Geom_point(aes(col=state, size=popdensity)) + Gg <- ggplot(midwest, aes(x=area, y=poptotal)) + Theme_set(theme_bw()) # pre-set the bw theme. options(scipen=999) # turn-off scientific notation like 1e+48 percollege percprof poppovertyknown percpovertyknown percbelowpoverty percchildbelowpovert percadultpoverty percelderlypoverty inmetro categoryĠ 561 ADAMS IL 0.052 66090 1270.961540 63917 1702 98 249. PID county state area poptotal popdensity popwhite popblack popamerindian popasian. Here is the head of the dataframe midwest. How do we add the smooth line showed in the ggplot visualisation.How do we get the markersize as per the popdensity of the point without doing all the heavy lifting (like plotting each point individually)?.This produces a plot nearly similar to the goal plot. Ms=1+12*((max_popdensity-data_point.popdensity)/(max_popdensity-min_popdensity)), label=name)
Min_popdensity, max_popdensity = midwest.min(), midwest.max()Īx.plot(data_point.area, data_point.poptotal, marker='o', linestyle='', Here's how we can go down to each data point and make the plot in Matplotlib. However, we don't still have the marker size by popdensity Matplotlib Sns.pairplot(x_vars=, y_vars=, data=midwest,Īgain, this only does a scatter plot by the category. While this does get us different categories in the scatter plot, it does not get them sized by popdensity. fig, ax = plt.subplots()Īx.plot(group.area, group.poptotal, marker='o', linestyle='', ms=10, However, we can group the dataframe by the `state' and then individually do a scatter plot for each group ( ref).
#Scatter plot matplotlib markersize code#
The above code by itself will not colour code different categories and will look like the following. I am looking at the correlation scatter plot, which looks like the following.ĭefault Pandas scatter plot ot(kind='scatter', x='area', y='poptotal', ylim=((0, 50000)), xlim=((0., 0.1))) I am trying to make a R ggplot2 plot picked from here in Python. 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.
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