I have a huge data set of time series data. In order to visualise the clustering in python, I want to plot time series graphs along with the dendrogram as shown below.
I tried to do it by using subgrid2plot() function in python by creating two subplots side by side. I filled first one with series graphs and second one with dendrograms. But once number of time series increased, it became blur.
Can someone suggest a nice way to plot this type of dendrogram? I have around 50,000 time series to cluster and visualise.
Convert data into JSON with json module of python and then use D3.js for graph ploting.
Check the Gallery from here where you can find dendrogram and time series graph
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I have a pandas data frame containing location data (x_m and y_m) and another variable represented by the color bar in the figure.
Sample figure showing the data points and a possible gradient arrow
How can I obtain the average gradient of all of the data points in my data set? I drew one of the possible solutions showing the gradient vector.
Thank you!
EDIT:
I ended up using scipy.interpolate.griddata, similar to what was done here: https://earthscience.stackexchange.com/questions/12057/how-to-interpolate-scattered-data-to-a-regular-grid-in-python
I have the recurrent issue of having matplotlib bar graphs containing too many categorical values in the X axis. Resize a figure automatically in matplotlib and Python matplotlib multiple bars does not make the trick because my x values are not x. I am having the idea of splitting the graph into two graphs when it get past a certain amount of data point in the graph. I cannot find anything about in the matplotlib document, nor anywhere.
Is there a matplotlib tool to do that? or i would need to write an algorithm that detects the length of the dataset?
I Posted this question about 3D plots of data frames:
3D plot of 2d Pandas data frame
and the user referred me very very helfully to this:
Plotting Pandas Crosstab Dataframe into 3D bar chart
It use useful and the code worked in principle, but it lookes like a mess (see image below) for several reasons:
I have huge number of values to plot (470 or so, along the y-axis) so perhaps a bar chart is not the best way (I am going for a histogram kind of look, so I assumed very narrow bars would be suitable)
my counts (z axis) do not give almost any information, because the differences I need to see are from 100 to the max value
how can I make the 3D plot that shows up interactive? (being able to rotate etc) - I have seen it done in blogs/videos but sure if it's something on Tools -> Preferences that I can't find
So re: the second issue, simple enough, I tried to just change the limits of the zbar as I would for a 2D Plot, by incorporating:
ax.set_zlim([110,150])
just before the axis labels, but obviously this is the wrong way:
SO do I have to limit the values from the original data set (i.e. filter out <110), or is there a way to do this from the plot?
I have a group of high-dimensional (250 dimensions) data. To get rid of unnecessary dimensions and to easily visualize data on a figure, I used class sklearn.manifold.MDS and its method fit_transform(data) and already got the transformed data in 2-dimension space.
I have plotted the figure out, which looks like
The problem is: now I have some new coming data. In my case, I want to take the figure shown above as the basic model. For new coming data, I want to implement the same MDS on them and also plot them on this figure, so that I can know which area and how large area the new data will occupy.
However, I realized that MDS class only has fit_transform() method but doesn't have independent transform() method. I want to know, if for new coming data, I do another fit_transform(new_data), can these transformed data be directly plotted on top of this figure?
p.s. I do fit_transform(new_data) after I do fit_transform(old_data)
I am not a programmer but I have been doing some data analysis using Python lately and recently stumbled across Matplotlib. I have a series of geo-coordinate data points that I am trying to visualize. The data consists of geo coordinates and the sqft of each location:
latitude, longitude, SQFT
I have about 25k of these data points and I would like to show the cumulative sqft of these on a 2d image; basically create a heatmap of the sqft. I have managed to use Matplotlib to create a hexbin with the lat and long data but this only gives me a count of the number of times an item falls into the bin. I cannot figure out how I incorporate the sqft into the bins. I have looked at the matplotlib docs and found that I can enter an additional variable, they call C, but I can't figure out how I need to format the data (or if this will even do what I need it to).