric
- Numeric
- Numeric Index
- Numeric formatting
- ORC
- ORC
- Object creation
- Object creation
- Objects
- Offset aliases
- Offsets
- Offsets
- OpenDocument Spreadsheets
- Operations
- Operations
- Operations
- Operations
- Operations
- Operations on columns
- Operations on columns
- Operations on columns
- Operators now preserve dtypes
- Optimization
- Option 1: pass rows explicitly to skip rows
- Option 1: using Liveserve
- Option 1: using mamba (recommended)
- Option 2: read column names and then data
- Option 2: using pip
- Option 2: using the rst extension
- Option 3: using Docker
- Option 4: using Gitpod
- Optional dependencies
- Optional dependencies
- Optional integer NA support
- Optionally disallow duplicate labels
- Orient options
- Other
- Other
- Other
- Other
- Other
- Other
- Other
- Other
- Other
- Other
- Other
- Other
- Other
- Other
- Other
- Other
- Other
- Other
- Other
- Other
- Other
- Other
- Other
- Other
- Other
- Other
- Other
- Other
- Other
- Other
- Other
- Other
- Other
- Other
- Other
- Other
- Other API changes
- Other API changes
- Other API changes
- Other API changes
- Other API changes
- Other API changes
- Other API changes
- Other API changes
- Other API changes
- Other API changes
- Other API changes
- Other API changes
- Other API changes
- Other API changes
- Other API changes
- Other API changes
- Other API changes
- Other API changes
- Other API changes
- Other API changes
- Other API changes
- Other Deprecations
- Other Deprecations
- Other Deprecations
- Other Deprecations
- Other Deprecations
- Other Fun and Useful Stuff
- Other considerations
- Other considerations
- Other considerations
- Other data sources
- Other deprecations
- Other deprecations
- Other development changes
- Other enhancements
- Other enhancements
- Other enhancements
- Other enhancements
- Other enhancements
- Other enhancements
- Other enhancements
- Other enhancements
- Other enhancements
- Other enhancements
- Other enhancements
- Other enhancements
- Other enhancements
- Other enhancements
- Other enhancements
- Other enhancements
- Other enhancements
- Other enhancements
- Other enhancements
- Other enhancements
- Other enhancements
- Other enhancements
- Other enhancements
- Other file formats
- Other new features
- Other plots
- Other sparse fixes
- Other useful features
- Output formatting enhancements
- Overlapping value columns
- Override constructor properties
- Overview
- Overview
- Overview
- Pandas 2.2.2 is now compatible with numpy 2.0
- Pandas 2.2.3 is now compatible with Python 3.13
- Parallel coordinates
- Parameter types
- Parametric offsets
- Parquet
- Parquet
- Parquet
- Parsing
- Parsing a CSV with mixed timezones
- Parsing date components in multi-columns
- Parsing dates
- Parsing datetime strings with timezone offsets
- Parsing mixed-timezones with read_csv()
- Parsing options
- Parsing specific columns
- Parsing timezone-aware format with different timezones in to_datetime
- Partial string indexing
- Partial string indexing changes
- Partial string indexing on DatetimeIndex when part of a MultiIndex
- Partitioning Parquet files
- Passing arguments to fsspec backends
- Passing integer data and a timezone to DatetimeIndex
- Patterns to avoid
- Performance
- Performance
- Performance
- Performance
- Performance
- Performance
- Performance considerations
- Performance dependencies (recommended)
- Performance enhancements
- Performance enhancements
- Performance enhancements
- Performance improvements
- Performance improvements
- Performance improvements
- Performance improvements
- Performance improvements
- Performance improvements
- Performance improvements
- Performance improvements
- Performance improvements
- Performance improvements
- Performance improvements
- Performance improvements
- Performance improvements
- Performance improvements
- Performance improvements
- Performance improvements
- Performance improvements
- Performance improvements
- Performance improvements
- Performance improvements
- Performance improvements
- Performance improvements
- Performance improvements
- Performance improvements
- Performance improvements
- Performance improvements
- Performance improvements
- Performance improvements
- Performance improvements
- Performance improvements
- Performance improvements
- Performance improvements
- Performance improvements
- Performance of query()
- Period
- Period
- Period
- Period
- Period
- Period
- Period
- Period
- Period
- Period aliases
- Period changes
- Period dtypes
- Period frequency enhancement
- Period properties
- Period subtraction
- Period('NaT') now returns pd.NaT
- PeriodIndex
- PeriodIndex and period_range
- PeriodIndex partial string indexing
- PeriodIndex resampling
- PeriodIndex.values now returns array of Period object
- Periods
- Pickle file IO now supports compression
- Pickling
- Pickling
- Pie plot
- Pipe
- Piping function calls
- Pivot
- Pivot Tables
- Pivot table
- Pivot table always returns a DataFrame
- Pivot tables
- Plain Cython
- Plot formatting
- Plot submethods
- Plots in examples
- Plotting
- Plotting
- Plotting
- Plotting
- Plotting
- Plotting
- Plotting
- Plotting
- Plotting
- Plotting
- Plotting
- Plotting
- Plotting
- Plotting
- Plotting
- Plotting
- Plotting
- Plotting
- Plotting
- Plotting
- Plotting
- Plotting
- Plotting
- Plotting
- Plotting and visualization
- Plotting backends
- Plotting backends
- Plotting directly with Matplotlib
- Plotting on a secondary y-axis
- Plotting tables
- Plotting tools
- Plotting with error bars
- Plotting with missing data
- Possible incompatibility for HDF5 formats created with pandas < 0.13.0
- Post-Release
- Potential porting issues for pandas <= 0.7.3 users
- Practical data analysis with Python
- Pre-commit
- Pre-release
- Preferred pytest idioms
- Prerequisites
- Preserve dtypes in DataFrame.combine_first()
- Previewing changes
- Previous API will work but with deprecations
- Previous behavior
- Prior version deprecations/changes
- Prior version deprecations/changes
- Prior version deprecations/changes
- Project governance
- Propagation in arithmetic and comparison operations
- Proper handling of np.nan in a string data-typed column with the Python engine
- Properties
- Properties
- Properties
- Providing a format argument
- Publishing
- Pure Python
- Pushing your changes
- PyArrow
- PyArrow backed string data type
- PyArrow will become a required dependency with pandas 3.0
- PyPy
- Python support
- Python version support
- QuarterBegin
- QuarterEnd
- Query MultiIndex
- Query timedelta64[ns]
- Query via data columns
- Querying
- Querying a table
- Querying, filtering, sampling
- Quick reference
- Quick workspace tour
- Quoting and Escape Characters
- Quoting, compression, and file format
- RIGHT JOIN
- RadViz
- Raise ValueError in DataFrame.to_dict(orient='index')
- Range Index
- RangeIndex
- Rank function for rolling and expanding windows
- Read and write XML documents
- Read-only NumPy arrays
- Read/write API
- Reading Excel files
- Reading HTML content
- Reading JSON
- Reading XML
- Reading a MultiIndex
- Reading an index with a MultiIndex
- Reading columns with a MultiIndex
- Reading directly from TAR archives
- Reading external data
- Reading external data
- Reading external data
- Reading from Stata format
- Reading multiple files to create a single DataFrame
- Reading multiple files to create a single DataFrame
- Reading tables
- Reading/writing remote files
- Reconstructing the level labels
- Reductions
- Reference tracking
- Regaining original data
- Registering custom accessors
- Regular expression replacement
- Reindexing
- Reindexing / selection / label manipulation
- Reindexing / selection / label manipulation
- Reindexing and altering labels
- Reindexing to align with another object
- Release
- Release process
- Releasing the GIL
- Removal of deprecated float indexers
- Removal of prior version deprecations/changes
- Removal of prior version deprecations/changes
- Removal of prior version deprecations/changes
- Removal of prior version deprecations/changes
- Removal of prior version deprecations/changes
- Removal of prior version deprecations/changes
- Removal of prior version deprecations/changes
- Removal of prior version deprecations/changes
- Removal of prior version deprecations/changes
- Removal of prior version deprecations/changes
- Removal of prior version deprecations/changes
- Removal of prior version deprecations/changes
- Removed artificial truncation in rolling variance and standard deviation
- Removing categories
- Removing unused categories
- Rename a column
- Rename a column
- Rename a column
- Renaming / mapping labels
- Renaming categories
- Renaming names in a MultiIndex
- Renaming names of an Index or MultiIndex
- Rendering the pandas documentation
- Reordering
- Reordering levels with reorder_levels
- Reorganization of the library: privacy changes
- Replace
- Replace missing values with a specified value
- Replace missing values with a specified value
- Replacing values
- Representing out-of-bounds spans
- Required dependencies
- Requirements
- Resample
- Resample API
- Resample a time series to another frequency
- Resampling
- Resampling
- Resampling
- Reset the index
- Reshaping
- Reshaping
- Reshaping
- Reshaping
- Reshaping
- Reshaping
- Reshaping
- Reshaping
- Reshaping
- Reshaping
- Reshaping
- Reshaping
- Reshaping
- Reshaping
- Reshaping
- Reshaping
- Reshaping
- Reshaping
- Reshaping
- Reshaping, sorting
- Reshaping, sorting, transposing
- Restore Matplotlib datetime converter registration
- Result dtype inference changes for resample operations
- Resulting keys
- Returning a Series to propagate names
- Returning a view versus a copy
- Reviewing pull requests
- Roles
- Rolling and expanding
- Rolling apply
- Rolling window
- Rolling window endpoints
- Rolling window functions
- Rolling/Expanding.apply() accepts raw=False to pass a Series to the function
- Rolling/expanding moments improvements
- Row or column-wise function application
- Running the performance test suite
- Running the test suite
- Running the test suite
- S3 file handling
- SAS
- SAS formats
- SELECT
- SPSS
- SPSS formats
- SQL
- SQL
- SQL
- SQL data types
- SQL databases
- SQL queries
- STATA
- Sample
- Scalar NA Value
- Scalar introspection
- Scales
- Scatter matrix plot
- Scatter plot
- Schema support
- SciPy sparse matrix from/to SparseDataFrame
- Search Page
- Second
- Section 1: short summary
- Section 2: extended summary
- Section 3: parameters
- Section 4: returns or yields
- Section 5: see also
- Section 6: notes
- Section 7: examples
- Security policy
- Select a single column
- Selecting
- Selecting a group
- Selecting columns based on dtype
- Selecting coordinates
- Selecting random samples
- Selecting using a where mask
- Selection
- Selection
- Selection by callable
- Selection by label
- Selection by label
- Selection by position
- Selection by position
- Selection choices
- Selection deprecations
- Selection of columns
- Selection of columns
- Selection of columns
- Semi-month offsets
- SemiMonthBegin
- SemiMonthEnd
- Serialization / IO / conversion
- Serialization / IO / conversion
- Serializing tz-naive Timestamps with to_json() with iso_dates=True
- Series
- Series
- Series
- Series
- Series and Index data-dtype incompatibilities
- Series creation
- Series is dict-like
- Series is ndarray-like
- Series operators for different indexes
- Series type promotion on assignment
- Series.argmax and Series.argmin
- Series.dt accessor
- Series.dt.strftime
- Series.dt.total_seconds
- Series.explode to split list-like values to rows
- Series.list accessor for PyArrow list data
- Series.select and DataFrame.select
- Series.str.cat has gained the join kwarg
- Series.struct accessor for PyArrow structured data
- Series.tolist() will now return Python types
- Series.unique for timezone-aware data
- SeriesGroupBy computations / descriptive stats
- Set / reset index
- Set an index
- Set operations on Index objects
- Set properties
- Setting
- Setting
- Setting Classes and Linking to External CSS
- Setting categories
- Setting metadata
- Setting startup options in Python/IPython environment
- Setting the plot style
- Setting with enlargement
- Setting with enlargement conditionally using numpy()
- Sharing docstrings
- Sharing styles
- Shifting / lagging
- Shorter truncated repr for Series and DataFrame
- Side effects
- Signature change for .rank
- Skip row between header and data
- Slice vs. exact match
- Slicing
- Slicing ranges
- Slicing with R's c
- Slicing with labels
- Sort table rows
- Sorting
- Sorting
- Sorting
- Sorting
- Sorting a MultiIndex
- Sorting and order
- Sorting by a MultiIndex column
- Sorting by a combination of columns and index levels
- Sorting by values
- Sorting by values
- Sorting by values
- Sorting with keys
- Sparse
- Sparse
- Sparse
- Sparse
- Sparse
- Sparse
- Sparse
- Sparse
- Sparse
- Sparse
- Sparse
- Sparse
- Sparse
- Sparse
- Sparse
- Sparse
- Sparse accessor
- Sparse accessor
- Sparse accessor
- Sparse calculation
- Sparse changes
- Sparse changes
- Sparse data structure refactor
- Sparse resampling
- Sparse subclasses
- SparseArray
- SparseDtype
- Special use of the == operator with list objects
- Specifying categorical dtype
- Specifying column data types
- Specifying date columns
- Specifying method for floating-point conversion
- Specifying sheets
- Specifying the parser engine
- Splitting
- Splitting an object into groups
- Splitting and replacing strings
- Sqlite fallback
- Stack
- Starting Gitpod
- Stata format
- Stats
- Step 1: install a C compiler
- Step 2: create an isolated environment
- Step 3: build and install pandas
- Sticky Headers
- Storer object
- Storing Interval and Period data in Series and DataFrame
- Storing MultiIndex DataFrames
- Storing mixed types in a table
- Storing pandas DataFrame objects in Apache Parquet format
- Storing types
- String
- String Methods
- String and datetime accessors
- String columns
- String handling
- String methods
- String methods enhancements
- String methods enhancements
- String processing
- String processing
- String processing
- Strings
- Strings
- Strings
- Strings
- Strings
- Strings
- Strings
- Strings
- Strings
- Strings
- Strings
- Strings
- Strings
- Struct accessor
- Style
- Style and formatting
- Style application
- Style export and import
- Style guidelines
- Styler
- Styler
- Styler
- Styler
- Styler
- Styler
- Styler Functions
- Styler Object and Customising the Display
- Styler Object and HTML
- Styler constructor
- Styler enhancements
- Styler properties
- Subclassing
- Subclassing pandas data structures
- Subclassing pandas data structures
- Submitting a pull request
- Subplots
- Sum/prod of all-NaN or empty Series/DataFrames is now consistently NaN
- Summarizing data: describe
- Support for SAS XPORT files
- Support for binary file handles in to_csv
- Support for integer NA
- Support for math functions in .eval()
- Support for non-unique indexes
- Support for short caption and table position in to_latex
- Supported syntax
- Suppressing tick resolution adjustment
- Swapping levels with swaplevel
- Table Attributes
- Table Styles
- Table format
- Table schema
- Table schema display
- Table schema output
- Tablewise function application
- Take methods
- Taking the first rows of each group
- Taking the nth row of each group
- Tasks
- Template Structure
- Test structure
- Test suite runner
- Test-driven development
- Testing a warning
- Testing an exception
- Testing extension arrays
- Testing for strings that match or contain a pattern
- Testing involving files
- Testing involving network connectivity
- Testing type hints in code using pandas
- Testing with continuous integration
- Text data types
- Text parsing API changes
- The .str-accessor performs stricter type checks
- The DataFrame.eval() method
- The PeriodIndex now has period dtype
- The aggregate() method
- The developer mailing list
- The filter method
- The in and not in operators
- The query() Method
- The transform() method
- The where() Method and Masking
- Thousand separators
- Thread-safety
- Tick
- Tick DateOffset normalize restrictions
- Time Series changes and improvements
- Time Series-related
- Time Series-related
- Time Series-related instance methods
- Time Zones
- Time Zones
- Time series
- Time span representation
- Time values in dt.end_time and to_timestamp(how='end')
- Time zone Series operations
- Time zone handling
- Time-specific operations
- Time/date components
- Time/date components
- Timedelta
- Timedelta
- Timedelta
- Timedelta
- Timedelta
- Timedelta
- Timedelta
- Timedelta
- Timedelta
- Timedelta
- Timedelta
- Timedelta
- Timedelta limitations
- Timedelta methods
- Timedelta mod method
- Timedelta properties
- TimedeltaIndex
- TimedeltaIndex
- TimedeltaIndex/scalar
- Timedeltas
- Timedeltas
- Timeseries
- Timestamp limitations
- Timestamps vs. time spans
- Timezone handling improvements
- Timezones
- Timezones
- Timezones
- Timezones
- Timezones
- Timezones
- Timezones
- Timezones
- Timezones
- Timezones
- Timezones
- Tips for a successful pull request
- Tips for getting your examples pass the doctests
- Tooltips and Captions
- Top n rows per group
- Top n rows with offset
- Top-level dealing with Interval data
- Top-level dealing with datetimelike data
- Top-level dealing with numeric data
- Top-level evaluation
- Top-level missing data
- Transform API
- Transform with multiple functions
- Transformation
- Transformation
- Transformation
- Transforming
- Transforming with a dict
- Transposing
- Truncating & fancy indexing
- Try operating inplace when setting values with loc and iloc
- Tutorials
- Type hints
- Types int64 and bool support enhancements
- UInt64 support improved
- UNION
- UPDATE
- UTC and fixed-offset timezones default to standard-library tzinfo objects
- UTC localization with Series
- Unicode formatting
- Unioning
- Upcoming changes in pandas 3.0
- Updated PyTables support
- Updating a pandas docstring
- Updating the development environment
- Updating your pull request
- Upsampling
- Upsampling
- Upsampling
- Use Other Libraries
- Use chunking
- Use efficient datatypes
- Use origin or offset to adjust the start of the bins
- User defined functions
- Using .apply on GroupBy resampling
- Using Docker
- Using Numba in rolling.apply and expanding.apply
- Using dropna=True with groupby transforms
- Using group_keys with transformers in DataFrameGroupBy.apply() and SeriesGroupBy.apply()
- Using hypothesis
- Using if/truth statements with pandas
- Using layout and targeting multiple axes
- Using ndarray
- Using offsets with Series / DatetimeIndex
- Using pandas datetime properties
- Using pytest
- Using slicers
- Using the TimedeltaIndex
- Using the in operator
- Using the origin parameter
- Utilities
- Validating type hints
- Value Counts
- Value counts (histogramming) / mode
- Value counts sets the resulting name to count
- Values
- Values considered "missing"
- Various enhancements
- Various tutorials
- Vectorized operations and label alignment with Series
- Vectorized string methods
- Version 0.10
- Version 0.11
- Version 0.12
- Version 0.13
- Version 0.14
- Version 0.15
- Version 0.16
- Version 0.17
- Version 0.18
- Version 0.19
- Version 0.20
- Version 0.21
- Version 0.22
- Version 0.23
- Version 0.24
- Version 0.25
- Version 0.4
- Version 0.5
- Version 0.6
- Version 0.7
- Version 0.8
- Version 0.9
- Version 1.0
- Version 1.1
- Version 1.2
- Version 1.3
- Version 1.4
- Version 1.5
- Version 2.0
- Version 2.1
- Version 2.2
- Version control, Git, and GitHub
- Version policy
- Video tutorials
- Viewing data
- Visualization
- WHERE
- Week
- WeekOfMonth
- Weighted window
- Weighted window functions
- What's new in 0.23.0 (May 15, 2018)
- What's new in 0.23.1 (June 12, 2018)
- What's new in 0.23.2 (July 5, 2018)
- What's new in 0.23.3 (July 7, 2018)
- What's new in 0.23.4 (August 3, 2018)
- What's new in 0.24.0 (January 25, 2019)
- What's new in 0.24.1 (February 3, 2019)
- What's new in 0.24.2 (March 12, 2019)
- What's new in 0.25.0 (July 18, 2019)
- What's new in 0.25.1 (August 21, 2019)
- What's new in 0.25.2 (October 15, 2019)
- What's new in 0.25.3 (October 31, 2019)
- What's new in 1.0.0 (January 29, 2020)
- What's new in 1.0.1 (February 5, 2020)
- What's new in 1.0.2 (March 12, 2020)
- What's new in 1.0.3 (March 17, 2020)
- What's new in 1.0.4 (May 28, 2020)
- What's new in 1.0.5 (June 17, 2020)
- What's new in 1.1.0 (July 28, 2020)
- What's new in 1.1.1 (August 20, 2020)
- What's new in 1.1.2 (September 8, 2020)
- What's new in 1.1.3 (October 5, 2020)
- What's new in 1.1.4 (October 30, 2020)
- What's new in 1.1.5 (December 07, 2020)
- What's new in 1.2.0 (December 26, 2020)
- What's new in 1.2.1 (January 20, 2021)
- What's new in 1.2.2 (February 09, 2021)
- What's new in 1.2.3 (March 02, 2021)
- What's new in 1.2.4 (April 12, 2021)
- What's new in 1.2.5 (June 22, 2021)
- What's new in 1.3.0 (July 2, 2021)
- What's new in 1.3.1 (July 25, 2021)
- What's new in 1.3.2 (August 15, 2021)
- What's new in 1.3.3 (September 12, 2021)
- What's new in 1.3.4 (October 17, 2021)
- What's new in 1.3.5 (December 12, 2021)
- What's new in 1.4.0 (January 22, 2022)
- What's new in 1.4.1 (February 12, 2022)
- What's new in 1.4.2 (April 2, 2022)
- What's new in 1.4.3 (June 23, 2022)
- What's new in 1.4.4 (August 31, 2022)
- What's new in 1.5.0 (September 19, 2022)
- What's new in 1.5.1 (October 19, 2022)
- What's new in 1.5.2 (November 21, 2022)
- What's new in 1.5.3 (January 18, 2023)
- What's new in 2.0.0 (April 3, 2023)
- What's new in 2.0.1 (April 24, 2023)
- What's new in 2.0.2 (May 29, 2023)
- What's new in 2.0.3 (June 28, 2023)
- What's new in 2.1.0 (Aug 30, 2023)
- What's new in 2.1.1 (September 20, 2023)
- What's new in 2.1.2 (October 26, 2023)
- What's new in 2.1.3 (November 10, 2023)
- What's new in 2.1.4 (December 8, 2023)
- What's new in 2.2.0 (January 19, 2024)
- What's new in 2.2.1 (February 22, 2024)
- What's new in 2.2.2 (April 10, 2024)
- What's new in 2.2.3 (September 20, 2024)
- Why does assignment fail when using chained indexing?
- Why more than one data structure?
- Why not make NumPy like R?
- Wide DataFrame printing
- Wide to long format
- Widgets
- Window and resample operations
- Window binary corr/cov operations return a MultiIndex DataFrame
- Window functions are now methods
- Window indexer
- Working with categories
- Working with options
- Working with time zones
- Writing CSVs to binary file objects
- Writing DataFrames
- Writing Excel files
- Writing Excel files to disk
- Writing Excel files to memory
- Writing JSON
- Writing XML
- Writing a docstring
- Writing a formatted string
- Writing out data
- Writing tests
- Writing to CSV format
- Writing to HTML files
- Writing to LaTeX files
- Writing to ORC files
- Writing to stata format
- XML
- XML
- XML
- XML Final Notes
- YearBegin
- YearEnd
- __str__ methods now call __repr__ rather than vice versa
- aggregate
- apply and applymap on DataFrame evaluates first row/column only once
- arrays.IntegerArray comparisons return arrays.BooleanArray
- arrays.IntegerArray now uses pandas.NA
- astype
- cast
- compare()
- concat()
- crosstab()
- cut()
- ddply
- defaults
- dtype in apply
- dtypes
- eval() performance comparison
- explode()
- factor
- factorize()
- float result for DataFrameGroupBy.mean(), DataFrameGroupBy.median(), and GDataFrameGroupBy.var(), SeriesGroupBy.mean(), SeriesGroupBy.median(), and SeriesGroupBy.var()
- from_dummies
- fsspec now used for filesystem handling
- get_dummies() always returns a DataFrame
- get_dummies() and from_dummies()
- gotchas
- groupby.apply consistent transform detection
- if-then...
- items
- iterrows
- itertuples
- mangle_dupe_cols in read_csv no longer renames unique columns conflicting with target names
- match / %in%
- melt() and wide_to_long()
- meltarray
- meltdf
- meltlist
- merge()
- merge() and DataFrame.join() no longer reorder levels when levels differ
- merge() and DataFrame.join() now consistently follow documented sort behavior
- merge_asof()
- merge_ordered()
- msgpack
- msgpack format
- netCDF
- np.nan as the NA representation for NumPy types
- numeric_only default value
- object conversion
- os.linesep is used for line_terminator of DataFrame.to_csv
- pandas Google BigQuery support has moved
- pandas Numba Engine
- pandas cookbook by Julia Evans
- pandas data table representation
- pandas development API
- pandas documentation
- pandas equivalents for some SQL analytic and aggregate functions
- pandas workshop by Stefanie Molin
- pandas-specific types
- pandas-stubs
- pandas.array() inference changes
- pandas.array: a new top-level method for creating arrays
- pandas.core.common removals
- pandas.errors
- pandas.eval() engines
- pandas.eval() parsers
- pandas.plotting
- pandas.testing
- pd.read_sas() changes
- pd.unique will now be consistent with extension types
- pivot()
- pivot() and pivot_table()
- pivot_table()
- plyr
- ptrepack
- query() Python versus pandas Syntax Comparison
- query() Use Cases
- read_xml now supports dtype, converters, and parse_dates
- read_xml now supports large XML using iterparse
- reshape / reshape2
- searchsorted
- smallest / largest values
- stack() and unstack()
- subset
- tapply
- to_numpy for NumPy nullable and Arrow types converts to suitable NumPy dtype
- to_timedelta
- unstack and pivot_table no longer raises ValueError for result that would exceed int32 limit
- upcasting
- with