I have a Folium map with shapefile of towns colored according to the number of people in the town.
I would like to add a slider that let me select only the town in a certain population range.
Is this doable?
Thanks in advance for any advice.
Giacomo
I looked online but I found only example with time values. Such as this: https://github.com/python-visualization/folium/blob/main/examples/TimeSliderChoropleth.ipynb
Related
I have this plot
I want this plot to be inverted i.e the categories with the highest avg buyer365 should appear at the top. Does anyone know how to deal with this? I really appreciate any help you can provide.
P.S code I used:
fig,axs=plt.subplots()
axs.scatter(trace1_summary["mean"],trace1_summary["cat3_items"])
axs.set(xlim=(-3,15),title= "89% Hdi Interval",ylabel="categories", xlabel="avg buyer365(standarized)")
axs.axvline(0.0,ls="--",color="k",alpha=0.5,label="average Buyer")
plt.legend()
You can plot the sorted in ascending order values:
axs.scatter("mean", "cat3_items", data=trace1_summary.sort_values("mean"))
I would like to plot the energy per spin < E >/N against the temperature T.
However, I am not sure how to "extract" the values from the table below and plot them.
Just do:
using Plots
plot(data.T, data.Emean)
This is the simplest way to get a column from a data frame.
You might also want to check out this notebook: https://github.com/bkamins/Julia-DataFrames-Tutorial/blob/master/02_basicinfo.ipynb in the section "
Most elementary get and set operations".
I have different measurements from two+ sensors. I want to compare the performance of each sensor for each measurement with errorbars (mean and std). I have no problems creating and formatting a standard errorbar plot for one y (sensor) and yerr per data point (measurement). But i'm trying to create a plot like this:
I can neither find the option to do this in the matplotlib documentation nor when i google it or search this site. The closest i found was this thread:
matplotlib: grouping error bars for each x-axes tick
But this solution doesn't work for me since my datapoints aren't numbers but a pandas dataframe index of strings.
So i found the solution in the Matplotlib documentation after all. Here's the link for people who might have the same question:
A bar plot with errorbars and height labels on individual bars.
I am looking for suggestion on the fastest way to select a time-series for a given latitude and longitude from an xarray dataset. xarray dataset that I am working with is 3 dimensional of the shape [400, 2000, 7200] where the first dimension is time (400), then latitude (2000) and longitude (7200). I simply need to read in individual time-series for each of the grid cells in a given rectangle. So I am reading in time-series one by one for each grid cell with the given rectangle.
For this selection I am using .sel option.
XR..sel(latitude=Y, longitude=X)
Where XR is a xarray dataset, and Y and X are the given latitude and longitude.
This works well but turns about to be very slow when repeated several times. Is there a faster option to do this?
Thank you for your help!
I am having a hard time of plotting the density of Pandas time series.
I have a data frame with perfectly organised timestamps, like below:
It's a web log, and I want to show the density of the timestamp, which indicates how many visitors in certain period of time.
My solution atm is extracting the year, month, week and day of each timestamp, and group them. Like below:
But I don't think it would be a efficient way of dealing with time. And I couldn't find any good info on this, more of them are about plot the calculated values on a date or something.
So, anybody have any suggestions on how to plot Pandas time series?
Much appreciated!
The best way to compute the values you want to plot is to use Series.resample; for example, to aggregate the count of dates daily, use this:
ser = pd.Series(1, index=dates)
ser.resample('D').sum()
The documentation there has more details depending on exactly how you want to resample & aggregate the data.
If you want to plot the result, you can use Pandas built-in plotting capabilities; for example:
ser.resample('D').sum().plot()
More info on plotting is here.