# Nanoplots > **Warning: Warning** > > [fmt_nanoplot()](../reference/GT.fmt_nanoplot.md#great_tables.GT.fmt_nanoplot) is still experimental. Nanoplots are tiny plots you can use in your table. They are simple by design, mainly because there isn't a lot of space to work with. With that simplicity, however, you do get a set of very succinct data visualizations that adapt nicely to the amount of data you feed into them. The main features of nanoplots include the following: - interactivity: you can hover over data and other elements to show values - choice of line and bar charting - you can annotate plots with a reference line and/or area - plenty of easy-to-use options for composing your plots # A simple line-based nanoplot Let's make some simple plots with a Polars DataFrame. Here we are using lists to define data values for each cell in the `numbers` column. The [fmt_nanoplot()](../reference/GT.fmt_nanoplot.md#great_tables.GT.fmt_nanoplot) method understands that these are input values for a line plot (the default type of nanoplot). ``` python from great_tables import GT import polars as pl random_numbers_df = pl.DataFrame( { "example": ["Row " + str(x) for x in range(1, 5)], "numbers": [ "20 23 6 7 37 23 21 4 7 16", "2.3 6.8 9.2 2.42 3.5 12.1 5.3 3.6 7.2 3.74", "-12 -5 6 3.7 0 8 -7.4", "2 0 15 7 8 10 1 24 17 13 6", ], } ) GT(random_numbers_df).fmt_nanoplot(columns="numbers") ```
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This looks a lot like the familiar sparklines you might see in tables where space for plots is limited. The input values, strings of space-separated values, can be considered here as *y* values and they are evenly spaced along the imaginary *x* axis. Hovering over (or touching) the values is something of a treat! You might notice that: - data values are automatically formatted for you in a compact fashion - the plot elements also display pertinent values This sort of interactively is baked into the rendered SVG graphics that [fmt_nanoplot()](../reference/GT.fmt_nanoplot.md#great_tables.GT.fmt_nanoplot) generates from your data and selection of options. Polars lets us express 'lists-of-values-per-cell' in different ways and **Great Tables** is pretty good at understanding different column *dtypes*. So, you can alternatively create the same table as above with the following code. ``` python random_numbers_df = pl.DataFrame( { "example": ["Row " + str(x) for x in range(1, 5)], "numbers": [ { "val": [20, 23, 6, 7, 37, 23, 21, 4, 7, 16] }, { "val": [2.3, 6.8, 9.2, 2.42, 3.5, 12.1, 5.3, 3.6, 7.2, 3.74] }, { "val": [-12, -5, 6, 3.7, 0, 8, -7.4] }, { "val": [2, 0, 15, 7, 8, 10, 1, 24, 17, 13, 6] }, ], } ) GT(random_numbers_df).fmt_nanoplot(columns="numbers") ``` Both forms of the `numbers` column in the two DataFrames look the same to [fmt_nanoplot()](../reference/GT.fmt_nanoplot.md#great_tables.GT.fmt_nanoplot). The key for the list of values (here, `"val"`) can be anything as long as it's repeated down the column. So the choice is yours on how you want to prepare those column values. # The reference line and the reference area You can insert two additional things which may be useful: a reference line and a reference area. You can define them either through literal values or via keywords (these are: `"mean"`, `"median"`, `"min"`, `"max"`, `"q1"`, `"q3"`, `"first"`, or `"last"`). Here's a reference line that corresponds to the mean data value of each nanoplot: ``` python GT(random_numbers_df).fmt_nanoplot(columns="numbers", reference_line="mean") ```
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This example uses a reference area that bounds the minimum value to the median value: ``` python GT(random_numbers_df).fmt_nanoplot(columns="numbers", reference_area=["min", "median"]) ```
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As an added touch, you don't need to worry about the order of the keywords provided to `reference_area=` (which could be potentially problematic if providing a literal value and a keyword). # Using `autoscale=` to have a common *y*-axis scale across plots There are lots of options. Like, if you want to ensure that the scale is shared across all of the nanoplots (so you can better get a sense of overall magnitude), you can set `autoscale=` to `True`: ``` python GT(random_numbers_df).fmt_nanoplot(columns="numbers", autoscale=True) ```
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If you hover along or touch the left side of any of the plots above, you'll see that each *y* scale runs from `-12.0` to `37.0`. Using `autoscale=True` is very useful if you want to compare the magnitudes of values across rows in addition to their trends. It won't, however, make much sense if the overall magnitudes of values vary wildly across rows (e.g., comparing changing currency values or stock prices over time). # Using the [nanoplot_options()](../reference/nanoplot_options.md#great_tables.nanoplot_options) helper function There are many options for customization. You can radically change the look of a collection of nanoplots with the [nanoplot_options()](../reference/nanoplot_options.md#great_tables.nanoplot_options) helper function. With that function, you invoke it in the `options=` argument of [fmt_nanoplot()](../reference/GT.fmt_nanoplot.md#great_tables.GT.fmt_nanoplot). You can modify the sizes and colors of different elements, decide which elements are even present, and much more! Here's an example where a line-based nanoplot retains all of its elements, but the overall appearance is greatly altered. ``` python from great_tables import nanoplot_options ( GT(random_numbers_df) .fmt_nanoplot( columns="numbers", options=nanoplot_options( data_point_radius=8, data_point_stroke_color="black", data_point_stroke_width=2, data_point_fill_color="white", data_line_type="straight", data_line_stroke_color="brown", data_line_stroke_width=2, data_area_fill_color="orange", vertical_guide_stroke_color="green", ), ) ) ```
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IiBzdHJva2U9InRyYW5zcGFyZW50IiBzdHJva2Utd2lkdGg9IjEyIiBmaWxsPSJ0cmFuc3BhcmVudCIgLz48dGV4dCB4PSI0NDguODg4ODg4ODg4ODg4OSIgeT0iMjAiIGZpbGw9InRyYW5zcGFyZW50IiBzdHJva2U9InRyYW5zcGFyZW50IiBmb250LXNpemU9IjMwcHgiPjMuNjA8L3RleHQ+PC9nPjxnIGNsYXNzPSJ2ZXJ0LWxpbmUiPjxyZWN0IHg9IjQ4NC40NDQ0NDQ0NDQ0NDQ0IiB5PSIwIiB3aWR0aD0iMjAiIGhlaWdodD0iMTMwIiBzdHJva2U9InRyYW5zcGFyZW50IiBzdHJva2Utd2lkdGg9IjEyIiBmaWxsPSJ0cmFuc3BhcmVudCIgLz48dGV4dCB4PSI1MDQuNDQ0NDQ0NDQ0NDQ0NCIgeT0iMjAiIGZpbGw9InRyYW5zcGFyZW50IiBzdHJva2U9InRyYW5zcGFyZW50IiBmb250LXNpemU9IjMwcHgiPjcuMjA8L3RleHQ+PC9nPjxnIGNsYXNzPSJ2ZXJ0LWxpbmUiPjxyZWN0IHg9IjU0MC4wIiB5PSIwIiB3aWR0aD0iMjAiIGhlaWdodD0iMTMwIiBzdHJva2U9InRyYW5zcGFyZW50IiBzdHJva2Utd2lkdGg9IjEyIiBmaWxsPSJ0cmFuc3BhcmVudCIgLz48dGV4dCB4PSI1NjAuMCIgeT0iMjAiIGZpbGw9InRyYW5zcGFyZW50IiBzdHJva2U9InRyYW5zcGFyZW50IiBmb250LXNpemU9IjMwcHgiPjMuNzQ8L3RleHQ+PC9nPjwvc3ZnPg==" />
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As can be seen, you have a lot of fine-grained control over the look of a nanoplot. # Making nanoplots with bars using `plot_type="bar"` We don't just support line plots in [fmt_nanoplot()](../reference/GT.fmt_nanoplot.md#great_tables.GT.fmt_nanoplot), we also have the option to show bar plots. The only thing you need to change is the value of `plot_type=` argument to `"bar"`: ``` python GT(random_numbers_df).fmt_nanoplot(columns="numbers", plot_type="bar") ```
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An important difference between line plots and bar plots is that the bars project from a zero line. Notice that some negative values in the bar-based nanoplot appear red and radiate downward from the gray zero line. Using `plot_type="bar"` still allows us to supply a reference line and a reference area with `reference_line=` and `reference_area=`. The `autoscale=` option works here as well. We also have a set of options just for bar plots available inside [nanoplot_options()](../reference/nanoplot_options.md#great_tables.nanoplot_options). Here's an example where we use all of the aforementioned customization possibilities: ``` python ( GT(random_numbers_df) .fmt_nanoplot( columns="numbers", plot_type="bar", autoscale=True, reference_line="min", reference_area=[0, "max"], options=nanoplot_options( data_bar_stroke_color="gray", data_bar_stroke_width=2, data_bar_fill_color="orange", data_bar_negative_stroke_color="blue", data_bar_negative_stroke_width=1, data_bar_negative_fill_color="lightblue", reference_line_color="pink", reference_area_fill_color="bisque", vertical_guide_stroke_color="blue", ), ) ) ```
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The customized bars use orange fills, gray strokes, and a pink reference line. Negative values are styled separately with blue strokes and light blue fills, making it easy to distinguish positive and negative trends at a glance. # Horizontal bar and line plots Single-value bar plots, running in the horizontal direction, can be made by simply invoking [fmt_nanoplot()](../reference/GT.fmt_nanoplot.md#great_tables.GT.fmt_nanoplot) on a column of numeric values. These plots are meant for comparison across rows so the method automatically scales the horizontal bars to facilitate this type of display. Here's a simple example that uses `plot_type="bar"` on the `numbers` column that contains a single numeric value in every cell. ``` python single_vals_df = pl.DataFrame( { "example": ["Row " + str(x) for x in range(1, 5)], "numbers": [2.75, 0, -3.2, 8] } ) GT(single_vals_df).fmt_nanoplot(columns="numbers", plot_type="bar") ```
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This, interestingly enough, works with the `"line"` type of nanoplot. The result is akin to a lollipop plot: ``` python GT(single_vals_df).fmt_nanoplot(columns="numbers") ```
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You get to customize the line and the data point marker with the latter display of single values, and that's a plus. Nonetheless, it is more common to see horizontal bar plots in tables and the extra customization of negative values makes that form of presentation more advantageous. # Line plots with paired *x* and *y* values Aside from a single stream of *y* values, we can plot pairs of *x* and *y* values. This works only for the `"line"` type of plot. We can set up a column of Polars `struct` values in a DataFrame to have this input data prepared for [fmt_nanoplot()](../reference/GT.fmt_nanoplot.md#great_tables.GT.fmt_nanoplot). Notice that the dictionary values in the enclosed list must have the `"x"` and `"y"` keys. Further to this, the list lengths for each of `"x"` and `"y"` must match (i.e., to make valid pairs of *x* and *y*). ``` python weather_2 = pl.DataFrame( { "station": ["Station " + str(x) for x in range(1, 4)], "temperatures": [ { "x": [6.1, 8.0, 10.1, 10.5, 11.2, 12.4, 13.1, 15.3], "y": [24.2, 28.2, 30.2, 30.5, 30.5, 33.1, 33.5, 32.7], }, { "x": [7.1, 8.2, 10.3, 10.75, 11.25, 12.5, 13.5, 14.2], "y": [18.2, 18.1, 20.3, 20.5, 21.4, 21.9, 23.1, 23.3], }, { "x": [6.3, 7.1, 10.3, 11.0, 12.07, 13.1, 15.12, 16.42], "y": [15.2, 17.77, 21.42, 21.63, 25.23, 26.84, 27.2, 27.44], }, ] } ) ( GT(weather_2) .fmt_nanoplot( columns="temperatures", plot_type="line", expand_x=[5, 16], expand_y=[10, 40], options=nanoplot_options( show_data_area=False, show_data_line=False ) ) ) ```
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The options for removing the *data area* and the *data line* (though the corresponding `show_*` arguments of [nanoplot_options()](../reference/nanoplot_options.md#great_tables.nanoplot_options)) make the finalized nanoplots look somewhat like scatter plots. Nanoplots bring data visualization directly into your table cells, giving readers an immediate visual sense of trends and distributions without leaving the tabular format. Between line plots, bar charts, reference annotations, and extensive customization through [nanoplot_options()](../reference/nanoplot_options.md#great_tables.nanoplot_options), you can tailor these compact visualizations to match your data and your presentation style.