datetime64[ns] 1.4.5 Dtype Gotchas Platform Gotchas Starting in 0.11.0, construction of DataFrame/Series will use default dtypes of int64 and float64, regardless of platform. This is not an apparent change 9.12.1 defaults By default integer types are int64 and float types are float64, REGARDLESS of platform (32-bit or 64-bit). The following will all result in int64 dtypes. In [238]: DataFrame([1, 2], a int64 dtype: object Numpy, however will choose platform-dependent types when creating arrays. The following WILL result in int32 on 32-bit platform. In [241]: frame = DataFrame(np.array([1, 2])) 9

datetime64[ns] 1.5.5 Dtype Gotchas Platform Gotchas Starting in 0.11.0, construction of DataFrame/Series will use default dtypes of int64 and float64, regardless of platform. This is not an apparent change raw binary file format like this for general data storage is not recommended, as it is not cross platform. We recommended either HDF5 or msgpack, both of which are supported by pandas’ IO facilities. 7 9.12.1 defaults By default integer types are int64 and float types are float64, REGARDLESS of platform (32-bit or 64-bit). The following will all result in int64 dtypes. In [248]: DataFrame([1, 2],

The easiest way to install pandas is to install it as part of the Anaconda distribution, a cross platform distribution for data analysis and scientific computing. This is the recommended installation method packages that make up the SciPy stack (IPython, NumPy, Matplotlib, ...) is with Anaconda, a cross-platform (Linux, macOS, Windows) Python distribution for data analytics and scientific computing. After manager that the Anaconda distribution is built upon. It is a package manager that is both cross- platform and language agnostic (it can play a similar role to a pip and virtualenv combination). Miniconda

datetime64[ns] 1.13.5 Dtype Gotchas Platform Gotchas Starting in 0.11.0, construction of DataFrame/Series will use default dtypes of int64 and float64, regardless of platform. This is not an apparent change majority of users to install pandas is to install it as part of the Anaconda distribution, a cross platform distribution for data analysis and scientific computing. This is the recommended installation method packages that make up the SciPy stack (IPython, NumPy, Matplotlib, ...) is with Anaconda, a cross-platform (Linux, Mac OS X, Windows) Python distribution for data analytics and scientific computing. After

an indexer array, coerce that array to a “platform int”, so that it can be directly used in 3rd party library operations like numpy.take. Previously, a platform int was defined as np.int_ which corresponds the same on many platform, but for 64 bit python on Windows, np.int_ is 32 bits, and np.intp is 64 bits. Changing this behavior improves performance for many operations on that platform. Previous behavior: if tz was set (GH13884) • Bug in Categorical.remove_unused_categories() changes .codes dtype to platform int (GH13261) • Bug in groupby with as_index=False returns all NaN’s when grouping on multiple

an indexer array, coerce that array to a “platform int”, so that it can be directly used in 3rd party library operations like numpy.take. Previously, a platform int was defined as np.int_ which corresponds the same on many platform, but for 64 bit python on Windows, np.int_ is 32 bits, and np.intp is 64 bits. Changing this behavior improves performance for many operations on that platform. Previous behavior: if tz was set (GH13884) • Bug in Categorical.remove_unused_categories() changes .codes dtype to platform int (GH13261) • Bug in groupby with as_index=False returns all NaN’s when grouping on multiple

an indexer array, coerce that array to a “platform int”, so that it can be directly used in 3rd party library operations like numpy.take. Previously, a platform int was defined as np.int_ which corresponds the same on many platform, but for 64 bit python on Windows, np.int_ is 32 bits, and np.intp is 64 bits. Changing this behavior improves performance for many operations on that platform. Previous behavior: if tz was set (GH13884) • Bug in Categorical.remove_unused_categories() changes .codes dtype to platform int (GH13261) • Bug in groupby with as_index=False returns all NaN’s when grouping on multiple

an indexer array, coerce that array to a “platform int”, so that it can be directly used in 3rd party library operations like numpy.take. Previously, a platform int was defined as np.int_ which corresponds the same on many platform, but for 64 bit python on Windows, np.int_ is 32 bits, and np.intp is 64 bits. Changing this behavior improves performance for many operations on that platform. Previous behavior: if tz was set (GH13884) • Bug in Categorical.remove_unused_categories() changes .codes dtype to platform int (GH13261) • Bug in groupby with as_index=False returns all NaN’s when grouping on multiple

an indexer array, coerce that array to a “platform int”, so that it can be directly used in 3rd party library operations like numpy.take. Previously, a platform int was defined as np.int_ which corresponds the same on many platform, but for 64 bit python on Windows, np.int_ is 32 bits, and np.intp is 64 bits. Changing this behavior improves performance for many operations on that platform. Previous behavior: if tz was set (GH13884) • Bug in Categorical.remove_unused_categories() changes .codes dtype to platform int (GH13261) • Bug in groupby with as_index=False returns all NaN’s when grouping on multiple

The easiest way to install pandas is to install it as part of the Anaconda distribution, a cross platform distribution for data analysis and scientific computing. This is the recommended installation method packages that make up the SciPy stack (IPython, NumPy, Matplotlib, ...) is with Anaconda, a cross-platform (Linux, macOS, Windows) Python distribution for data analytics and scientific computing. After manager that the Anaconda distribution is built upon. It is a package manager that is both cross-platform and language agnostic (it can play a similar role to a pip and virtualenv combination). Miniconda