# Tag Info

## Hot answers tagged visualization

5

You could take an Information Theory approach by finding the lowest Kullback–Leibler divergence between the distributions. There is a KL divergence option within SciPy's entropy function. >>> from scipy.stats import entropy >>> p = [0.21,0.24,0.36,0.56,0.67,0.72,0.74,0.83,0.84,0.87] # Data removed to make equal sizes: [0.91,0.94,0.97] >...

5

In my case I am able to find graphviz executables manually in anaconda3\Library\bin\graphviz, but I still would get the GraphViz's Executables not found error. I have unsuccessfully tried zhangqianyuan's suggestion as well as specific orders of module installation and using python-graphviz (official solution, widely discussed here). Only thing I didn't try ...

4

I got your problem like this way: You want to show labels on x and y axis on seaborn heatmap. So for that sns.heatmap() function has two parameter they are xticklabels for x-axis and yticklabels for y-axis labels. follow below code snippet import seaborn as sns # for data visualization flight = sns.load_dataset('flights') # load flights datset from GitHub ...

4

My go to library would be matplotlib, with which it is relatively easy to generate something similar. I don't have the correct font family to render the exact output as above, but this hopefully illustrates the point Source Code import pandas as pd import numpy as np import matplotlib.pyplot as plt # Create the data to plot on # create a 2d array of ...

3

Why not do both? Like you mention, it might be worth first computing the percentage of all values that are values. Generally you might also have a percentage in mind that is acceptable, like up to 10% missing values, if they are scattered at random throughout your dataset. There are libraries built specifically for visualising missing data, such as ...

3

With Wolfram Language you may use "Icon" Entity and ConstantArray to create lists of "Crayola" ColorData colored icons and display with Multicolumn 24 columns wide. palette = <|"SeaGreen" -> 135, "Razzmatazz" -> 146, "Yellow" -> 18, "TurquoiseBlue" -> 13|>; Multicolumn[ Flatten@ KeyValueMap[ With[{i = Graphics[{ColorData["...

3

They are quite similar but : Histograms results of aggregation into bins (loss of information due to aggregation: high-resolution point (2.324) is reduced to bins resolution [2,3]) Beeswarm plot displays the exact value of points on the axis. It is a 1D-scatter plot. However, if two points are very close to each other they will overlap if the point size ...

2

What worked for my use case: Generating model diagrams in Django. But it can also be extended to generate diagrams for any other applications. I installed the GraphViz for viewing graph from .dot file. Can be installed from graphviz.org. Create a dot file associated with the project: python manage.py graph_models -a > dotfile.dot Or you could create ...

2

Consider using the Earth Mover's Distance (i.e., the Wasserstein-1 distance), which (similar to the KL-divergence) can be used to compute the "distance" between sets of points (or rather the empirical distribution induced by them). There is a method in scipy for it, as well as this library. Some notes: You do not need to have the same number of points in ...

2

You need to get used to so called wide and long table format, from there you should get the trick rapidly. By then, I would use unstack in order to have three columns and do something like this: unstacked_data = describedWidth.unstack() unstacked_data = unstacked_data.reset_index() unstacked_data.columns = ['metric', 'run', 'y'] sns.barplot(data=...

2

The first returns a probability density of the distributions. As you can see, they integrate to 1, i.e. they cover the same area (because they are probabilities, not the raw data). The second returns actual frequencies, and that's why you have the actual scale of the data. Different histograms having different scales.

2

I ended up creating a new magic, based on the %dot magic, called %dotMJ. It works by looking for <text> objects in the SVG (corresponding to labels in GV), checking if they are LaTeX code (start/end with $), and if so converting them to a <div> and wrapping with a <foreignObject>. This allows MathJax to "see" the LaTeX code and convert it ... 2 The question is if you really want to treat this as a time series problem. I say this because there may be no obvious/persistent autocorrelation (meaning that$y$is contingent on$y_{t-1}\$). My take (not knowing the data) would be that each hotel has an own unobserved "identity" (like location, reputation etc) apart from star rating. Bookings (and thus ...

2

I used a simple Neural Network with 3 Inputs - 1 hidden layer with 8 neurons - 3 Outputs (Labels) with k-fold crossvalidation in keras for your data set. import pandas as pd from keras.models import Sequential from keras.layers import Dense from keras.wrappers.scikit_learn import KerasClassifier from keras.utils import np_utils from sklearn.model_selection ...

2

Matplotlib and Seaborn are graphical plotting libraries, there is no straightforward way to display data visualizations in the terminal. The best option is to run Jupyter Notebook with --no-browser flag on the remote instance. Then, paste the URL from the remote instance in your local computer browser. You make have make sure the ports and permissions are ...

2

If there are 20 independent variables and 1 dependent variable, a linear regression model can be viewed as a 20-dimensional hyperplane in 21-dimensional space. The hyperplane is not "vertical" with respect to any independent variable. So if you pick a value for each of the 20 independent variables and draw a vertical line at the point consisting of those ...

2

Adding to other answers here, I first want to note, that fitting a regression is different to plotting regression results. Fitting simply requires to minimize the sum of squared residuals to determine optimal coefficients (or weights). A single coefficient gives you the „marginal effect“ of the corresponding variable/feature. Obviously it is impossible ...

2

My question is is there a way to aggregate all the frequency distributions for all the fitness values among the 30 runs? so that I can only show a single plot for all the runs. Yes absolutely, you can do that with boxplots or violin plots, one boxplot (or violin plot) for each fitness value. The height and shape shows the distribution of the values across ...

2

The graph resulting from this kind of dataset is also known as a Network Graph and the kind of analysis you are trying to do is known as Social Network Analysis. There are many prominent Python libraries for visualization and subsequent analysis of network graphs. The most widely used is NetworkX. It is easy to add nodes and directed edges in a NetworkX ...

2

Since the variables isPWKinText and H1_2_Len have the same value for all examples in your dataset they have zero variance and contain no information. There is simply just no inference you can make based on them. That is why they are not relevant and shown blank. Please note, that this depends on your dataset, of course. The two variable might just be ...

2

I would do a big confusion matrix and, by inspecting it, decide what classes to add to a smaller confusion matrix in order to clearly illustrate the main sources of confusion for the model.

1

Looking at the documentation, it seems there is no obvious way. Looking at the source code of the plt.bar method (held on Axes objects), and searching for uses of the width parameter, it starts getting quite complicated and I don't think it is meant to be used for this purpose explicitly! Lots of internal methods, starting with a _, like self._convert_dx is ...

1

Thanks to @Peter. It's called Venn Diagram and here's my needed Javascript Library. https://www.benfrederickson.com/venn-diagrams-with-d3.js/

1

To me, it would make intuitive sense to visualize/analyze the data before imputing the missing values, as imputation will skew distributions and may lead to false assumptions about the real data before imputation. I think there's a crucial detail to answer here which might deepen your analysis a bit. How are you planning to imput the missing values? That ...

1

As We should not remove any data ... we can use vector norm from the origin(l2 norm) given data_a,data_b,data_c are arrays. import numpy as np import pandas as pd from numpy.linalg import norm l2_a=norm(data_a) l2_b=norm(data_b) l2_c=norm(data_c) print(l2_a,l2_b,l2_c) output: 2.619885493680974 1.5779100101083077 1.6631897065578538. as l2_a,...

1

One approach would be to fit a linear regression and look at Cooks Distance to detect outliers. https://en.m.wikipedia.org/wiki/Cook%27s_distance Some R background: http://r-statistics.co/Outlier-Treatment-With-R.html Some Python discussion: https://stackoverflow.com/a/52322232/9524424

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I would recommend using an Alluvial Diagram (Sankey Diagram). You can actually show multiple stages between emotions. So while if you google Sankey you will likely mainly see one two stage demonstrations...that is from A to B, you can easily code it to show from A to B to C, etc.

1

I think you've done a good job identifying the trade-offs in this situation. If you impute missing values before visualization, then you won't be visualizing the "true" data. But sometimes a lot of data is missing, and if you drop all examples with missing attributes then you're unlikely to be visualizing a representative sample of the data you might use ...

1

I had tried the suggestion from @foxthatruns's answer to no avail. I found that changing my numeric column to float64 solved the problem (reference). df['my_column']=df['my_column'].astype('float64') This was done with Python 3.7, seaborn 0.9.0, numpy 1.16.4.

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This should do the trick. You have to melt your data frame to use x,y and hue in your seaborn barplot. yfreq['type'] = yfreq.index yfreq = yfreq.melt(id_vars = 'type') sns.barplot(x = 'variable', y = 'value', hue = 'type', data = yfreq)

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