One way you could be encountering this error is if you have multiple Python installations on your system and you don’t have pandas installed in the Python installation you’re currently using. In Linux/Mac installation you’re using. If it’s something like “/usr/bin/python”, you’re using the Python from the system, which is not recommended. It is highly recommended to use conda, for quick installation and for pyarrow using conda. The following is a summary of the environment in which read_orc() can work. System Conda PyPI Linux Successful Failed(pyarrow==3.0 Successful) macOS Successful Failed Windows Failed

One way you could be encountering this error is if you have multiple Python installations on your system and you don’t have pandas installed in the Python installation you’re currently using. In Linux/Mac installation you’re using. If it’s something like “/usr/bin/python”, you’re using the Python from the system, which is not recommended. It is highly recommended to use conda, for quick installation and for pyarrow using conda. The following is a summary of the environment in which read_orc() can work. System Conda PyPI Linux Successful Failed(pyarrow==3.0 Successful) macOS Successful Failed Windows Failed

The returned dtype of unique() now matches the input dtype. (GH27874) • Changed the default configuration value for options.matplotlib.register_converters from True to "auto" (GH18720). Now, pandas custom underlying array is a numpy.ndarray. However, pandas and 3rd party libraries may extend NumPy’s type system to add support for custom arrays (see dtypes). To get the actual data inside a Index or Series, does not support timezone-aware datetimes). Pandas and third-party libraries extend NumPy’s type system in a few places. This section describes the extensions pandas has made internally. See Extension

One way you could be encountering this error is if you have multiple Python installations on your system and you don’t have pandas installed in the Python installation you’re currently using. In Linux/Mac installation you’re using. If it’s something like “/usr/bin/python”, you’re using the Python from the system, which is not recommended. It is highly recommended to use conda, for quick installation and for dtype('float64') This is often a NumPy dtype. However, pandas and 3rd-party libraries extend NumPy’s type system in a few places, in which case the dtype would be an ExtensionDtype. Some examples within pandas

One way you could be encountering this error is if you have multiple Python installations on your system and you don’t have pandas installed in the Python installation you’re currently using. In Linux/Mac installation you’re using. If it’s something like “/usr/bin/python”, you’re using the Python from the system, which is not recommended. It is highly recommended to use conda, for quick installation and for dtype('float64') This is often a NumPy dtype. However, pandas and 3rd-party libraries extend NumPy’s type system in a few places, in which case the dtype would be an ExtensionDtype. Some examples within pandas

One way you could be encountering this error is if you have multiple Python installations on your system and you don’t have pandas installed in the Python installation you’re currently using. In Linux/Mac installation you’re using. If it’s something like “/usr/bin/python”, you’re using the Python from the system, which is not recommended. It is highly recommended to use conda, for quick installation and for Notes tz- data 2022.1(pypi)/ 2022a(for system tzdata) Allows the use of zoneinfo timezones with pandas. Note: You only need to install the pypi package if your system does not already provide the IANA tz

One way you could be encountering this error is if you have multiple Python installations on your system and you don’t have pandas installed in the Python installation you’re currently using. In Linux/Mac installation you’re using. If it’s something like “/usr/bin/python”, you’re using the Python from the system, which is not recommended. It is highly recommended to use conda, for quick installation and for pyarrow using conda. The following is a summary of the environment in which read_orc() can work. System Conda PyPI Linux Successful Failed(pyarrow==3.0 Successful) macOS Successful Failed Windows Failed

Locations and Names . . . . . . . . . . . . . . . . . . . . . . . 1028 24.1.1.3 General Parsing Configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1029 24.1.1.4 NA and Missing Data Handling Improved the import time of pandas by about 2.25x. (GH16764) • Support for PEP 519 – Adding a file system path protocol on most readers (e.g. read_csv()) and writers (e.g. DataFrame.to_csv()) (GH13823). __fspath__ method to pd.HDFStore, pd.ExcelFile, and pd.ExcelWriter to work prop- erly with the file system path protocol (GH13823). • The validate argument for merge() now checks whether a merge is one-to-one

One way you could be encountering this error is if you have multiple Python installations on your system and you don’t have pandas installed in the Python installation you’re currently using. In Linux/Mac installation you’re using. If it’s something like “/usr/bin/python”, you’re using the Python from the system, which is not recommended. It is highly recommended to use conda, for quick installation and for pyarrow using conda. The following is a summary of the environment in which read_orc() can work. System Conda PyPI Linux Successful Failed(pyarrow==3.0 Successful) macOS Successful Failed Windows Failed

One way you could be encountering this error is if you have multiple Python installations on your system and you don’t have pandas installed in the Python installation you’re currently using. In Linux/Mac installation you’re using. If it’s something like “/usr/bin/python”, you’re using the Python from the system, which is not recommended. It is highly recommended to use conda, for quick installation and for pyarrow using conda. The following is a summary of the environment in which read_orc() can work. System Conda PyPI Linux Successful Failed(pyarrow==3.0 Successful) macOS Successful Failed Windows Failed