display. a wider frame will trigger a summary view, unless ex- pand_repr is True and HTML output is disabled. • max_rows: max dataframe rows display. a longer frame will trigger a summary view. • width: characters, used to determine the width of lines when expand_repr is active, Setting this to None will trigger auto-detection of terminal width, this only works for proper terminals, not IPython frontends such

are restricted to lie between -127 and 100 in Stata, and so variables with values above 100 will trigger a conversion to int16. nan values in floating points data types are stored as the basic missing data these checks fail then you can comment: @github-actions pre-commit on that pull request. This will trigger a workflow which will autofix formatting errors. Delete your merged branch (optional) Once your branch from a newer branch then you can comment: @meeseeksdev backport version-branch This will trigger a workflow which will backport a given change to a branch (e.g. @meeseeksdev backport 1.2.x) 4.6

) 401 pandas: powerful Python data analysis toolkit, Release 1.3.3 with values above 100 will trigger a conversion to int16. nan values in floating points data types are stored as the basic missing data these checks fail then you can comment: @github-actions pre-commit on that pull request. This will trigger a workflow which will autofix formatting errors. 2696 Chapter 4. Development pandas: powerful Python branch from a newer branch then you can comment: @meeseeksdev backport version-branch This will trigger a workflow which will backport a given change to a branch (e.g. @meeseeksdev backport 1.2.x) 4.6

) 401 pandas: powerful Python data analysis toolkit, Release 1.3.4 with values above 100 will trigger a conversion to int16. nan values in floating points data types are stored as the basic missing data these checks fail then you can comment: @github-actions pre-commit on that pull request. This will trigger a workflow which will autofix formatting errors. 2696 Chapter 4. Development pandas: powerful Python branch from a newer branch then you can comment: @meeseeksdev backport version-branch This will trigger a workflow which will backport a given change to a branch (e.g. @meeseeksdev backport 1.2.x) 4.6

) 403 pandas: powerful Python data analysis toolkit, Release 1.4.2 with values above 100 will trigger a conversion to int16. nan values in floating points data types are stored as the basic missing data these checks fail then you can comment: @github-actions pre-commit on that pull request. This will trigger a workflow which will autofix formatting errors. To automatically fix formatting errors on each branch from a newer branch then you can comment: @meeseeksdev backport version-branch This will trigger a workflow which will backport a given change to a branch (e.g. @meeseeksdev backport 1.2.x) 4.6

are restricted to lie between -127 and 100 in Stata, and so variables with values above 100 will trigger a conversion to int16. nan values in floating points data types are stored as the basic missing data these checks fail then you can comment: @github-actions pre-commit on that pull request. This will trigger a workflow which will autofix formatting errors. To automatically fix formatting errors on each branch from a newer branch then you can comment: @meeseeksdev backport version-branch This will trigger a workflow which will backport a given change to a branch (e.g. @meeseeksdev backport 1.2.x) 4.6

are restricted to lie between -127 and 100 in Stata, and so variables with values above 100 will trigger a conversion to int16. nan values in floating points data types are stored as the basic missing data can also write a Github comment in the merged pull request to trigger the backport: @meeseeksdev backport version-branch This will trigger a workflow which will backport a given change to a branch (e explicit categories= that differed from that in the Series created an invalid object which could trigger segfaults. (GH25318) • Fixed regression in to_timedelta() losing precision when converting floating

are restricted to lie between -127 and 100 in Stata, and so variables with values above 100 will trigger a conversion to int16. nan values in floating points data types are stored as the basic missing data explicit categories= that differed from that in the Series created an invalid object which could trigger segfaults. (GH25318) • Fixed regression in to_timedelta() losing precision when converting floating now show a FutureWarning. In the future this will raise a KeyError (GH15747). This warning will trigger on a DataFrame or a Series for using .loc[] or [[]] when passing a list-of-labels with at least 1

are restricted to lie between -127 and 100 in Stata, and so variables with values above 100 will trigger a conversion to int16. nan values in floating points data types are stored as the basic missing data explicit categories= that differed from that in the Series created an invalid object which could trigger segfaults. (GH25318) • Fixed regression in to_timedelta() losing precision when converting floating now show a FutureWarning. In the future this will raise a KeyError (GH15747). This warning will trigger on a DataFrame or a Series for using .loc[] or [[]] when passing a list-of-labels with at least 1

are restricted to lie between -127 and 100 in Stata, and so variables with values above 100 will trigger a conversion to int16. nan values in floating points data types are stored as the basic missing data explicit categories= that differed from that in the Series created an invalid object which could trigger segfaults. (GH25318) • Fixed regression in to_timedelta() losing precision when converting floating now show a FutureWarning. In the future this will raise a KeyError (GH15747). This warning will trigger on a DataFrame or a Series for using .loc[] or [[]] when passing a list-of-labels with at least 1