Minimum Ver- sion Notes SciPy 1.7.1 Miscellaneous statistical functions numba 0.53.1 Alternative execution engine for rolling operations (see Enhancing Perfor- mance) xarray 0.19.0 pandas-like API for N-dimensional group index will be passed as NumPy arrays to the JITed user defined function, and no alternative execution attempts will be tried. Other useful features Automatic exclusion of “nuisance” columns Again For example, this occurs when each data point is a full time series read from an experiment, and the task is to extract underlying conditions. In these cases it can be useful to perform forward-looking rolling

Minimum Ver- sion Notes SciPy 1.4.1 Miscellaneous statistical functions numba 0.50.1 Alternative execution engine for rolling operations (see Enhancing Perfor- mance) xarray 0.15.1 pandas-like API for N-dimensional group index will be passed as NumPy arrays to the JITed user defined function, and no alternative execution attempts will be tried. 2.18.10 Other useful features Automatic exclusion of “nuisance” columns For example, this occurs when each data point is a full time series read from an experiment, and the task is to extract underlying conditions. In these cases it can be useful to perform forward-looking rolling

Minimum Ver- sion Notes SciPy 1.4.1 Miscellaneous statistical functions numba 0.50.1 Alternative execution engine for rolling operations (see Enhancing Perfor- mance) xarray 0.15.1 pandas-like API for N-dimensional group index will be passed as NumPy arrays to the JITed user defined function, and no alternative execution attempts will be tried. 2.18.10 Other useful features Automatic exclusion of “nuisance” columns For example, this occurs when each data point is a full time series read from an experiment, and the task is to extract underlying conditions. In these cases it can be useful to perform forward-looking rolling

Minimum Ver- sion Notes SciPy 1.12.0 Miscellaneous statistical functions numba 0.46.0 Alternative execution engine for rolling operations (see Enhancing Perfor- mance) xarray 0.12.3 pandas-like API for N-dimensional group index will be passed as NumPy arrays to the JITed user defined function, and no alternative execution attempts will be tried. 2.18.10 Other useful features Automatic exclusion of “nuisance” columns For example, this occurs when each data point is a full time series read from an experiment, and the task is to extract underlying conditions. In these cases it can be useful to perform forward-looking rolling

Minimum Ver- sion Notes SciPy 1.12.0 Miscellaneous statistical functions numba 0.46.0 Alternative execution engine for rolling operations (see Enhancing Perfor- mance) xarray 0.12.3 pandas-like API for N-dimensional group index will be passed as NumPy arrays to the JITed user defined function, and no alternative execution attempts will be tried. 2.18.10 Other useful features Automatic exclusion of “nuisance” columns For example, this occurs when each data point is a full time series read from an experiment, and the task is to extract underlying conditions. In these cases it can be useful to perform forward-looking rolling

Minimum Ver- sion Notes SciPy 1.12.0 Miscellaneous statistical functions numba 0.46.0 Alternative execution engine for rolling operations (see Enhancing Perfor- mance) xarray 0.12.3 pandas-like API for N-dimensional group index will be passed as NumPy arrays to the JITed user defined function, and no alternative execution attempts will be tried. 2.18.10 Other useful features Automatic exclusion of “nuisance” columns For example, this occurs when each data point is a full time series read from an experiment, and the task is to extract underlying conditions. In these cases it can be useful to perform forward-looking rolling

HTML parser for read_html (see note) matplotlib 2.2.3 Visualization numba 0.46.0 Alternative execution engine for rolling operations openpyxl 2.6.0 Reading / writing for xlsx files pandas-gbq 0.12 group index will be passed as NumPy arrays to the JITed user defined function, and no alternative execution attempts will be tried. Note: In terms of performance, the first time a function is run using the For example, this occurs when each data point is a full time series read from an experiment, and the task is to extract underlying conditions. In these cases it can be useful to perform forward-looking rolling

HTML parser for read_html (see note) matplotlib 2.2.3 Visualization numba 0.46.0 Alternative execution engine for rolling operations openpyxl 2.6.0 Reading / writing for xlsx files pandas-gbq 0.12 group index will be passed as NumPy arrays to the JITed user defined function, and no alternative execution attempts will be tried. Note: In terms of performance, the first time a function is run using the For example, this occurs when each data point is a full time series read from an experiment, and the task is to extract underlying conditions. In these cases it can be useful to perform forward-looking rolling

HTML parser for read_html (see note) matplotlib 2.2.2 Visualization numba 0.46.0 Alternative execution engine for rolling operations openpyxl 2.5.7 Reading / writing for xlsx files pandas-gbq 0.12 For example, this occurs when each data point is a full time series read from an experiment, and the task is to extract underlying conditions. In these cases it can be useful to perform forward-looking rolling group index will be passed as numpy arrays to the JITed user defined function, and no alternative execution attempts will be tried. Note: In terms of performance, the first time a function is run using the

HTML parser for read_html (see note) matplotlib 2.2.2 Visualization numba 0.46.0 Alternative execution engine for rolling operations openpyxl 2.5.7 Reading / writing for xlsx files pandas-gbq 0.12 For example, this occurs when each data point is a full time series read from an experiment, and the task is to extract underlying conditions. In these cases it can be useful to perform forward-looking rolling group index will be passed as numpy arrays to the JITed user defined function, and no alternative execution attempts will be tried. Note: In terms of performance, the first time a function is run using the