I have a dataset which looks like this,
It has four attributes and the fifth column (which I added by myself) is the cluster of each row to which the row belongs.
I want to build something like a Scatter Plot for this dataset, but I am unable to do so. I have tried searching it up and the best I could find was this following question on Stackoverflow,
How to make a 4d plot with matplotlib using arbitrary data
Using this, I was able to make a Scatter Plot but it can only be done for three attributes while fourth attribute being the cluster of each row.
Can anyone help me figure out how would it be possible to do the same to make a Scatter Plot for a dataset similar to mine?
I would recommend something like seaborn's pairplot:
import seaborn as sns
sns.pairplot(df, hue="cluster")
See the images in the link, of what it looks like.
This creates several pairwise scatterplots instead of trying to make a 3D plot and arbitrarily flatten one of the dimensions.
Related
i have a shiny appp created which plots metagenome data using ggplot2, phyloseq and plotly with dplyr and tidyr. It creates pretty good stacked barplots and heatmaps only problem is it reorders sample names at x-axis e.g. 1-10 are arranged as 1,10,2,,5,6... how to correct that bug?
I would like to ask a question regarding Matplotlib 3D Scatter Plots
I have a data frame, consists of 3 columns and 55 rows.
I formatted it to a numpy array with
dataframe.to_numpy()
I have also some subset of my dataframe which should be coloured differently on the plot.
For example:
subset1
subset2
I would like plot my data frame with 3D Scatter Plots while coloring the points of different subsets differently.
I have tried bunch of methods but always getting error because of the shape of the subsets. Is there any more efficient way to do it?
I would appreciate for your suggestions.
I am a recent migrant from Matlab to Python and have recently worked with Numpy and Matplotlib. I recoded one of my scripts from Matlab, which employs Matlab's contourf-function, into Python using matplotlib's corresponding contourf-function. I managed to replicate the output in Python, apart that the contourf-plots are not exacly the same, for a reason that is unknown to me. As I run the contourf-function in matplotlib, I get this otherwise nice figure but it has these sharp edges on the contour-levels on top and bottom, which should not be there (see Figure 1 below, matplotlib-output). Now, when I export the arrays I used in Python to Matlab (i.e. the exactly same data set that was used to generate the matplotlib-contourf-plot) and use Matlab's contourf-function, I get a slightly different output, without those sharp contour-level edges (see Figure 2 below, Matlab-output). I used the same number of levels in both figures. In figure 3 I have made a scatterplot of the same data, which shows that there are no such sharp edges in the data as shown in the contourf-plot (I added contour-lines just for reference). Example dataset can be downloaded through Dropbox-link given below. The data set contains three txt-files: X, Y, Z. Each of them are an 500x500 arrays, which can be directly used with contourf(), i.e. plt.contourf(X,Y,Z,...). The code that used was
plt.contourf(X,Y,Z,10, cmap=plt.cm.jet)
plt.contour(X,Y,Z,10,colors='black', linewidths=0.5)
plt.axis('equal')
plt.axis('off')
Does anyone have an idea why this happens? I would appreciate any insight on this!
Cheers,
Jussi
Below are the details of my setup:
Python 3.7.0
IPython 6.5.0
matplotlib 2.2.3
Matplotlib output
Matlab output
Matplotlib-scatter
Link to data set
The confusing thing about the matlab plot is that its colorbar shows much more levels than there are actually in the plot. Hence you don't see the actual intervals that are contoured.
You would achieve the same result in matplotlib by choosing 12 instead of 11 levels.
import numpy as np
import matplotlib.pyplot as plt
X, Y, Z = [np.loadtxt("data/roundcontourdata/{}.txt".format(i)) for i in list("XYZ")]
levels = np.linspace(Z.min(), Z.max(), 12)
cntr = plt.contourf(X,Y,Z,levels, cmap=plt.cm.jet)
plt.contour(X,Y,Z,levels,colors='black', linewidths=0.5)
plt.colorbar(cntr)
plt.axis('equal')
plt.axis('off')
plt.show()
So in conclusion, both plots are correct and show the same data. Just the levels being automatically chosen are different. This can be circumvented by choosing custom levels depending on the desired visual appearance.
I am trying to produce a figure/plot with more than a single heatmap (matrix with color shading according to the cell value). At the moment using Plots;
pyplot() and heatmap(mat) is enough to produce a heatmap.
It is not clear to me how to produce a single figure with more though. After looking at this page example subplots for how to use the layout, and then the example histogram, I cannot seem to produce working examples for the two together.
The question is how to produce a figure with two different matrices displayed via heatmap or some other function to do the same?
(as an extra side, could you also explain the context of the 'using' statement and how it relates to the 'backend'?)
The easiest way is to make a Vector of heatmaps, then plot those
using Plots
hms = [heatmap(randn(10,10)) for i in 1:16];
plot(hms..., layout = (4,4), colorbar = false)
The using statement calls the Plots library. The "backend" is another package, loaded by Plots, that does the actual plotting. Plots itself has no plotting capabilities - it translates the plot call to a plot call for the backend package.
Explanation of the code above:
Plotting with Plots is a two-step process. 1: plot generates a Plot object with all the information for the plot; 2: when a Plot object is returned to the console, it automatically calls julia´s display function, which then generates the plot. But you can do other things with the Plot object first, like put it in an array.
The heatmap call is a short form of plot(randn(10,10), seriestype = :heatmap), so it just creates a Plot object. 16 Plot objects are stored in the vector.
Passing a number of Plot objects to plot creates a new, larger Plot, with each of the incoming Plot objects as subplots. The splat operator ... simply passes each element of the Array{Plot} to plot as an individual argument.
I can use DataFrameGroupBy.boxplot(...) to create a boxplot in the following way:
In [15]: df = pd.DataFrame({"gene_length":[100,100,100,200,200,200,300,300,300],
...: "gene_id":[1,1,1,2,2,2,3,3,3],
...: "density":[0.4,1.1,1.2,1.9,2.0,2.5,2.2,3.0,3.3],
...: "cohort":["USA","EUR","FIJ","USA","EUR","FIJ","USA","EUR","FIJ"]})
In [17]: df.groupby("cohort").boxplot(column="density",by="gene_id")
In [18]: plt.show()
This produces the following image:
This is exactly what I want, except instead of making three subplots, I want all the plots to be in one plot (with different colors for USA, EUR, and FIJ). I've tried
In [17]: df.groupby("cohort").boxplot(column="density",subplots=False,by="gene_id")
but it produces the error
KeyError: 'gene_id'
I think the problem has something to do with the fact that by="gene_id" is a keyword sent to the matplotlib boxplot method. If someone has a better way of producing the plot I am after, perhaps by using DataFrame.boxplot(?) instead, please respond here. Thanks so much!
To use the pure pandas functions, I think you should not GroupBy before calling boxplot, but instead, request to group by certain columns in the call to boxplot on the DataFrame itself:
df.boxplot(column='density',by=['gene_id','cohort'])
To get a better-looking result, you might want to consider using the Seaborn library. It is designed to help precisely with this sort of tasks:
sns.boxplot(data=df,x='gene_id',y='density',hue='cohort')
EDIT to take into account comment below
If you want to have each of your cohort boxplots stacked/superimposed for each gene_id, it's a bit more complicated (plus you might end up with quite an ugly output). You cannot do this using Seaborn, AFAIK, but you could with pandas directly, by using the position= parameter to boxplot (see doc). The catch it to generate the correct sequence of positions to place the boxplots where you want them, but you'll have to fix the tick labels and the legend yourself.
pos = [i for i in range(len(df.gene_id.unique())) for _ in range(len(df.cohort.unique()))]
df.boxplot(column='density',by=['gene_id','cohort'],positions=pos)
An alternative would be to use seaborn.swarmplot instead of using boxplot. A swarmplot plots every point instead of the synthetic representation of boxplots, but you can use the parameter split=False to get the points colored by cohort but stacked on top of each other for each gene_id.
sns.swarmplot(data=df,x='gene_id',y='density',hue='cohort', split=False)
Without knowing the actual content of your dataframe (number of points per gene and per cohort, and how separate they are in each cohort), it's hard to say which solution would be the most appropriate.