If I understand right your question, you are looking to plot selected numerical columns against a selected categorical column of your dataset, am I right ?
If so, you can have the use of dplyr, tidyr and ggplot2 packages to achieve this.
Starting with this dataframe:
id num1 num2 num3 cat cat2
1 C -0.48892284 1.417909 2.8884577 a ...
If districts are visualized in a scatterplot which subsidy is labeled as y-axis and area as x-axis, subsidy per area should be shown as the slope of the scatterplot. If subsidy per area is around the nationwide average of around 25, the slope of the scatterplot should be pretty much around 25.
You theory of exception on small areas can be visualized as an ...
As per business statement it is a exceptional scenario when minimum subsidy will be provided. So for displaying common behavior of data you can drop these exceptional values from table. You can use box-plot to visualize spread of data and then remove else, if you are aware with max range in normal scenario you can drop rows having value more than that.
I quote Hands-On Machine Learning with Scikit-Learn and TensorFlow
t-SNE Reduces dimensionality while trying to keep similar instances close and dissimilar instances apart. It is mostly used for visualization, in particular to visualize clusters of instances in high-dimensional space (e.g., to visualize the MNIST images in 2D).
When visualizing your ...
If by 'inference' you mean clustering analysis, I have a trick that may be helpful - plugging in the input properties to the t-SNE outputs.
For example, let's say you are applying t-SNE on a customer data set, and it outputs a few cleanly separated clusters. As you can figure out where each individual customer lies on the t-SNE plot, you can identify the ...
ETA New Current Best Answer: It turns out Google Sheets has a "Timeline" chart type that implements something very similar to the charts on Google Finance. It is not clunky and so far seems perfect!
It turns out you can do this in Excel after all:
I'm working on it now but this seems ...
You can use plotly in python to get this done.
import plotly.graph_objects as go
import numpy as np
x = np.array([1, 2, 3, 4, 5])
y = np.array([1, 3, 2, 3, 1])
fig = go.Figure()
fig.add_trace(go.Scatter(x=x, y=y, name="linear",
fig.add_trace(go.Scatter(x=x, y=y + 5, name="spline",
With Wolfram Language you may use GeoRegionValuePlot with the ColorFunction option to customise the colour scaling.
With some "AdministrativeDivision" "Population" data for the Philippines.
popPH = EntityValue[