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 Release 1.0.0 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 a ExtensionDtype. Some examples within pandas are

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

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 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 a ExtensionDtype. Some examples within pandas are

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 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 a ExtensionDtype. Some examples within pandas are

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 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 a ExtensionDtype. Some examples within pandas are

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

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