release. Some of the result attributes may change names in order to foster naming consistency with the rest of statsmodels. We will provide every effort to provide compatibility with older versions of pandas read_csv(StringIO(lines), index_col=0, parse_dates=True)[::-1] 136 /Library/Frameworks/EPD64.framework/Versions/7.3/lib/python2.7/urllib.pyc in urlopen(url, data, proxies) 84 opener = _urlopener 85 return opener.open(url) 87 else: 88 return opener.open(url, data) /Library/Frameworks/EPD64.framework/Versions/7.3/lib/python2.7/urllib.pyc in open(self, fullurl, data) 205 try: 206 if data is None:

has been changed from set_axis(axis, labels) to set_axis(labels, axis=0), for consistency with the rest of the API. The old signature is deprecated and will show a FutureWarning (GH14636) • Series.argmin() 23 version of cython to avoid problems with character encodings (GH14699) • Switched the test framework to use pytest (GH13097) • Reorganization of tests directory layout (GH14854, GH15707). 1.5.4 Deprecations Before, if NaN values were present in the Series, calling .str.cat() on it would return NaN, unlike the rest of the Series.str.* API. This behavior has been amended to ignore NaN values by default. (GH11435)

23 version of cython to avoid problems with character encodings (GH14699) • Switched the test framework to use pytest (GH13097) • Reorganization of tests directory layout (GH14854, GH15707). 1.3.4 Deprecations Before, if NaN values were present in the Series, calling .str.cat() on it would return NaN, unlike the rest of the Series.str.* API. This behavior has been amended to ignore NaN values by default. (GH11435) 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.

23 version of cython to avoid problems with character encodings (GH14699) • Switched the test framework to use pytest (GH13097) • Reorganization of tests directory layout (GH14854, GH15707). 1.2.4 Deprecations Before, if NaN values were present in the Series, calling .str.cat() on it would return NaN, unlike the rest of the Series.str.* API. This behavior has been amended to ignore NaN values by default. (GH11435) 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.

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

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