argsort() now places missing values at the end of the array, making it consistent with NumPy and the rest of pandas (GH21801). In [60]: cat = pd.Categorical(['b', None, 'a'], categories=['a', 'b'], ordered=True) 5, 3.6, and 3.7. 2.3 Installing pandas 2.3.1 Installing with Anaconda Installing pandas and the rest of the NumPy and SciPy stack can be a little difficult for inexperienced users. The simplest way and scientific computing. After running the installer, the user will have access to pandas and the rest of the SciPy stack without needing to install anything else, and without needing to wait for any software

argsort() now places missing values at the end of the array, making it consistent with NumPy and the rest of pandas (GH21801). In [60]: cat = pd.Categorical(['b', None, 'a'], categories=['a', 'b'], ordered=True) above, 3.6, and 3.7. 2.2 Installing pandas 2.2.1 Installing with Anaconda Installing pandas and the rest of the NumPy and SciPy stack can be a little difficult for inexperienced users. The simplest way and scientific computing. After running the installer, the user will have access to pandas and the rest of the SciPy stack without needing to install anything else, and without needing to wait for any software

above, 3.7, and 3.8. 2.1.2 Installing pandas Installing with Anaconda Installing pandas and the rest of the NumPy and SciPy stack can be a little difficult for inexperienced users. The simplest way and scientific computing. After running the installer, the user will have access to pandas and the rest of the SciPy stack without needing to install anything else, and without needing to wait for any software As you can see, the columns A, B, C, and D are automatically tab completed. E is there as well; the rest of the attributes have been truncated for brevity. 52 Chapter 2. Getting started pandas: powerful

been refactored to no longer sub-class ndarray but instead subclass PandasObject, similarly to the rest of the pandas objects. This change allows very easy sub-classing and creation of new index types. been refactored to no longer sub-class ndarray but instead subclass PandasObject, similarly to the rest of the pandas objects. This change allows very easy sub-classing and cre- ation of new index types name propogation in TimeGrouper/resample (GH4161) • TimeGrouper has a more compatible API to the rest of the groupers (e.g. groups was missing) (GH3881) • Bug in multiple grouping with a TimeGrouper

been refactored to no longer sub-class ndarray but instead subclass PandasObject, similarly to the rest of the pandas objects. This change allows very easy sub-classing and creation of new index types. been refactored to no longer sub-class ndarray but instead subclass PandasObject, similarly to the rest of the pandas objects. This change allows very easy sub-classing and cre- ation of new index types name propogation in TimeGrouper/resample (GH4161) • TimeGrouper has a more compatible API to the rest of the groupers (e.g. groups was missing) (GH3881) • Bug in multiple grouping with a TimeGrouper

extension array (GH21978, GH19056, GH22835). To conform to this interface and for consistency with the rest of pandas, some API breaking changes were made: • SparseArray is no longer a subclass of numpy.ndarray 5, 3.6, and 3.7. 2.3 Installing pandas 2.3.1 Installing with Anaconda Installing pandas and the rest of the NumPy and SciPy stack can be a little difficult for inexperienced users. The simplest way and scientific computing. After running the installer, the user will have access to pandas and the rest of the SciPy stack without needing to install anything else, and without needing to wait for any software

1 and above, 3.7, and 3.8. Installing pandas Installing with Anaconda Installing pandas and the rest of the NumPy and SciPy stack can be a little difficult for inexperienced users. The simplest way and scientific computing. After running the installer, the user will have access to pandas and the rest of the SciPy stack without needing to install anything else, and without needing to wait for any software As you can see, the columns A, B, C, and D are automatically tab completed. E is there as well; the rest of the attributes have been truncated for brevity. 14 Chapter 1. Getting started pandas: powerful

1 and above, 3.7, and 3.8. Installing pandas Installing with Anaconda Installing pandas and the rest of the NumPy and SciPy stack can be a little difficult for inexperienced users. The simplest way and scientific computing. After running the installer, the user will have access to pandas and the rest of the SciPy stack without needing to install anything else, and without needing to wait for any software As you can see, the columns A, B, C, and D are automatically tab completed. E is there as well; the rest of the attributes have been truncated for brevity. 14 Chapter 1. Getting started pandas: powerful

1 and above, 3.7, and 3.8. Installing pandas Installing with Anaconda Installing pandas and the rest of the NumPy and SciPy stack can be a little difficult for inexperienced users. The simplest way and scientific computing. After running the installer, the user will have access to pandas and the rest of the SciPy stack without needing to install anything else, and without needing to wait for any software As you can see, the columns A, B, C, and D are automatically tab completed. E is there as well; the rest of the attributes have been truncated for brevity. 16 Chapter 2. Getting started pandas: powerful

been refactored to no longer sub-class ndarray but instead subclass PandasObject, similarly to the rest of the pandas objects. This change allows very easy sub-classing and creation of new index types. been refactored to no longer sub-class ndarray but instead subclass PandasObject, similarly to the rest of the pandas objects. This change allows very easy sub-classing and cre- ation of new index types name propogation in TimeGrouper/resample (GH4161) • TimeGrouper has a more compatible API to the rest of the groupers (e.g. groups was missing) (GH3881) • Bug in multiple grouping with a TimeGrouper