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Plotting a map using Geoview and using size/ colour option

I'm trying to visualize a dataset which I've filtered down to just longitude/latitude, country name, year and a count of deaths. I'm trying to plot that using geoviews as I wish to add lot more to my dataset and interactive map would be a great add on
My code is as follows: (for_plot is the dataframe)
# Plotting the graph
Best = gv.Dataset(for_plot)
points = Best.to(gv.Points, ['longitude', 'latitude'], ['deaths', 'country'])
(gts.Wikipedia * points).opts(
opts.Points(width=600, height=350, tools=['hover'],
size='deaths', cmap='viridis'))
This creates a perfect graph put the 'size' function doesn't work. If I change size to color, graph is not generated. I'm okay with either but just need atleast one marker.
Thanks for any help
Tried to switch values for color instead of size, works with year but not deaths

How can I plot only particular values in xarray?

I am using data from cdasws to plot dynamic spectra. I am following the example found here https://cdaweb.gsfc.nasa.gov/WebServices/REST/jupyter/CdasWsExample.html
This is my code which I have modified to obtain a dynamic spectra for STEREO.
from cdasws import CdasWs
from cdasws.datarepresentation import DataRepresentation
import matplotlib.pyplot as plt
cdas = CdasWs()
import numpy as np
datasets = cdas.get_datasets(observatoryGroup='STEREO')
for index, dataset in enumerate(datasets):
print(dataset['Id'], dataset['Label'])
variables = cdas.get_variables('STEREO_LEVEL2_SWAVES')
for variable_1 in variables:
print(variable_1['Name'], variable_1['LongDescription'])
data = cdas.get_data('STEREO_LEVEL2_SWAVES', ['avg_intens_ahead'],
'2020-07-11T02:00:00Z', '2020-07-11T03:00:00Z',
dataRepresentation = DataRepresentation.XARRAY)[1]
print(data)
plt.figure(figsize = (15,7))
# plt.ylim(100,1000)
plt.xticks(fontsize=18)
plt.yticks(fontsize=18)
plt.yscale('log')
sorted_data.transpose().plot()
plt.xlabel("Time",size=18)
plt.ylabel("Frequency (kHz)",size=18)
plt.show()
Using this code gives a plot that looks something like this,
My question is, is there anyway of plotting this spectrum only for a particular frequency? For example, I want to plot just the intensity values at 636 kHz, is there any way I can do that?
Any help is greatly appreciated, I dont understand xarray, I have never worked with it before.
Edit -
Using the command,
data_stereo.avg_intens_ahead.loc[:,625].plot()
generates a plot that looks like,
While this is useful, what I needed is;
for the dynamic spectrum, if i choose a particular frequency like 600khz, can it display something like this (i have just added white boxes to clarify what i mean) -

If you still want the plot to be 2D, but to include a subset of your data along one of the dimensions, you can provide an array of indices or a slice object. For example:
data_stereo.avg_intens_ahead.sel(
frequency=[625]
).plot()
Or
# include a 10% band on either side
data_stereo.avg_intens_ahead.sel(
frequency=slice(625*0.9, 625*1.1)
).plot()
Alternatively, if you would actually like your plot to show white space outside this selected area, you could mask your data with where:
data_stereo.avg_intens_ahead.where(
data_stereo.frequency==625
).plot()

Which parts of my dataframe are being plotted?

The goal is to plot the data frame I'm working with on a single chart, with a line for each value of init_population where the y-axis is count and x-axis is tick_number.
I've figured out how to use groupby() and plot() together to make this:
As you can see, all the lines are there nicely, but I'm pretty confident that the blue at the top that doesn't follow the relationship the other lines are following is actually a different column of data.
So that this is reproducible, the data is available here.
import pandas as pd
max_runs_data = pd.read_csv('clean_table.csv')
del max_runs_data['visualization']
max_runs_data.columns = ['run_number','init_population', 'tick', 'turtle_count']
max_runs_data.set_index('tick', inplace = True)
test_plot_1 = max_runs_data.groupby('init_population')['turtle_count'].plot()
test_plot_2 = max_runs_data.groupby('init_population').plot(y='turtle_count')
test_plot_1 is the linked image, test_plot_2 is a separate plot for each group.
Is it obvious how to specify the columns for x and y without losing the grouping on a single chart?
Thanks

How can you set the x-axis in matplotlib?

I have data of shipping dates (1=Jan, 2=Feb ect..) and revenue corresponding to it in a pandas dataframe.
Data Frame Here
My code for the line graph that I am trying to make is:
finalhelp.plot(x='shippeddate',y='revenue',title='Revenue Per Month')
It returns a line graph like this
linegraph
I tried to fix it by using the code
fig = finalhelp.plot(x='shippeddate',y='revenue',title='Revenue Per Month',yticks=([0,20000,40000,60000,80000,100000]), legend=False,)
fig.set_xticklabels(['','Jan','Feb','March','April','May','June','July','August','Sept','Oct','Nov','Dec'])
I would like to find a way to set each of the x axis to one of the corresponding months, right now it still returns only Jan-June.
It returns this image
newlinegraph

You need to set_xticks and set_xticklabels:
fig.set_xticks(df['shippeddate'])
fig.set_xticklabels(['Jan','Feb','March','April','May','June','July','August','Sept','Oct','Nov','Dec'])

Setting col_colors in seaborn clustermap from pandas

I have a clustermap generated from a pandas dataframe. Two of the columns are used to generate the clustermap and I need to use a 3rd column to generate a col_colors bar using sns.palplot(sns.light_palette('red')) palette (values will be from 0 - 1, light - dark colors).
The pseudo-code looks something like this:
df=pd.DataFrame(input, columns = ['Source', 'amplicon', 'coverage', 'GC'])
tiles = df.pivot("Source", "amplicon", "coverage")
col_colors = [values from df['GC']]
sns.clustermap(tiles, vmin=0, vmax=2, col_colors=col_colors)
I'm battling to find details on how to setup the col_colors so the correct values are linked to the appropriate tiles. Some direction would be greatly appreciated.

This example will be much easier to explain with sample data. I don't know what your data looks like, but say you had a bunch of GC content measurements For instance:
import seaborn as sns
import numpy as np
import pandas as pd
data = {'16S':np.random.normal(.52, 0.05, 12),
'ITS':np.random.normal(.52, 0.05, 12),
'Source':np.random.choice(['soil', 'water', 'air'], 12, replace=True)}
df=pd.DataFrame(data)
df[:3]
16S ITS Source
0 0.493087 0.460066 air
1 0.607229 0.592945 water
2 0.577155 0.440726 water
So data is GC content, and then there is a column describing the source. Say we want to plot a cluster map of the GC content where we use the Source column to define the network
#create a color palette with the same number of colors as unique values in the Source column
network_pal = sns.light_palette('red', len(df.Source.unique()))
#Create a dictionary where the key is the category and the values are the
#colors from the palette we just created
network_lut = dict(zip(df.Source.unique(), network_pal))
#get the series of all of the categories
networks = df.Source
#map the colors to the series. Now we have a list of colors the same
#length as our dataframe, where unique values are mapped to the same color
network_colors = pd.Series(networks).map(network_lut)
#plot the heatmap with the 16S and ITS categories with the network colors
#defined by Source column
sns.clustermap(df[['16S', 'ITS']], row_colors=network_colors, cmap='BuGn_r')
Basically what most of the above code is doing is creating a vector of colors that corrospond to the Source column of the data frame. You could of course create this manually, where the first color in the list would be mapped to the first row in the dataframe and the second color would be mapped to the second row and so on (this order will change when you plot it), however that would be a lot of work. I used a red color palette as that is what you mentioned in your question though I might recommend using a different palette. I colored by rows, however you can do the same thing for columns. Hope this helps!