pandas: powerful Python data analysis toolkit¶
Date: Jul 07, 2018 Version: 0.23.3
Binary Installers: https://pypi.org/project/pandas
Source Repository: http://github.com/pandas-dev/pandas
Issues & Ideas: https://github.com/pandas-dev/pandas/issues
Q&A Support: http://stackoverflow.com/questions/tagged/pandas
Developer Mailing List: http://groups.google.com/group/pydata
pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. It is already well on its way toward this goal.
pandas is well suited for many different kinds of data:
- Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet
- Ordered and unordered (not necessarily fixed-frequency) time series data.
- Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels
- Any other form of observational / statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure
The two primary data structures of pandas, Series
(1-dimensional)
and DataFrame
(2-dimensional), handle the vast majority of typical use
cases in finance, statistics, social science, and many areas of
engineering. For R users, DataFrame
provides everything that R’s
data.frame
provides and much more. pandas is built on top of NumPy and is intended to integrate well within a scientific
computing environment with many other 3rd party libraries.
Here are just a few of the things that pandas does well:
- Easy handling of missing data (represented as NaN) in floating point as well as non-floating point data
- Size mutability: columns can be inserted and deleted from DataFrame and higher dimensional objects
- Automatic and explicit data alignment: objects can be explicitly aligned to a set of labels, or the user can simply ignore the labels and let Series, DataFrame, etc. automatically align the data for you in computations
- Powerful, flexible group by functionality to perform split-apply-combine operations on data sets, for both aggregating and transforming data
- Make it easy to convert ragged, differently-indexed data in other Python and NumPy data structures into DataFrame objects
- Intelligent label-based slicing, fancy indexing, and subsetting of large data sets
- Intuitive merging and joining data sets
- Flexible reshaping and pivoting of data sets
- Hierarchical labeling of axes (possible to have multiple labels per tick)
- Robust IO tools for loading data from flat files (CSV and delimited), Excel files, databases, and saving / loading data from the ultrafast HDF5 format
- Time series-specific functionality: date range generation and frequency conversion, moving window statistics, moving window linear regressions, date shifting and lagging, etc.
Many of these principles are here to address the shortcomings frequently experienced using other languages / scientific research environments. For data scientists, working with data is typically divided into multiple stages: munging and cleaning data, analyzing / modeling it, then organizing the results of the analysis into a form suitable for plotting or tabular display. pandas is the ideal tool for all of these tasks.
Some other notes
- pandas is fast. Many of the low-level algorithmic bits have been extensively tweaked in Cython code. However, as with anything else generalization usually sacrifices performance. So if you focus on one feature for your application you may be able to create a faster specialized tool.
- pandas is a dependency of statsmodels, making it an important part of the statistical computing ecosystem in Python.
- pandas has been used extensively in production in financial applications.
Note
This documentation assumes general familiarity with NumPy. If you haven’t used NumPy much or at all, do invest some time in learning about NumPy first.
See the package overview for more detail about what’s in the library.
- What’s New
- v0.23.3 (July 7, 2018)
- v0.23.2
- v0.23.1
- v0.23.0 (May 15, 2018)
- New features
- JSON read/write round-trippable with
orient='table'
.assign()
accepts dependent arguments- Merging on a combination of columns and index levels
- Sorting by a combination of columns and index levels
- Extending Pandas with Custom Types (Experimental)
- New
observed
keyword for excluding unobserved categories ingroupby
- Rolling/Expanding.apply() accepts
raw=False
to pass aSeries
to the function DataFrame.interpolate
has gained thelimit_area
kwargget_dummies
now supportsdtype
argument- Timedelta mod method
.rank()
handlesinf
values whenNaN
are presentSeries.str.cat
has gained thejoin
kwargDataFrame.astype
performs column-wise conversion toCategorical
- Other Enhancements
- JSON read/write round-trippable with
- Backwards incompatible API changes
- Dependencies have increased minimum versions
- Instantiation from dicts preserves dict insertion order for python 3.6+
- Deprecate Panel
- pandas.core.common removals
- Changes to make output of
DataFrame.apply
consistent - Concatenation will no longer sort
- Build Changes
- Index Division By Zero Fills Correctly
- Extraction of matching patterns from strings
- Default value for the
ordered
parameter ofCategoricalDtype
- Better pretty-printing of DataFrames in a terminal
- Datetimelike API Changes
- Other API Changes
- Deprecations
- Removal of prior version deprecations/changes
- Performance Improvements
- Documentation Changes
- Bug Fixes
- New features
- v0.22.0 (December 29, 2017)
- v0.21.1 (December 12, 2017)
- v0.21.0 (October 27, 2017)
- New features
- Integration with Apache Parquet file format
infer_objects
type conversion- Improved warnings when attempting to create columns
drop
now also accepts index/columns keywordsrename
,reindex
now also accept axis keywordCategoricalDtype
for specifying categoricalsGroupBy
objects now have apipe
methodCategorical.rename_categories
accepts a dict-like- Other Enhancements
- Backwards incompatible API changes
- Dependencies have increased minimum versions
- Sum/Prod of all-NaN or empty Series/DataFrames is now consistently NaN
- Indexing with a list with missing labels is Deprecated
- NA naming Changes
- Iteration of Series/Index will now return Python scalars
- Indexing with a Boolean Index
PeriodIndex
resampling- Improved error handling during item assignment in pd.eval
- Dtype Conversions
- MultiIndex Constructor with a Single Level
- UTC Localization with Series
- Consistency of Range Functions
- No Automatic Matplotlib Converters
- Other API Changes
- Deprecations
- Removal of prior version deprecations/changes
- Performance Improvements
- Documentation Changes
- Bug Fixes
- New features
- v0.20.3 (July 7, 2017)
- v0.20.2 (June 4, 2017)
- v0.20.1 (May 5, 2017)
- New features
agg
API for DataFrame/Seriesdtype
keyword for data IO.to_datetime()
has gained anorigin
parameter- Groupby Enhancements
- Better support for compressed URLs in
read_csv
- Pickle file I/O now supports compression
- UInt64 Support Improved
- GroupBy on Categoricals
- Table Schema Output
- SciPy sparse matrix from/to SparseDataFrame
- Excel output for styled DataFrames
- IntervalIndex
- Other Enhancements
- Backwards incompatible API changes
- Possible incompatibility for HDF5 formats created with pandas < 0.13.0
- Map on Index types now return other Index types
- Accessing datetime fields of Index now return Index
- pd.unique will now be consistent with extension types
- S3 File Handling
- Partial String Indexing Changes
- Concat of different float dtypes will not automatically upcast
- Pandas Google BigQuery support has moved
- Memory Usage for Index is more Accurate
- DataFrame.sort_index changes
- Groupby Describe Formatting
- Window Binary Corr/Cov operations return a MultiIndex DataFrame
- HDFStore where string comparison
- Index.intersection and inner join now preserve the order of the left Index
- Pivot Table always returns a DataFrame
- Other API Changes
- Reorganization of the library: Privacy Changes
- Deprecations
- Removal of prior version deprecations/changes
- Performance Improvements
- Bug Fixes
- New features
- v0.19.2 (December 24, 2016)
- v0.19.1 (November 3, 2016)
- v0.19.0 (October 2, 2016)
- New features
merge_asof
for asof-style time-series joining.rolling()
is now time-series awareread_csv
has improved support for duplicate column namesread_csv
supports parsingCategorical
directly- Categorical Concatenation
- Semi-Month Offsets
- New Index methods
- Google BigQuery Enhancements
- Fine-grained numpy errstate
get_dummies
now returns integer dtypes- Downcast values to smallest possible dtype in
to_numeric
- pandas development API
- Other enhancements
- API changes
Series.tolist()
will now return Python typesSeries
operators for different indexesSeries
type promotion on assignment.to_datetime()
changes- Merging changes
.describe()
changesPeriod
changes- Index
+
/-
no longer used for set operations Index.difference
and.symmetric_difference
changesIndex.unique
consistently returnsIndex
MultiIndex
constructors,groupby
andset_index
preserve categorical dtypesread_csv
will progressively enumerate chunks- Sparse Changes
- Indexer dtype changes
- Other API Changes
- Deprecations
- Removal of prior version deprecations/changes
- Performance Improvements
- Bug Fixes
- New features
- v0.18.1 (May 3, 2016)
- v0.18.0 (March 13, 2016)
- New features
- Window functions are now methods
- Changes to rename
- Range Index
- Changes to str.extract
- Addition of str.extractall
- Changes to str.cat
- Datetimelike rounding
- Formatting of Integers in FloatIndex
- Changes to dtype assignment behaviors
- to_xarray
- Latex Representation
pd.read_sas()
changes- Other enhancements
- Backwards incompatible API changes
- Performance Improvements
- Bug Fixes
- New features
- v0.17.1 (November 21, 2015)
- v0.17.0 (October 9, 2015)
- New features
- Datetime with TZ
- Releasing the GIL
- Plot submethods
- Additional methods for
dt
accessor - Period Frequency Enhancement
- Support for SAS XPORT files
- Support for Math Functions in .eval()
- Changes to Excel with
MultiIndex
- Google BigQuery Enhancements
- Display Alignment with Unicode East Asian Width
- Other enhancements
- Backwards incompatible API changes
- Changes to sorting API
- Changes to to_datetime and to_timedelta
- Changes to Index Comparisons
- Changes to Boolean Comparisons vs. None
- HDFStore dropna behavior
- Changes to
display.precision
option - Changes to
Categorical.unique
- Changes to
bool
passed asheader
in Parsers - Other API Changes
- Deprecations
- Removal of prior version deprecations/changes
- Performance Improvements
- Bug Fixes
- New features
- v0.16.2 (June 12, 2015)
- v0.16.1 (May 11, 2015)
- v0.16.0 (March 22, 2015)
- v0.15.2 (December 12, 2014)
- v0.15.1 (November 9, 2014)
- v0.15.0 (October 18, 2014)
- v0.14.1 (July 11, 2014)
- v0.14.0 (May 31 , 2014)
- v0.13.1 (February 3, 2014)
- v0.13.0 (January 3, 2014)
- v0.12.0 (July 24, 2013)
- v0.11.0 (April 22, 2013)
- v0.10.1 (January 22, 2013)
- v0.10.0 (December 17, 2012)
- v0.9.1 (November 14, 2012)
- v0.9.0 (October 7, 2012)
- v0.8.1 (July 22, 2012)
- v0.8.0 (June 29, 2012)
- v.0.7.3 (April 12, 2012)
- v.0.7.2 (March 16, 2012)
- v.0.7.1 (February 29, 2012)
- v.0.7.0 (February 9, 2012)
- v.0.6.1 (December 13, 2011)
- v.0.6.0 (November 25, 2011)
- v.0.5.0 (October 24, 2011)
- v.0.4.3 through v0.4.1 (September 25 - October 9, 2011)
- Installation
- Contributing to pandas
- Package overview
- 10 Minutes to pandas
- Tutorials
- Cookbook
- Intro to Data Structures
- Series
- DataFrame
- From dict of Series or dicts
- From dict of ndarrays / lists
- From structured or record array
- From a list of dicts
- From a dict of tuples
- From a Series
- Alternate Constructors
- Column selection, addition, deletion
- Assigning New Columns in Method Chains
- Indexing / Selection
- Data alignment and arithmetic
- Transposing
- DataFrame interoperability with NumPy functions
- Console display
- DataFrame column attribute access and IPython completion
- Panel
- Deprecate Panel
- Essential Basic Functionality
- Head and Tail
- Attributes and the raw ndarray(s)
- 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
- Working with Text Data
- Options and Settings
- 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
- Computational tools
- Working with missing data
- 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
- Automatic exclusion of “nuisance” columns
- Handling of (un)observed Categorical values
- NA and NaT group handling
- Grouping with ordered factors
- Grouping with a Grouper specification
- Taking the first rows of each group
- Taking the nth row of each group
- Enumerate group items
- Enumerate groups
- Plotting
- Piping function calls
- Examples
- Merge, join, and concatenate
- Concatenating objects
- Database-style DataFrame joining/merging
- Brief primer on merge methods (relational algebra)
- Checking for duplicate keys
- The merge indicator
- Merge Dtypes
- Joining on index
- Joining key columns on an index
- Joining a single Index to a Multi-index
- Joining with two multi-indexes
- Merging on a combination of columns and index levels
- Overlapping value columns
- Joining multiple DataFrame or Panel objects
- Merging together values within Series or DataFrame columns
- Timeseries friendly merging
- Reshaping and Pivot Tables
- 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
- Categorical Data
- Visualization
- Styling
- IO Tools (Text, CSV, HDF5, …)
- CSV & Text files
- Parsing options
- Specifying column data types
- Specifying Categorical dtype
- Naming and Using Columns
- Duplicate names parsing
- Comments and Empty Lines
- Dealing with Unicode Data
- Index columns and trailing delimiters
- Date Handling
- Specifying method for floating-point conversion
- Thousand Separators
- NA Values
- Infinity
- Returning Series
- Boolean values
- Handling “bad” lines
- Dialect
- Quoting and Escape Characters
- Files with Fixed Width Columns
- Indexes
- Automatically “sniffing” the delimiter
- Reading multiple files to create a single DataFrame
- Iterating through files chunk by chunk
- Specifying the parser engine
- Reading remote files
- Writing out Data
- JSON
- HTML
- Excel files
- Clipboard
- Pickling
- msgpack
- HDF5 (PyTables)
- Feather
- Parquet
- SQL Queries
- Google BigQuery
- Stata Format
- SAS Formats
- Other file formats
- Performance Considerations
- CSV & Text files
- Enhancing Performance
- Sparse data structures
- Frequently Asked Questions (FAQ)
- rpy2 / R interface
- pandas Ecosystem
- Comparison with R / R libraries
- Comparison with SQL
- Comparison with SAS
- Comparison with Stata
- API Reference
- Input/Output
- General functions
- Series
- Constructor
- Attributes
- pandas.Series.index
- pandas.Series.values
- pandas.Series.dtype
- pandas.Series.ftype
- pandas.Series.shape
- pandas.Series.nbytes
- pandas.Series.ndim
- pandas.Series.size
- pandas.Series.strides
- pandas.Series.itemsize
- pandas.Series.base
- pandas.Series.T
- pandas.Series.memory_usage
- pandas.Series.hasnans
- pandas.Series.flags
- pandas.Series.empty
- pandas.Series.dtypes
- pandas.Series.ftypes
- pandas.Series.data
- pandas.Series.is_copy
- pandas.Series.name
- pandas.Series.put
- Conversion
- Indexing, iteration
- Binary operator functions
- pandas.Series.add
- pandas.Series.sub
- pandas.Series.mul
- pandas.Series.div
- pandas.Series.truediv
- pandas.Series.floordiv
- pandas.Series.mod
- pandas.Series.pow
- pandas.Series.radd
- pandas.Series.rsub
- pandas.Series.rmul
- pandas.Series.rdiv
- pandas.Series.rtruediv
- pandas.Series.rfloordiv
- pandas.Series.rmod
- pandas.Series.rpow
- pandas.Series.combine
- pandas.Series.combine_first
- pandas.Series.round
- pandas.Series.lt
- pandas.Series.gt
- pandas.Series.le
- pandas.Series.ge
- pandas.Series.ne
- pandas.Series.eq
- pandas.Series.product
- pandas.Series.dot
- Function application, GroupBy & Window
- Computations / Descriptive Stats
- pandas.Series.abs
- pandas.Series.all
- pandas.Series.any
- pandas.Series.autocorr
- pandas.Series.between
- pandas.Series.clip
- pandas.Series.clip_lower
- pandas.Series.clip_upper
- pandas.Series.corr
- pandas.Series.count
- pandas.Series.cov
- pandas.Series.cummax
- pandas.Series.cummin
- pandas.Series.cumprod
- pandas.Series.cumsum
- pandas.Series.describe
- pandas.Series.diff
- pandas.Series.factorize
- pandas.Series.kurt
- pandas.Series.mad
- pandas.Series.max
- pandas.Series.mean
- pandas.Series.median
- pandas.Series.min
- pandas.Series.mode
- pandas.Series.nlargest
- pandas.Series.nsmallest
- pandas.Series.pct_change
- pandas.Series.prod
- pandas.Series.quantile
- pandas.Series.rank
- pandas.Series.sem
- pandas.Series.skew
- pandas.Series.std
- pandas.Series.sum
- pandas.Series.var
- pandas.Series.kurtosis
- pandas.Series.unique
- pandas.Series.nunique
- pandas.Series.is_unique
- pandas.Series.is_monotonic
- pandas.Series.is_monotonic_increasing
- pandas.Series.is_monotonic_decreasing
- pandas.Series.value_counts
- pandas.Series.compound
- pandas.Series.nonzero
- pandas.Series.ptp
- Reindexing / Selection / Label manipulation
- pandas.Series.align
- pandas.Series.drop
- pandas.Series.drop_duplicates
- pandas.Series.duplicated
- pandas.Series.equals
- pandas.Series.first
- pandas.Series.head
- pandas.Series.idxmax
- pandas.Series.idxmin
- pandas.Series.isin
- pandas.Series.last
- pandas.Series.reindex
- pandas.Series.reindex_like
- pandas.Series.rename
- pandas.Series.rename_axis
- pandas.Series.reset_index
- pandas.Series.sample
- pandas.Series.select
- pandas.Series.set_axis
- pandas.Series.take
- pandas.Series.tail
- pandas.Series.truncate
- pandas.Series.where
- pandas.Series.mask
- pandas.Series.add_prefix
- pandas.Series.add_suffix
- pandas.Series.filter
- Missing data handling
- Reshaping, sorting
- pandas.Series.argsort
- pandas.Series.argmin
- pandas.Series.argmax
- pandas.Series.reorder_levels
- pandas.Series.sort_values
- pandas.Series.sort_index
- pandas.Series.swaplevel
- pandas.Series.unstack
- pandas.Series.searchsorted
- pandas.Series.ravel
- pandas.Series.repeat
- pandas.Series.squeeze
- pandas.Series.view
- pandas.Series.sortlevel
- Combining / joining / merging
- Time series-related
- Datetimelike Properties
- pandas.Series.dt.date
- pandas.Series.dt.time
- pandas.Series.dt.year
- pandas.Series.dt.month
- pandas.Series.dt.day
- pandas.Series.dt.hour
- pandas.Series.dt.minute
- pandas.Series.dt.second
- pandas.Series.dt.microsecond
- pandas.Series.dt.nanosecond
- pandas.Series.dt.week
- pandas.Series.dt.weekofyear
- pandas.Series.dt.dayofweek
- pandas.Series.dt.weekday
- pandas.Series.dt.dayofyear
- pandas.Series.dt.quarter
- pandas.Series.dt.is_month_start
- pandas.Series.dt.is_month_end
- pandas.Series.dt.is_quarter_start
- pandas.Series.dt.is_quarter_end
- pandas.Series.dt.is_year_start
- pandas.Series.dt.is_year_end
- pandas.Series.dt.is_leap_year
- pandas.Series.dt.daysinmonth
- pandas.Series.dt.days_in_month
- pandas.Series.dt.tz
- pandas.Series.dt.freq
- pandas.Series.dt.to_period
- pandas.Series.dt.to_pydatetime
- pandas.Series.dt.tz_localize
- pandas.Series.dt.tz_convert
- pandas.Series.dt.normalize
- pandas.Series.dt.strftime
- pandas.Series.dt.round
- pandas.Series.dt.floor
- pandas.Series.dt.ceil
- pandas.Series.dt.month_name
- pandas.Series.dt.day_name
- pandas.Series.dt.days
- pandas.Series.dt.seconds
- pandas.Series.dt.microseconds
- pandas.Series.dt.nanoseconds
- pandas.Series.dt.components
- pandas.Series.dt.to_pytimedelta
- pandas.Series.dt.total_seconds
- String handling
- pandas.Series.str.capitalize
- pandas.Series.str.cat
- pandas.Series.str.center
- pandas.Series.str.contains
- pandas.Series.str.count
- pandas.Series.str.decode
- pandas.Series.str.encode
- pandas.Series.str.endswith
- pandas.Series.str.extract
- pandas.Series.str.extractall
- pandas.Series.str.find
- pandas.Series.str.findall
- pandas.Series.str.get
- pandas.Series.str.index
- pandas.Series.str.join
- pandas.Series.str.len
- pandas.Series.str.ljust
- pandas.Series.str.lower
- pandas.Series.str.lstrip
- pandas.Series.str.match
- pandas.Series.str.normalize
- pandas.Series.str.pad
- pandas.Series.str.partition
- pandas.Series.str.repeat
- pandas.Series.str.replace
- pandas.Series.str.rfind
- pandas.Series.str.rindex
- pandas.Series.str.rjust
- pandas.Series.str.rpartition
- pandas.Series.str.rstrip
- pandas.Series.str.slice
- pandas.Series.str.slice_replace
- pandas.Series.str.split
- pandas.Series.str.rsplit
- pandas.Series.str.startswith
- pandas.Series.str.strip
- pandas.Series.str.swapcase
- pandas.Series.str.title
- pandas.Series.str.translate
- pandas.Series.str.upper
- pandas.Series.str.wrap
- pandas.Series.str.zfill
- pandas.Series.str.isalnum
- pandas.Series.str.isalpha
- pandas.Series.str.isdigit
- pandas.Series.str.isspace
- pandas.Series.str.islower
- pandas.Series.str.isupper
- pandas.Series.str.istitle
- pandas.Series.str.isnumeric
- pandas.Series.str.isdecimal
- pandas.Series.str.get_dummies
- Categorical
- pandas.api.types.CategoricalDtype
- pandas.api.types.CategoricalDtype.categories
- pandas.api.types.CategoricalDtype.ordered
- pandas.Categorical
- pandas.Categorical.from_codes
- pandas.Categorical.dtype
- pandas.Categorical.categories
- pandas.Categorical.ordered
- pandas.Categorical.codes
- pandas.Categorical.__array__
- pandas.Series.cat.categories
- pandas.Series.cat.ordered
- pandas.Series.cat.codes
- pandas.Series.cat.rename_categories
- pandas.Series.cat.reorder_categories
- pandas.Series.cat.add_categories
- pandas.Series.cat.remove_categories
- pandas.Series.cat.remove_unused_categories
- pandas.Series.cat.set_categories
- pandas.Series.cat.as_ordered
- pandas.Series.cat.as_unordered
- Plotting
- Serialization / IO / Conversion
- pandas.Series.to_pickle
- pandas.Series.to_csv
- pandas.Series.to_dict
- pandas.Series.to_excel
- pandas.Series.to_frame
- pandas.Series.to_xarray
- pandas.Series.to_hdf
- pandas.Series.to_sql
- pandas.Series.to_msgpack
- pandas.Series.to_json
- pandas.Series.to_sparse
- pandas.Series.to_dense
- pandas.Series.to_string
- pandas.Series.to_clipboard
- pandas.Series.to_latex
- Sparse
- DataFrame
- Constructor
- Attributes and underlying data
- pandas.DataFrame.index
- pandas.DataFrame.columns
- pandas.DataFrame.dtypes
- pandas.DataFrame.ftypes
- pandas.DataFrame.get_dtype_counts
- pandas.DataFrame.get_ftype_counts
- pandas.DataFrame.select_dtypes
- pandas.DataFrame.values
- pandas.DataFrame.get_values
- pandas.DataFrame.axes
- pandas.DataFrame.ndim
- pandas.DataFrame.size
- pandas.DataFrame.shape
- pandas.DataFrame.memory_usage
- pandas.DataFrame.empty
- pandas.DataFrame.is_copy
- Conversion
- Indexing, iteration
- pandas.DataFrame.head
- pandas.DataFrame.at
- pandas.DataFrame.iat
- pandas.DataFrame.loc
- pandas.DataFrame.iloc
- pandas.DataFrame.insert
- pandas.DataFrame.insert
- pandas.DataFrame.__iter__
- pandas.DataFrame.items
- pandas.DataFrame.keys
- pandas.DataFrame.iteritems
- pandas.DataFrame.iterrows
- pandas.DataFrame.itertuples
- pandas.DataFrame.lookup
- pandas.DataFrame.pop
- pandas.DataFrame.tail
- pandas.DataFrame.xs
- pandas.DataFrame.get
- pandas.DataFrame.isin
- pandas.DataFrame.where
- pandas.DataFrame.mask
- pandas.DataFrame.query
- Binary operator functions
- pandas.DataFrame.add
- pandas.DataFrame.sub
- pandas.DataFrame.mul
- pandas.DataFrame.div
- pandas.DataFrame.truediv
- pandas.DataFrame.floordiv
- pandas.DataFrame.mod
- pandas.DataFrame.pow
- pandas.DataFrame.dot
- pandas.DataFrame.radd
- pandas.DataFrame.rsub
- pandas.DataFrame.rmul
- pandas.DataFrame.rdiv
- pandas.DataFrame.rtruediv
- pandas.DataFrame.rfloordiv
- pandas.DataFrame.rmod
- pandas.DataFrame.rpow
- pandas.DataFrame.lt
- pandas.DataFrame.gt
- pandas.DataFrame.le
- pandas.DataFrame.ge
- pandas.DataFrame.ne
- pandas.DataFrame.eq
- pandas.DataFrame.combine
- pandas.DataFrame.combine_first
- Function application, GroupBy & Window
- Computations / Descriptive Stats
- pandas.DataFrame.abs
- pandas.DataFrame.all
- pandas.DataFrame.any
- pandas.DataFrame.clip
- pandas.DataFrame.clip_lower
- pandas.DataFrame.clip_upper
- pandas.DataFrame.compound
- pandas.DataFrame.corr
- pandas.DataFrame.corrwith
- pandas.DataFrame.count
- pandas.DataFrame.cov
- pandas.DataFrame.cummax
- pandas.DataFrame.cummin
- pandas.DataFrame.cumprod
- pandas.DataFrame.cumsum
- pandas.DataFrame.describe
- pandas.DataFrame.diff
- pandas.DataFrame.eval
- pandas.DataFrame.kurt
- pandas.DataFrame.kurtosis
- pandas.DataFrame.mad
- pandas.DataFrame.max
- pandas.DataFrame.mean
- pandas.DataFrame.median
- pandas.DataFrame.min
- pandas.DataFrame.mode
- pandas.DataFrame.pct_change
- pandas.DataFrame.prod
- pandas.DataFrame.product
- pandas.DataFrame.quantile
- pandas.DataFrame.rank
- pandas.DataFrame.round
- pandas.DataFrame.sem
- pandas.DataFrame.skew
- pandas.DataFrame.sum
- pandas.DataFrame.std
- pandas.DataFrame.var
- pandas.DataFrame.nunique
- Reindexing / Selection / Label manipulation
- pandas.DataFrame.add_prefix
- pandas.DataFrame.add_suffix
- pandas.DataFrame.align
- pandas.DataFrame.at_time
- pandas.DataFrame.between_time
- pandas.DataFrame.drop
- pandas.DataFrame.drop_duplicates
- pandas.DataFrame.duplicated
- pandas.DataFrame.equals
- pandas.DataFrame.filter
- pandas.DataFrame.first
- pandas.DataFrame.head
- pandas.DataFrame.idxmax
- pandas.DataFrame.idxmin
- pandas.DataFrame.last
- pandas.DataFrame.reindex
- pandas.DataFrame.reindex_axis
- pandas.DataFrame.reindex_like
- pandas.DataFrame.rename
- pandas.DataFrame.rename_axis
- pandas.DataFrame.reset_index
- pandas.DataFrame.sample
- pandas.DataFrame.select
- pandas.DataFrame.set_axis
- pandas.DataFrame.set_index
- pandas.DataFrame.tail
- pandas.DataFrame.take
- pandas.DataFrame.truncate
- Missing data handling
- Reshaping, sorting, transposing
- pandas.DataFrame.pivot
- pandas.DataFrame.pivot_table
- pandas.DataFrame.reorder_levels
- pandas.DataFrame.sort_values
- pandas.DataFrame.sort_index
- pandas.DataFrame.nlargest
- pandas.DataFrame.nsmallest
- pandas.DataFrame.swaplevel
- pandas.DataFrame.stack
- pandas.DataFrame.unstack
- pandas.DataFrame.swapaxes
- pandas.DataFrame.melt
- pandas.DataFrame.squeeze
- pandas.DataFrame.to_panel
- pandas.DataFrame.to_xarray
- pandas.DataFrame.T
- pandas.DataFrame.transpose
- Combining / joining / merging
- Time series-related
- pandas.DataFrame.asfreq
- pandas.DataFrame.asof
- pandas.DataFrame.shift
- pandas.DataFrame.slice_shift
- pandas.DataFrame.tshift
- pandas.DataFrame.first_valid_index
- pandas.DataFrame.last_valid_index
- pandas.DataFrame.resample
- pandas.DataFrame.to_period
- pandas.DataFrame.to_timestamp
- pandas.DataFrame.tz_convert
- pandas.DataFrame.tz_localize
- Plotting
- pandas.DataFrame.plot
- pandas.DataFrame.plot.area
- pandas.DataFrame.plot.bar
- pandas.DataFrame.plot.barh
- pandas.DataFrame.plot.box
- pandas.DataFrame.plot.density
- pandas.DataFrame.plot.hexbin
- pandas.DataFrame.plot.hist
- pandas.DataFrame.plot.kde
- pandas.DataFrame.plot.line
- pandas.DataFrame.plot.pie
- pandas.DataFrame.plot.scatter
- pandas.DataFrame.boxplot
- pandas.DataFrame.hist
- Serialization / IO / Conversion
- pandas.DataFrame.from_csv
- pandas.DataFrame.from_dict
- pandas.DataFrame.from_items
- pandas.DataFrame.from_records
- pandas.DataFrame.info
- pandas.DataFrame.to_parquet
- pandas.DataFrame.to_pickle
- pandas.DataFrame.to_csv
- pandas.DataFrame.to_hdf
- pandas.DataFrame.to_sql
- pandas.DataFrame.to_dict
- pandas.DataFrame.to_excel
- pandas.DataFrame.to_json
- pandas.DataFrame.to_html
- pandas.DataFrame.to_feather
- pandas.DataFrame.to_latex
- pandas.DataFrame.to_stata
- pandas.DataFrame.to_msgpack
- pandas.DataFrame.to_gbq
- pandas.DataFrame.to_records
- pandas.DataFrame.to_sparse
- pandas.DataFrame.to_dense
- pandas.DataFrame.to_string
- pandas.DataFrame.to_clipboard
- pandas.DataFrame.style
- Sparse
- Panel
- Constructor
- Attributes and underlying data
- Conversion
- Getting and setting
- Indexing, iteration, slicing
- Binary operator functions
- pandas.Panel.add
- pandas.Panel.sub
- pandas.Panel.mul
- pandas.Panel.div
- pandas.Panel.truediv
- pandas.Panel.floordiv
- pandas.Panel.mod
- pandas.Panel.pow
- pandas.Panel.radd
- pandas.Panel.rsub
- pandas.Panel.rmul
- pandas.Panel.rdiv
- pandas.Panel.rtruediv
- pandas.Panel.rfloordiv
- pandas.Panel.rmod
- pandas.Panel.rpow
- pandas.Panel.lt
- pandas.Panel.gt
- pandas.Panel.le
- pandas.Panel.ge
- pandas.Panel.ne
- pandas.Panel.eq
- Function application, GroupBy
- Computations / Descriptive Stats
- pandas.Panel.abs
- pandas.Panel.clip
- pandas.Panel.clip_lower
- pandas.Panel.clip_upper
- pandas.Panel.count
- pandas.Panel.cummax
- pandas.Panel.cummin
- pandas.Panel.cumprod
- pandas.Panel.cumsum
- pandas.Panel.max
- pandas.Panel.mean
- pandas.Panel.median
- pandas.Panel.min
- pandas.Panel.pct_change
- pandas.Panel.prod
- pandas.Panel.sem
- pandas.Panel.skew
- pandas.Panel.sum
- pandas.Panel.std
- pandas.Panel.var
- Reindexing / Selection / Label manipulation
- pandas.Panel.add_prefix
- pandas.Panel.add_suffix
- pandas.Panel.drop
- pandas.Panel.equals
- pandas.Panel.filter
- pandas.Panel.first
- pandas.Panel.last
- pandas.Panel.reindex
- pandas.Panel.reindex_axis
- pandas.Panel.reindex_like
- pandas.Panel.rename
- pandas.Panel.sample
- pandas.Panel.select
- pandas.Panel.take
- pandas.Panel.truncate
- Missing data handling
- Reshaping, sorting, transposing
- Combining / joining / merging
- Time series-related
- Serialization / IO / Conversion
- Index
- pandas.Index
- pandas.Index.T
- pandas.Index.base
- pandas.Index.data
- pandas.Index.dtype
- pandas.Index.dtype_str
- pandas.Index.flags
- pandas.Index.hasnans
- pandas.Index.inferred_type
- pandas.Index.is_monotonic
- pandas.Index.is_monotonic_decreasing
- pandas.Index.is_monotonic_increasing
- pandas.Index.is_unique
- pandas.Index.itemsize
- pandas.Index.nbytes
- pandas.Index.ndim
- pandas.Index.shape
- pandas.Index.size
- pandas.Index.strides
- pandas.Index.values
- pandas.Index.all
- pandas.Index.any
- pandas.Index.append
- pandas.Index.argmax
- pandas.Index.argmin
- pandas.Index.argsort
- pandas.Index.asof
- pandas.Index.asof_locs
- pandas.Index.astype
- pandas.Index.contains
- pandas.Index.copy
- pandas.Index.delete
- pandas.Index.difference
- pandas.Index.drop
- pandas.Index.drop_duplicates
- pandas.Index.dropna
- pandas.Index.duplicated
- pandas.Index.equals
- pandas.Index.factorize
- pandas.Index.fillna
- pandas.Index.format
- pandas.Index.get_duplicates
- pandas.Index.get_indexer
- pandas.Index.get_indexer_for
- pandas.Index.get_indexer_non_unique
- pandas.Index.get_level_values
- pandas.Index.get_loc
- pandas.Index.get_slice_bound
- pandas.Index.get_value
- pandas.Index.get_values
- pandas.Index.groupby
- pandas.Index.identical
- pandas.Index.insert
- pandas.Index.intersection
- pandas.Index.is_
- pandas.Index.is_categorical
- pandas.Index.isin
- pandas.Index.isna
- pandas.Index.isnull
- pandas.Index.item
- pandas.Index.join
- pandas.Index.map
- pandas.Index.max
- pandas.Index.memory_usage
- pandas.Index.min
- pandas.Index.notna
- pandas.Index.notnull
- pandas.Index.nunique
- pandas.Index.putmask
- pandas.Index.ravel
- pandas.Index.reindex
- pandas.Index.rename
- pandas.Index.repeat
- pandas.Index.searchsorted
- pandas.Index.set_names
- pandas.Index.set_value
- pandas.Index.shift
- pandas.Index.slice_indexer
- pandas.Index.slice_locs
- pandas.Index.sort_values
- pandas.Index.sortlevel
- pandas.Index.str
- pandas.Index.summary
- pandas.Index.symmetric_difference
- pandas.Index.take
- pandas.Index.to_frame
- pandas.Index.to_native_types
- pandas.Index.to_series
- pandas.Index.tolist
- pandas.Index.transpose
- pandas.Index.union
- pandas.Index.unique
- pandas.Index.value_counts
- pandas.Index.where
- Attributes
- pandas.Index.values
- pandas.Index.is_monotonic
- pandas.Index.is_monotonic_increasing
- pandas.Index.is_monotonic_decreasing
- pandas.Index.is_unique
- pandas.Index.has_duplicates
- pandas.Index.hasnans
- pandas.Index.dtype
- pandas.Index.dtype_str
- pandas.Index.inferred_type
- pandas.Index.is_all_dates
- pandas.Index.shape
- pandas.Index.name
- pandas.Index.names
- pandas.Index.nbytes
- pandas.Index.ndim
- pandas.Index.size
- pandas.Index.empty
- pandas.Index.strides
- pandas.Index.itemsize
- pandas.Index.base
- pandas.Index.T
- pandas.Index.memory_usage
- Modifying and Computations
- pandas.Index.all
- pandas.Index.any
- pandas.Index.argmin
- pandas.Index.argmax
- pandas.Index.copy
- pandas.Index.delete
- pandas.Index.drop
- pandas.Index.drop_duplicates
- pandas.Index.duplicated
- pandas.Index.equals
- pandas.Index.factorize
- pandas.Index.identical
- pandas.Index.insert
- pandas.Index.is_
- pandas.Index.is_boolean
- pandas.Index.is_categorical
- pandas.Index.is_floating
- pandas.Index.is_integer
- pandas.Index.is_interval
- pandas.Index.is_lexsorted_for_tuple
- pandas.Index.is_mixed
- pandas.Index.is_numeric
- pandas.Index.is_object
- pandas.Index.min
- pandas.Index.max
- pandas.Index.reindex
- pandas.Index.rename
- pandas.Index.repeat
- pandas.Index.where
- pandas.Index.take
- pandas.Index.putmask
- pandas.Index.set_names
- pandas.Index.unique
- pandas.Index.nunique
- pandas.Index.value_counts
- Missing Values
- Conversion
- Sorting
- Time-specific operations
- Combining / joining / set operations
- Selecting
- pandas.Index.asof
- pandas.Index.asof_locs
- pandas.Index.contains
- pandas.Index.get_duplicates
- pandas.Index.get_indexer
- pandas.Index.get_indexer_for
- pandas.Index.get_indexer_non_unique
- pandas.Index.get_level_values
- pandas.Index.get_loc
- pandas.Index.get_slice_bound
- pandas.Index.get_value
- pandas.Index.get_values
- pandas.Index.set_value
- pandas.Index.isin
- pandas.Index.slice_indexer
- pandas.Index.slice_locs
- pandas.Index
- Numeric Index
- CategoricalIndex
- pandas.CategoricalIndex
- pandas.CategoricalIndex.rename_categories
- pandas.CategoricalIndex.reorder_categories
- pandas.CategoricalIndex.add_categories
- pandas.CategoricalIndex.remove_categories
- pandas.CategoricalIndex.remove_unused_categories
- pandas.CategoricalIndex.set_categories
- pandas.CategoricalIndex.as_ordered
- pandas.CategoricalIndex.as_unordered
- pandas.CategoricalIndex.map
- Categorical Components
- pandas.CategoricalIndex.codes
- pandas.CategoricalIndex.categories
- pandas.CategoricalIndex.ordered
- pandas.CategoricalIndex.rename_categories
- pandas.CategoricalIndex.reorder_categories
- pandas.CategoricalIndex.add_categories
- pandas.CategoricalIndex.remove_categories
- pandas.CategoricalIndex.remove_unused_categories
- pandas.CategoricalIndex.set_categories
- pandas.CategoricalIndex.as_ordered
- pandas.CategoricalIndex.as_unordered
- pandas.CategoricalIndex.map
- pandas.CategoricalIndex
- IntervalIndex
- pandas.IntervalIndex
- pandas.IntervalIndex.closed
- pandas.IntervalIndex.is_non_overlapping_monotonic
- pandas.IntervalIndex.left
- pandas.IntervalIndex.length
- pandas.IntervalIndex.mid
- pandas.IntervalIndex.right
- pandas.IntervalIndex.values
- pandas.IntervalIndex.contains
- pandas.IntervalIndex.from_arrays
- pandas.IntervalIndex.from_breaks
- pandas.IntervalIndex.from_tuples
- pandas.IntervalIndex.get_indexer
- pandas.IntervalIndex.get_loc
- IntervalIndex Components
- pandas.IntervalIndex.from_arrays
- pandas.IntervalIndex.from_tuples
- pandas.IntervalIndex.from_breaks
- pandas.IntervalIndex.contains
- pandas.IntervalIndex.left
- pandas.IntervalIndex.right
- pandas.IntervalIndex.mid
- pandas.IntervalIndex.closed
- pandas.IntervalIndex.length
- pandas.IntervalIndex.values
- pandas.IntervalIndex.is_non_overlapping_monotonic
- pandas.IntervalIndex.get_loc
- pandas.IntervalIndex.get_indexer
- pandas.IntervalIndex
- MultiIndex
- pandas.MultiIndex
- pandas.MultiIndex.names
- pandas.MultiIndex.nlevels
- pandas.MultiIndex.levshape
- pandas.MultiIndex.from_arrays
- pandas.MultiIndex.from_tuples
- pandas.MultiIndex.from_product
- pandas.MultiIndex.set_levels
- pandas.MultiIndex.set_labels
- pandas.MultiIndex.to_hierarchical
- pandas.MultiIndex.to_frame
- pandas.MultiIndex.is_lexsorted
- pandas.MultiIndex.sortlevel
- pandas.MultiIndex.droplevel
- pandas.MultiIndex.swaplevel
- pandas.MultiIndex.reorder_levels
- pandas.MultiIndex.remove_unused_levels
- pandas.IndexSlice
- MultiIndex Constructors
- MultiIndex Attributes
- MultiIndex Components
- pandas.MultiIndex.set_levels
- pandas.MultiIndex.set_labels
- pandas.MultiIndex.to_hierarchical
- pandas.MultiIndex.to_frame
- pandas.MultiIndex.is_lexsorted
- pandas.MultiIndex.sortlevel
- pandas.MultiIndex.droplevel
- pandas.MultiIndex.swaplevel
- pandas.MultiIndex.reorder_levels
- pandas.MultiIndex.remove_unused_levels
- pandas.MultiIndex.unique
- MultiIndex Selecting
- pandas.MultiIndex
- DatetimeIndex
- pandas.DatetimeIndex
- pandas.DatetimeIndex.year
- pandas.DatetimeIndex.month
- pandas.DatetimeIndex.day
- pandas.DatetimeIndex.hour
- pandas.DatetimeIndex.minute
- pandas.DatetimeIndex.second
- pandas.DatetimeIndex.microsecond
- pandas.DatetimeIndex.nanosecond
- pandas.DatetimeIndex.date
- pandas.DatetimeIndex.time
- pandas.DatetimeIndex.dayofyear
- pandas.DatetimeIndex.weekofyear
- pandas.DatetimeIndex.week
- pandas.DatetimeIndex.dayofweek
- pandas.DatetimeIndex.weekday
- pandas.DatetimeIndex.quarter
- pandas.DatetimeIndex.freq
- pandas.DatetimeIndex.freqstr
- pandas.DatetimeIndex.is_month_start
- pandas.DatetimeIndex.is_month_end
- pandas.DatetimeIndex.is_quarter_start
- pandas.DatetimeIndex.is_quarter_end
- pandas.DatetimeIndex.is_year_start
- pandas.DatetimeIndex.is_year_end
- pandas.DatetimeIndex.is_leap_year
- pandas.DatetimeIndex.inferred_freq
- pandas.DatetimeIndex.normalize
- pandas.DatetimeIndex.strftime
- pandas.DatetimeIndex.snap
- pandas.DatetimeIndex.tz_convert
- pandas.DatetimeIndex.tz_localize
- pandas.DatetimeIndex.round
- pandas.DatetimeIndex.floor
- pandas.DatetimeIndex.ceil
- pandas.DatetimeIndex.to_period
- pandas.DatetimeIndex.to_perioddelta
- pandas.DatetimeIndex.to_pydatetime
- pandas.DatetimeIndex.to_series
- pandas.DatetimeIndex.to_frame
- pandas.DatetimeIndex.month_name
- pandas.DatetimeIndex.day_name
- Time/Date Components
- pandas.DatetimeIndex.year
- pandas.DatetimeIndex.month
- pandas.DatetimeIndex.day
- pandas.DatetimeIndex.hour
- pandas.DatetimeIndex.minute
- pandas.DatetimeIndex.second
- pandas.DatetimeIndex.microsecond
- pandas.DatetimeIndex.nanosecond
- pandas.DatetimeIndex.date
- pandas.DatetimeIndex.time
- pandas.DatetimeIndex.dayofyear
- pandas.DatetimeIndex.weekofyear
- pandas.DatetimeIndex.week
- pandas.DatetimeIndex.dayofweek
- pandas.DatetimeIndex.weekday
- pandas.DatetimeIndex.quarter
- pandas.DatetimeIndex.tz
- pandas.DatetimeIndex.freq
- pandas.DatetimeIndex.freqstr
- pandas.DatetimeIndex.is_month_start
- pandas.DatetimeIndex.is_month_end
- pandas.DatetimeIndex.is_quarter_start
- pandas.DatetimeIndex.is_quarter_end
- pandas.DatetimeIndex.is_year_start
- pandas.DatetimeIndex.is_year_end
- pandas.DatetimeIndex.is_leap_year
- pandas.DatetimeIndex.inferred_freq
- Selecting
- Time-specific operations
- Conversion
- pandas.DatetimeIndex
- TimedeltaIndex
- pandas.TimedeltaIndex
- pandas.TimedeltaIndex.days
- pandas.TimedeltaIndex.seconds
- pandas.TimedeltaIndex.microseconds
- pandas.TimedeltaIndex.nanoseconds
- pandas.TimedeltaIndex.components
- pandas.TimedeltaIndex.inferred_freq
- pandas.TimedeltaIndex.to_pytimedelta
- pandas.TimedeltaIndex.to_series
- pandas.TimedeltaIndex.round
- pandas.TimedeltaIndex.floor
- pandas.TimedeltaIndex.ceil
- pandas.TimedeltaIndex.to_frame
- Components
- Conversion
- pandas.TimedeltaIndex
- PeriodIndex
- pandas.PeriodIndex
- pandas.PeriodIndex.day
- pandas.PeriodIndex.dayofweek
- pandas.PeriodIndex.dayofyear
- pandas.PeriodIndex.days_in_month
- pandas.PeriodIndex.daysinmonth
- pandas.PeriodIndex.freq
- pandas.PeriodIndex.freqstr
- pandas.PeriodIndex.hour
- pandas.PeriodIndex.is_leap_year
- pandas.PeriodIndex.minute
- pandas.PeriodIndex.month
- pandas.PeriodIndex.quarter
- pandas.PeriodIndex.second
- pandas.PeriodIndex.week
- pandas.PeriodIndex.weekday
- pandas.PeriodIndex.weekofyear
- pandas.PeriodIndex.year
- pandas.PeriodIndex.asfreq
- pandas.PeriodIndex.strftime
- pandas.PeriodIndex.to_timestamp
- pandas.PeriodIndex.tz_convert
- pandas.PeriodIndex.tz_localize
- Attributes
- pandas.PeriodIndex.day
- pandas.PeriodIndex.dayofweek
- pandas.PeriodIndex.dayofyear
- pandas.PeriodIndex.days_in_month
- pandas.PeriodIndex.daysinmonth
- pandas.PeriodIndex.end_time
- pandas.PeriodIndex.freq
- pandas.PeriodIndex.freqstr
- pandas.PeriodIndex.hour
- pandas.PeriodIndex.is_leap_year
- pandas.PeriodIndex.minute
- pandas.PeriodIndex.month
- pandas.PeriodIndex.quarter
- pandas.PeriodIndex.qyear
- pandas.PeriodIndex.second
- pandas.PeriodIndex.start_time
- pandas.PeriodIndex.week
- pandas.PeriodIndex.weekday
- pandas.PeriodIndex.weekofyear
- pandas.PeriodIndex.year
- Methods
- pandas.PeriodIndex
- Scalars
- Period
- Attributes
- pandas.Period.day
- pandas.Period.dayofweek
- pandas.Period.dayofyear
- pandas.Period.days_in_month
- pandas.Period.daysinmonth
- pandas.Period.end_time
- pandas.Period.freq
- pandas.Period.freqstr
- pandas.Period.hour
- pandas.Period.is_leap_year
- pandas.Period.minute
- pandas.Period.month
- pandas.Period.ordinal
- pandas.Period.quarter
- pandas.Period.qyear
- pandas.Period.second
- pandas.Period.start_time
- pandas.Period.week
- pandas.Period.weekday
- pandas.Period.weekofyear
- pandas.Period.year
- Methods
- Timestamp
- Properties
- pandas.Timestamp.asm8
- pandas.Timestamp.day
- pandas.Timestamp.dayofweek
- pandas.Timestamp.dayofyear
- pandas.Timestamp.days_in_month
- pandas.Timestamp.daysinmonth
- pandas.Timestamp.fold
- pandas.Timestamp.hour
- pandas.Timestamp.is_leap_year
- pandas.Timestamp.is_month_end
- pandas.Timestamp.is_month_start
- pandas.Timestamp.is_quarter_end
- pandas.Timestamp.is_quarter_start
- pandas.Timestamp.is_year_end
- pandas.Timestamp.is_year_start
- pandas.Timestamp.max
- pandas.Timestamp.microsecond
- pandas.Timestamp.min
- pandas.Timestamp.minute
- pandas.Timestamp.month
- pandas.Timestamp.nanosecond
- pandas.Timestamp.quarter
- pandas.Timestamp.resolution
- pandas.Timestamp.second
- pandas.Timestamp.tz
- pandas.Timestamp.tzinfo
- pandas.Timestamp.value
- pandas.Timestamp.week
- pandas.Timestamp.weekofyear
- pandas.Timestamp.year
- Methods
- pandas.Timestamp.astimezone
- pandas.Timestamp.ceil
- pandas.Timestamp.combine
- pandas.Timestamp.ctime
- pandas.Timestamp.date
- pandas.Timestamp.day_name
- pandas.Timestamp.dst
- pandas.Timestamp.floor
- pandas.Timestamp.freq
- pandas.Timestamp.freqstr
- pandas.Timestamp.fromordinal
- pandas.Timestamp.fromtimestamp
- pandas.Timestamp.isocalendar
- pandas.Timestamp.isoformat
- pandas.Timestamp.isoweekday
- pandas.Timestamp.month_name
- pandas.Timestamp.normalize
- pandas.Timestamp.now
- pandas.Timestamp.replace
- pandas.Timestamp.round
- pandas.Timestamp.strftime
- pandas.Timestamp.strptime
- pandas.Timestamp.time
- pandas.Timestamp.timestamp
- pandas.Timestamp.timetuple
- pandas.Timestamp.timetz
- pandas.Timestamp.to_datetime64
- pandas.Timestamp.to_julian_date
- pandas.Timestamp.to_period
- pandas.Timestamp.to_pydatetime
- pandas.Timestamp.today
- pandas.Timestamp.toordinal
- pandas.Timestamp.tz_convert
- pandas.Timestamp.tz_localize
- pandas.Timestamp.tzname
- pandas.Timestamp.utcfromtimestamp
- pandas.Timestamp.utcnow
- pandas.Timestamp.utcoffset
- pandas.Timestamp.utctimetuple
- pandas.Timestamp.weekday
- Interval
- Properties
- Timedelta
- Properties
- pandas.Timedelta.asm8
- pandas.Timedelta.components
- pandas.Timedelta.days
- pandas.Timedelta.delta
- pandas.Timedelta.freq
- pandas.Timedelta.is_populated
- pandas.Timedelta.max
- pandas.Timedelta.microseconds
- pandas.Timedelta.min
- pandas.Timedelta.nanoseconds
- pandas.Timedelta.resolution
- pandas.Timedelta.seconds
- pandas.Timedelta.value
- pandas.Timedelta.view
- Methods
- Frequencies
- Window
- Standard moving window functions
- pandas.core.window.Rolling.count
- pandas.core.window.Rolling.sum
- pandas.core.window.Rolling.mean
- pandas.core.window.Rolling.median
- pandas.core.window.Rolling.var
- pandas.core.window.Rolling.std
- pandas.core.window.Rolling.min
- pandas.core.window.Rolling.max
- pandas.core.window.Rolling.corr
- pandas.core.window.Rolling.cov
- pandas.core.window.Rolling.skew
- pandas.core.window.Rolling.kurt
- pandas.core.window.Rolling.apply
- pandas.core.window.Rolling.aggregate
- pandas.core.window.Rolling.quantile
- pandas.core.window.Window.mean
- pandas.core.window.Window.sum
- Standard expanding window functions
- pandas.core.window.Expanding.count
- pandas.core.window.Expanding.sum
- pandas.core.window.Expanding.mean
- pandas.core.window.Expanding.median
- pandas.core.window.Expanding.var
- pandas.core.window.Expanding.std
- pandas.core.window.Expanding.min
- pandas.core.window.Expanding.max
- pandas.core.window.Expanding.corr
- pandas.core.window.Expanding.cov
- pandas.core.window.Expanding.skew
- pandas.core.window.Expanding.kurt
- pandas.core.window.Expanding.apply
- pandas.core.window.Expanding.aggregate
- pandas.core.window.Expanding.quantile
- Exponentially-weighted moving window functions
- Standard moving window functions
- GroupBy
- Indexing, iteration
- Function application
- Computations / Descriptive Stats
- pandas.core.groupby.GroupBy.all
- pandas.core.groupby.GroupBy.any
- pandas.core.groupby.GroupBy.bfill
- pandas.core.groupby.GroupBy.count
- pandas.core.groupby.GroupBy.cumcount
- pandas.core.groupby.GroupBy.ffill
- pandas.core.groupby.GroupBy.first
- pandas.core.groupby.GroupBy.head
- pandas.core.groupby.GroupBy.last
- pandas.core.groupby.GroupBy.max
- pandas.core.groupby.GroupBy.mean
- pandas.core.groupby.GroupBy.median
- pandas.core.groupby.GroupBy.min
- pandas.core.groupby.GroupBy.ngroup
- pandas.core.groupby.GroupBy.nth
- pandas.core.groupby.GroupBy.ohlc
- pandas.core.groupby.GroupBy.prod
- pandas.core.groupby.GroupBy.rank
- pandas.core.groupby.GroupBy.pct_change
- pandas.core.groupby.GroupBy.size
- pandas.core.groupby.GroupBy.sem
- pandas.core.groupby.GroupBy.std
- pandas.core.groupby.GroupBy.sum
- pandas.core.groupby.GroupBy.var
- pandas.core.groupby.GroupBy.tail
- pandas.core.groupby.DataFrameGroupBy.agg
- pandas.core.groupby.DataFrameGroupBy.all
- pandas.core.groupby.DataFrameGroupBy.any
- pandas.core.groupby.DataFrameGroupBy.bfill
- pandas.core.groupby.DataFrameGroupBy.corr
- pandas.core.groupby.DataFrameGroupBy.count
- pandas.core.groupby.DataFrameGroupBy.cov
- pandas.core.groupby.DataFrameGroupBy.cummax
- pandas.core.groupby.DataFrameGroupBy.cummin
- pandas.core.groupby.DataFrameGroupBy.cumprod
- pandas.core.groupby.DataFrameGroupBy.cumsum
- pandas.core.groupby.DataFrameGroupBy.describe
- pandas.core.groupby.DataFrameGroupBy.diff
- pandas.core.groupby.DataFrameGroupBy.ffill
- pandas.core.groupby.DataFrameGroupBy.fillna
- pandas.core.groupby.DataFrameGroupBy.filter
- pandas.core.groupby.DataFrameGroupBy.hist
- pandas.core.groupby.DataFrameGroupBy.idxmax
- pandas.core.groupby.DataFrameGroupBy.idxmin
- pandas.core.groupby.DataFrameGroupBy.mad
- pandas.core.groupby.DataFrameGroupBy.pct_change
- pandas.core.groupby.DataFrameGroupBy.plot
- pandas.core.groupby.DataFrameGroupBy.quantile
- pandas.core.groupby.DataFrameGroupBy.rank
- pandas.core.groupby.DataFrameGroupBy.resample
- pandas.core.groupby.DataFrameGroupBy.shift
- pandas.core.groupby.DataFrameGroupBy.size
- pandas.core.groupby.DataFrameGroupBy.skew
- pandas.core.groupby.DataFrameGroupBy.take
- pandas.core.groupby.DataFrameGroupBy.tshift
- pandas.core.groupby.SeriesGroupBy.nlargest
- pandas.core.groupby.SeriesGroupBy.nsmallest
- pandas.core.groupby.SeriesGroupBy.nunique
- pandas.core.groupby.SeriesGroupBy.unique
- pandas.core.groupby.SeriesGroupBy.value_counts
- pandas.core.groupby.SeriesGroupBy.is_monotonic_increasing
- pandas.core.groupby.SeriesGroupBy.is_monotonic_decreasing
- pandas.core.groupby.DataFrameGroupBy.corrwith
- pandas.core.groupby.DataFrameGroupBy.boxplot
- Resampling
- Indexing, iteration
- Function application
- Upsampling
- pandas.core.resample.Resampler.ffill
- pandas.core.resample.Resampler.backfill
- pandas.core.resample.Resampler.bfill
- pandas.core.resample.Resampler.pad
- pandas.core.resample.Resampler.nearest
- pandas.core.resample.Resampler.fillna
- pandas.core.resample.Resampler.asfreq
- pandas.core.resample.Resampler.interpolate
- Computations / Descriptive Stats
- pandas.core.resample.Resampler.count
- pandas.core.resample.Resampler.nunique
- pandas.core.resample.Resampler.first
- pandas.core.resample.Resampler.last
- pandas.core.resample.Resampler.max
- pandas.core.resample.Resampler.mean
- pandas.core.resample.Resampler.median
- pandas.core.resample.Resampler.min
- pandas.core.resample.Resampler.ohlc
- pandas.core.resample.Resampler.prod
- pandas.core.resample.Resampler.size
- pandas.core.resample.Resampler.sem
- pandas.core.resample.Resampler.std
- pandas.core.resample.Resampler.sum
- pandas.core.resample.Resampler.var
- Style
- Styler Constructor
- Styler Attributes
- Style Application
- pandas.io.formats.style.Styler.apply
- pandas.io.formats.style.Styler.applymap
- pandas.io.formats.style.Styler.where
- pandas.io.formats.style.Styler.format
- pandas.io.formats.style.Styler.set_precision
- pandas.io.formats.style.Styler.set_table_styles
- pandas.io.formats.style.Styler.set_table_attributes
- pandas.io.formats.style.Styler.set_caption
- pandas.io.formats.style.Styler.set_properties
- pandas.io.formats.style.Styler.set_uuid
- pandas.io.formats.style.Styler.clear
- Builtin Styles
- Style Export and Import
- Plotting
- General utility functions
- Working with options
- Testing functions
- Exceptions and warnings
- Data types related functionality
- pandas.api.types.union_categoricals
- pandas.api.types.infer_dtype
- pandas.api.types.pandas_dtype
- pandas.api.types.is_bool_dtype
- pandas.api.types.is_categorical_dtype
- pandas.api.types.is_complex_dtype
- pandas.api.types.is_datetime64_any_dtype
- pandas.api.types.is_datetime64_dtype
- pandas.api.types.is_datetime64_ns_dtype
- pandas.api.types.is_datetime64tz_dtype
- pandas.api.types.is_extension_type
- pandas.api.types.is_float_dtype
- pandas.api.types.is_int64_dtype
- pandas.api.types.is_integer_dtype
- pandas.api.types.is_interval_dtype
- pandas.api.types.is_numeric_dtype
- pandas.api.types.is_object_dtype
- pandas.api.types.is_period_dtype
- pandas.api.types.is_signed_integer_dtype
- pandas.api.types.is_string_dtype
- pandas.api.types.is_timedelta64_dtype
- pandas.api.types.is_timedelta64_ns_dtype
- pandas.api.types.is_unsigned_integer_dtype
- pandas.api.types.is_sparse
- pandas.api.types.is_dict_like
- pandas.api.types.is_file_like
- pandas.api.types.is_list_like
- pandas.api.types.is_named_tuple
- pandas.api.types.is_iterator
- pandas.api.types.is_bool
- pandas.api.types.is_categorical
- pandas.api.types.is_complex
- pandas.api.types.is_datetimetz
- pandas.api.types.is_float
- pandas.api.types.is_hashable
- pandas.api.types.is_integer
- pandas.api.types.is_interval
- pandas.api.types.is_number
- pandas.api.types.is_period
- pandas.api.types.is_re
- pandas.api.types.is_re_compilable
- pandas.api.types.is_scalar
- Extensions
- pandas.api.extensions.register_dataframe_accessor
- pandas.api.extensions.register_series_accessor
- pandas.api.extensions.register_index_accessor
- pandas.api.extensions.ExtensionDtype
- pandas.api.extensions.ExtensionArray
- pandas.api.extensions.ExtensionArray.dtype
- pandas.api.extensions.ExtensionArray.nbytes
- pandas.api.extensions.ExtensionArray.ndim
- pandas.api.extensions.ExtensionArray.shape
- pandas.api.extensions.ExtensionArray.argsort
- pandas.api.extensions.ExtensionArray.astype
- pandas.api.extensions.ExtensionArray.copy
- pandas.api.extensions.ExtensionArray.factorize
- pandas.api.extensions.ExtensionArray.fillna
- pandas.api.extensions.ExtensionArray.isna
- pandas.api.extensions.ExtensionArray.take
- pandas.api.extensions.ExtensionArray.unique
- Developer
- Internals
- Extending Pandas
- Release Notes
- pandas 0.23.2
- pandas 0.23.1
- pandas 0.23.0
- pandas 0.22.0
- pandas 0.21.1
- pandas 0.21.0
- pandas 0.20.0 / 0.20.1
- pandas 0.19.2
- pandas 0.19.1
- pandas 0.19.0
- pandas 0.18.1
- pandas 0.18.0
- pandas 0.17.1
- pandas 0.17.0
- pandas 0.16.2
- pandas 0.16.1
- pandas 0.16.0
- pandas 0.15.2
- pandas 0.15.1
- pandas 0.15.0
- pandas 0.14.1
- pandas 0.14.0
- pandas 0.13.1
- pandas 0.13.0
- pandas 0.12.0
- pandas 0.11.0
- pandas 0.10.1
- pandas 0.10.0
- pandas 0.9.1
- pandas 0.9.0
- pandas 0.8.1
- pandas 0.8.0
- pandas 0.7.3
- pandas 0.7.2
- pandas 0.7.1
- pandas 0.7.0
- pandas 0.6.1
- pandas 0.6.0
- pandas 0.5.0
- pandas 0.4.3
- pandas 0.4.2
- pandas 0.4.1
- pandas 0.4.0
- pandas 0.3.0