From 131e05c4f7562496f67a80c7fbf61ce461a6f0ed Mon Sep 17 00:00:00 2001 From: rich-iannone Date: Mon, 11 Dec 2023 15:12:18 +0000 Subject: [PATCH] deploy: 08bbbb6b02a3f28f6afe3cde480018061ae7614a --- examples-qmd/GT.html | 94 +++--- examples-qmd/fmt-number.html | 96 +++--- examples-qmd/table-manipulation.html | 188 +++++------ examples/index.html | 378 ++++++++++----------- get-started/basic-column-labels.html | 282 ++++++++-------- get-started/basic-formatting.html | 470 +++++++++++++-------------- get-started/basic-header.html | 284 ++++++++-------- get-started/basic-stub.html | 282 ++++++++-------- get-started/column-selection.html | 376 ++++++++++----------- get-started/index.html | 284 ++++++++-------- reference/GT.cols_align.html | 94 +++--- reference/GT.cols_label.html | 188 +++++------ reference/GT.cols_move.html | 94 +++--- reference/GT.cols_move_to_end.html | 94 +++--- reference/GT.cols_move_to_start.html | 188 +++++------ reference/GT.fmt_bytes.html | 94 +++--- reference/GT.fmt_currency.html | 94 +++--- reference/GT.fmt_date.html | 94 +++--- reference/GT.fmt_integer.html | 94 +++--- reference/GT.fmt_number.html | 94 +++--- reference/GT.fmt_roman.html | 94 +++--- reference/GT.fmt_scientific.html | 94 +++--- reference/GT.fmt_time.html | 94 +++--- reference/GT.html | 470 +++++++++++++-------------- reference/GT.tab_header.html | 188 +++++------ reference/GT.tab_source_note.html | 94 +++--- reference/GT.tab_spanner.html | 188 +++++------ reference/GT.tab_stubhead.html | 188 +++++------ reference/GT.tab_style.html | 94 +++--- search.json | 8 +- 30 files changed, 2687 insertions(+), 2687 deletions(-) diff --git a/examples-qmd/GT.html b/examples-qmd/GT.html index 6c127a1aa..46590475f 100644 --- a/examples-qmd/GT.html +++ b/examples-qmd/GT.html @@ -221,60 +221,60 @@

GT

gt.GT(exibble)
-
+
diff --git a/examples-qmd/fmt-number.html b/examples-qmd/fmt-number.html index 82b0a38c5..e044644aa 100644 --- a/examples-qmd/fmt-number.html +++ b/examples-qmd/fmt-number.html @@ -220,60 +220,60 @@

fmt_number

gt.GT(exibble).fmt_number(columns='num', decimals=3).cols_label(char = "character")
-
+
@@ -417,7 +417,7 @@

fmt_number

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value='#FFFFFF'), table_border_top_include=OptionsInfo(scss=False, category='table', type='boolean', value=True), table_border_top_style=OptionsInfo(scss=True, category='table', type='value', value='solid'), table_border_top_width=OptionsInfo(scss=True, category='table', type='px', value='2px'), table_border_top_color=OptionsInfo(scss=True, category='table', type='value', value='#A8A8A8'), table_border_right_style=OptionsInfo(scss=True, category='table', type='value', value='none'), table_border_right_width=OptionsInfo(scss=True, category='table', type='px', value='2px'), table_border_right_color=OptionsInfo(scss=True, category='table', type='value', value='#D3D3D3'), table_border_bottom_include=OptionsInfo(scss=False, category='table', type='boolean', value=True), table_border_bottom_style=OptionsInfo(scss=True, category='table', type='value', value='solid'), table_border_bottom_width=OptionsInfo(scss=True, category='table', type='px', value='2px'), table_border_bottom_color=OptionsInfo(scss=True, category='table', type='value', value='#A8A8A8'), table_border_left_style=OptionsInfo(scss=True, category='table', type='value', value='none'), table_border_left_width=OptionsInfo(scss=True, category='table', type='px', value='2px'), table_border_left_color=OptionsInfo(scss=True, category='table', type='value', value='#D3D3D3'), heading_background_color=OptionsInfo(scss=True, category='heading', type='value', value=None), heading_align=OptionsInfo(scss=True, category='heading', type='value', value='center'), heading_title_font_size=OptionsInfo(scss=True, category='heading', type='px', value='125%'), heading_title_font_weight=OptionsInfo(scss=True, category='heading', type='value', value='initial'), heading_subtitle_font_size=OptionsInfo(scss=True, category='heading', type='px', value='85%'), heading_subtitle_font_weight=OptionsInfo(scss=True, category='heading', type='value', value='initial'), heading_padding=OptionsInfo(scss=True, category='heading', type='px', value='4px'), heading_padding_horizontal=OptionsInfo(scss=True, category='heading', type='px', value='5px'), heading_border_bottom_style=OptionsInfo(scss=True, category='heading', type='value', value='solid'), heading_border_bottom_width=OptionsInfo(scss=True, category='heading', type='px', value='2px'), heading_border_bottom_color=OptionsInfo(scss=True, category='heading', type='value', value='#D3D3D3'), heading_border_lr_style=OptionsInfo(scss=True, category='heading', type='value', value='none'), heading_border_lr_width=OptionsInfo(scss=True, category='heading', type='px', value='1px'), heading_border_lr_color=OptionsInfo(scss=True, category='heading', type='value', value='#D3D3D3'), column_labels_background_color=OptionsInfo(scss=True, category='column_labels', type='value', value=None), column_labels_font_size=OptionsInfo(scss=True, category='column_labels', type='px', value='100%'), column_labels_font_weight=OptionsInfo(scss=True, category='column_labels', type='value', value='normal'), column_labels_text_transform=OptionsInfo(scss=True, category='column_labels', type='value', value='inherit'), column_labels_padding=OptionsInfo(scss=True, category='column_labels', type='px', value='5px'), column_labels_padding_horizontal=OptionsInfo(scss=True, category='column_labels', type='px', value='5px'), column_labels_vlines_style=OptionsInfo(scss=True, category='table_body', type='value', value='none'), column_labels_vlines_width=OptionsInfo(scss=True, category='table_body', type='px', value='1px'), column_labels_vlines_color=OptionsInfo(scss=True, category='table_body', type='value', value='#D3D3D3'), column_labels_border_top_style=OptionsInfo(scss=True, category='column_labels', type='value', value='solid'), column_labels_border_top_width=OptionsInfo(scss=True, category='column_labels', type='px', value='2px'), column_labels_border_top_color=OptionsInfo(scss=True, category='column_labels', type='value', value='#D3D3D3'), column_labels_border_bottom_style=OptionsInfo(scss=True, category='column_labels', type='value', value='solid'), column_labels_border_bottom_width=OptionsInfo(scss=True, category='column_labels', type='px', value='2px'), column_labels_border_bottom_color=OptionsInfo(scss=True, category='column_labels', type='value', value='#D3D3D3'), column_labels_border_lr_style=OptionsInfo(scss=True, category='column_labels', type='value', value='none'), column_labels_border_lr_width=OptionsInfo(scss=True, category='column_labels', type='px', value='1px'), column_labels_border_lr_color=OptionsInfo(scss=True, category='column_labels', type='value', value='#D3D3D3'), column_labels_hidden=OptionsInfo(scss=False, category='column_labels', type='boolean', value=False), row_group_background_color=OptionsInfo(scss=True, category='row_group', type='value', value=None), row_group_font_size=OptionsInfo(scss=True, category='row_group', type='px', value='100%'), row_group_font_weight=OptionsInfo(scss=True, category='row_group', type='value', value='initial'), row_group_text_transform=OptionsInfo(scss=True, category='row_group', type='value', value='inherit'), row_group_padding=OptionsInfo(scss=True, category='row_group', type='px', value='8px'), row_group_padding_horizontal=OptionsInfo(scss=True, category='row_group', type='px', value='5px'), row_group_border_top_style=OptionsInfo(scss=True, category='row_group', type='value', value='solid'), row_group_border_top_width=OptionsInfo(scss=True, category='row_group', type='px', value='2px'), row_group_border_top_color=OptionsInfo(scss=True, category='row_group', type='value', value='#D3D3D3'), row_group_border_right_style=OptionsInfo(scss=True, category='row_group', type='value', value='none'), row_group_border_right_width=OptionsInfo(scss=True, category='row_group', type='px', value='1px'), row_group_border_right_color=OptionsInfo(scss=True, category='row_group', type='value', value='#D3D3D3'), row_group_border_bottom_style=OptionsInfo(scss=True, category='row_group', type='value', value='solid'), row_group_border_bottom_width=OptionsInfo(scss=True, category='row_group', type='px', value='2px'), row_group_border_bottom_color=OptionsInfo(scss=True, category='row_group', type='value', value='#D3D3D3'), row_group_border_left_style=OptionsInfo(scss=True, category='row_group', type='value', value='none'), row_group_border_left_width=OptionsInfo(scss=True, category='row_group', type='px', value='1px'), row_group_border_left_color=OptionsInfo(scss=True, category='row_group', type='value', value='#D3D3D3'), row_group_default_label=OptionsInfo(scss=False, category='row_group', type='value', value=None), row_group_as_column=OptionsInfo(scss=False, category='row_group', type='boolean', value=False), table_body_hlines_style=OptionsInfo(scss=True, category='table_body', type='value', value='solid'), table_body_hlines_width=OptionsInfo(scss=True, category='table_body', type='px', value='1px'), table_body_hlines_color=OptionsInfo(scss=True, category='table_body', type='value', value='#D3D3D3'), table_body_vlines_style=OptionsInfo(scss=True, category='table_body', type='value', value='none'), table_body_vlines_width=OptionsInfo(scss=True, category='table_body', type='px', value='1px'), table_body_vlines_color=OptionsInfo(scss=True, category='table_body', type='value', value='#D3D3D3'), table_body_border_top_style=OptionsInfo(scss=True, category='table_body', type='value', value='solid'), table_body_border_top_width=OptionsInfo(scss=True, category='table_body', type='px', value='2px'), table_body_border_top_color=OptionsInfo(scss=True, category='table_body', type='value', value='#D3D3D3'), table_body_border_bottom_style=OptionsInfo(scss=True, category='table_body', type='value', value='solid'), table_body_border_bottom_width=OptionsInfo(scss=True, category='table_body', type='px', value='2px'), table_body_border_bottom_color=OptionsInfo(scss=True, category='table_body', type='value', value='#D3D3D3'), data_row_padding=OptionsInfo(scss=True, category='data_row', type='px', value='8px'), data_row_padding_horizontal=OptionsInfo(scss=True, category='data_row', type='px', value='5px'), stub_background_color=OptionsInfo(scss=True, category='stub', type='value', value=None), stub_font_size=OptionsInfo(scss=True, category='stub', type='px', value='100%'), stub_font_weight=OptionsInfo(scss=True, category='stub', type='value', value='initial'), stub_text_transform=OptionsInfo(scss=True, category='stub', type='value', value='inherit'), stub_border_style=OptionsInfo(scss=True, category='stub', type='value', value='solid'), stub_border_width=OptionsInfo(scss=True, category='stub', type='px', value='2px'), stub_border_color=OptionsInfo(scss=True, category='stub', type='value', value='#D3D3D3'), stub_row_group_background_color=OptionsInfo(scss=True, category='stub', type='value', value=None), stub_row_group_font_size=OptionsInfo(scss=True, category='stub', type='px', value='100%'), stub_row_group_font_weight=OptionsInfo(scss=True, category='stub', type='value', value='initial'), stub_row_group_text_transform=OptionsInfo(scss=True, category='stub', type='value', value='inherit'), stub_row_group_border_style=OptionsInfo(scss=True, category='stub', type='value', value='solid'), stub_row_group_border_width=OptionsInfo(scss=True, category='stub', type='px', value='2px'), stub_row_group_border_color=OptionsInfo(scss=True, category='stub', type='value', value='#D3D3D3'), summary_row_padding=OptionsInfo(scss=True, category='summary_row', type='px', value='8px'), summary_row_padding_horizontal=OptionsInfo(scss=True, category='summary_row', type='px', value='5px'), summary_row_background_color=OptionsInfo(scss=True, category='summary_row', type='value', value=None), summary_row_text_transform=OptionsInfo(scss=True, category='summary_row', type='value', value='inherit'), summary_row_border_style=OptionsInfo(scss=True, category='summary_row', type='value', value='solid'), summary_row_border_width=OptionsInfo(scss=True, category='summary_row', type='px', value='2px'), summary_row_border_color=OptionsInfo(scss=True, category='summary_row', type='value', value='#D3D3D3'), grand_summary_row_padding=OptionsInfo(scss=True, category='grand_summary_row', type='px', value='8px'), grand_summary_row_padding_horizontal=OptionsInfo(scss=True, category='grand_summary_row', type='px', value='5px'), grand_summary_row_background_color=OptionsInfo(scss=True, category='grand_summary_row', type='value', value=None), grand_summary_row_text_transform=OptionsInfo(scss=True, category='grand_summary_row', type='value', value='inherit'), grand_summary_row_border_style=OptionsInfo(scss=True, category='grand_summary_row', type='value', value='double'), grand_summary_row_border_width=OptionsInfo(scss=True, category='grand_summary_row', type='px', value='6px'), grand_summary_row_border_color=OptionsInfo(scss=True, category='grand_summary_row', type='value', value='#D3D3D3'), footnotes_font_size=OptionsInfo(scss=True, category='footnotes', type='px', value='90%'), footnotes_padding=OptionsInfo(scss=True, category='footnotes', type='px', value='4px'), footnotes_padding_horizontal=OptionsInfo(scss=True, category='footnotes', type='px', value='5px'), footnotes_background_color=OptionsInfo(scss=True, category='footnotes', type='value', value=None), footnotes_margin=OptionsInfo(scss=True, category='footnotes', type='px', value='0px'), footnotes_border_bottom_style=OptionsInfo(scss=True, category='footnotes', type='value', value='none'), footnotes_border_bottom_width=OptionsInfo(scss=True, category='footnotes', type='px', value='2px'), footnotes_border_bottom_color=OptionsInfo(scss=True, category='footnotes', type='value', value='#D3D3D3'), footnotes_border_lr_style=OptionsInfo(scss=True, category='footnotes', type='value', value='none'), footnotes_border_lr_width=OptionsInfo(scss=True, category='footnotes', type='px', value='2px'), footnotes_border_lr_color=OptionsInfo(scss=True, category='footnotes', type='value', value='#D3D3D3'), footnotes_marks=OptionsInfo(scss=False, category='footnotes', type='values', value='numbers'), footnotes_multiline=OptionsInfo(scss=False, category='footnotes', type='boolean', value=True), footnotes_sep=OptionsInfo(scss=False, category='footnotes', type='value', value=' '), source_notes_padding=OptionsInfo(scss=True, category='source_notes', type='px', value='4px'), source_notes_padding_horizontal=OptionsInfo(scss=True, category='source_notes', type='px', value='5px'), source_notes_background_color=OptionsInfo(scss=True, category='source_notes', type='value', value=None), source_notes_font_size=OptionsInfo(scss=True, category='source_notes', type='px', value='90%'), source_notes_border_bottom_style=OptionsInfo(scss=True, category='source_notes', type='value', value='none'), source_notes_border_bottom_width=OptionsInfo(scss=True, category='source_notes', type='px', value='2px'), source_notes_border_bottom_color=OptionsInfo(scss=True, category='source_notes', type='value', value='#D3D3D3'), source_notes_border_lr_style=OptionsInfo(scss=True, category='source_notes', type='value', value='none'), source_notes_border_lr_width=OptionsInfo(scss=True, category='source_notes', type='px', value='2px'), source_notes_border_lr_color=OptionsInfo(scss=True, category='source_notes', type='value', value='#D3D3D3'), source_notes_multiline=OptionsInfo(scss=False, category='source_notes', type='boolean', value=True), source_notes_sep=OptionsInfo(scss=False, category='source_notes', type='value', value=' '), row_striping_background_color=OptionsInfo(scss=True, category='row', type='value', value='rgba(128,128,128,0.05)'), row_striping_include_stub=OptionsInfo(scss=False, category='row', type='boolean', value=False), row_striping_include_table_body=OptionsInfo(scss=False, category='row', type='boolean', value=False), container_width=OptionsInfo(scss=False, category='container', type='px', value='auto'), container_height=OptionsInfo(scss=False, category='container', type='px', value='auto'), container_padding_x=OptionsInfo(scss=False, category='container', type='px', value='0px'), container_padding_y=OptionsInfo(scss=False, category='container', type='px', value='10px'), container_overflow_x=OptionsInfo(scss=False, category='container', type='overflow', value='auto'), container_overflow_y=OptionsInfo(scss=False, category='container', type='overflow', value='auto'), page_orientation=OptionsInfo(scss=False, category='page', type='value', value='portrait'), page_numbering=OptionsInfo(scss=False, category='page', type='boolean', value=False), page_header_use_tbl_headings=OptionsInfo(scss=False, category='page', type='boolean', value=False), page_footer_use_tbl_notes=OptionsInfo(scss=False, category='page', type='boolean', value=False), page_width=OptionsInfo(scss=False, category='page', type='value', value='8.5in'), page_height=OptionsInfo(scss=False, category='page', type='value', value='11.0in'), page_margin_left=OptionsInfo(scss=False, category='page', type='value', value='1.0in'), page_margin_right=OptionsInfo(scss=False, category='page', type='value', value='1.0in'), page_margin_top=OptionsInfo(scss=False, category='page', type='value', value='1.0in'), page_margin_bottom=OptionsInfo(scss=False, category='page', type='value', value='1.0in'), page_header_height=OptionsInfo(scss=False, category='page', type='value', value='0.5in'), page_footer_height=OptionsInfo(scss=False, category='page', type='value', value='0.5in'), quarto_disable_processing=OptionsInfo(scss=False, category='quarto', type='logical', value=False), quarto_use_bootstrap=OptionsInfo(scss=False, category='quarto', type='logical', value=False)), _has_built=False)

In a variation of the previous table, we can combine large-number suffixing with a declaration of the number of significant digits to use. With things like population figures, n_sigfig=3 is a very good option.

diff --git a/examples-qmd/table-manipulation.html b/examples-qmd/table-manipulation.html index 686fc0402..23168d55f 100644 --- a/examples-qmd/table-manipulation.html +++ b/examples-qmd/table-manipulation.html @@ -225,60 +225,60 @@

table manipulation

).fmt_number(columns="msrp", decimals=2, scale_by=1 / 10000)
-
+
@@ -330,60 +330,60 @@

table manipulation

gt.GT(exibble.iloc[[4, 3, 7, 1],], groupname_col="group")
-
+
diff --git a/examples/index.html b/examples/index.html index 14baed201..7790004db 100644 --- a/examples/index.html +++ b/examples/index.html @@ -203,60 +203,60 @@

Examples

-
+
@@ -318,7 +318,7 @@

Examples

- + @@ -364,60 +364,60 @@

Examples

-
+
Reference: McNeil, D. R. (1977) Interactive Data Analysis. Wiley.
@@ -593,60 +593,60 @@

Examples

-
+
@@ -822,60 +822,60 @@

Examples

-
+
diff --git a/get-started/basic-column-labels.html b/get-started/basic-column-labels.html index 8a6b23354..6b20dfccc 100644 --- a/get-started/basic-column-labels.html +++ b/get-started/basic-column-labels.html @@ -431,60 +431,60 @@

Column spanners

gt_airquality
-
+
@@ -633,60 +633,60 @@

Move and label colu )
-
+

@@ -827,60 +827,60 @@

Specifying col )
-
+

diff --git a/get-started/basic-formatting.html b/get-started/basic-formatting.html index bc1b8b884..038e4edc1 100644 --- a/get-started/basic-formatting.html +++ b/get-started/basic-formatting.html @@ -275,60 +275,60 @@

Formatting values

gt_ex
-
+
@@ -384,60 +384,60 @@

Formatting values

gt_ex
-
+
@@ -496,60 +496,60 @@

Formatting values

gt_ex
-
+
@@ -605,60 +605,60 @@

Formatting values

gt_ex
-
+
@@ -714,60 +714,60 @@

Formatting values

gt_ex
-
+
diff --git a/get-started/basic-header.html b/get-started/basic-header.html index 2b0d79d2b..1eee1f70b 100644 --- a/get-started/basic-header.html +++ b/get-started/basic-header.html @@ -275,60 +275,60 @@

Header and Footer

)
-
+
@@ -404,60 +404,60 @@

Header and Footer

gt_tbl
-
+
@@ -502,60 +502,60 @@

Header and Footer

)
-
+
@@ -585,7 +585,7 @@

Header and Footer

- + diff --git a/get-started/basic-stub.html b/get-started/basic-stub.html index 6dcb47d03..4fe447066 100644 --- a/get-started/basic-stub.html +++ b/get-started/basic-stub.html @@ -278,60 +278,60 @@

Row names

GT(islands_mini, rowname_col = "name")
-
+
Reference: McNeil, D. R. (1977) Interactive Data Analysis. Wiley.
@@ -396,60 +396,60 @@

Row names

)
-
+
@@ -519,60 +519,60 @@

Row groups

GT(island_groups, rowname_col="name", groupname_col="group").tab_stubhead(label="landmass")
-
+
diff --git a/get-started/column-selection.html b/get-started/column-selection.html index 38f521c22..c9b94c5f9 100644 --- a/get-started/column-selection.html +++ b/get-started/column-selection.html @@ -283,60 +283,60 @@

Column selection

gt_ex
-
+
@@ -455,60 +455,60 @@

String and in 
gt_ex.cols_move_to_start(["date", 1, -1])
-
+

@@ -635,60 +635,60 @@

Function selectors

gt_ex.cols_move_to_start(lambda x: "c" in x)
-
+
@@ -814,60 +814,60 @@

Polars selectors

GT(pl_df).cols_move_to_start(cs.starts_with("c") | cs.starts_with("f"))
-
+
diff --git a/get-started/index.html b/get-started/index.html index 84a5b778d..d1f4f715f 100644 --- a/get-started/index.html +++ b/get-started/index.html @@ -292,60 +292,60 @@

A basic gtgt_tbl
-
+

@@ -432,60 +432,60 @@

Some beautiful exa
-
+

@@ -547,7 +547,7 @@

Some beautiful exa

- + @@ -592,60 +592,60 @@

Some beautiful exa
-
+

Reference: McNeil, D. R. (1977) Interactive Data Analysis. Wiley.
diff --git a/reference/GT.cols_align.html b/reference/GT.cols_align.html index 63aed71c3..9df0cf157 100644 --- a/reference/GT.cols_align.html +++ b/reference/GT.cols_align.html @@ -654,60 +654,60 @@

Examples

)
-
+
diff --git a/reference/GT.cols_label.html b/reference/GT.cols_label.html index 5d409ee31..0732d87a4 100644 --- a/reference/GT.cols_label.html +++ b/reference/GT.cols_label.html @@ -653,60 +653,60 @@

Examples

)
-
+
@@ -761,60 +761,60 @@

Examples

)
-
+
diff --git a/reference/GT.cols_move.html b/reference/GT.cols_move.html index bb8596dd6..3438c53dc 100644 --- a/reference/GT.cols_move.html +++ b/reference/GT.cols_move.html @@ -658,60 +658,60 @@

Examples

)
-
+
diff --git a/reference/GT.cols_move_to_end.html b/reference/GT.cols_move_to_end.html index 818d270cd..8c7ec2a81 100644 --- a/reference/GT.cols_move_to_end.html +++ b/reference/GT.cols_move_to_end.html @@ -645,60 +645,60 @@

Examples

gt.GT(countrypops_mini).cols_move_to_end(columns="year")
-
+
diff --git a/reference/GT.cols_move_to_start.html b/reference/GT.cols_move_to_start.html index 25d637b60..fe1b2ec38 100644 --- a/reference/GT.cols_move_to_start.html +++ b/reference/GT.cols_move_to_start.html @@ -646,60 +646,60 @@

Examples

gt.GT(countrypops_mini).cols_move_to_start(columns="year")
-
+
@@ -747,60 +747,60 @@

Examples

gt.GT(countrypops_mini).cols_move_to_start(columns=["year", "population"])
-
+
diff --git a/reference/GT.fmt_bytes.html b/reference/GT.fmt_bytes.html index c91ff0b81..60838c63b 100644 --- a/reference/GT.fmt_bytes.html +++ b/reference/GT.fmt_bytes.html @@ -725,60 +725,60 @@

Examples

gt.GT(gt.data.exibble[["num"]]).fmt_bytes(columns="num", standard="decimal")
-
+
diff --git a/reference/GT.fmt_currency.html b/reference/GT.fmt_currency.html index 91510a774..c2c75dad1 100644 --- a/reference/GT.fmt_currency.html +++ b/reference/GT.fmt_currency.html @@ -740,60 +740,60 @@

Examples

gt.GT(gt.data.exibble).fmt_currency(columns="currency", decimals=3, use_seps=False)
-
+
diff --git a/reference/GT.fmt_date.html b/reference/GT.fmt_date.html index a18ccfb58..bf210b42e 100644 --- a/reference/GT.fmt_date.html +++ b/reference/GT.fmt_date.html @@ -774,60 +774,60 @@

Examples

gt.GT(exibble_mini).fmt_date(columns="date", date_style="month_day_year")
-
+
diff --git a/reference/GT.fmt_integer.html b/reference/GT.fmt_integer.html index 5fc9b5338..2d61c3844 100644 --- a/reference/GT.fmt_integer.html +++ b/reference/GT.fmt_integer.html @@ -703,60 +703,60 @@

Examples

gt.GT(gt.data.exibble).fmt_integer(columns="num", use_seps=False)
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diff --git a/reference/GT.fmt_number.html b/reference/GT.fmt_number.html index d9ccc8010..83fe9a2cd 100644 --- a/reference/GT.fmt_number.html +++ b/reference/GT.fmt_number.html @@ -733,60 +733,60 @@

Examples

gt.GT(gt.data.exibble).fmt_number(columns="num", decimals=3, use_seps=False)
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diff --git a/reference/GT.fmt_roman.html b/reference/GT.fmt_roman.html index eb7259a21..8b2bae9f3 100644 --- a/reference/GT.fmt_roman.html +++ b/reference/GT.fmt_roman.html @@ -662,60 +662,60 @@

Examples

gt.GT(numbers_tbl, rowname_col="arabic").fmt_roman(columns="roman")
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diff --git a/reference/GT.fmt_scientific.html b/reference/GT.fmt_scientific.html index 3f957722b..aaeae1594 100644 --- a/reference/GT.fmt_scientific.html +++ b/reference/GT.fmt_scientific.html @@ -732,60 +732,60 @@

Examples

gt.GT(gt.data.exibble).fmt_scientific(columns="num")
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diff --git a/reference/GT.fmt_time.html b/reference/GT.fmt_time.html index 744dbd4ec..4a1f6b0a2 100644 --- a/reference/GT.fmt_time.html +++ b/reference/GT.fmt_time.html @@ -720,60 +720,60 @@

Examples

gt.GT(exibble_mini).fmt_time(columns="time", time_style="h_m_s_p")
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diff --git a/reference/GT.html b/reference/GT.html index 527df782e..aecdf01b7 100644 --- a/reference/GT.html +++ b/reference/GT.html @@ -665,60 +665,60 @@

Examples

gt.GT(gt.exibble)
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Examples

gt.GT(gt.exibble, rowname_col="row")
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Examples

gt.GT(gt.exibble, rowname_col="row", groupname_col="group")
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Examples

gt.GT(gt.exibble, rowname_col="row", auto_align=False)
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Examples

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diff --git a/reference/GT.tab_header.html b/reference/GT.tab_header.html index f9c5a73c7..e803d0cc2 100644 --- a/reference/GT.tab_header.html +++ b/reference/GT.tab_header.html @@ -660,60 +660,60 @@

Examples

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Examples

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diff --git a/reference/GT.tab_source_note.html b/reference/GT.tab_source_note.html index 820c59605..65e09347e 100644 --- a/reference/GT.tab_source_note.html +++ b/reference/GT.tab_source_note.html @@ -645,60 +645,60 @@

Examples

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diff --git a/reference/GT.tab_spanner.html b/reference/GT.tab_spanner.html index 7875150ea..1c0bb26da 100644 --- a/reference/GT.tab_spanner.html +++ b/reference/GT.tab_spanner.html @@ -686,60 +686,60 @@

Examples

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Examples

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diff --git a/reference/GT.tab_stubhead.html b/reference/GT.tab_stubhead.html index a47924a70..8cd4e778c 100644 --- a/reference/GT.tab_stubhead.html +++ b/reference/GT.tab_stubhead.html @@ -642,60 +642,60 @@

Examples

gt.GT(gtcars_mini, rowname_col="model").tab_stubhead(label="car")
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Examples

gt.GT(gtcars_mini, rowname_col="model").tab_stubhead(label=gt.md("*Car*"))
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diff --git a/reference/GT.tab_style.html b/reference/GT.tab_style.html index 778ebbdeb..80509a50a 100644 --- a/reference/GT.tab_style.html +++ b/reference/GT.tab_style.html @@ -651,60 +651,60 @@

Examples

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diff --git a/search.json b/search.json index 82915a40a..703144883 100644 --- a/search.json +++ b/search.json @@ -18,7 +18,7 @@ "href": "examples-qmd/fmt-number.html", "title": "fmt_number", "section": "", - "text": "import great_tables as gt\nfrom great_tables.data import exibble, countrypops\n\nUse the exibble dataset to create a gt table. With the fmt_number() method, we’ll format the num column to have three decimal places (with decimals=3) and omit the use of digit separators (with use_seps=False).\n\ngt.GT(exibble).fmt_number(columns='num', decimals=3).cols_label(char = \"character\")\n\n\n\n\n\n\nnum\ncharacter\nfctr\ndate\ntime\ndatetime\ncurrency\nrow\ngroup\n\n\n 0.111\n apricot\n one\n 2015-01-15\n 13:35\n 2018-01-01 02:22\n 49.95\n row_1\n grp_a\n\n\n 2.222\n banana\n two\n 2015-02-15\n 14:40\n 2018-02-02 14:33\n 17.95\n row_2\n grp_a\n\n\n 33.330\n coconut\n three\n 2015-03-15\n 15:45\n 2018-03-03 03:44\n 1.39\n row_3\n grp_a\n\n\n 444.400\n durian\n four\n 2015-04-15\n 16:50\n 2018-04-04 15:55\n 65100.0\n row_4\n grp_a\n\n\n 5,550.000\n \n five\n 2015-05-15\n 17:55\n 2018-05-05 04:00\n 1325.81\n row_5\n grp_b\n\n\n nan\n fig\n six\n 2015-06-15\n \n 2018-06-06 16:11\n 13.255\n row_6\n grp_b\n\n\n 777,000.000\n grapefruit\n seven\n \n 19:10\n 2018-07-07 05:22\n \n row_7\n grp_b\n\n\n 8,880,000.000\n honeydew\n eight\n 2015-08-15\n 20:20\n \n 0.44\n row_8\n grp_b\n\n\n\n\n\n\n\n \n\n\nUse a modified version of the countrypops dataset to create a gt table with row labels. Format all columns to use large-number suffixing (e.g., where '10,000,000' becomes '10M') with the suffixing=True option.\n\nfrom siuba import *\nres = (countrypops\n >> select(_.country_code_3, _.year, _.population)\n >> filter(_.country_code_3.isin(['CHN', 'IND', 'USA', 'PAK', 'IDN']))\n >> filter(_.year > 1975, _.year % 5 == 0)\n >> spread(_.year, _.population)\n >> arrange(-_[2015])\n)\n\n# TODO: implement `suffixing`\n(gt.GT(res)\n .fmt_integer(columns=1980, scale_by=1/10000)\n .fmt_number(columns=1985)\n)\n\nAttributeError: 'int' object has no attribute 'text'\n\n\nGT(_tbl_data= country_code_3 1980 1985 1990 1995 2000 \\\n0 CHN 981235000 1051040000 1135185000 1204855000 1262645000 \n2 IND 696828385 780242084 870452165 964279129 1059633675 \n4 USA 227225000 237924000 249623000 266278000 282162411 \n1 IDN 148177096 165791694 182159874 198140162 214072421 \n3 PAK 80624057 97121552 115414069 133117476 154369924 \n\n 2005 2010 2015 2020 \n0 1303720000 1337705000 1379860000 1411100000 \n2 1154638713 1240613620 1322866505 1396387127 \n4 295516599 309327143 320738994 331511512 \n1 228805144 244016173 259091970 271857970 \n3 174372098 194454498 210969298 227196741 , _body=<great_tables._gt_data.Body object at 0x7f8474b8b2b0>, _boxhead=Boxhead([ColInfo(var='country_code_3', type=<ColInfoTypeEnum.default: 1>, column_label='country_code_3', column_align='left', column_width=None), ColInfo(var=1980, type=<ColInfoTypeEnum.default: 1>, column_label=1980, column_align='right', column_width=None), ColInfo(var=1985, type=<ColInfoTypeEnum.default: 1>, column_label=1985, column_align='right', column_width=None), ColInfo(var=1990, type=<ColInfoTypeEnum.default: 1>, column_label=1990, column_align='right', column_width=None), ColInfo(var=1995, type=<ColInfoTypeEnum.default: 1>, column_label=1995, column_align='right', column_width=None), ColInfo(var=2000, type=<ColInfoTypeEnum.default: 1>, column_label=2000, column_align='right', column_width=None), ColInfo(var=2005, type=<ColInfoTypeEnum.default: 1>, column_label=2005, column_align='right', column_width=None), ColInfo(var=2010, type=<ColInfoTypeEnum.default: 1>, column_label=2010, column_align='right', column_width=None), ColInfo(var=2015, type=<ColInfoTypeEnum.default: 1>, column_label=2015, column_align='right', column_width=None), ColInfo(var=2020, type=<ColInfoTypeEnum.default: 1>, column_label=2020, column_align='right', column_width=None)]), _stub=Stub([RowInfo(rownum_i=0, group_id=None, rowname=None, group_label=None, built=False), RowInfo(rownum_i=1, group_id=None, rowname=None, group_label=None, built=False), RowInfo(rownum_i=2, group_id=None, rowname=None, group_label=None, built=False), RowInfo(rownum_i=3, group_id=None, rowname=None, group_label=None, built=False), RowInfo(rownum_i=4, group_id=None, rowname=None, group_label=None, built=False)]), _row_groups=[], _group_rows=GroupRows([]), _spanners=Spanners([]), _heading=Heading(title=None, subtitle=None, preheader=None), _stubhead=None, _source_notes=[], _footnotes=[], _styles=[], _locale=<great_tables._gt_data.Locale object at 0x7f8434309450>, _formats=[<great_tables._gt_data.FormatInfo object at 0x7f843430a410>, <great_tables._gt_data.FormatInfo object at 0x7f843430a590>], _options=Options(table_id=OptionsInfo(scss=False, category='table', type='value', value=None), table_caption=OptionsInfo(scss=False, category='table', type='value', value=None), table_width=OptionsInfo(scss=True, category='table', type='px', value='auto'), table_layout=OptionsInfo(scss=True, category='table', type='value', value='fixed'), table_margin_left=OptionsInfo(scss=True, category='table', type='px', value='auto'), table_margin_right=OptionsInfo(scss=True, category='table', type='px', value='auto'), table_background_color=OptionsInfo(scss=True, category='table', type='value', value='#FFFFFF'), table_additional_css=OptionsInfo(scss=False, category='table', type='values', value=None), table_font_names=OptionsInfo(scss=False, 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type='px', value='2px'), source_notes_border_lr_color=OptionsInfo(scss=True, category='source_notes', type='value', value='#D3D3D3'), source_notes_multiline=OptionsInfo(scss=False, category='source_notes', type='boolean', value=True), source_notes_sep=OptionsInfo(scss=False, category='source_notes', type='value', value=' '), row_striping_background_color=OptionsInfo(scss=True, category='row', type='value', value='rgba(128,128,128,0.05)'), row_striping_include_stub=OptionsInfo(scss=False, category='row', type='boolean', value=False), row_striping_include_table_body=OptionsInfo(scss=False, category='row', type='boolean', value=False), container_width=OptionsInfo(scss=False, category='container', type='px', value='auto'), container_height=OptionsInfo(scss=False, category='container', type='px', value='auto'), container_padding_x=OptionsInfo(scss=False, category='container', type='px', value='0px'), container_padding_y=OptionsInfo(scss=False, category='container', type='px', value='10px'), container_overflow_x=OptionsInfo(scss=False, category='container', type='overflow', value='auto'), container_overflow_y=OptionsInfo(scss=False, category='container', type='overflow', value='auto'), page_orientation=OptionsInfo(scss=False, category='page', type='value', value='portrait'), page_numbering=OptionsInfo(scss=False, category='page', type='boolean', value=False), page_header_use_tbl_headings=OptionsInfo(scss=False, category='page', type='boolean', value=False), page_footer_use_tbl_notes=OptionsInfo(scss=False, category='page', type='boolean', value=False), page_width=OptionsInfo(scss=False, category='page', type='value', value='8.5in'), page_height=OptionsInfo(scss=False, category='page', type='value', value='11.0in'), page_margin_left=OptionsInfo(scss=False, category='page', type='value', value='1.0in'), page_margin_right=OptionsInfo(scss=False, category='page', type='value', value='1.0in'), page_margin_top=OptionsInfo(scss=False, category='page', type='value', value='1.0in'), page_margin_bottom=OptionsInfo(scss=False, category='page', type='value', value='1.0in'), page_header_height=OptionsInfo(scss=False, category='page', type='value', value='0.5in'), page_footer_height=OptionsInfo(scss=False, category='page', type='value', value='0.5in'), quarto_disable_processing=OptionsInfo(scss=False, category='quarto', type='logical', value=False), quarto_use_bootstrap=OptionsInfo(scss=False, category='quarto', type='logical', value=False)), _has_built=False)\n\n\nIn a variation of the previous table, we can combine large-number suffixing with a declaration of the number of significant digits to use. With things like population figures, n_sigfig=3 is a very good option.\n\n#countrypops |>\n# dplyr::select(country_code_3, year, population) |>\n# dplyr::filter(country_code_3 %in% c('CHN', 'IND', 'USA', 'PAK', 'IDN')) |>\n# dplyr::filter(year > 1975 & year %% 5 == 0) |>\n# tidyr::spread(year, population) |>\n# dplyr::arrange(desc(`2015`)) |>\n# gt(rowname_col='country_code_3') |>\n# fmt_number(suffixing=True, n_sigfig=3)\n\nThere can be cases where you want to show numbers to a large number of decimal places but also drop the unnecessary trailing zeros for low-precision values. Let’s take a portion of the towny dataset and format the latitude and longitude columns with fmt_number(). We’ll have up to 5 digits displayed as decimal values, but we’ll also unconditionally drop any runs of trailing zeros in the decimal part with drop_trailing_zeros=True.\n\ntowny |>\n dplyr::select(name, latitude, longitude) |>\n dplyr::slice_head(n=10) |>\n gt() |>\n fmt_number(decimals=5, drop_trailing_zeros=True) |>\n # replace -name with [latitude, longitude]\n ## cols_merge(columns=-name, pattern='{1}, {2}') |>\n cols_label(\n name~'Municipality',\n latitude='Location'\n )" + "text": "import great_tables as gt\nfrom great_tables.data import exibble, countrypops\n\nUse the exibble dataset to create a gt table. With the fmt_number() method, we’ll format the num column to have three decimal places (with decimals=3) and omit the use of digit separators (with use_seps=False).\n\ngt.GT(exibble).fmt_number(columns='num', decimals=3).cols_label(char = \"character\")\n\n\n\n\n\n\nnum\ncharacter\nfctr\ndate\ntime\ndatetime\ncurrency\nrow\ngroup\n\n\n 0.111\n apricot\n one\n 2015-01-15\n 13:35\n 2018-01-01 02:22\n 49.95\n row_1\n grp_a\n\n\n 2.222\n banana\n two\n 2015-02-15\n 14:40\n 2018-02-02 14:33\n 17.95\n row_2\n grp_a\n\n\n 33.330\n coconut\n three\n 2015-03-15\n 15:45\n 2018-03-03 03:44\n 1.39\n row_3\n grp_a\n\n\n 444.400\n durian\n four\n 2015-04-15\n 16:50\n 2018-04-04 15:55\n 65100.0\n row_4\n grp_a\n\n\n 5,550.000\n \n five\n 2015-05-15\n 17:55\n 2018-05-05 04:00\n 1325.81\n row_5\n grp_b\n\n\n nan\n fig\n six\n 2015-06-15\n \n 2018-06-06 16:11\n 13.255\n row_6\n grp_b\n\n\n 777,000.000\n grapefruit\n seven\n \n 19:10\n 2018-07-07 05:22\n \n row_7\n grp_b\n\n\n 8,880,000.000\n honeydew\n eight\n 2015-08-15\n 20:20\n \n 0.44\n row_8\n grp_b\n\n\n\n\n\n\n\n \n\n\nUse a modified version of the countrypops dataset to create a gt table with row labels. Format all columns to use large-number suffixing (e.g., where '10,000,000' becomes '10M') with the suffixing=True option.\n\nfrom siuba import *\nres = (countrypops\n >> select(_.country_code_3, _.year, _.population)\n >> filter(_.country_code_3.isin(['CHN', 'IND', 'USA', 'PAK', 'IDN']))\n >> filter(_.year > 1975, _.year % 5 == 0)\n >> spread(_.year, _.population)\n >> arrange(-_[2015])\n)\n\n# TODO: implement `suffixing`\n(gt.GT(res)\n .fmt_integer(columns=1980, scale_by=1/10000)\n .fmt_number(columns=1985)\n)\n\nAttributeError: 'int' object has no attribute 'text'\n\n\nGT(_tbl_data= country_code_3 1980 1985 1990 1995 2000 \\\n0 CHN 981235000 1051040000 1135185000 1204855000 1262645000 \n2 IND 696828385 780242084 870452165 964279129 1059633675 \n4 USA 227225000 237924000 249623000 266278000 282162411 \n1 IDN 148177096 165791694 182159874 198140162 214072421 \n3 PAK 80624057 97121552 115414069 133117476 154369924 \n\n 2005 2010 2015 2020 \n0 1303720000 1337705000 1379860000 1411100000 \n2 1154638713 1240613620 1322866505 1396387127 \n4 295516599 309327143 320738994 331511512 \n1 228805144 244016173 259091970 271857970 \n3 174372098 194454498 210969298 227196741 , _body=<great_tables._gt_data.Body object at 0x7fab64a277f0>, _boxhead=Boxhead([ColInfo(var='country_code_3', type=<ColInfoTypeEnum.default: 1>, column_label='country_code_3', column_align='left', column_width=None), ColInfo(var=1980, type=<ColInfoTypeEnum.default: 1>, column_label=1980, column_align='right', column_width=None), ColInfo(var=1985, type=<ColInfoTypeEnum.default: 1>, column_label=1985, column_align='right', column_width=None), ColInfo(var=1990, type=<ColInfoTypeEnum.default: 1>, column_label=1990, column_align='right', column_width=None), ColInfo(var=1995, type=<ColInfoTypeEnum.default: 1>, column_label=1995, column_align='right', column_width=None), ColInfo(var=2000, type=<ColInfoTypeEnum.default: 1>, column_label=2000, column_align='right', column_width=None), ColInfo(var=2005, type=<ColInfoTypeEnum.default: 1>, column_label=2005, column_align='right', column_width=None), ColInfo(var=2010, type=<ColInfoTypeEnum.default: 1>, column_label=2010, column_align='right', column_width=None), ColInfo(var=2015, type=<ColInfoTypeEnum.default: 1>, column_label=2015, column_align='right', column_width=None), ColInfo(var=2020, type=<ColInfoTypeEnum.default: 1>, column_label=2020, column_align='right', column_width=None)]), _stub=Stub([RowInfo(rownum_i=0, group_id=None, rowname=None, group_label=None, built=False), RowInfo(rownum_i=1, group_id=None, rowname=None, group_label=None, built=False), RowInfo(rownum_i=2, group_id=None, rowname=None, group_label=None, built=False), RowInfo(rownum_i=3, group_id=None, rowname=None, group_label=None, built=False), RowInfo(rownum_i=4, group_id=None, rowname=None, group_label=None, built=False)]), _row_groups=[], _group_rows=GroupRows([]), _spanners=Spanners([]), _heading=Heading(title=None, subtitle=None, preheader=None), _stubhead=None, _source_notes=[], _footnotes=[], _styles=[], _locale=<great_tables._gt_data.Locale object at 0x7fab643fceb0>, _formats=[<great_tables._gt_data.FormatInfo object at 0x7fab643fe470>, <great_tables._gt_data.FormatInfo object at 0x7fab643fe5f0>], _options=Options(table_id=OptionsInfo(scss=False, category='table', type='value', value=None), table_caption=OptionsInfo(scss=False, category='table', type='value', value=None), table_width=OptionsInfo(scss=True, category='table', type='px', value='auto'), table_layout=OptionsInfo(scss=True, category='table', type='value', value='fixed'), table_margin_left=OptionsInfo(scss=True, category='table', type='px', value='auto'), table_margin_right=OptionsInfo(scss=True, category='table', type='px', value='auto'), table_background_color=OptionsInfo(scss=True, category='table', type='value', value='#FFFFFF'), table_additional_css=OptionsInfo(scss=False, category='table', type='values', value=None), table_font_names=OptionsInfo(scss=False, 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footnotes_sep=OptionsInfo(scss=False, category='footnotes', type='value', value=' '), source_notes_padding=OptionsInfo(scss=True, category='source_notes', type='px', value='4px'), source_notes_padding_horizontal=OptionsInfo(scss=True, category='source_notes', type='px', value='5px'), source_notes_background_color=OptionsInfo(scss=True, category='source_notes', type='value', value=None), source_notes_font_size=OptionsInfo(scss=True, category='source_notes', type='px', value='90%'), source_notes_border_bottom_style=OptionsInfo(scss=True, category='source_notes', type='value', value='none'), source_notes_border_bottom_width=OptionsInfo(scss=True, category='source_notes', type='px', value='2px'), source_notes_border_bottom_color=OptionsInfo(scss=True, category='source_notes', type='value', value='#D3D3D3'), source_notes_border_lr_style=OptionsInfo(scss=True, category='source_notes', type='value', value='none'), source_notes_border_lr_width=OptionsInfo(scss=True, category='source_notes', type='px', value='2px'), source_notes_border_lr_color=OptionsInfo(scss=True, category='source_notes', type='value', value='#D3D3D3'), source_notes_multiline=OptionsInfo(scss=False, category='source_notes', type='boolean', value=True), source_notes_sep=OptionsInfo(scss=False, category='source_notes', type='value', value=' '), row_striping_background_color=OptionsInfo(scss=True, category='row', type='value', value='rgba(128,128,128,0.05)'), row_striping_include_stub=OptionsInfo(scss=False, category='row', type='boolean', value=False), row_striping_include_table_body=OptionsInfo(scss=False, category='row', type='boolean', value=False), container_width=OptionsInfo(scss=False, category='container', type='px', value='auto'), container_height=OptionsInfo(scss=False, category='container', type='px', value='auto'), container_padding_x=OptionsInfo(scss=False, category='container', type='px', value='0px'), container_padding_y=OptionsInfo(scss=False, category='container', type='px', value='10px'), container_overflow_x=OptionsInfo(scss=False, category='container', type='overflow', value='auto'), container_overflow_y=OptionsInfo(scss=False, category='container', type='overflow', value='auto'), page_orientation=OptionsInfo(scss=False, category='page', type='value', value='portrait'), page_numbering=OptionsInfo(scss=False, category='page', type='boolean', value=False), page_header_use_tbl_headings=OptionsInfo(scss=False, category='page', type='boolean', value=False), page_footer_use_tbl_notes=OptionsInfo(scss=False, category='page', type='boolean', value=False), page_width=OptionsInfo(scss=False, category='page', type='value', value='8.5in'), page_height=OptionsInfo(scss=False, category='page', type='value', value='11.0in'), page_margin_left=OptionsInfo(scss=False, category='page', type='value', value='1.0in'), page_margin_right=OptionsInfo(scss=False, category='page', type='value', value='1.0in'), page_margin_top=OptionsInfo(scss=False, category='page', type='value', value='1.0in'), page_margin_bottom=OptionsInfo(scss=False, category='page', type='value', value='1.0in'), page_header_height=OptionsInfo(scss=False, category='page', type='value', value='0.5in'), page_footer_height=OptionsInfo(scss=False, category='page', type='value', value='0.5in'), quarto_disable_processing=OptionsInfo(scss=False, category='quarto', type='logical', value=False), quarto_use_bootstrap=OptionsInfo(scss=False, category='quarto', type='logical', value=False)), _has_built=False)\n\n\nIn a variation of the previous table, we can combine large-number suffixing with a declaration of the number of significant digits to use. With things like population figures, n_sigfig=3 is a very good option.\n\n#countrypops |>\n# dplyr::select(country_code_3, year, population) |>\n# dplyr::filter(country_code_3 %in% c('CHN', 'IND', 'USA', 'PAK', 'IDN')) |>\n# dplyr::filter(year > 1975 & year %% 5 == 0) |>\n# tidyr::spread(year, population) |>\n# dplyr::arrange(desc(`2015`)) |>\n# gt(rowname_col='country_code_3') |>\n# fmt_number(suffixing=True, n_sigfig=3)\n\nThere can be cases where you want to show numbers to a large number of decimal places but also drop the unnecessary trailing zeros for low-precision values. Let’s take a portion of the towny dataset and format the latitude and longitude columns with fmt_number(). We’ll have up to 5 digits displayed as decimal values, but we’ll also unconditionally drop any runs of trailing zeros in the decimal part with drop_trailing_zeros=True.\n\ntowny |>\n dplyr::select(name, latitude, longitude) |>\n dplyr::slice_head(n=10) |>\n gt() |>\n fmt_number(decimals=5, drop_trailing_zeros=True) |>\n # replace -name with [latitude, longitude]\n ## cols_merge(columns=-name, pattern='{1}, {2}') |>\n cols_label(\n name~'Municipality',\n latitude='Location'\n )" }, { "objectID": "examples-qmd/table-manipulation.html", @@ -662,7 +662,7 @@ "href": "get-started/basic-header.html", "title": "Header and Footer", "section": "", - "text": "The way that we add parts like the Table Header and footnotes in the Table Footer is to use the tab_*() family of methods. A Table Header is easy to add so let’s see how the previous table looks with a title and a subtitle. We can add this part using the tab_header() method:\n\nfrom great_tables import GT, md, html\nfrom great_tables.data import islands\n\nislands_mini = islands.head(10)\n\n# Make a display table with the `islands_tbl`\n# table; put a heading just above the column labels\n(GT(islands_mini)\n .tab_header(\ntitle = \"Large Landmasses of the World\",\nsubtitle = \"The top ten largest are presented\"\n )\n)\n\n\n\n\n\n \n Large Landmasses of the World\n \n \n The top ten largest are presented\n \nname\nsize\n\n\n Africa\n 11506\n\n\n Antarctica\n 5500\n\n\n Asia\n 16988\n\n\n Australia\n 2968\n\n\n Axel Heiberg\n 16\n\n\n Baffin\n 184\n\n\n Banks\n 23\n\n\n Borneo\n 280\n\n\n Britain\n 84\n\n\n Celebes\n 73\n\n\n\n\n\n\n\n \n\n\nThe Header table part provides an opportunity to describe the data that’s presented. The subtitle, which functions as a subtitle, is an optional part of the Header. We may also style the title and subtitle using Markdown! We do this by wrapping the values passed to title or subtitle with the md() function. Here is an example with the table data truncated for brevity:\n\n# Make a display table with the `islands_tbl`\n# table; put a heading just above the column labels\ngt_tbl = (GT(islands.head(2))\n .tab_header(\ntitle = md(\"Large Landmasses of the World\"),\nsubtitle = md(\"The top ten largest are presented\")\n )\n)\n\ngt_tbl\n\n\n\n\n\n \n Large Landmasses of the World\n \n \n The top ten largest are presented\n \nname\nsize\n\n\n Africa\n 11506\n\n\n Antarctica\n 5500\n\n\n\n\n\n\n\n \n\n\nA source note can be added to the table’s footer through use of the tab_source_note() method. It works in the same way as tab_header() (it also allows for Markdown inputs) except it can be called multiple times—each invocation results in the addition of a source note.\n\n# Display the `islands_tbl` data with a heading and\n# two source notes\n(gt_tbl\n .tab_source_note(\n source_note = \"Source: The World Almanac and Book of Facts, 1975, page 406.\"\n )\n .tab_source_note(\n source_note = md(\"Reference: McNeil, D. R. (1977) *Interactive Data Analysis*. Wiley.\")\n )\n\n)\n\n\n\n\n\n \n Large Landmasses of the World\n \n \n The top ten largest are presented\n \nname\nsize\n\n\n Africa\n 11506\n\n\n Antarctica\n 5500\n\n\n \n \n \n Source: The World Almanac and Book of Facts, 1975, page 406.\n \n\n\n \n \n \n\n\n\n\n\n\n \n\n\n\n🚧 Footnotes are not yet implemented, but footnotes might go here." + "text": "The way that we add parts like the Table Header and footnotes in the Table Footer is to use the tab_*() family of methods. A Table Header is easy to add so let’s see how the previous table looks with a title and a subtitle. We can add this part using the tab_header() method:\n\nfrom great_tables import GT, md, html\nfrom great_tables.data import islands\n\nislands_mini = islands.head(10)\n\n# Make a display table with the `islands_tbl`\n# table; put a heading just above the column labels\n(GT(islands_mini)\n .tab_header(\ntitle = \"Large Landmasses of the World\",\nsubtitle = \"The top ten largest are presented\"\n )\n)\n\n\n\n\n\n \n Large Landmasses of the World\n \n \n The top ten largest are presented\n \nname\nsize\n\n\n Africa\n 11506\n\n\n Antarctica\n 5500\n\n\n Asia\n 16988\n\n\n Australia\n 2968\n\n\n Axel Heiberg\n 16\n\n\n Baffin\n 184\n\n\n Banks\n 23\n\n\n Borneo\n 280\n\n\n Britain\n 84\n\n\n Celebes\n 73\n\n\n\n\n\n\n\n \n\n\nThe Header table part provides an opportunity to describe the data that’s presented. The subtitle, which functions as a subtitle, is an optional part of the Header. We may also style the title and subtitle using Markdown! We do this by wrapping the values passed to title or subtitle with the md() function. Here is an example with the table data truncated for brevity:\n\n# Make a display table with the `islands_tbl`\n# table; put a heading just above the column labels\ngt_tbl = (GT(islands.head(2))\n .tab_header(\ntitle = md(\"Large Landmasses of the World\"),\nsubtitle = md(\"The top ten largest are presented\")\n )\n)\n\ngt_tbl\n\n\n\n\n\n \n Large Landmasses of the World\n \n \n The top ten largest are presented\n \nname\nsize\n\n\n Africa\n 11506\n\n\n Antarctica\n 5500\n\n\n\n\n\n\n\n \n\n\nA source note can be added to the table’s footer through use of the tab_source_note() method. It works in the same way as tab_header() (it also allows for Markdown inputs) except it can be called multiple times—each invocation results in the addition of a source note.\n\n# Display the `islands_tbl` data with a heading and\n# two source notes\n(gt_tbl\n .tab_source_note(\n source_note = \"Source: The World Almanac and Book of Facts, 1975, page 406.\"\n )\n .tab_source_note(\n source_note = md(\"Reference: McNeil, D. R. (1977) *Interactive Data Analysis*. Wiley.\")\n )\n\n)\n\n\n\n\n\n \n Large Landmasses of the World\n \n \n The top ten largest are presented\n \nname\nsize\n\n\n Africa\n 11506\n\n\n Antarctica\n 5500\n\n\n \n \n \n Source: The World Almanac and Book of Facts, 1975, page 406.\n \n\n\n \n Reference: McNeil, D. R. (1977) Interactive Data Analysis. Wiley.\n \n\n\n\n\n\n\n \n\n\n\n🚧 Footnotes are not yet implemented, but footnotes might go here." }, { "objectID": "get-started/column-selection.html", @@ -746,14 +746,14 @@ "href": "get-started/index.html#some-beautiful-examples", "title": "Intro", "section": "Some beautiful examples", - "text": "Some beautiful examples\nIn the following pages we’ll use great_tables to turn DataFrames into beautiful tables, like the ones below.\n\n\nShow the Code\nfrom great_tables import GT, md, html\nfrom great_tables.data import islands\n\nislands_mini = islands.head(10)\n\n(\n GT(islands_mini, rowname_col = \"name\")\n .tab_header(\n title = \"Large Landmasses of the World\",\n subtitle = \"The top ten largest are presented\"\n )\n .tab_source_note(\n source_note = \"Source: The World Almanac and Book of Facts, 1975, page 406.\"\n )\n .tab_source_note(\n source_note = md(\"Reference: McNeil, D. R. (1977) *Interactive Data Analysis*. Wiley.\")\n )\n . tab_stubhead(label = \"landmass\")\n)\n\n\n\n\n\n\n \n Large Landmasses of the World\n \n \n The top ten largest are presented\n \nlandmass\nsize\n\n\n Africa\n 11506\n\n\n Antarctica\n 5500\n\n\n Asia\n 16988\n\n\n Australia\n 2968\n\n\n Axel Heiberg\n 16\n\n\n Baffin\n 184\n\n\n Banks\n 23\n\n\n Borneo\n 280\n\n\n Britain\n 84\n\n\n Celebes\n 73\n\n\n \n \n \n Source: The World Almanac and Book of Facts, 1975, page 406.\n \n\n\n \n \n \n\n\n\n\n\n\n \n\n\n\n\nShow the Code\nfrom great_tables.data import airquality\n\nairquality_m = airquality.head(10).assign(Year = 1973)\n\ngt_airquality = (\n GT(airquality_m)\n .tab_header(\n title = \"New York Air Quality Measurements\",\n subtitle = \"Daily measurements in New York City (May 1-10, 1973)\"\n )\n .tab_spanner(\n label = \"Time\",\n columns = [\"Year\", \"Month\", \"Day\"]\n )\n .tab_spanner(\n label = \"Measurement\",\n columns = [\"Ozone\", \"Solar.R\", \"Wind\", \"Temp\"]\n )\n .cols_move_to_start(columns = [\"Year\", \"Month\", \"Day\"])\n .cols_label(\n Ozone = html(\"Ozone,<br>ppbV\"),\n Solar_R = html(\"Solar R.,<br>cal/m<sup>2</sup>\"),\n Wind = html(\"Wind,<br>mph\"),\n Temp = html(\"Temp,<br>°F\")\n )\n)\n\ngt_airquality\n\n\n\n\n\n\n \n New York Air Quality Measurements\n \n \n Daily measurements in New York City (May 1-10, 1973)\n \n\n \n Time\n \n \n Measurement\n \n Solar R.,cal/m2\n\n\n Year\n Month\n Day\n Ozone,ppbV\n Wind,mph\n Temp,°F\n\n\n\n 1973\n 5\n 1\n 41.0\n 7.4\n 67\n 190.0\n\n\n 1973\n 5\n 2\n 36.0\n 8.0\n 72\n 118.0\n\n\n 1973\n 5\n 3\n 12.0\n 12.6\n 74\n 149.0\n\n\n 1973\n 5\n 4\n 18.0\n 11.5\n 62\n 313.0\n\n\n 1973\n 5\n 5\n \n 14.3\n 56\n \n\n\n 1973\n 5\n 6\n 28.0\n 14.9\n 66\n \n\n\n 1973\n 5\n 7\n 23.0\n 8.6\n 65\n 299.0\n\n\n 1973\n 5\n 8\n 19.0\n 13.8\n 59\n 99.0\n\n\n 1973\n 5\n 9\n 8.0\n 20.1\n 61\n 19.0\n\n\n 1973\n 5\n 10\n \n 8.6\n 69\n 194.0" + "text": "Some beautiful examples\nIn the following pages we’ll use great_tables to turn DataFrames into beautiful tables, like the ones below.\n\n\nShow the Code\nfrom great_tables import GT, md, html\nfrom great_tables.data import islands\n\nislands_mini = islands.head(10)\n\n(\n GT(islands_mini, rowname_col = \"name\")\n .tab_header(\n title = \"Large Landmasses of the World\",\n subtitle = \"The top ten largest are presented\"\n )\n .tab_source_note(\n source_note = \"Source: The World Almanac and Book of Facts, 1975, page 406.\"\n )\n .tab_source_note(\n source_note = md(\"Reference: McNeil, D. R. (1977) *Interactive Data Analysis*. Wiley.\")\n )\n . tab_stubhead(label = \"landmass\")\n)\n\n\n\n\n\n\n \n Large Landmasses of the World\n \n \n The top ten largest are presented\n \nlandmass\nsize\n\n\n Africa\n 11506\n\n\n Antarctica\n 5500\n\n\n Asia\n 16988\n\n\n Australia\n 2968\n\n\n Axel Heiberg\n 16\n\n\n Baffin\n 184\n\n\n Banks\n 23\n\n\n Borneo\n 280\n\n\n Britain\n 84\n\n\n Celebes\n 73\n\n\n \n \n \n Source: The World Almanac and Book of Facts, 1975, page 406.\n \n\n\n \n Reference: McNeil, D. R. (1977) Interactive Data Analysis. Wiley.\n \n\n\n\n\n\n\n \n\n\n\n\nShow the Code\nfrom great_tables.data import airquality\n\nairquality_m = airquality.head(10).assign(Year = 1973)\n\ngt_airquality = (\n GT(airquality_m)\n .tab_header(\n title = \"New York Air Quality Measurements\",\n subtitle = \"Daily measurements in New York City (May 1-10, 1973)\"\n )\n .tab_spanner(\n label = \"Time\",\n columns = [\"Year\", \"Month\", \"Day\"]\n )\n .tab_spanner(\n label = \"Measurement\",\n columns = [\"Ozone\", \"Solar.R\", \"Wind\", \"Temp\"]\n )\n .cols_move_to_start(columns = [\"Year\", \"Month\", \"Day\"])\n .cols_label(\n Ozone = html(\"Ozone,<br>ppbV\"),\n Solar_R = html(\"Solar R.,<br>cal/m<sup>2</sup>\"),\n Wind = html(\"Wind,<br>mph\"),\n Temp = html(\"Temp,<br>°F\")\n )\n)\n\ngt_airquality\n\n\n\n\n\n\n \n New York Air Quality Measurements\n \n \n Daily measurements in New York City (May 1-10, 1973)\n \n\n \n Time\n \n \n Measurement\n \n Solar R.,cal/m2\n\n\n Year\n Month\n Day\n Ozone,ppbV\n Wind,mph\n Temp,°F\n\n\n\n 1973\n 5\n 1\n 41.0\n 7.4\n 67\n 190.0\n\n\n 1973\n 5\n 2\n 36.0\n 8.0\n 72\n 118.0\n\n\n 1973\n 5\n 3\n 12.0\n 12.6\n 74\n 149.0\n\n\n 1973\n 5\n 4\n 18.0\n 11.5\n 62\n 313.0\n\n\n 1973\n 5\n 5\n \n 14.3\n 56\n \n\n\n 1973\n 5\n 6\n 28.0\n 14.9\n 66\n \n\n\n 1973\n 5\n 7\n 23.0\n 8.6\n 65\n 299.0\n\n\n 1973\n 5\n 8\n 19.0\n 13.8\n 59\n 99.0\n\n\n 1973\n 5\n 9\n 8.0\n 20.1\n 61\n 19.0\n\n\n 1973\n 5\n 10\n \n 8.6\n 69\n 194.0" }, { "objectID": "examples/index.html", "href": "examples/index.html", "title": "Examples", "section": "", - "text": "Code\nfrom great_tables import GT, md, html\nfrom great_tables.data import islands\n\nislands_mini = islands.head(10)\n\n(\n GT(islands_mini, rowname_col = \"name\")\n .tab_header(\n title = \"Large Landmasses of the World\",\n subtitle = \"The top ten largest are presented\"\n )\n .tab_source_note(\n source_note = \"Source: The World Almanac and Book of Facts, 1975, page 406.\"\n )\n .tab_source_note(\n source_note = md(\"Reference: McNeil, D. R. (1977) *Interactive Data Analysis*. Wiley.\")\n )\n . tab_stubhead(label = \"landmass\")\n)\n\n\n\n\n\n\n \n Large Landmasses of the World\n \n \n The top ten largest are presented\n \nlandmass\nsize\n\n\n Africa\n 11506\n\n\n Antarctica\n 5500\n\n\n Asia\n 16988\n\n\n Australia\n 2968\n\n\n Axel Heiberg\n 16\n\n\n Baffin\n 184\n\n\n Banks\n 23\n\n\n Borneo\n 280\n\n\n Britain\n 84\n\n\n Celebes\n 73\n\n\n \n \n \n Source: The World Almanac and Book of Facts, 1975, page 406.\n \n\n\n \n \n \n\n\n\n\n\n\n \n\n\n\n\n\n\nCode\nfrom great_tables import GT, md, html\nfrom great_tables.data import airquality\n\nairquality_mini = airquality.head(10).assign(Year = 1973)\n\n(\n GT(airquality_mini)\n .tab_header(\n title = \"New York Air Quality Measurements\",\n subtitle = \"Daily measurements in New York City (May 1-10, 1973)\"\n )\n .tab_spanner(\n label = \"Time\",\n columns = [\"Year\", \"Month\", \"Day\"]\n )\n .tab_spanner(\n label = \"Measurement\",\n columns = [\"Ozone\", \"Solar_R\", \"Wind\", \"Temp\"]\n )\n .cols_move_to_start(columns = [\"Year\", \"Month\", \"Day\"])\n .cols_label(\n Ozone = html(\"Ozone,<br>ppbV\"),\n Solar_R = html(\"Solar R.,<br>cal/m<sup>2</sup>\"),\n Wind = html(\"Wind,<br>mph\"),\n Temp = html(\"Temp,<br>°F\")\n )\n)\n\n\n\n\n\n\n \n New York Air Quality Measurements\n \n \n Daily measurements in New York City (May 1-10, 1973)\n \n\n \n Time\n \n \n Measurement\n \n\n\n Year\n Month\n Day\n Ozone,ppbV\n Solar R.,cal/m2\n Wind,mph\n Temp,°F\n\n\n\n 1973\n 5\n 1\n 41.0\n 190.0\n 7.4\n 67\n\n\n 1973\n 5\n 2\n 36.0\n 118.0\n 8.0\n 72\n\n\n 1973\n 5\n 3\n 12.0\n 149.0\n 12.6\n 74\n\n\n 1973\n 5\n 4\n 18.0\n 313.0\n 11.5\n 62\n\n\n 1973\n 5\n 5\n \n \n 14.3\n 56\n\n\n 1973\n 5\n 6\n 28.0\n \n 14.9\n 66\n\n\n 1973\n 5\n 7\n 23.0\n 299.0\n 8.6\n 65\n\n\n 1973\n 5\n 8\n 19.0\n 99.0\n 13.8\n 59\n\n\n 1973\n 5\n 9\n 8.0\n 19.0\n 20.1\n 61\n\n\n 1973\n 5\n 10\n \n 194.0\n 8.6\n 69\n\n\n\n\n\n\n\n \n\n\n\n\n\n\nCode\nfrom great_tables import GT\nfrom great_tables.data import countrypops\n\nimport polars as pl\nimport polars.selectors as cs\n\n# Get vectors of 2-letter country codes for each region of Oceania\ncountries = {\n \"Australasia\": [\"AU\", \"NZ\"],\n \"Melanesia\": [\"NC\", \"PG\", \"SB\", \"VU\"],\n \"Micronesia\": [\"FM\", \"GU\", \"KI\", \"MH\", \"MP\", \"NR\", \"PW\"],\n \"Polynesia\": [\"PF\", \"WS\", \"TO\", \"TV\"],\n}\n\n# a dictionary mapping region to country (e.g. AU -> Australasia)\nregion_to_country = {\n region: country for country, regions in countries.items() for region in regions\n}\n\nkeep_rows = countrypops.country_code_2.isin(list(region_to_country)) & countrypops.year.isin(\n [2000, 2010, 2020]\n)\n\n# Create a gt table based on a preprocessed `countrypops`\nwide_pops = (\n pl.from_pandas(countrypops)\n .filter(\n pl.col(\"country_code_2\").is_in(list(region_to_country))\n & pl.col(\"year\").is_in([2000, 2010, 2020])\n )\n .with_columns(pl.col(\"country_code_2\").replace(region_to_country).alias(\"region\"))\n .pivot(index=[\"country_name\", \"region\"], columns=\"year\", values=\"population\")\n .sort(\"2020\", descending=True)\n)\n\n(\n GT(wide_pops, rowname_col=\"country_name\", groupname_col=\"region\")\n .tab_header(title=\"Populations of Oceania's Countries in 2000, 2010, and 2020\")\n .tab_spanner(label=\"Total Population\", columns=cs.all())\n .fmt_integer()\n)\n\n\n\n\n\n\n \n Populations of Oceania's Countries in 2000, 2010, and 2020\n \n\n \n \n Total Population\n \n\n\n 2000\n 2010\n 2020\n\n\n\n Australasia\n Australia\n 19028802\n 22031750\n 25655289\n\n\n Melanesia\n Papua New Guinea\n 5508297\n 7583269\n 9749640\n\n\n New Zealand\n 3857700\n 4350700\n 5090200\n\n\n Solomon Islands\n 429978\n 540394\n 691191\n\n\n Vanuatu\n 192074\n 245453\n 311685\n\n\n Polynesia\n French Polynesia\n 250927\n 283788\n 301920\n\n\n New Caledonia\n 213230\n 249750\n 272460\n\n\n Samoa\n 184008\n 194672\n 214929\n\n\n Micronesia\n Guam\n 160188\n 164905\n 169231\n\n\n Kiribati\n 88826\n 107995\n 126463\n\n\n Micronesia (Federated States)\n 111709\n 107588\n 112106\n\n\n Tonga\n 102603\n 107383\n 105254\n\n\n Northern Mariana Islands\n 80338\n 54087\n 49587\n\n\n Marshall Islands\n 54224\n 53416\n 43413\n\n\n Palau\n 19726\n 18540\n 17972\n\n\n Nauru\n 10377\n 10241\n 12315\n\n\n Tuvalu\n 9638\n 10550\n 11069\n\n\n\n\n\n\n\n \n\n\n\n\n\n\nCode\nfrom great_tables import GT, md, html\nfrom great_tables.data import towny\n\n\ntowny_mini = (towny[[\"name\", \"website\", \"density_2021\", \"land_area_km2\",\n \"latitude\", \"longitude\"]].sort_values(\"density_2021\",\n ascending=False).head(10))\n\ntowny_mini[\"url_name\"] = ([\"[\"] + towny_mini[\"name\"] + [\"]\"] +\n [\"(\"] + towny_mini[\"website\"] + [\")\"])\n\ntowny_mini[\"location\"] =( [\"[map](http://maps.google.com/?ie=UTF8&hq=&ll=\"] +\n towny_mini[\"latitude\"].astype(str) + [\",\"] +\n towny_mini[\"longitude\"].astype(str) + [\"&z=13)\"])\n\n\n(\n GT(towny_mini[[\"url_name\", \"location\", \"land_area_km2\",\"density_2021\"]], rowname_col = \"url_name\")\n .tab_header(\n title = \"The Municipalities of Ontario\",\n subtitle = \"The top 10 highest population density in 2021\"\n )\n\n .tab_stubhead(label = \"Municipality\")\n\n .fmt_markdown(columns = [\"url_name\",\"location\"])\n\n .fmt_number(columns = [\"land_area_km2\", \"density_2021\"], decimals=1)\n\n .cols_label(\n land_area_km2 = html(\"land area, <br>km<sup>2</sup>\"),\n density_2021 = html(\"density, <br>people/km<sup>2</sup>\")\n )\n)\n\n\n\n\n\n\n \n The Municipalities of Ontario\n \n \n The top 10 highest population density in 2021\n \nMunicipality\nlocation\nland area, km2\ndensity, people/km2\n\n\n Toronto\n map\n 631.1\n 4,427.8\n\n\n Brampton\n map\n 265.9\n 2,469.0\n\n\n Mississauga\n map\n 292.7\n 2,452.6\n\n\n Newmarket\n map\n 38.5\n 2,284.2\n\n\n Richmond Hill\n map\n 100.8\n 2,004.4\n\n\n Orangeville\n map\n 15.2\n 1,989.9\n\n\n Ajax\n map\n 66.6\n 1,900.8\n\n\n Waterloo\n map\n 64.1\n 1,895.7\n\n\n Kitchener\n map\n 136.8\n 1,877.7\n\n\n Guelph\n map\n 87.4\n 1,644.1" + "text": "Code\nfrom great_tables import GT, md, html\nfrom great_tables.data import islands\n\nislands_mini = islands.head(10)\n\n(\n GT(islands_mini, rowname_col = \"name\")\n .tab_header(\n title = \"Large Landmasses of the World\",\n subtitle = \"The top ten largest are presented\"\n )\n .tab_source_note(\n source_note = \"Source: The World Almanac and Book of Facts, 1975, page 406.\"\n )\n .tab_source_note(\n source_note = md(\"Reference: McNeil, D. R. (1977) *Interactive Data Analysis*. Wiley.\")\n )\n . tab_stubhead(label = \"landmass\")\n)\n\n\n\n\n\n\n \n Large Landmasses of the World\n \n \n The top ten largest are presented\n \nlandmass\nsize\n\n\n Africa\n 11506\n\n\n Antarctica\n 5500\n\n\n Asia\n 16988\n\n\n Australia\n 2968\n\n\n Axel Heiberg\n 16\n\n\n Baffin\n 184\n\n\n Banks\n 23\n\n\n Borneo\n 280\n\n\n Britain\n 84\n\n\n Celebes\n 73\n\n\n \n \n \n Source: The World Almanac and Book of Facts, 1975, page 406.\n \n\n\n \n Reference: McNeil, D. R. (1977) Interactive Data Analysis. Wiley.\n \n\n\n\n\n\n\n \n\n\n\n\n\n\nCode\nfrom great_tables import GT, md, html\nfrom great_tables.data import airquality\n\nairquality_mini = airquality.head(10).assign(Year = 1973)\n\n(\n GT(airquality_mini)\n .tab_header(\n title = \"New York Air Quality Measurements\",\n subtitle = \"Daily measurements in New York City (May 1-10, 1973)\"\n )\n .tab_spanner(\n label = \"Time\",\n columns = [\"Year\", \"Month\", \"Day\"]\n )\n .tab_spanner(\n label = \"Measurement\",\n columns = [\"Ozone\", \"Solar_R\", \"Wind\", \"Temp\"]\n )\n .cols_move_to_start(columns = [\"Year\", \"Month\", \"Day\"])\n .cols_label(\n Ozone = html(\"Ozone,<br>ppbV\"),\n Solar_R = html(\"Solar R.,<br>cal/m<sup>2</sup>\"),\n Wind = html(\"Wind,<br>mph\"),\n Temp = html(\"Temp,<br>°F\")\n )\n)\n\n\n\n\n\n\n \n New York Air Quality Measurements\n \n \n Daily measurements in New York City (May 1-10, 1973)\n \n\n \n Time\n \n \n Measurement\n \n\n\n Year\n Month\n Day\n Ozone,ppbV\n Solar R.,cal/m2\n Wind,mph\n Temp,°F\n\n\n\n 1973\n 5\n 1\n 41.0\n 190.0\n 7.4\n 67\n\n\n 1973\n 5\n 2\n 36.0\n 118.0\n 8.0\n 72\n\n\n 1973\n 5\n 3\n 12.0\n 149.0\n 12.6\n 74\n\n\n 1973\n 5\n 4\n 18.0\n 313.0\n 11.5\n 62\n\n\n 1973\n 5\n 5\n \n \n 14.3\n 56\n\n\n 1973\n 5\n 6\n 28.0\n \n 14.9\n 66\n\n\n 1973\n 5\n 7\n 23.0\n 299.0\n 8.6\n 65\n\n\n 1973\n 5\n 8\n 19.0\n 99.0\n 13.8\n 59\n\n\n 1973\n 5\n 9\n 8.0\n 19.0\n 20.1\n 61\n\n\n 1973\n 5\n 10\n \n 194.0\n 8.6\n 69\n\n\n\n\n\n\n\n \n\n\n\n\n\n\nCode\nfrom great_tables import GT\nfrom great_tables.data import countrypops\n\nimport polars as pl\nimport polars.selectors as cs\n\n# Get vectors of 2-letter country codes for each region of Oceania\ncountries = {\n \"Australasia\": [\"AU\", \"NZ\"],\n \"Melanesia\": [\"NC\", \"PG\", \"SB\", \"VU\"],\n \"Micronesia\": [\"FM\", \"GU\", \"KI\", \"MH\", \"MP\", \"NR\", \"PW\"],\n \"Polynesia\": [\"PF\", \"WS\", \"TO\", \"TV\"],\n}\n\n# a dictionary mapping region to country (e.g. AU -> Australasia)\nregion_to_country = {\n region: country for country, regions in countries.items() for region in regions\n}\n\nkeep_rows = countrypops.country_code_2.isin(list(region_to_country)) & countrypops.year.isin(\n [2000, 2010, 2020]\n)\n\n# Create a gt table based on a preprocessed `countrypops`\nwide_pops = (\n pl.from_pandas(countrypops)\n .filter(\n pl.col(\"country_code_2\").is_in(list(region_to_country))\n & pl.col(\"year\").is_in([2000, 2010, 2020])\n )\n .with_columns(pl.col(\"country_code_2\").replace(region_to_country).alias(\"region\"))\n .pivot(index=[\"country_name\", \"region\"], columns=\"year\", values=\"population\")\n .sort(\"2020\", descending=True)\n)\n\n(\n GT(wide_pops, rowname_col=\"country_name\", groupname_col=\"region\")\n .tab_header(title=\"Populations of Oceania's Countries in 2000, 2010, and 2020\")\n .tab_spanner(label=\"Total Population\", columns=cs.all())\n .fmt_integer()\n)\n\n\n\n\n\n\n \n Populations of Oceania's Countries in 2000, 2010, and 2020\n \n\n \n \n Total Population\n \n\n\n 2000\n 2010\n 2020\n\n\n\n Australasia\n Australia\n 19028802\n 22031750\n 25655289\n\n\n Melanesia\n Papua New Guinea\n 5508297\n 7583269\n 9749640\n\n\n New Zealand\n 3857700\n 4350700\n 5090200\n\n\n Solomon Islands\n 429978\n 540394\n 691191\n\n\n Vanuatu\n 192074\n 245453\n 311685\n\n\n Polynesia\n French Polynesia\n 250927\n 283788\n 301920\n\n\n New Caledonia\n 213230\n 249750\n 272460\n\n\n Samoa\n 184008\n 194672\n 214929\n\n\n Micronesia\n Guam\n 160188\n 164905\n 169231\n\n\n Kiribati\n 88826\n 107995\n 126463\n\n\n Micronesia (Federated States)\n 111709\n 107588\n 112106\n\n\n Tonga\n 102603\n 107383\n 105254\n\n\n Northern Mariana Islands\n 80338\n 54087\n 49587\n\n\n Marshall Islands\n 54224\n 53416\n 43413\n\n\n Palau\n 19726\n 18540\n 17972\n\n\n Nauru\n 10377\n 10241\n 12315\n\n\n Tuvalu\n 9638\n 10550\n 11069\n\n\n\n\n\n\n\n \n\n\n\n\n\n\nCode\nfrom great_tables import GT, md, html\nfrom great_tables.data import towny\n\n\ntowny_mini = (towny[[\"name\", \"website\", \"density_2021\", \"land_area_km2\",\n \"latitude\", \"longitude\"]].sort_values(\"density_2021\",\n ascending=False).head(10))\n\ntowny_mini[\"url_name\"] = ([\"[\"] + towny_mini[\"name\"] + [\"]\"] +\n [\"(\"] + towny_mini[\"website\"] + [\")\"])\n\ntowny_mini[\"location\"] =( [\"[map](http://maps.google.com/?ie=UTF8&hq=&ll=\"] +\n towny_mini[\"latitude\"].astype(str) + [\",\"] +\n towny_mini[\"longitude\"].astype(str) + [\"&z=13)\"])\n\n\n(\n GT(towny_mini[[\"url_name\", \"location\", \"land_area_km2\",\"density_2021\"]], rowname_col = \"url_name\")\n .tab_header(\n title = \"The Municipalities of Ontario\",\n subtitle = \"The top 10 highest population density in 2021\"\n )\n\n .tab_stubhead(label = \"Municipality\")\n\n .fmt_markdown(columns = [\"url_name\",\"location\"])\n\n .fmt_number(columns = [\"land_area_km2\", \"density_2021\"], decimals=1)\n\n .cols_label(\n land_area_km2 = html(\"land area, <br>km<sup>2</sup>\"),\n density_2021 = html(\"density, <br>people/km<sup>2</sup>\")\n )\n)\n\n\n\n\n\n\n \n The Municipalities of Ontario\n \n \n The top 10 highest population density in 2021\n \nMunicipality\nlocation\nland area, km2\ndensity, people/km2\n\n\n Toronto\n map\n 631.1\n 4,427.8\n\n\n Brampton\n map\n 265.9\n 2,469.0\n\n\n Mississauga\n map\n 292.7\n 2,452.6\n\n\n Newmarket\n map\n 38.5\n 2,284.2\n\n\n Richmond Hill\n map\n 100.8\n 2,004.4\n\n\n Orangeville\n map\n 15.2\n 1,989.9\n\n\n Ajax\n map\n 66.6\n 1,900.8\n\n\n Waterloo\n map\n 64.1\n 1,895.7\n\n\n Kitchener\n map\n 136.8\n 1,877.7\n\n\n Guelph\n map\n 87.4\n 1,644.1" }, { "objectID": "get-started/overview.html",