# Working with Datasets

**Great Tables** includes sixteen built-in datasets that are used throughout this User Guide and in the API documentation. These datasets cover a range of subject areas and sizes, from the small [exibble](../reference/data.exibble.md#great_tables.data.exibble) toy table (8 rows) to the larger [pizzaplace](../reference/data.pizzaplace.md#great_tables.data.pizzaplace) dataset (nearly 50,000 rows). You can load any of them as a Pandas DataFrame, a Polars DataFrame, or both, depending on how you prefer to work.

These built-in datasets exist so you can experiment with Great Tables features without needing to bring your own data. They make it easy to follow along with examples, test table-building code, and explore different formatting options. They also demonstrate the range of data types and structures that Great Tables handles well, from small categorical tables to large transactional datasets with dates, currencies, and numeric measurements.


# Accessing Datasets Directly

The simplest way to use a dataset is to import it by name from `great_tables.data`. This returns a Pandas DataFrame by default (or a Polars DataFrame if Pandas is not installed).


``` python
from great_tables import GT
from great_tables.data import exibble

GT(exibble)
```


<style>
#tjqgflpywy table {
          font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Helvetica Neue', 'Fira Sans', 'Droid Sans', Arial, sans-serif;
          -webkit-font-smoothing: antialiased;
          -moz-osx-font-smoothing: grayscale;
        }

#tjqgflpywy thead, tbody, tfoot, tr, td, th { border-style: none; }
 tr { background-color: transparent; }
#tjqgflpywy p { margin: 0; padding: 0; }
 #tjqgflpywy .gt_table { display: table; border-collapse: collapse; line-height: normal; margin-left: auto; margin-right: auto; color: #333333; font-size: 16px; font-weight: normal; font-style: normal; background-color: #FFFFFF; width: auto; border-top-style: solid; border-top-width: 2px; border-top-color: #A8A8A8; border-right-style: none; border-right-width: 2px; border-right-color: #D3D3D3; border-bottom-style: solid; border-bottom-width: 2px; border-bottom-color: #A8A8A8; border-left-style: none; border-left-width: 2px; border-left-color: #D3D3D3; }
 #tjqgflpywy .gt_caption { padding-top: 4px; padding-bottom: 4px; }
 #tjqgflpywy .gt_title { color: #333333; font-size: 125%; font-weight: initial; padding-top: 4px; padding-bottom: 4px; padding-left: 5px; padding-right: 5px; border-bottom-color: #FFFFFF; border-bottom-width: 0; }
 #tjqgflpywy .gt_subtitle { color: #333333; font-size: 85%; font-weight: initial; padding-top: 3px; padding-bottom: 5px; padding-left: 5px; padding-right: 5px; border-top-color: #FFFFFF; border-top-width: 0; }
 #tjqgflpywy .gt_heading { background-color: #FFFFFF; text-align: center; border-bottom-color: #FFFFFF; border-left-style: none; border-left-width: 1px; border-left-color: #D3D3D3; border-right-style: none; border-right-width: 1px; border-right-color: #D3D3D3; }
 #tjqgflpywy .gt_bottom_border { border-bottom-style: solid; border-bottom-width: 2px; border-bottom-color: #D3D3D3; }
 #tjqgflpywy .gt_col_headings { border-top-style: solid; border-top-width: 2px; border-top-color: #D3D3D3; border-bottom-style: solid; border-bottom-width: 2px; border-bottom-color: #D3D3D3; border-left-style: none; border-left-width: 1px; border-left-color: #D3D3D3; border-right-style: none; border-right-width: 1px; border-right-color: #D3D3D3; }
 #tjqgflpywy .gt_col_heading { color: #333333; background-color: #FFFFFF; font-size: 100%; font-weight: normal; text-transform: inherit; border-left-style: none; border-left-width: 1px; border-left-color: #D3D3D3; border-right-style: none; border-right-width: 1px; border-right-color: #D3D3D3; vertical-align: bottom; padding-top: 5px; padding-bottom: 5px; padding-left: 5px; padding-right: 5px; overflow-x: hidden; }
 #tjqgflpywy .gt_column_spanner_outer { color: #333333; background-color: #FFFFFF; font-size: 100%; font-weight: normal; text-transform: inherit; padding-top: 0; padding-bottom: 0; padding-left: 4px; padding-right: 4px; }
 #tjqgflpywy .gt_column_spanner_outer:first-child { padding-left: 0; }
 #tjqgflpywy .gt_column_spanner_outer:last-child { padding-right: 0; }
 #tjqgflpywy .gt_column_spanner { border-bottom-style: solid; border-bottom-width: 2px; border-bottom-color: #D3D3D3; vertical-align: bottom; padding-top: 5px; padding-bottom: 5px; overflow-x: hidden; display: inline-block; width: 100%; }
 #tjqgflpywy .gt_spanner_row { border-bottom-style: hidden; }
 #tjqgflpywy .gt_group_heading { padding-top: 8px; padding-bottom: 8px; padding-left: 5px; padding-right: 5px; color: #333333; background-color: #FFFFFF; font-size: 100%; font-weight: initial; text-transform: inherit; border-top-style: solid; border-top-width: 2px; border-top-color: #D3D3D3; border-bottom-style: solid; border-bottom-width: 2px; border-bottom-color: #D3D3D3; border-left-style: none; border-left-width: 1px; border-left-color: #D3D3D3; border-right-style: none; border-right-width: 1px; border-right-color: #D3D3D3; vertical-align: middle; text-align: left; }
 #tjqgflpywy .gt_empty_group_heading { padding: 0.5px; color: #333333; background-color: #FFFFFF; font-size: 100%; font-weight: initial; border-top-style: solid; border-top-width: 2px; border-top-color: #D3D3D3; border-bottom-style: solid; border-bottom-width: 2px; border-bottom-color: #D3D3D3; vertical-align: middle; }
 #tjqgflpywy .gt_from_md> :first-child { margin-top: 0; }
 #tjqgflpywy .gt_from_md> :last-child { margin-bottom: 0; }
 #tjqgflpywy .gt_row { padding-top: 8px; padding-bottom: 8px; padding-left: 5px; padding-right: 5px; margin: 10px; border-top-style: solid; border-top-width: 1px; border-top-color: #D3D3D3; border-left-style: none; border-left-width: 1px; border-left-color: #D3D3D3; border-right-style: none; border-right-width: 1px; border-right-color: #D3D3D3; vertical-align: middle; overflow-x: hidden; }
 #tjqgflpywy .gt_stub { color: #333333; background-color: #FFFFFF; font-size: 100%; font-weight: initial; text-transform: inherit; border-right-style: solid; border-right-width: 2px; border-right-color: #D3D3D3; padding-left: 5px; padding-right: 5px; }
 #tjqgflpywy .gt_indent_1 { text-indent: 5px; }
 #tjqgflpywy .gt_indent_2 { text-indent: calc(5px * 2); }
 #tjqgflpywy .gt_indent_3 { text-indent: calc(5px * 3); }
 #tjqgflpywy .gt_indent_4 { text-indent: calc(5px * 4); }
 #tjqgflpywy .gt_indent_5 { text-indent: calc(5px * 5); }
 #tjqgflpywy .gt_stub_row_group { color: #333333; background-color: #FFFFFF; font-size: 100%; font-weight: initial; text-transform: inherit; border-right-style: solid; border-right-width: 2px; border-right-color: #D3D3D3; padding-left: 5px; padding-right: 5px; vertical-align: top; }
 #tjqgflpywy .gt_row_group_first td { border-top-width: 2px; }
 #tjqgflpywy .gt_row_group_first th { border-top-width: 2px; }
 #tjqgflpywy .gt_striped { color: #333333; background-color: #F4F4F4; }
 #tjqgflpywy .gt_table_body { border-top-style: solid; border-top-width: 2px; border-top-color: #D3D3D3; border-bottom-style: solid; border-bottom-width: 2px; border-bottom-color: #D3D3D3; }
 #tjqgflpywy .gt_summary_row { color: #333333; background-color: #FFFFFF; text-transform: inherit; padding-top: 8px; padding-bottom: 8px; padding-left: 5px; padding-right: 5px; }
 #tjqgflpywy .gt_first_summary_row { border-top-style: solid; border-top-width: 2px; border-top-color: #D3D3D3; }
 #tjqgflpywy .gt_last_summary_row_top { border-bottom-style: solid; border-bottom-width: 2px; border-bottom-color: #D3D3D3; }
 #tjqgflpywy .gt_grand_summary_row { color: #333333; background-color: #FFFFFF; text-transform: inherit; padding-top: 8px; padding-bottom: 8px; padding-left: 5px; padding-right: 5px; }
 #tjqgflpywy .gt_first_grand_summary_row_bottom { border-top-style: double; border-top-width: 6px; border-top-color: #D3D3D3; }
 #tjqgflpywy .gt_last_grand_summary_row_top { border-bottom-style: double; border-bottom-width: 6px; border-bottom-color: #D3D3D3; }
 #tjqgflpywy .gt_sourcenotes { color: #333333; background-color: #FFFFFF; border-bottom-style: none; border-bottom-width: 2px; border-bottom-color: #D3D3D3; border-left-style: none; border-left-width: 2px; border-left-color: #D3D3D3; border-right-style: none; border-right-width: 2px; border-right-color: #D3D3D3; }
 #tjqgflpywy .gt_sourcenote { font-size: 90%; padding-top: 4px; padding-bottom: 4px; padding-left: 5px; padding-right: 5px; text-align: left; }
 #tjqgflpywy .gt_left { text-align: left; }
 #tjqgflpywy .gt_center { text-align: center; }
 #tjqgflpywy .gt_right { text-align: right; font-variant-numeric: tabular-nums; }
 #tjqgflpywy .gt_font_normal { font-weight: normal; }
 #tjqgflpywy .gt_font_bold { font-weight: bold; }
 #tjqgflpywy .gt_font_italic { font-style: italic; }
 #tjqgflpywy .gt_super { font-size: 65%; }
 #tjqgflpywy .gt_footnotes { color: font-color(#FFFFFF); background-color: #FFFFFF; border-bottom-style: none; border-bottom-width: 2px; border-bottom-color: #D3D3D3; border-left-style: none; border-left-width: 2px; border-left-color: #D3D3D3; border-right-style: none; border-right-width: 2px; border-right-color: #D3D3D3; }
 #tjqgflpywy .gt_footnote { margin: 0px; font-size: 90%; padding-top: 4px; padding-bottom: 4px; padding-left: 5px; padding-right: 5px; }
 #tjqgflpywy .gt_sourcenotes { color: #333333; background-color: #FFFFFF; border-bottom-style: none; border-bottom-width: 2px; border-bottom-color: #D3D3D3; border-left-style: none; border-left-width: 2px; border-left-color: #D3D3D3; border-right-style: none; border-right-width: 2px; border-right-color: #D3D3D3; }
 #tjqgflpywy .gt_sourcenote { font-size: 90%; padding-top: 4px; padding-bottom: 4px; padding-left: 5px; padding-right: 5px; text-align: left; }
 #tjqgflpywy .gt_footnote_marks { font-size: 75%; vertical-align: 0.4em; position: initial; }
 #tjqgflpywy .gt_asterisk { font-size: 100%; vertical-align: 0; }
 
</style>

| num | char | fctr | date | time | datetime | currency | row | group |
|----|----|----|----|----|----|----|----|----|
| 0.1111 | apricot | one | 2015-01-15 | 13:35 | 2018-01-01 02:22 | 49.95 | row_1 | grp_a |
| 2.222 | banana | two | 2015-02-15 | 14:40 | 2018-02-02 14:33 | 17.95 | row_2 | grp_a |
| 33.33 | coconut | three | 2015-03-15 | 15:45 | 2018-03-03 03:44 | 1.39 | row_3 | grp_a |
| 444.4 | durian | four | 2015-04-15 | 16:50 | 2018-04-04 15:55 | 65100.0 | row_4 | grp_a |
| 5550.0 |  | five | 2015-05-15 | 17:55 | 2018-05-05 04:00 | 1325.81 | row_5 | grp_b |
|  | fig | six | 2015-06-15 |  | 2018-06-06 16:11 | 13.255 | row_6 | grp_b |
| 777000.0 | grapefruit | seven |  | 19:10 | 2018-07-07 05:22 |  | row_7 | grp_b |
| 8880000.0 | honeydew | eight | 2015-08-15 | 20:20 |  | 0.44 | row_8 | grp_b |


The [exibble](../reference/data.exibble.md#great_tables.data.exibble) dataset is also available directly on the top-level `great_tables` module, which can be convenient for quick experimentation:


``` python
import great_tables as gt

GT(gt.exibble)
```


<style>
#gglwrvxppt table {
          font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Helvetica Neue', 'Fira Sans', 'Droid Sans', Arial, sans-serif;
          -webkit-font-smoothing: antialiased;
          -moz-osx-font-smoothing: grayscale;
        }

#gglwrvxppt thead, tbody, tfoot, tr, td, th { border-style: none; }
 tr { background-color: transparent; }
#gglwrvxppt p { margin: 0; padding: 0; }
 #gglwrvxppt .gt_table { display: table; border-collapse: collapse; line-height: normal; margin-left: auto; margin-right: auto; color: #333333; font-size: 16px; font-weight: normal; font-style: normal; background-color: #FFFFFF; width: auto; border-top-style: solid; border-top-width: 2px; border-top-color: #A8A8A8; border-right-style: none; border-right-width: 2px; border-right-color: #D3D3D3; border-bottom-style: solid; border-bottom-width: 2px; border-bottom-color: #A8A8A8; border-left-style: none; border-left-width: 2px; border-left-color: #D3D3D3; }
 #gglwrvxppt .gt_caption { padding-top: 4px; padding-bottom: 4px; }
 #gglwrvxppt .gt_title { color: #333333; font-size: 125%; font-weight: initial; padding-top: 4px; padding-bottom: 4px; padding-left: 5px; padding-right: 5px; border-bottom-color: #FFFFFF; border-bottom-width: 0; }
 #gglwrvxppt .gt_subtitle { color: #333333; font-size: 85%; font-weight: initial; padding-top: 3px; padding-bottom: 5px; padding-left: 5px; padding-right: 5px; border-top-color: #FFFFFF; border-top-width: 0; }
 #gglwrvxppt .gt_heading { background-color: #FFFFFF; text-align: center; border-bottom-color: #FFFFFF; border-left-style: none; border-left-width: 1px; border-left-color: #D3D3D3; border-right-style: none; border-right-width: 1px; border-right-color: #D3D3D3; }
 #gglwrvxppt .gt_bottom_border { border-bottom-style: solid; border-bottom-width: 2px; border-bottom-color: #D3D3D3; }
 #gglwrvxppt .gt_col_headings { border-top-style: solid; border-top-width: 2px; border-top-color: #D3D3D3; border-bottom-style: solid; border-bottom-width: 2px; border-bottom-color: #D3D3D3; border-left-style: none; border-left-width: 1px; border-left-color: #D3D3D3; border-right-style: none; border-right-width: 1px; border-right-color: #D3D3D3; }
 #gglwrvxppt .gt_col_heading { color: #333333; background-color: #FFFFFF; font-size: 100%; font-weight: normal; text-transform: inherit; border-left-style: none; border-left-width: 1px; border-left-color: #D3D3D3; border-right-style: none; border-right-width: 1px; border-right-color: #D3D3D3; vertical-align: bottom; padding-top: 5px; padding-bottom: 5px; padding-left: 5px; padding-right: 5px; overflow-x: hidden; }
 #gglwrvxppt .gt_column_spanner_outer { color: #333333; background-color: #FFFFFF; font-size: 100%; font-weight: normal; text-transform: inherit; padding-top: 0; padding-bottom: 0; padding-left: 4px; padding-right: 4px; }
 #gglwrvxppt .gt_column_spanner_outer:first-child { padding-left: 0; }
 #gglwrvxppt .gt_column_spanner_outer:last-child { padding-right: 0; }
 #gglwrvxppt .gt_column_spanner { border-bottom-style: solid; border-bottom-width: 2px; border-bottom-color: #D3D3D3; vertical-align: bottom; padding-top: 5px; padding-bottom: 5px; overflow-x: hidden; display: inline-block; width: 100%; }
 #gglwrvxppt .gt_spanner_row { border-bottom-style: hidden; }
 #gglwrvxppt .gt_group_heading { padding-top: 8px; padding-bottom: 8px; padding-left: 5px; padding-right: 5px; color: #333333; background-color: #FFFFFF; font-size: 100%; font-weight: initial; text-transform: inherit; border-top-style: solid; border-top-width: 2px; border-top-color: #D3D3D3; border-bottom-style: solid; border-bottom-width: 2px; border-bottom-color: #D3D3D3; border-left-style: none; border-left-width: 1px; border-left-color: #D3D3D3; border-right-style: none; border-right-width: 1px; border-right-color: #D3D3D3; vertical-align: middle; text-align: left; }
 #gglwrvxppt .gt_empty_group_heading { padding: 0.5px; color: #333333; background-color: #FFFFFF; font-size: 100%; font-weight: initial; border-top-style: solid; border-top-width: 2px; border-top-color: #D3D3D3; border-bottom-style: solid; border-bottom-width: 2px; border-bottom-color: #D3D3D3; vertical-align: middle; }
 #gglwrvxppt .gt_from_md> :first-child { margin-top: 0; }
 #gglwrvxppt .gt_from_md> :last-child { margin-bottom: 0; }
 #gglwrvxppt .gt_row { padding-top: 8px; padding-bottom: 8px; padding-left: 5px; padding-right: 5px; margin: 10px; border-top-style: solid; border-top-width: 1px; border-top-color: #D3D3D3; border-left-style: none; border-left-width: 1px; border-left-color: #D3D3D3; border-right-style: none; border-right-width: 1px; border-right-color: #D3D3D3; vertical-align: middle; overflow-x: hidden; }
 #gglwrvxppt .gt_stub { color: #333333; background-color: #FFFFFF; font-size: 100%; font-weight: initial; text-transform: inherit; border-right-style: solid; border-right-width: 2px; border-right-color: #D3D3D3; padding-left: 5px; padding-right: 5px; }
 #gglwrvxppt .gt_indent_1 { text-indent: 5px; }
 #gglwrvxppt .gt_indent_2 { text-indent: calc(5px * 2); }
 #gglwrvxppt .gt_indent_3 { text-indent: calc(5px * 3); }
 #gglwrvxppt .gt_indent_4 { text-indent: calc(5px * 4); }
 #gglwrvxppt .gt_indent_5 { text-indent: calc(5px * 5); }
 #gglwrvxppt .gt_stub_row_group { color: #333333; background-color: #FFFFFF; font-size: 100%; font-weight: initial; text-transform: inherit; border-right-style: solid; border-right-width: 2px; border-right-color: #D3D3D3; padding-left: 5px; padding-right: 5px; vertical-align: top; }
 #gglwrvxppt .gt_row_group_first td { border-top-width: 2px; }
 #gglwrvxppt .gt_row_group_first th { border-top-width: 2px; }
 #gglwrvxppt .gt_striped { color: #333333; background-color: #F4F4F4; }
 #gglwrvxppt .gt_table_body { border-top-style: solid; border-top-width: 2px; border-top-color: #D3D3D3; border-bottom-style: solid; border-bottom-width: 2px; border-bottom-color: #D3D3D3; }
 #gglwrvxppt .gt_summary_row { color: #333333; background-color: #FFFFFF; text-transform: inherit; padding-top: 8px; padding-bottom: 8px; padding-left: 5px; padding-right: 5px; }
 #gglwrvxppt .gt_first_summary_row { border-top-style: solid; border-top-width: 2px; border-top-color: #D3D3D3; }
 #gglwrvxppt .gt_last_summary_row_top { border-bottom-style: solid; border-bottom-width: 2px; border-bottom-color: #D3D3D3; }
 #gglwrvxppt .gt_grand_summary_row { color: #333333; background-color: #FFFFFF; text-transform: inherit; padding-top: 8px; padding-bottom: 8px; padding-left: 5px; padding-right: 5px; }
 #gglwrvxppt .gt_first_grand_summary_row_bottom { border-top-style: double; border-top-width: 6px; border-top-color: #D3D3D3; }
 #gglwrvxppt .gt_last_grand_summary_row_top { border-bottom-style: double; border-bottom-width: 6px; border-bottom-color: #D3D3D3; }
 #gglwrvxppt .gt_sourcenotes { color: #333333; background-color: #FFFFFF; border-bottom-style: none; border-bottom-width: 2px; border-bottom-color: #D3D3D3; border-left-style: none; border-left-width: 2px; border-left-color: #D3D3D3; border-right-style: none; border-right-width: 2px; border-right-color: #D3D3D3; }
 #gglwrvxppt .gt_sourcenote { font-size: 90%; padding-top: 4px; padding-bottom: 4px; padding-left: 5px; padding-right: 5px; text-align: left; }
 #gglwrvxppt .gt_left { text-align: left; }
 #gglwrvxppt .gt_center { text-align: center; }
 #gglwrvxppt .gt_right { text-align: right; font-variant-numeric: tabular-nums; }
 #gglwrvxppt .gt_font_normal { font-weight: normal; }
 #gglwrvxppt .gt_font_bold { font-weight: bold; }
 #gglwrvxppt .gt_font_italic { font-style: italic; }
 #gglwrvxppt .gt_super { font-size: 65%; }
 #gglwrvxppt .gt_footnotes { color: font-color(#FFFFFF); background-color: #FFFFFF; border-bottom-style: none; border-bottom-width: 2px; border-bottom-color: #D3D3D3; border-left-style: none; border-left-width: 2px; border-left-color: #D3D3D3; border-right-style: none; border-right-width: 2px; border-right-color: #D3D3D3; }
 #gglwrvxppt .gt_footnote { margin: 0px; font-size: 90%; padding-top: 4px; padding-bottom: 4px; padding-left: 5px; padding-right: 5px; }
 #gglwrvxppt .gt_sourcenotes { color: #333333; background-color: #FFFFFF; border-bottom-style: none; border-bottom-width: 2px; border-bottom-color: #D3D3D3; border-left-style: none; border-left-width: 2px; border-left-color: #D3D3D3; border-right-style: none; border-right-width: 2px; border-right-color: #D3D3D3; }
 #gglwrvxppt .gt_sourcenote { font-size: 90%; padding-top: 4px; padding-bottom: 4px; padding-left: 5px; padding-right: 5px; text-align: left; }
 #gglwrvxppt .gt_footnote_marks { font-size: 75%; vertical-align: 0.4em; position: initial; }
 #gglwrvxppt .gt_asterisk { font-size: 100%; vertical-align: 0; }
 
</style>

| num | char | fctr | date | time | datetime | currency | row | group |
|----|----|----|----|----|----|----|----|----|
| 0.1111 | apricot | one | 2015-01-15 | 13:35 | 2018-01-01 02:22 | 49.95 | row_1 | grp_a |
| 2.222 | banana | two | 2015-02-15 | 14:40 | 2018-02-02 14:33 | 17.95 | row_2 | grp_a |
| 33.33 | coconut | three | 2015-03-15 | 15:45 | 2018-03-03 03:44 | 1.39 | row_3 | grp_a |
| 444.4 | durian | four | 2015-04-15 | 16:50 | 2018-04-04 15:55 | 65100.0 | row_4 | grp_a |
| 5550.0 |  | five | 2015-05-15 | 17:55 | 2018-05-05 04:00 | 1325.81 | row_5 | grp_b |
|  | fig | six | 2015-06-15 |  | 2018-06-06 16:11 | 13.255 | row_6 | grp_b |
| 777000.0 | grapefruit | seven |  | 19:10 | 2018-07-07 05:22 |  | row_7 | grp_b |
| 8880000.0 | honeydew | eight | 2015-08-15 | 20:20 |  | 0.44 | row_8 | grp_b |


All sixteen datasets are available this way: [countrypops](../reference/data.countrypops.md#great_tables.data.countrypops), [sza](../reference/data.sza.md#great_tables.data.sza), [gtcars](../reference/data.gtcars.md#great_tables.data.gtcars), [sp500](../reference/data.sp500.md#great_tables.data.sp500), [pizzaplace](../reference/data.pizzaplace.md#great_tables.data.pizzaplace), [exibble](../reference/data.exibble.md#great_tables.data.exibble), [towny](../reference/data.towny.md#great_tables.data.towny), [peeps](../reference/data.peeps.md#great_tables.data.peeps), [films](../reference/data.films.md#great_tables.data.films), [metro](../reference/data.metro.md#great_tables.data.metro), [gibraltar](../reference/data.gibraltar.md#great_tables.data.gibraltar), [constants](../reference/data.constants.md#great_tables.data.constants), [illness](../reference/data.illness.md#great_tables.data.illness), [reactions](../reference/data.reactions.md#great_tables.data.reactions), [photolysis](../reference/data.photolysis.md#great_tables.data.photolysis), and [nuclides](../reference/data.nuclides.md#great_tables.data.nuclides). Each one has its own documentation page in the API reference with a full description of its columns and contents.

This direct import approach is the most concise option and works well when you're writing examples or exploring the package. Since the default backend is Pandas, existing code that imports datasets this way will continue to work without any changes.


# Choosing a Backend with `data.pd` and `data.pl`

If you want to be explicit about whether you get a Pandas or Polars DataFrame, use the `data.pd` and `data.pl` namespaces. Being explicit about your backend is especially important in shared codebases or library code where you want to guarantee the DataFrame type, regardless of what packages happen to be installed in a given environment. Datasets accessed through these namespaces are loaded on first access and cached for subsequent use, so there's no performance penalty for repeated access.

To get a dataset as a Polars DataFrame, use `data.pl`:


``` python
from great_tables import GT, data

GT(data.pl.exibble)
```


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 #rwizyxphfj .gt_asterisk { font-size: 100%; vertical-align: 0; }
 
</style>

| num | char | fctr | date | time | datetime | currency | row | group |
|----|----|----|----|----|----|----|----|----|
| 0.1111 | apricot | one | 2015-01-15 | 13:35 | 2018-01-01 02:22 | 49.95 | row_1 | grp_a |
| 2.222 | banana | two | 2015-02-15 | 14:40 | 2018-02-02 14:33 | 17.95 | row_2 | grp_a |
| 33.33 | coconut | three | 2015-03-15 | 15:45 | 2018-03-03 03:44 | 1.39 | row_3 | grp_a |
| 444.4 | durian | four | 2015-04-15 | 16:50 | 2018-04-04 15:55 | 65100.0 | row_4 | grp_a |
| 5550.0 | None | five | 2015-05-15 | 17:55 | 2018-05-05 04:00 | 1325.81 | row_5 | grp_b |
| None | fig | six | 2015-06-15 | None | 2018-06-06 16:11 | 13.255 | row_6 | grp_b |
| 777000.0 | grapefruit | seven | None | 19:10 | 2018-07-07 05:22 | None | row_7 | grp_b |
| 8880000.0 | honeydew | eight | 2015-08-15 | 20:20 | None | 0.44 | row_8 | grp_b |


To explicitly request a Pandas DataFrame, use `data.pd`:


``` python
GT(data.pd.exibble)
```


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 #duccxdarvc .gt_footnote { margin: 0px; font-size: 90%; padding-top: 4px; padding-bottom: 4px; padding-left: 5px; padding-right: 5px; }
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 #duccxdarvc .gt_sourcenote { font-size: 90%; padding-top: 4px; padding-bottom: 4px; padding-left: 5px; padding-right: 5px; text-align: left; }
 #duccxdarvc .gt_footnote_marks { font-size: 75%; vertical-align: 0.4em; position: initial; }
 #duccxdarvc .gt_asterisk { font-size: 100%; vertical-align: 0; }
 
</style>

| num | char | fctr | date | time | datetime | currency | row | group |
|----|----|----|----|----|----|----|----|----|
| 0.1111 | apricot | one | 2015-01-15 | 13:35 | 2018-01-01 02:22 | 49.95 | row_1 | grp_a |
| 2.222 | banana | two | 2015-02-15 | 14:40 | 2018-02-02 14:33 | 17.95 | row_2 | grp_a |
| 33.33 | coconut | three | 2015-03-15 | 15:45 | 2018-03-03 03:44 | 1.39 | row_3 | grp_a |
| 444.4 | durian | four | 2015-04-15 | 16:50 | 2018-04-04 15:55 | 65100.0 | row_4 | grp_a |
| 5550.0 |  | five | 2015-05-15 | 17:55 | 2018-05-05 04:00 | 1325.81 | row_5 | grp_b |
|  | fig | six | 2015-06-15 |  | 2018-06-06 16:11 | 13.255 | row_6 | grp_b |
| 777000.0 | grapefruit | seven |  | 19:10 | 2018-07-07 05:22 |  | row_7 | grp_b |
| 8880000.0 | honeydew | eight | 2015-08-15 | 20:20 |  | 0.44 | row_8 | grp_b |


The `data.pl` namespace is especially useful in Polars-only workflows. It reads directly from the underlying CSV files using Polars' own reader, so there is no dependency on Pandas and no need to convert with `pl.from_pandas()`. Similarly, `data.pd` always uses the Pandas CSV reader regardless of what other libraries are installed.


# Using [load_dataset()](../reference/load_dataset.md#great_tables.load_dataset)

The [load_dataset()](../reference/load_dataset.md#great_tables.load_dataset) function provides a programmatic way to load any dataset in a specific format. This is convenient when the dataset name or backend is determined at runtime, for example in a loop, a parameterized notebook, or a function that accepts the table type as an argument.


``` python
from great_tables import GT, load_dataset

gtcars_pl = load_dataset(dataset="gtcars", tbl_type="polars")

(
    GT(gtcars_pl.head(5))
    .cols_hide(columns=["trim", "trsmn", "drivetrain", "bdy_style"])
)
```


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 #elkpziodqt .gt_footnote_marks { font-size: 75%; vertical-align: 0.4em; position: initial; }
 #elkpziodqt .gt_asterisk { font-size: 100%; vertical-align: 0; }
 
</style>

| mfr | model | year | hp | hp_rpm | trq | trq_rpm | mpg_c | mpg_h | ctry_origin | msrp |
|----|----|----|----|----|----|----|----|----|----|----|
| Ford | GT | 2017 | 647.0 | 6250.0 | 550.0 | 5900.0 | 11.0 | 18.0 | United States | 447000.0 |
| Ferrari | 458 Speciale | 2015 | 597.0 | 9000.0 | 398.0 | 6000.0 | 13.0 | 17.0 | Italy | 291744.0 |
| Ferrari | 458 Spider | 2015 | 562.0 | 9000.0 | 398.0 | 6000.0 | 13.0 | 17.0 | Italy | 263553.0 |
| Ferrari | 458 Italia | 2014 | 562.0 | 9000.0 | 398.0 | 6000.0 | 13.0 | 17.0 | Italy | 233509.0 |
| Ferrari | 488 GTB | 2016 | 661.0 | 8000.0 | 561.0 | 3000.0 | 15.0 | 22.0 | Italy | 245400.0 |


The `tbl_type=` parameter accepts `"pandas"` (the default) or `"polars"`, and the `dataset=` parameter accepts any of the sixteen dataset names listed above. Here is the same idea with a Pandas DataFrame:


``` python
sp500_pd = load_dataset(dataset="sp500", tbl_type="pandas")

GT(sp500_pd.head(5))
```


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 #vyvrubmaxt .gt_last_summary_row_top { border-bottom-style: solid; border-bottom-width: 2px; border-bottom-color: #D3D3D3; }
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 #vyvrubmaxt .gt_sourcenotes { color: #333333; background-color: #FFFFFF; border-bottom-style: none; border-bottom-width: 2px; border-bottom-color: #D3D3D3; border-left-style: none; border-left-width: 2px; border-left-color: #D3D3D3; border-right-style: none; border-right-width: 2px; border-right-color: #D3D3D3; }
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 #vyvrubmaxt .gt_footnote { margin: 0px; font-size: 90%; padding-top: 4px; padding-bottom: 4px; padding-left: 5px; padding-right: 5px; }
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 #vyvrubmaxt .gt_sourcenote { font-size: 90%; padding-top: 4px; padding-bottom: 4px; padding-left: 5px; padding-right: 5px; text-align: left; }
 #vyvrubmaxt .gt_footnote_marks { font-size: 75%; vertical-align: 0.4em; position: initial; }
 #vyvrubmaxt .gt_asterisk { font-size: 100%; vertical-align: 0; }
 
</style>

| date       | open      | high      | low     | close     | volume       | adj_close |
|------------|-----------|-----------|---------|-----------|--------------|-----------|
| 2015-12-31 | 2060.5901 | 2062.54   | 2043.62 | 2043.9399 | 2655330000.0 | 2043.9399 |
| 2015-12-30 | 2077.3401 | 2077.3401 | 2061.97 | 2063.3601 | 2367430000.0 | 2063.3601 |
| 2015-12-29 | 2060.54   | 2081.5601 | 2060.54 | 2078.3601 | 2542000000.0 | 2078.3601 |
| 2015-12-28 | 2057.77   | 2057.77   | 2044.2  | 2056.5    | 2492510000.0 | 2056.5    |
| 2015-12-24 | 2063.52   | 2067.3601 | 2058.73 | 2060.99   | 1411860000.0 | 2060.99   |


If you pass an unrecognized dataset name or table type, [load_dataset()](../reference/load_dataset.md#great_tables.load_dataset) raises a `ValueError` with a message listing the valid options. This makes it safe to use in automated workflows where a typo might otherwise lead to a confusing `AttributeError`.


# Summary of Access Patterns

The table below summarizes the three approaches. All of them read directly from the package's bundled CSV files, so no network access is needed and the data is always available.

| Pattern | Returns | Use when… |
|----|----|----|
| [data.exibble](../reference/data.exibble.md#great_tables.data.exibble) | Pandas (default) | You want the simplest import |
| `data.pd.exibble` | Pandas (always) | You want to be explicit about Pandas |
| `data.pl.exibble` | Polars (always) | You're working in a Polars-only workflow |
| `load_dataset("exibble", tbl_type="polars")` | Polars | The dataset name or type is a variable |

Whichever approach you choose, the resulting DataFrame can be passed directly to [GT()](../reference/GT.md#great_tables.GT) to start building a table. In the rest of this User Guide we'll mostly use the simple `from great_tables.data import ...` style, but everything you see works just as well with a Polars DataFrame loaded through `data.pl` or [load_dataset()](../reference/load_dataset.md#great_tables.load_dataset).