version of Python and set of libraries. Run the following commands from a terminal window: conda create -n name_of_my_env python This will create a minimal environment with only Python installed in it. To put execution engine for rolling operations (see Enhancing Perfor- mance) xarray 0.12.3 pandas-like API for N-dimensional data Excel files Dependency Minimum Version Notes xlrd 1.2.0 Reading Excel xlwt 1.3 take into account the typical orientation of time series and cross-sectional data sets. When using the N-dimensional array (ndarrays) to store 2- and 3-dimensional data, a burden is placed on the user to consider

version of Python and set of libraries. Run the following commands from a terminal window: conda create -n name_of_my_env python This will create a minimal environment with only Python installed in it. To put tabulate 0.8.3 Printing in Markdown-friendly format (see tabulate) xarray 0.8.2 pandas-like API for N-dimensional data xclip Clipboard I/O on linux xlrd 1.1.0 Excel reading xlwt 1.2.0 Excel writing 12 columns] To see the first N rows of a DataFrame, use the head() method with the required number of rows (in this case 8) as argument. Note: Interested in the last N rows instead? pandas also provides

version of Python and set of libraries. Run the following commands from a terminal window: conda create -n name_of_my_env python This will create a minimal environment with only Python installed in it. To put tabulate 0.8.3 Printing in Markdown-friendly format (see tabulate) xarray 0.8.2 pandas-like API for N-dimensional data xclip Clipboard I/O on linux xlrd 1.1.0 Excel reading xlwt 1.2.0 Excel writing 21.0750 NaN S To see the first N rows of a DataFrame, use the head() method with the required number of rows (in this case 8) as argument. Note: Interested in the last N rows instead? pandas also provides

version of Python and set of libraries. Run the following commands from a terminal window: conda create -n name_of_my_env python This will create a minimal environment with only Python installed in it. To put tabulate 0.8.3 Printing in Markdown-friendly format (see tabulate) xarray 0.12.3 pandas-like API for N-dimensional data xclip Clipboard I/O on linux xlrd 1.2.0 Excel reading xlwt 1.3.0 Excel writing take into account the typical orientation of time series and cross-sectional data sets. When using the N-dimensional array (ndarrays) to store 2- and 3-dimensional data, a burden is placed on the user to consider

version of Python and set of libraries. Run the following commands from a terminal window: conda create -n name_of_my_env python This will create a minimal environment with only Python installed in it. To put tabulate 0.8.3 Printing in Markdown-friendly format (see tabulate) xarray 0.12.3 pandas-like API for N-dimensional data xclip Clipboard I/O on linux xlrd 1.2.0 Excel reading xlwt 1.3.0 Excel writing take into account the typical orientation of time series and cross-sectional data sets. When using the N-dimensional array (ndarrays) to store 2- and 3-dimensional data, a burden is placed on the user to consider

integer-arrays is no longer allowed in Timestamp, DatetimeIndex, TimedeltaIndex, use obj + n * obj.freq instead of obj + n (GH22535) • Removed Series.ptp (GH21614) • Removed Series.from_array (GH18258) • Removed from DataFrame.update(), use “errors” instead (GH23585) • Removed the previously deprecated keyword “n” from DatetimeIndex.shift(), TimedeltaIndex. shift(), PeriodIndex.shift(), use “periods” instead (GH22458) version of Python and set of libraries. Run the following commands from a terminal window: conda create -n name_of_my_env python This will create a minimal environment with only Python installed in it. To put

version of Python and set of libraries. Run the following commands from a terminal window: conda create -n name_of_my_env python This will create a minimal environment with only Python installed in it. To put execution engine for rolling operations (see Enhancing Perfor- mance) xarray 0.15.1 pandas-like API for N-dimensional data Excel files Dependency Minimum Version Notes xlrd 2.0.1 Reading Excel xlwt 1.3 take into account the typical orientation of time series and cross-sectional data sets. When using the N-dimensional array (ndarrays) to store 2- and 3-dimensional data, a burden is placed on the user to consider

version of Python and set of libraries. Run the following commands from a terminal window: conda create -n name_of_my_env python This will create a minimal environment with only Python installed in it. To put tabulate 0.8.3 Printing in Markdown-friendly format (see tabulate) xarray 0.8.2 pandas-like API for N-dimensional data xclip Clipboard I/O on linux xlrd 1.1.0 Excel reading xlwt 1.2.0 Excel writing 12 columns] To see the first N rows of a DataFrame, use the head() method with the required number of rows (in this case 8) as argument. Note: Interested in the last N rows instead? pandas also provides

version of Python and set of libraries. Run the following commands from a terminal window: conda create -n name_of_my_env python This will create a minimal environment with only Python installed in it. To put tabulate 0.8.3 Printing in Markdown-friendly format (see tabulate) xarray 0.8.2 pandas-like API for N-dimensional data xclip Clipboard I/O on linux xlrd 1.1.0 Excel reading xlwt 1.2.0 Excel writing 21.0750 NaN S To see the first N rows of a DataFrame, use the head() method with the required number of rows (in this case 8) as argument. Note: Interested in the last N rows instead? pandas also provides

version of Python and set of libraries. Run the following commands from a terminal window: conda create -n name_of_my_env python This will create a minimal environment with only Python installed in it. To put execution engine for rolling operations (see Enhancing Perfor- mance) xarray 0.12.3 pandas-like API for N-dimensional data Excel files Dependency Minimum Version Notes xlrd 1.2.0 Reading Excel xlwt 1.3 take into account the typical orientation of time series and cross-sectional data sets. When using the N-dimensional array (ndarrays) to store 2- and 3-dimensional data, a burden is placed on the user to consider