lets_plot.geom_text(mapping=None, *, data=None, stat=None, position=None, show_legend=None, sampling=None, tooltips=None, map=None, map_join=None, use_crs=None, label_format=None, na_text=None, nudge_x=None, nudge_y=None, size_unit=None, color_by=None, **other_args)#

Add a text directly to the plot.


Set of aesthetic mappings created by aes() function. Aesthetic mappings describe the way that variables in the data are mapped to plot “aesthetics”.

datadict or Pandas or Polars DataFrame or GeoDataFrame

The data to be displayed in this layer. If None, the default, the data is inherited from the plot data as specified in the call to ggplot.

statstr, default=’identity’

The statistical transformation to use on the data for this layer, as a string. Supported transformations: ‘identity’ (leaves the data unchanged), ‘count’ (counts number of points with same x-axis coordinate), ‘bin’ (counts number of points with x-axis coordinate in the same bin), ‘smooth’ (performs smoothing - linear default), ‘density’ (computes and draws kernel density estimate).

positionstr or FeatureSpec, default=’identity’

Position adjustment. Either a position adjustment name: ‘dodge’, ‘dodgev’, ‘jitter’, ‘nudge’, ‘jitterdodge’, ‘fill’, ‘stack’ or ‘identity’, or the result of calling a position adjustment function (e.g., position_dodge() etc.).

show_legendbool, default=True

False - do not show legend for this layer.


Result of the call to the sampling_xxx() function. To prevent any sampling for this layer pass value “none” (string “none”).


Result of the call to the layer_tooltips() function. Specify appearance, style and content.

mapGeoDataFrame or Geocoder

Data containing coordinates of points.

map_joinstr or list

Keys used to join map coordinates with data. First value in pair - column/columns in data. Second value in pair - column/columns in map.

use_crsstr, optional, default=”EPSG:4326” (aka WGS84)

EPSG code of the coordinate reference system (CRS) or the keyword “provided”. If an EPSG code is given, then all the coordinates in GeoDataFrame (see the map parameter) will be projected to this CRS. Specify “provided” to disable any further re-projection and to keep the GeoDataFrame’s original CRS.


Format used to transform label mapping values to a string. Examples:

  • ‘.2f’ -> ‘12.45’

  • ‘Num {}’ -> ‘Num 12.456789’

  • ‘TTL: {.2f}$’ -> ‘TTL: 12.45$’

For more info see https://lets-plot.org/python/pages/formats.html.

na_textstr, default=’n/a’

Text to show for missing values.


Horizontal adjustment to nudge labels by.


Vertical adjustment to nudge labels by.

size_unit{‘x’, ‘y’}

Relate the size of the text to the length of the unit step along one of the axes. If None, no fitting is performed.

color_by{‘fill’, ‘color’, ‘paint_a’, ‘paint_b’, ‘paint_c’}, default=’color’

Define the color aesthetic for the geometry.


Other arguments passed on to the layer. These are often aesthetics settings used to set an aesthetic to a fixed value, like color=’red’, fill=’blue’, size=3 or shape=21. They may also be parameters to the paired geom/stat.


Geom object specification.


Adds text directly to the plot.

geom_text() understands the following aesthetics mappings:

  • x : x-axis value.

  • y : y-axis value.

  • alpha : transparency level of a layer. Accept values between 0 and 1.

  • color (colour) : color of the geometry. String in the following formats: RGB/RGBA (e.g. “rgb(0, 0, 255)”); HEX (e.g. “#0000FF”); color name (e.g. “red”); role name (“pen”, “paper” or “brush”).

  • size : font size.

  • label : text to add to plot.

  • family : font family. Possible values: ‘sans’, ‘serif’, ‘mono’, any other like: “Times New Roman”. The default is ‘sans’.

  • fontface : font style and weight. Possible values: ‘plain’, ‘bold’, ‘italic’, ‘bold italic’. The default is ‘plain’.

  • hjust : horizontal text alignment. Possible values: ‘left’, ‘middle’, ‘right’ or number between 0 (‘left’) and 1 (‘right’). There are two special alignments: ‘inward’ (aligns text towards the plot center) and ‘outward’ (away from the plot center).

  • vjust : vertical text alignment. Possible values: ‘bottom’, ‘center’, ‘top’ or number between 0 (‘bottom’) and 1 (‘top’). There are two special alignments: ‘inward’ (aligns text towards the plot center) and ‘outward’ (away from the plot center).

  • angle : text rotation angle in degrees.

  • lineheight : line height multiplier applied to the font size in the case of multi-line text.

The data and map parameters of GeoDataFrame type support shapes Point and MultiPoint.

The map parameter of Geocoder type implicitly invokes centroids() function.

The conventions for the values of map_join parameter are as follows:

  • Joining data and GeoDataFrame object

    Data has a column named ‘State_name’ and GeoDataFrame has a matching column named ‘state’:

    • map_join=[‘State_Name’, ‘state’]

    • map_join=[[‘State_Name’], [‘state’]]

  • Joining data and Geocoder object

    Data has a column named ‘State_name’. The matching key in Geocoder is always ‘state’ (providing it is a state-level geocoder) and can be omitted:

    • map_join=’State_Name’

    • map_join=[‘State_Name’]

  • Joining data by composite key

    Joining by composite key works like in examples above, but instead of using a string for a simple key you need to use an array of strings for a composite key. The names in the composite key must be in the same order as in the US street addresses convention: ‘city’, ‘county’, ‘state’, ‘country’. For example, the data has columns ‘State_name’ and ‘County_name’. Joining with a 2-keys county level Geocoder object (the Geocoder keys ‘county’ and ‘state’ are omitted in this case):

    • map_join=[‘County_name’, ‘State_Name’]


1from lets_plot import *
3ggplot() + geom_text(x=0, y=0, label='Lorem ipsum')

 1import pandas as pd
 2from lets_plot import *
 4df = pd.DataFrame({
 5    "x": [0, -1, -1, -1, 0, 0, 1, 1, 1],
 6    "y": [0, -1, 1, 0, -1, 1, -1, 0, 1],
 7    "hjust": [.5, 1, 1, 1, .5, .5, 0, 0, 0],
 8    "vjust": [.5, 1, 0, .5, 1, 0, 1, .5, 0],
10    label=lambda r: ("hjust=" + r["hjust"].astype(str)).str.cat(
11        "vjust=" + r["vjust"].astype(str),
12        sep='\n'
13    )
15ggplot(df, aes("x", "y")) + \
16    geom_text(aes(label="label", hjust="hjust", vjust="vjust")) + \
17    geom_point() + \
18    xlim(-1.2, 1.2) + ylim(-1.2, 1.2)

 1import numpy as np
 2from lets_plot import *
 4n = 10
 6x = np.arange(n)
 7y = np.random.normal(loc=10, scale=2, size=n)
 8ggplot({'x': x, 'y': y}, aes(x='x', y='y')) + \
 9    geom_bar(stat='identity', fill='#2b8cbe', tooltips='none') + \
10    geom_text(aes(label='y'), position=position_nudge(y=1), \
11              label_format='.1f', angle=45, color='#2b8cbe')

 1from lets_plot import *
 2from lets_plot.geo_data import *
 4cities = ["New York", "Los Angeles"]
 5states = ["NY", "CA"]
 6titles = ['{0} ({1})'.format(city, state) \
 7          for city, state in zip(cities, states)]
 8data = {"city": cities, "state": states, "title": titles}
 9centroids = geocode_cities(data["city"]).get_centroids()
10ggplot(data) + geom_livemap(tiles=maptiles_lets_plot(theme='dark')) + \
11    geom_text(aes(label="title"), map=centroids, map_join="city", \
12              size=7, color="yellow", fontface='bold')
The geodata is provided by © OpenStreetMap contributors and is made available here under the Open Database License (ODbL).