datetime64[ns] 1.2.5 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 as flexible containers for lower dimensional data. For example, DataFrame is a container for Series, and Panel is a container for DataFrame objects. We would like to be able to insert and remove objects [5]: df.fo df.foo1 df.foo2 7.3 Panel Panel is a somewhat less-used, but still important container for 3-dimensional data. The term panel data is derived from econometrics and is partially responsible

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 as flexible containers for lower dimensional data. For example, DataFrame is a container for Series, and Panel is a container for DataFrame objects. We would like to be able to insert and remove objects analysis toolkit, Release 0.13.1 8.3 Panel Panel is a somewhat less-used, but still important container for 3-dimensional data. The term panel data is derived from econometrics and is partially responsible

homogeneous data had its own labels and extra care was necessary to keep those in sync with the parent container’s labels. This should not have any visible user/API behavior changes (GH6745) 1.1.1 API changes 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 as flexible containers for lower dimensional data. For example, DataFrame is a container for Series, and Panel is a container for DataFrame objects. We would like to be able to insert and remove objects

a new type of index object that is useful for supporting indexing with dupli- cates. This is a container around a Categorical (introduced in v0.15.0) and allows efficient indexing and storage of an index homogeneous data had its own labels and extra care was necessary to keep those in sync with the parent container’s labels. This should not have any visible user/API behavior changes (GH6745) 1.9.1 API changes 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

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 popular 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 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). 5 pandas:

homogeneous data had its own labels and extra care was necessary to keep those in sync with the parent container’s labels. This should not have any visible user/API behavior changes (GH6745) 1.5.1 API changes datetime64[ns] 1.9.5 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

homogeneous data had its own labels and extra care was necessary to keep those in sync with the parent container’s labels. This should not have any visible user/API behavior changes (GH6745) 1.4.1 API changes datetime64[ns] 1.8.5 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

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