update_layout ( geo = dict ( scope = 'north america', showland = True, landcolor = "rgb(212, 212, 212)", subunitcolor = "rgb(255, 255, 255)", countrycolor = "rgb(255, 255, 255)", showlakes = True, lakecolor = "rgb(255, 255, 255)", showsubunits = True, showcountries = True, resolution = 50, projection = dict ( type = 'conic conformal', rotation_lon = - 100 ), lonaxis = dict ( showgrid = True, gridwidth = 0.5, range =, dtick = 5 ), lataxis = dict ( showgrid = True, gridwidth = 0.5, range =, dtick = 5 ) ), title = 'US Precipitation 06-30-2015Source: NOAA', ) fig. astype ( str ) + ' inches', marker = dict ( color = df, colorscale = scl, reversescale = True, opacity = 0.7, size = 2, colorbar = dict ( titleside = "right", outlinecolor = "rgba(68, 68, 68, 0)", ticks = "outside", showticksuffix = "last", dtick = 0.1 ) ) )) fig. Scattergeo ( lat = df, lon = df, text = df. Import aph_objects as go import pandas as pd df = pd. update_layout ( title = 'Most trafficked US airports(Hover for airport names)', geo = dict ( scope = 'usa', projection_type = 'albers usa', showland = True, landcolor = "rgb(250, 250, 250)", subunitcolor = "rgb(217, 217, 217)", countrycolor = "rgb(217, 217, 217)", countrywidth = 0.5, subunitwidth = 0.5 ), ) fig. max (), colorbar_title = "Incoming flightsFebruary 2011" ))) fig. Scattergeo ( locationmode = 'USA-states', lon = df, lat = df, text = df, mode = 'markers', marker = dict ( size = 8, opacity = 0.8, reversescale = True, autocolorscale = False, symbol = 'square', line = dict ( width = 1, color = 'rgba(102, 102, 102)' ), colorscale = 'Blues', cmin = 0, color = df, cmax = df. Scatter_plot.Import aph_objects as go import pandas as pd df = pd. For plotting the bubble chart on an excel sheet, use BubbleChart class from openpyxl.chart submodule. ![]() Scatter_plot = plt.scatter(x, y, facecolor="b", marker="o") Bubble charts are similar to scatter charts but use a third dimension to determine the size of the bubbles. """Return the value in ee closest to x, y."""ĭist, idx = (self.scaled((x, y)), k=1, p=1)Ĭursor = FollowDotCursor(ax, x, y, formatter=fmt, tolerance=20) Xytext = self.offsets, textcoords = 'offset points', va = 'bottom',īoxstyle='round,pad=0.5', fc='yellow', alpha=0.75),Īrrowstyle='->', connectionstyle='arc3,rad=0')) ![]() X, y = inv.transform().ravel()Īt_text(self.formatter(x, y)) This post will cover some basic concepts for styling scatter plots in Matplotlib such as how to adjust: Color of scatter points Size of scatter points Transparency of scatter points Import Modules In 8: import matplotlib. # event.inaxes is always the current axis. ee = spatial.cKDTree(self.scaled(self._points)) Self.scale = y.ptp() / self.scale if self.scale else 1 X = np.asarray(mdates.date2num(x), dtype='float') For that purpose, you can set the type argument to. """Display the x,y location of the nearest data point.ĭef _init_(self, ax, x, y, tolerance=5, formatter=fmt, offsets=(-20, 20)): A connected scatter plot is similar to a line plot, but the breakpoints are marked with dots or other symbol. ![]() ![]() With the FollowDotCursor the data coordinate displayed is always a point in the underlying data which is nearest the mouse. With the DataCursor the data coordinates displayed are where the user clicks - which might not be exactly the same coordinates as the underlying data. Here is a derivative example ( FollowDotCursor) which highlights and annotates data points when a user hovers the mouse over a point. Joe Kington has written a wonderful example ( DataCursor) of how to add an annotation displaying the data coordinates when a user clicks on on artist (such as a scatter plot). 3 Answers Sorted by: 23 scatter can only do one kind of marker at a time, so you have to plot the different types separately. Later, in response to user clicks, you could then use dot.set_offsets((x, y)) to change the location of the dot. If what you are really after is highlighting the point selected by the user, then you could superimpose another dot (with dot = ax.scatter(.)) on top of the point selected.
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