From e61e32a9cd468d41c440df234975858a31b26009 Mon Sep 17 00:00:00 2001 From: Phani Paladugula Date: Wed, 2 Jul 2025 22:38:40 +0530 Subject: [PATCH 1/2] Update __init__.py Added a quick explanation to better understand for the new users --- pandas/arrays/__init__.py | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) diff --git a/pandas/arrays/__init__.py b/pandas/arrays/__init__.py index b5c1c98da1c78..0b6583594fba1 100644 --- a/pandas/arrays/__init__.py +++ b/pandas/arrays/__init__.py @@ -4,6 +4,22 @@ See :ref:`extending.extension-types` for more. """ +# Explanations for the ExtensionArrays listed below: +# +# ArrowExtensionArray: Wraps an Apache Arrow array for high-performance data handling. +# ArrowStringArray: A specific version for string data backed by Apache Arrow. +# BooleanArray: Stores boolean data (True, False) but with the ability to hold missing values (NA). +# Categorical: For data that belongs to a fixed, finite set of categories. Very memory efficient. +# DatetimeArray: Handles timezone-aware or timezone-naive date and time data. +# FloatingArray: For floating-point (decimal) numbers, with support for missing values. +# IntegerArray: For integer (whole) numbers, with support for missing values. +# IntervalArray: Stores data representing intervals or ranges (e.g., 1-5, 6-10). +# NumpyExtensionArray: A wrapper around a standard NumPy array, for compatibility within the extension system. +# PeriodArray: For data representing regular time periods (e.g., months, quarters, years). +# SparseArray: Memory-efficient array for data that is mostly zero or NA. +# StringArray: Dedicated array for handling string data, with support for missing values. +# TimedeltaArray: For data representing durations or differences in time (e.g., 2 days, 5 hours). + from pandas.core.arrays import ( ArrowExtensionArray, ArrowStringArray, From 6e7edaf9bc3adfd01d807386dac152aca5e17862 Mon Sep 17 00:00:00 2001 From: Phani Paladugula Date: Wed, 2 Jul 2025 22:50:34 +0530 Subject: [PATCH 2/2] Update __init__.py DOC: Wrap long comment lines to fix E501 error --- pandas/arrays/__init__.py | 33 ++++++++++++++++++++++++++------- 1 file changed, 26 insertions(+), 7 deletions(-) diff --git a/pandas/arrays/__init__.py b/pandas/arrays/__init__.py index 0b6583594fba1..7302e4acf85d4 100644 --- a/pandas/arrays/__init__.py +++ b/pandas/arrays/__init__.py @@ -7,18 +7,37 @@ # Explanations for the ExtensionArrays listed below: # # ArrowExtensionArray: Wraps an Apache Arrow array for high-performance data handling. +# # ArrowStringArray: A specific version for string data backed by Apache Arrow. -# BooleanArray: Stores boolean data (True, False) but with the ability to hold missing values (NA). -# Categorical: For data that belongs to a fixed, finite set of categories. Very memory efficient. +# +# BooleanArray: Stores boolean data (True, False) but with the +# ability to hold missing values (NA). +# +# Categorical: For data that belongs to a fixed, finite set of +# categories. Very memory efficient. +# # DatetimeArray: Handles timezone-aware or timezone-naive date and time data. -# FloatingArray: For floating-point (decimal) numbers, with support for missing values. +# +# FloatingArray: For floating-point (decimal) numbers, with support +# for missing values. +# # IntegerArray: For integer (whole) numbers, with support for missing values. +# # IntervalArray: Stores data representing intervals or ranges (e.g., 1-5, 6-10). -# NumpyExtensionArray: A wrapper around a standard NumPy array, for compatibility within the extension system. -# PeriodArray: For data representing regular time periods (e.g., months, quarters, years). +# +# NumpyExtensionArray: A wrapper around a standard NumPy array, for compatibility +# within the extension system. +# +# PeriodArray: For data representing regular time periods (e.g., months, +# quarters, years). +# # SparseArray: Memory-efficient array for data that is mostly zero or NA. -# StringArray: Dedicated array for handling string data, with support for missing values. -# TimedeltaArray: For data representing durations or differences in time (e.g., 2 days, 5 hours). +# +# StringArray: Dedicated array for handling string data, with support +# for missing values. +# +# TimedeltaArray: For data representing durations or differences in time +# (e.g., 2 days, 5 hours). from pandas.core.arrays import ( ArrowExtensionArray,