1.11.1 New features • Add to_clipboard function to pandas namespace for writing objects to the system clipboard (GH774) • Add itertuples method to DataFrame for iterating through the rows of a dataframe Anaconda you should definitely read the gotchas about HTML parsing libraries Note: – if you’re on a system with apt-get you can do sudo apt-get build-dep python-lxml to get the necessary dependencies for running Python. To deal with this issue you should convert the underlying NumPy array to the native system byte order before passing it to Series/DataFrame/Panel constructors using something similar to the

1.31.1 New features • Add to_clipboard function to pandas namespace for writing objects to the system clipboard (GH774) • Add itertuples method to DataFrame for iterating through the rows of a dataframe 2.1, 4.1.3 and 4.0.2 have been confirmed for 64 and 32-bit Ubuntu/Debian Note: – if you’re on a system with apt-get you can do sudo apt-get build-dep python-lxml to get the necessary dependencies for pandas from your development directory. Thus, you can always be using the development version on your system without being inside the clone directory. 3.4 Contributing to the documentation If you’re not the

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

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 be raised. For more, see here (GH16270) • Added support for PEP 518 (pyproject.toml) to the build system (GH16745) • RangeIndex.append() now returns a RangeIndex object when possible (GH16212) • Series

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 EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY

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 EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY

1.1.1 New features • Add to_clipboard function to pandas namespace for writing objects to the system clipboard (#774) • Add itertuples method to DataFrame for iterating through the rows of a dataframe EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY

2 1.2.1 New features • Add to_clipboard function to pandas namespace for writing objects to the system clipboard (GH774) • Add itertuples method to DataFrame for iterating through the rows of a dataframe EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY