network performance. Here we recommend the following steps to implement: 1. Remove UUID and cell_ids Ignore the uuid and set cell_ids to False. This will prevent unnecessary HTML. This is sub-optimal: s4 = df4.style This is better: [35]: from pandas.io.formats.style import Styler s4 = Styler(df4, uuid_len=0, cell_ids=False) 2. Use table styles Use table styles where possible (e.g. for all cells or HTML, or shorten the default class names with string replace functions. [40]: html = Styler(df4, uuid_len=0, cell_ids=False)\ .set_table_styles([{'selector': 'td', 'props': props}, {'selector': '.col1'

network per- formance. Here we recommend the following steps to implement: 1. Remove UUID and cell_ids Ignore the uuid and set cell_ids to False. This will prevent unnecessary HTML. This is sub-optimal: s4 = df4.style This is better: [35]: from pandas.io.formats.style import Styler s4 = Styler(df4, uuid_len=0, cell_ids=False) 2. Use table styles Use table styles where possible (e.g. for all cells or HTML, or shorten the default class names with string replace functions. [40]: html = Styler(df4, uuid_len=0, cell_ids=False)\ .set_table_styles([{'selector': 'td', 'props': props}, {'selector': '.col1'

network per- formance. Here we recommend the following steps to implement: 1. Remove UUID and cell_ids Ignore the uuid and set cell_ids to False. This will prevent unnecessary HTML. This is sub-optimal: s4 = df4.style This is better: [35]: from pandas.io.formats.style import Styler s4 = Styler(df4, uuid_len=0, cell_ids=False) 2. Use table styles Use table styles where possible (e.g. for all cells or HTML, or shorten the default class names with string replace functions. [40]: html = Styler(df4, uuid_len=0, cell_ids=False)\ .set_table_styles([{'selector': 'td', 'props': props}, {'selector': '.col1'

network per- formance. Here we recommend the following steps to implement: 1. Remove UUID and cell_ids Ignore the uuid and set cell_ids to False. This will prevent unnecessary HTML. This is sub-optimal: Release 1.4.2 This is better: [39]: from pandas.io.formats.style import Styler s4 = Styler(df4, uuid_len=0, cell_ids=False) 2. Use table styles Use table styles where possible (e.g. for all cells or "row": "r", "col_trim": "", "row_trim": "", "level": "l", "data": "", "blank": "", } html = Styler(df4, uuid_len=0, cell_ids=False) html.set_table_styles([{'selector': 'td', 'props': props}, {'selector': '.c1'

network per- formance. Here we recommend the following steps to implement: 1. Remove UUID and cell_ids Ignore the uuid and set cell_ids to False. This will prevent unnecessary HTML. This is sub-optimal: Release 1.4.4 This is better: [39]: from pandas.io.formats.style import Styler s4 = Styler(df4, uuid_len=0, cell_ids=False) 2. Use table styles Use table styles where possible (e.g. for all cells or "row": "r", "col_trim": "", "row_trim": "", "level": "l", "data": "", "blank": "", } html = Styler(df4, uuid_len=0, cell_ids=False) html.set_table_styles([{'selector': 'td', 'props': props}, {'selector': '.c1'

network per- formance. Here we recommend the following steps to implement: 1. Remove UUID and cell_ids Ignore the uuid and set cell_ids to False. This will prevent unnecessary HTML. This is sub-optimal: Release 1.5.0rc0 This is better: [39]: from pandas.io.formats.style import Styler s4 = Styler(df4, uuid_len=0, cell_ids=False) 2. Use table styles Use table styles where possible (e.g. for all cells or "row": "r", "col_trim": "", "row_trim": "", "level": "l", "data": "", "blank": "", } html = Styler(df4, uuid_len=0, cell_ids=False) html.set_table_styles([{'selector': 'td', 'props': props}, {'selector': '.c1'

row/column identifier with a UUID unique to each DataFrame so that the style from one doesn’t collide with the styling from another within the same notebook or page (you can set the uuid if you’d like to tie together formats.style.Styler class pandas.io.formats.style.Styler(data, precision=None, table_styles=None, uuid=None, caption=None, table_attributes=None) Helps style a DataFrame or Series according to the data precision table_styles: list-like, default None list of {selector: (attr, value)} dicts; see Notes uuid: str, default None a unique identifier to avoid CSS collisons; generated automatically caption:

row/column identifier with a UUID unique to each DataFrame so that the style from one doesn’t collide with the styling from another within the same notebook or page (you can set the uuid if you’d like to tie together formats.style.Styler class pandas.io.formats.style.Styler(data, precision=None, table_styles=None, uuid=None, caption=None, table_attributes=None) Helps style a DataFrame or Series according to the data precision table_styles: list-like, default None list of {selector: (attr, value)} dicts; see Notes uuid: str, default None a unique identifier to avoid CSS collisons; generated automatically caption:

row/column identifier with a UUID unique to each DataFrame so that the style from one doesn’t collide with the styling from another within the same notebook or page (you can set the uuid if you’d like to tie together table or column based styles where possible to limit overall HTML length, as well as setting a shorter UUID to avoid unnecessary repeated data transmission. Terms • Style function: a function that’s passed formats.style.Styler(data, precision=None, table_styles=None, uuid=None, caption=None, table_attributes=None, cell_ids=True, na_rep=None, uuid_len=5) Helps style a DataFrame or Series according to the data

row/column identifier with a UUID unique to each DataFrame so that the style from one doesn’t collide with the styling from another within the same notebook or page (you can set the uuid if you’d like to tie together table or column based styles where possible to limit overall HTML length, as well as setting a shorter UUID to avoid unnecessary repeated data transmission. Terms • Style function: a function that’s passed formats.style.Styler(data, precision=None, table_styles=None, uuid=None, caption=None, table_attributes=None, cell_ids=True, na_rep=None, uuid_len=5) Helps style a DataFrame or Series according to the data