Table Of Contents
- What’s New
- Installation
- Contributing to pandas
- Package overview
- 10 Minutes to pandas
- Tutorials
- Cookbook
- Intro to Data Structures
- Essential Basic Functionality
- Working with Text Data
- Options and Settings
- Indexing and Selecting Data
- MultiIndex / Advanced Indexing
- Computational tools
- Working with missing data
- Group By: split-apply-combine
- Merge, join, and concatenate
- Reshaping and Pivot Tables
- Time Series / Date functionality
- Time Deltas
- Categorical Data
- Visualization
- Styling
- IO Tools (Text, CSV, HDF5, ...)
- Remote Data Access
- 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
- API Reference
- Input/Output
- General functions
- Series
- DataFrame
- Panel
- Index
- Numeric Index
- CategoricalIndex
- IntervalIndex
- MultiIndex
- DatetimeIndex
- TimedeltaIndex
- PeriodIndex
- Scalars
- Window
- GroupBy
- Resampling
- Style
- 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
- Developer
- Internals
- Release Notes
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pandas.api.types.is_period¶
-
pandas.api.types.
is_period
(arr)[source]¶ Check whether an array-like is a periodical index.
Parameters: arr : array-like
The array-like to check.
Returns: boolean : Whether or not the array-like is a periodical index.
Examples
>>> is_period([1, 2, 3]) False >>> is_period(pd.Index([1, 2, 3])) False >>> is_period(pd.PeriodIndex(["2017-01-01"], freq="D")) True