is observed. In [42]: weekmask_egypt = ’Sun Mon Tue Wed Thu’ # They also observe International Workers’ Day so let’s # add that for a couple of years In [43]: holidays = [’2012-05-01’, datetime(2013, data using the interface that pandas provides. See the end of the 0.8.0 section for a “porting” guide listing potential issues for users migrating legacy codebases from pandas 0.7 or earlier to 0.8.0 is observed. In [82]: weekmask_egypt = ’Sun Mon Tue Wed Thu’ # They also observe International Workers’ Day so let’s # add that for a couple of years In [83]: holidays = [’2012-05-01’, datetime(2013,

Python programming language. To the getting started guides To the user guide To the reference guide To the development guide CONTENTS 1 pandas: powerful Python data analysis toolkit, Release 1.0.0 Kleene logic). For example: In [8]: pd.NA | True Out[8]: True For more, see NA section in the user guide on missing data. 1.3.2 Dedicated string data type We’ve added StringDtype, an extension type dedicated install using the pip or conda methods described above. Installing from source See the contributing guide for complete instructions on building from the git source tree. Further, see creating a development

read_excel() supports reading OpenDocument tables. Specify engine='odf' to enable. Consult the IO User Guide for more details (GH9070) • Interval, IntervalIndex, and IntervalArray have gained an is_empty attribute the issue tracker. For more information, see the Python 3 statement and the Porting to Python 3 guide. 2.2 Python version support Officially Python 2.7, 3.5, 3.6, and 3.7. 2.3 Installing pandas 2 using the pip or conda methods described above. 2.3.6 Installing from source See the contributing guide for complete instructions on building from the git source tree. Further, see creating a development

read_excel() supports reading OpenDocument tables. Specify engine='odf' to enable. Consult the IO User Guide for more details (GH9070) • Interval, IntervalIndex, and IntervalArray have gained an is_empty attribute using the pip or conda methods described above. 2.2.6 Installing from source See the contributing guide for complete instructions on building from the git source tree. Further, see creating a development to all of our contributors. If you’re interested in contributing, please visit the contributing guide. pandas is a NumFOCUS sponsored project. This will help ensure the success of development of pandas

tutorials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110 2 User Guide 113 2.1 10 minutes to pandas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . request . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2460 4.2 pandas code style guide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2460 4.2.1 Patterns Python programming language. To the getting started guides To the user guide To the reference guide To the development guide CONTENTS 1 pandas: powerful Python data analysis toolkit, Release 1.1.1

tutorials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110 2 User Guide 113 2.1 10 minutes to pandas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . request . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2460 4.2 pandas code style guide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2460 4.2.1 Patterns Python programming language. To the getting started guides To the user guide To the reference guide To the development guide CONTENTS 1 pandas: powerful Python data analysis toolkit, Release 1.1.0

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222 2 User Guide 225 2.1 IO tools (text, CSV, HDF5, ...) . . . . . . . . . . . . . . . . . . . . . . . . . . . . to pandas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2364 4.2 pandas code style guide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2367 4.2.1 Patterns Python programming language. To the getting started guides To the user guide To the reference guide To the development guide CONTENTS 1 pandas: powerful Python data analysis toolkit, Release 1.0.5

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222 2 User Guide 225 2.1 IO tools (text, CSV, HDF5, ...) . . . . . . . . . . . . . . . . . . . . . . . . . . . . to pandas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2360 4.2 pandas code style guide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2363 4.2.1 Patterns Python programming language. To the getting started guides To the user guide To the reference guide To the development guide CONTENTS 1 pandas: powerful Python data analysis toolkit, Release 1.0.4

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 224 3 User Guide 227 3.1 IO tools (text, CSV, HDF5, ...) . . . . . . . . . . . . . . . . . . . . . . . . . . . . to pandas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2350 5.2 pandas code style guide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2353 5.2.1 Patterns Python programming language. To the getting started guides To the user guide To the reference guide To the development guide CONTENTS 1 pandas: powerful Python data analysis toolkit, Release 1.0.3

58. You can find the Hypothesis docs here, and a pandas- specific introduction in the contributing guide. (GH22280) • Building pandas on macOS now targets minimum macOS 10.9 if run on macOS 10.9 or above the issue tracker. For more information, see the Python 3 statement and the Porting to Python 3 guide. 2.2 Python version support Officially Python 2.7, 3.5, 3.6, and 3.7. 2.3 Installing pandas 2 using the pip or conda methods described above. 2.3.6 Installing from source See the contributing guide for complete instructions on building from the git source tree. Further, see creating a development